PDF format - Documentation
Akka Documentation
Release 1.3.1
Typesafe Inc
March 08, 2012
CONTENTS
1
Introduction
1.1 What is Akka? . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Why Akka? . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Getting Started . . . . . . . . . . . . . . . . . . . . . . . .
1.4 Getting Started Tutorial (Scala): First Chapter . . . . . . .
1.5 Getting Started Tutorial (Scala with Eclipse): First Chapter
1.6 Getting Started Tutorial (Java): First Chapter . . . . . . . .
1.7 Use-case and Deployment Scenarios . . . . . . . . . . . . .
1.8 Examples of use-cases for Akka . . . . . . . . . . . . . . .
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1
1
2
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43
General
2.1 Akka and the Java Memory Model .
2.2 Configuration . . . . . . . . . . . .
2.3 Event Handler . . . . . . . . . . .
2.4 SLF4J . . . . . . . . . . . . . . . .
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45
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3
Common utilities
3.1 Scheduler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4
Scala API
4.1 Actors (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Typed Actors (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 ActorRegistry (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Futures (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Dataflow Concurrency (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6 Agents (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7 Software Transactional Memory (Scala) . . . . . . . . . . . . . . . . . . . . .
4.8 Transactors (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.9 Remote Actors (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.10 Serialization (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.11 Fault Tolerance Through Supervisor Hierarchies (Scala) . . . . . . . . . . . .
4.12 Dispatchers (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.13 Routing (Scala) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.14 FSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.15 HTTP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.16 ZeroMQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.17 HTTP Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.18 Testing Actor Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.19 Tutorial: write a scalable, fault-tolerant, network chat server and client (Scala)
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167
Java API
178
5.1 Actors (Java) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
5.2 Typed Actors (Java) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
i
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
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7
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ActorRegistry (Java) . . . . . . . . . . . . . . . . . .
Futures (Java) . . . . . . . . . . . . . . . . . . . . . .
Dataflow Concurrency (Java) . . . . . . . . . . . . .
Software Transactional Memory (Java) . . . . . . . .
Transactors (Java) . . . . . . . . . . . . . . . . . . .
Remote Actors (Java) . . . . . . . . . . . . . . . . .
Serialization (Java) . . . . . . . . . . . . . . . . . . .
Fault Tolerance Through Supervisor Hierarchies (Java)
Dispatchers (Java) . . . . . . . . . . . . . . . . . . .
Routing (Java) . . . . . . . . . . . . . . . . . . . . .
Guice Integration . . . . . . . . . . . . . . . . . . . .
Information for Developers
6.1 Building Akka . . . . . .
6.2 Developer Guidelines . .
6.3 Documentation Guidelines
6.4 Team . . . . . . . . . . .
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237
237
239
240
242
Project Information
7.1 Migration Guides . . . . . . . . .
7.2 Release Notes . . . . . . . . . .
7.3 Scaladoc API . . . . . . . . . . .
7.4 Documentation for Other Versions
7.5 Issue Tracking . . . . . . . . . .
7.6 Licenses . . . . . . . . . . . . .
7.7 Sponsors . . . . . . . . . . . . .
7.8 Support . . . . . . . . . . . . . .
7.9 Mailing List . . . . . . . . . . .
7.10 Downloads . . . . . . . . . . . .
7.11 Source Code . . . . . . . . . . .
7.12 Maven Repository . . . . . . . .
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243
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277
Additional Information
8.1 Durable Mailboxes . . . . . . . . . . . . . . . . . . .
8.2 Add-on Modules . . . . . . . . . . . . . . . . . . . .
8.3 Articles & Presentations . . . . . . . . . . . . . . . .
8.4 Benchmarks . . . . . . . . . . . . . . . . . . . . . .
8.5 Here is a list of recipies for all things Akka . . . . . .
8.6 External Sample Projects . . . . . . . . . . . . . . . .
8.7 Projects using the removed Akka Persistence modules
8.8 Companies and Open Source projects using Akka . . .
8.9 Third-party Integrations . . . . . . . . . . . . . . . .
8.10 Other Language Bindings . . . . . . . . . . . . . . .
8.11 Feature Stability Matrix . . . . . . . . . . . . . . . .
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Links
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294
Python Module Index
295
Index
296
ii
CHAPTER
ONE
INTRODUCTION
1.1 What is Akka?
Simpler Scalability, Fault-Tolerance, Concurrency & Remoting through Actors
We believe that writing correct concurrent, fault-tolerant and scalable applications is too hard. Most of the time it’s
because we are using the wrong tools and the wrong level of abstraction. Akka is here to change that. Using the
Actor Model together with Software Transactional Memory we raise the abstraction level and provide a better platform to build correct concurrent and scalable applications. For fault-tolerance we adopt the Let it crash/Embrace
failure model which have been used with great success in the telecom industry to build applications that self-heals,
systems that never stop. Actors also provides the abstraction for transparent distribution and the basis for truly
scalable and fault-tolerant applications. Akka is Open Source and available under the Apache 2 License.
Download from http://akka.io/downloads/
1.1.1 Akka implements a unique hybrid
• Actors (Java), which gives you:
– Simple and high-level abstractions for concurrency and parallelism.
– Asynchronous, non-blocking and highly performant event-driven programming model.
– Very lightweight event-driven processes (create ~6.5 million actors on 4GB RAM).
• Fault Tolerance Through Supervisor Hierarchies (Java) through supervisor hierarchies with “let-it-crash”
semantics. Excellent for writing highly fault-tolerant systems that never stop, systems that self-heal.
• Software Transactional Memory (Java) (STM). (Distributed transactions coming soon).
• Transactors (Java): combine actors and STM into transactional actors. Allows you to compose atomic
message flows with automatic retry and rollback.
• Remote Actors (Java): highly performant distributed actors with remote supervision and error management.
• Java API and Scala API
1.1.2 Akka can be used in two different ways
• As a library: used by a web app, to be put into ‘WEB-INF/lib’ or as a regular JAR on your classpath.
• As a microkernel: stand-alone kernel, embedding a servlet container and all the other modules.
See the Use-case and Deployment Scenarios for details.
1
Akka Documentation, Release 1.3.1
1.2 Why Akka?
1.2.1 What features can the Akka platform offer, over the competition?
Akka is an unified runtime and programming model for:
• Scale up (Concurrency)
• Scale out (Remoting)
• Fault tolerance
One thing to learn and admin, with high cohesion and coherent semantics.
Akka is a very scalable piece of software, not only in the performance sense, but in the size of applications it is
useful for. The core of Akka, akka-actor, is very small and easily dropped into an existing project where you need
asynchronicity and lockless concurrency without hassle.
You can choose to include only the parts of akka you need in your application and then there’s the whole package,
the Akka Microkernel, which is a standalone container to deploy your Akka application in. With CPUs growing
more and more cores every cycle, Akka is the alternative that provides outstanding performance even if you’re
only running it on one machine. Akka also supplies a wide array of concurrency-paradigms, allowing for users to
choose the right tool for the job.
The integration possibilities for Akka Actors are immense through the Apache Camel integration. We provide
Software Transactional Memory concurrency control through the excellent Multiverse project, and have integrated
that with Actors, creating Transactors for coordinated concurrent transactions. We have Agents and Dataflow
concurrency as well.
1.2.2 What’s a good use-case for Akka?
(Web, Cloud, Application) Services - Actors lets you manage service failures (Supervisors), load management
(back-off strategies, timeouts and processing-isolation), both horizontal and vertical scalability (add more cores
and/or add more machines). Think payment processing, invoicing, order matching, datacrunching, messaging.
Really any highly transactional systems like banking, betting, games.
Here’s what some of the Akka users have to say about
http://stackoverflow.com/questions/4493001/good-use-case-for-akka
how
they
are
using
Akka:
1.2.3 Cloudy Akka
And that’s all in the ApacheV2-licensed open source project. On top of that we have a commercial product called
Cloudy Akka which provides the following features:
1. Management through Dashboard, JMX and REST
2. Monitoring through Dashboard, JMX and SNMP
3. Dapper-style tracing of messages across components and remote nodes
4. A configurable alert system
5. Real-time statistics
6. Very low overhead monitoring agents (should always be on in production)
7. Consolidation of statistics and logging information to a single node
8. Data analysis through Hadoop
9. Storage of statistics data for later processing
10. Provisioning and rolling upgrades through a dashboard
Read more here.
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1.3 Getting Started
The best way to start learning Akka is to try the Getting Started Tutorial, which comes in several flavours depending on you development environment preferences:
• Getting Started Tutorial (Java): First Chapter for Java development, either
– as standalone project, running from the command line,
– or as Maven project and running it from within Maven
• Getting Started Tutorial (Scala): First Chapter for Scala development, either
– as standalone project, running from the command line,
– or as SBT (Simple Build Tool) project and running it from within SBT
• Getting Started Tutorial (Scala with Eclipse): First Chapter for Scala development with Eclipse
The Getting Started Tutorial describes everything you need to get going, and you don’t need to read the rest of
this page if you study the tutorial. For later look back reference this page describes the essential parts for getting
started with different development environments.
1.3.1 Prerequisites
Akka requires that you have Java 1.6 or later installed on you machine.
1.3.2 Download
There are several ways to download Akka. You can download the full distribution with microkernel, which
includes all modules. You can download just the core distribution. Or you can use a build tool like Maven or SBT
to download dependencies from the Akka Maven repository.
1.3.3 Modules
Akka is split up into two different parts:
• Akka - The core modules. Reflects all the sections under Scala API and Java API.
• Akka Modules - The microkernel and add-on modules, described in Add-on Modules.
Akka is very modular and has many JARs for containing different features. The core distribution has seven
modules:
• akka-actor-1.3.1 – Standard Actors
• akka-typed-actor-1.3.1 – Typed Actors
• akka-remote-1.3.1 – Remote Actors
• akka-stm-1.3.1 – STM (Software Transactional Memory), transactors and transactional datastructures
• akka-http-1.3.1 – Akka Mist for continuation-based asynchronous HTTP and also Jersey integration
• akka-slf4j-1.3.1 – SLF4J Event Handler Listener
• akka-testkit-1.3.1 – Toolkit for testing Actors
We also have Akka Modules containing add-on modules outside the core of Akka.
• akka-kernel-1.3.1 – Akka microkernel for running a bare-bones mini application server (embeds
Jetty etc.)
• akka-amqp-1.3.1 – AMQP integration
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• akka-camel-1.3.1 – Apache Camel Actors integration (it’s the best way to have your Akka application
communicate with the rest of the world)
• akka-camel-typed-1.3.1 – Apache Camel Typed Actors integration
• akka-scalaz-1.3.1 – Support for the Scalaz library
• akka-spring-1.3.1 – Spring framework integration
• akka-osgi-dependencies-bundle-1.3.1 – OSGi support
How to see the JARs dependencies of each Akka module is described in the Dependencies section. Worth noting
is that akka-actor has zero external dependencies (apart from the scala-library.jar JAR).
1.3.4 Using a release distribution
Download the release you need, Akka core or Akka Modules, from http://akka.io/downloads and unzip it.
Microkernel
The Akka Modules distribution includes the microkernel. To run the microkernel:
• Set the AKKA_HOME environment variable to the root of the Akka distribution.
• To start the kernel use the scripts in the bin directory and deploy all samples applications from ./deploy
dir.
More information is available in the documentation of the Microkernel in Add-on Modules.
1.3.5 Using a build tool
Akka can be used with build tools that support Maven repositories. The Akka Maven repository can be found at
http://repo.akka.io/releases and Typesafe provides http://repo.typesafe.com/typesafe/releases/ that proxies several
other repositories, including akka.io.
1.3.6 Using Akka with Maven
Information about how to use Akka with Maven, including how to create an Akka Maven project from scratch,
can be found in the Getting Started Tutorial (Java): First Chapter.
Summary of the essential parts for using Akka with Maven:
1. Add this repository to your pom.xml:
<repository>
<id>typesafe</id>
<name>Typesafe Repository</name>
<url>http://repo.typesafe.com/typesafe/releases/</url>
</repository>
2. Add the Akka dependencies. For example, here is the dependency for Akka Actor 1.3.1:
<dependency>
<groupId>se.scalablesolutions.akka</groupId>
<artifactId>akka-actor</artifactId>
<version>1.3.1</version>
</dependency>
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1.3.7 Using Akka with SBT
Information about how to use Akka with SBT, including how to create an Akka SBT project from scratch, can be
found in the Getting Started Tutorial (Scala): First Chapter.
Summary of the essential parts for using Akka with SBT:
SBT installation instructions on https://github.com/harrah/xsbt/wiki/Setup
build.sbt file:
name := "My Project"
version := "1.0"
scalaVersion := "2.9.1"
resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/"
libraryDependencies += "se.scalablesolutions.akka" % "akka-actor" % "1.3.1"
1.3.8 Using Akka with Eclipse
Information about how to use Akka with Eclipse, including how to create an Akka Eclipse project from scratch,
can be found in the Getting Started Tutorial (Scala with Eclipse): First Chapter.
Setup SBT project and then use sbteclipse to generate Eclipse project.
1.3.9 Using Akka with IntelliJ IDEA
Setup SBT project and then use sbt-idea to generate IntelliJ IDEA project.
1.3.10 Build from sources
Akka uses Git and is hosted at Github.
• Akka: clone the Akka repository from http://github.com/akka/akka
• Akka Modules: clone the Akka Modules repository from http://github.com/akka/akka-modules
Continue reading the page on Building Akka
1.3.11 Need help?
If you have questions you can get help on the Akka Mailing List.
You can also ask for commercial support.
Thanks for being a part of the Akka community.
1.4 Getting Started Tutorial (Scala): First Chapter
1.4.1 Introduction
Welcome to the first tutorial on how to get started with Akka and Scala. We assume that you already know what
Akka and Scala are and will now focus on the steps necessary to start your first project.
There are two variations of this first tutorial:
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• 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.
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.
1.4.2 Tutorial source code
If you want don’t want to type in the code and/or set up an SBT 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:
$ git clone git://github.com/akka/akka.git
Then you can navigate down to the tutorial:
$ cd akka/akka-tutorials/akka-tutorial-first
1.4.3 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 Scala 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:
$ export JAVA_HOME=..root of java distribution..
$ export PATH=$PATH:$JAVA_HOME/bin
You can test your installation by invoking java:
$ java -version
java version "1.6.0_24"
Java(TM) SE Runtime Environment (build 1.6.0_24-b07-334-10M3326)
Java HotSpot(TM) 64-Bit Server VM (build 19.1-b02-334, mixed mode)
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1.4.4 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-actors-1.3.1.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 the
AKKA_HOME variable:
$ cd /Users/jboner/tools/akka-actors-1.3.1
$ export AKKA_HOME=‘pwd‘
$ echo $AKKA_HOME
/Users/jboner/tools/akka-actors-1.3.1
The distribution looks like this:
$ ls -1
config
doc
lib
src
• In the config directory we have the Akka conf files.
• In the doc directory we have the documentation, API, doc JARs, and also the source files for the tutorials.
• 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-1.3.1.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 core distribution has seven
modules:
• akka-actor-1.3.1.jar – Standard Actors
• akka-typed-actor-1.3.1.jar – Typed Actors
• akka-remote-1.3.1.jar – Remote Actors
• akka-stm-1.3.1.jar – STM (Software Transactional Memory), transactors and transactional datastructures
• akka-http-1.3.1.jar – Akka Mist for continuation-based asynchronous HTTP and also Jersey integration
• akka-slf4j-1.3.1.jar – SLF4J Event Handler Listener for logging with SLF4J
• akka-testkit-1.3.1.jar – Toolkit for testing Actors
We also have Akka Modules containing add-on modules outside the core of Akka. You can download the Akka
Modules distribution from http://akka.io/downloads/. 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-1.3.1.jar – Akka microkernel for running a bare-bones mini application server (embeds Jetty etc.)
• akka-amqp-1.3.1.jar – AMQP integration
• akka-camel-1.3.1.jar – Apache Camel Actors integration (it’s the best way to have your Akka
application communicate with the rest of the world)
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• akka-camel-typed-1.3.1.jar – Apache Camel Typed Actors integration
• akka-scalaz-1.3.1.jar – Support for the Scalaz library
• akka-spring-1.3.1.jar – Spring framework integration
• akka-osgi-dependencies-bundle-1.3.1.jar – OSGi support
1.4.5 Downloading and installing Scala
To build and run the tutorial sample from the command line, you have to install the Scala distribution. If you
prefer to use SBT to build and run the sample then you can skip this section and jump to the next one.
Scala can be downloaded from http://www.scala-lang.org/downloads. Browse there and download the Scala 2.9.1
release. If you pick the tgz or zip distribution 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-2.9.1/bin (if that is the directory where you installed Scala) is on
your PATH:
$ export PATH=$PATH:scala-2.9.1/bin
You can test your installation by invoking scala:
$ scala -version
Scala code runner version 2.9.1.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).
Some tools require you to set the SCALA_HOME environment variable to the root of the Scala distribution, however
Akka does not require that.
1.4.6 Downloading and installing SBT
SBT, short for ‘Simple Build Tool’ is an excellent build system written in Scala. It uses 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 and is probably the easiest
way of getting through this tutorial. If you want to use SBT for this tutorial then follow the following instructions,
if not you can skip this section and the next.
To install SBT and create a project for this tutorial it is easiest to follow the instructions on
https://github.com/harrah/xsbt/wiki/Setup.
Now we need to create our first Akka project. You could add the dependencies manually to the build script, but
the easiest way is to use Akka’s SBT Plugin, covered in the next section.
1.4.7 Creating an Akka SBT project
If you have not already done so, now is the time to create an SBT project for our tutorial. You do that by adding
the following content to build.sbt file in the directory you want to create your project in:
name := "My Project"
version := "1.0"
scalaVersion := "2.9.1"
resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/"
libraryDependencies += "se.scalablesolutions.akka" % "akka-actor" % "1.3.1"
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Create a directory src/main/scala in which you will store the Scala source files.
Not needed in this tutorial, but if you would like to use additional Akka modules beyond akka-actor, you can
add these as libraryDependencies in build.sbt. Note that there must be a blank line between each.
Here is an example adding akka-remote and akka-stm:
libraryDependencies += "se.scalablesolutions.akka" % "akka-actor" % "1.3.1"
libraryDependencies += "se.scalablesolutions.akka" % "akka-remote" % "1.3.1"
libraryDependencies += "se.scalablesolutions.akka" % "akka-stm" % "1.3.1"
So, now we are all set.
SBT itself needs a whole bunch of dependencies but our project will only need one; akka-actor-1.3.1.jar.
SBT will download that as well.
1.4.8 Start writing the code
Now it’s about time to start hacking.
We start by creating a Pi.scala file and adding these import statements at the top of the file:
package akka.tutorial.first.scala
import
import
import
import
import
akka.actor.{Actor, PoisonPill}
Actor._
akka.routing.{Routing, CyclicIterator}
Routing._
akka.dispatch.Dispatchers
import java.util.concurrent.CountDownLatch
If you are using SBT in this tutorial then create the file in the src/main/scala directory.
If you are using the command line tools then create the file wherever you want. I will create it in a directory called
tutorial at the root of the Akka distribution, e.g. in $AKKA_HOME/tutorial/Pi.scala.
1.4.9 Creating the messages
The design we are aiming for is to have one Master actor initiating the computation, creating a set of Worker
actors. 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 prints out the result, shuts down all workers and then
itself.
With this in mind, let’s now create the messages that we want to have flowing in the system. We need three
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
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):
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sealed trait PiMessage
case object Calculate extends PiMessage
case class Work(start: Int, nrOfElements: Int) extends PiMessage
case class Result(value: Double) extends PiMessage
1.4.10 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(start, nrOfElements) =>
self reply Result(calculatePiFor(start, nrOfElements)) // perform the work
}
}
As you can see we have now created an Actor with a receive method as a handler for the Work message. In
this handler we invoke the calculatePiFor(..) method, wrap the result in a Result message and send 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 for future use.
The only thing missing in our Worker actor is the implementation on the calculatePiFor(..) method.
While there are many ways we can implement this algorithm in Scala, in this introductory tutorial we have chosen
an imperative style using a for comprehension and an accumulator:
def calculatePiFor(start: Int, nrOfElements: Int): Double = {
var acc = 0.0
for (i <- start until (start + nrOfElements))
acc += 4.0 * (1 - (i % 2) * 2) / (2 * i + 1)
acc
}
1.4.11 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.actorOf
There are two versions of actorOf; one of them taking a actor type and the other one an instance of an actor. The
former one (actorOf[MyActor]) is used when the actor class has a no-argument constructor while the second
one (actorOf(new MyActor(..))) is used when the actor class has a constructor that takes arguments.
This is the only way to create an instance of an Actor and the actorOf method ensures this. The latter version is
using call-by-name and lazily creates the actor within the scope of the actorOf method. The actorOf method
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instantiates the actor and returns, not an instance to the actor, but an instance to an ActorRef. This reference
is the handle through which you communicate with the actor. It is immutable, serializable and location-aware
meaning that it “remembers” its original actor even if it is sent to other nodes across the network and can be seen
as the equivalent to the Erlang actor’s PID.
The actor’s life-cycle is:
• Created – Actor.actorOf[MyActor] – can not receive messages
• Started – actorRef.start() – can receive messages
• Stopped – actorRef.stop() – can not receive messages
Once the actor has been stopped it is dead and can not be started again.
Now we have a router that is representing all our workers in a single abstraction. If you paid attention to the code
above, you saw 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 have to pass in 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:
class Master(
nrOfWorkers: Int, nrOfMessages: Int, nrOfElements: Int, latch: CountDownLatch)
extends Actor {
var pi: Double = _
var nrOfResults: Int = _
var start: Long = _
// 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()
def receive = { ... }
override def preStart() {
start = System.currentTimeMillis
}
override def postStop() {
// tell the world that the calculation is complete
println(
"\n\tPi estimate: \t\t%s\n\tCalculation time: \t%s millis"
.format(pi, (System.currentTimeMillis - start)))
latch.countDown()
}
}
A couple of things are worth explaining further.
First, we are passing in a java.util.concurrent.CountDownLatch to the Master actor. This latch is
only used for plumbing (in this specific tutorial), to have a simple way of 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 but other abstractions and functions like Channel, Future and ? to
achieve the same thing in a non-blocking way. But for simplicity let’s stick to a CountDownLatch for now.
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 print-
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ing out the result (the approximation of Pi) and the time it took to calculate it. In this call 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 it also sends a
Broadcast(PoisonPill) message to the router, which will send out the PoisonPill message to all the
actors it is representing (in our case all the Worker actors). PoisonPill is a special kind of message that tells
the receiver to shut itself down using the normal shutdown method; self.stop. 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 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 that matches the number
of tasks sent out, the Master actor considers itself done and shuts down.
Let’s capture this in code:
// message handler
def receive = {
case Calculate =>
// schedule work
for (i <- 0 until nrOfMessages) router ! Work(i * nrOfElements, 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()
}
1.4.12 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 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 wait for it to finish:
object Pi extends App {
calculate(nrOfWorkers = 4, nrOfElements = 10000, nrOfMessages = 10000)
... // actors and messages
def calculate(nrOfWorkers: Int, nrOfElements: Int, nrOfMessages: Int) {
// this latch is only plumbing to know when the calculation is completed
val latch = new CountDownLatch(1)
// create the master
val master = actorOf(
new Master(nrOfWorkers, nrOfMessages, nrOfElements, latch)).start()
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// start the calculation
master ! Calculate
// wait for master to shut down
latch.await()
}
}
That’s it. Now we are done.
But before we package it up and run it, let’s take a look at the full code now, with package declaration, imports
and all:
package akka.tutorial.first.scala
import
import
import
import
akka.actor.{Actor, PoisonPill}
Actor._
akka.routing.{Routing, CyclicIterator}
Routing._
import java.util.concurrent.CountDownLatch
object Pi extends App {
calculate(nrOfWorkers = 4, nrOfElements = 10000, nrOfMessages = 10000)
// ====================
// ===== Messages =====
// ====================
sealed trait PiMessage
case object Calculate extends PiMessage
case class Work(start: Int, nrOfElements: Int) extends PiMessage
case class Result(value: Double) extends PiMessage
// ==================
// ===== Worker =====
// ==================
class Worker extends Actor {
// define the work
def calculatePiFor(start: Int, nrOfElements: Int): Double = {
var acc = 0.0
for (i <- start until (start + nrOfElements))
acc += 4.0 * (1 - (i % 2) * 2) / (2 * i + 1)
acc
}
def receive = {
case Work(start, nrOfElements) =>
self reply Result(calculatePiFor(start, nrOfElements)) // perform the work
}
}
// ==================
// ===== Master =====
// ==================
class Master(
nrOfWorkers: Int, nrOfMessages: Int, nrOfElements: Int, latch: CountDownLatch)
extends Actor {
var pi: Double = _
var nrOfResults: Int = _
var start: Long = _
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// 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()
// message handler
def receive = {
case Calculate =>
// schedule work
//for (start <- 0 until nrOfMessages) router ! Work(start, nrOfElements)
for (i <- 0 until nrOfMessages) router ! Work(i * nrOfElements, 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()
}
override def preStart() {
start = System.currentTimeMillis
}
override def postStop() {
// tell the world that the calculation is complete
println(
"\n\tPi estimate: \t\t%s\n\tCalculation time: \t%s millis"
.format(pi, (System.currentTimeMillis - start)))
latch.countDown()
}
}
// ==================
// ===== Run it =====
// ==================
def calculate(nrOfWorkers: Int, nrOfElements: Int, nrOfMessages: Int) {
// this latch is only plumbing to know when the calculation is completed
val latch = new CountDownLatch(1)
// create the master
val master = actorOf(
new Master(nrOfWorkers, nrOfMessages, nrOfElements, latch)).start()
// start the calculation
master ! Calculate
// wait for master to shut down
latch.await()
}
}
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1.4.13 Run it as a command line application
If you have not typed in (or copied) the code for the tutorial as $AKKA_HOME/tutorial/Pi.scala 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 Scala’s compiler scalac. Our application depends
on the akka-actor-1.3.1.jar JAR file, so let’s add that to the compiler classpath when we compile the
source:
$ scalac -cp lib/akka/akka-actor-1.3.1.jar tutorial/Pi.scala
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-1.3.1.jar JAR file to the classpath, and this time we also need to add the
Scala runtime library scala-library.jar and the classes we compiled ourselves:
$ java \
-cp lib/scala-library.jar:lib/akka/akka-actor-1.3.1.jar:. \
akka.tutorial.first.scala.Pi
AKKA_HOME is defined as [/Users/jboner/tools/akka-actors-1.3.1]
loading config from [/Users/jboner/tools/akka-actors-1.3.1/config/akka.conf].
Pi estimate:
Calculation time:
3.1435501812459323
858 millis
Yippee! It is working.
If you have not defined the AKKA_HOME environment variable then Akka can’t find the akka.conf configuration file and will print out a Can’t load akka.conf warning. This is ok since it will then just use the
defaults.
1.4.14 Run it inside SBT
If you used SBT, then you can run the application directly inside SBT. First you need to compile the project:
$ sbt
> compile
...
When this in done we can run our application directly inside SBT:
> run
...
Pi estimate:
Calculation time:
3.1435501812459323
942 millis
Yippee! It is working.
If you have not defined an the AKKA_HOME environment variable then Akka can’t find the akka.conf configuration file and will print out a Can’t load akka.conf warning. This is ok since it will then just use the
defaults.
1.4.15 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.
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Now we are ready to take on more advanced problems. In the next tutorial we will build on this one, refactor it
into more idiomatic Akka and Scala code, and introduce a few new concepts and abstractions. Whenever you feel
ready, join me in the Getting Started Tutorial: Second Chapter.
Happy hakking.
1.5 Getting Started Tutorial (Scala with Eclipse): First Chapter
1.5.1 Introduction
Welcome to the first tutorial on how to get started with Akka and Scala. We assume that you already know what
Akka and Scala are and will now focus on the steps necessary to start your first project. We will be using Eclipse,
and the Scala plugin for Eclipse.
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.
1.5.2 Tutorial source code
If you want don’t want to type in the code and/or set up an SBT 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.
1.5.3 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 recent version of Eclipse (at least 3.6
- Helios).
If you want to run the example from the command line as well, 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:
$ export JAVA_HOME=..root of java distribution..
$ export PATH=$PATH:$JAVA_HOME/bin
You can test your installation by invoking java:
$ java -version
java version "1.6.0_24"
Java(TM) SE Runtime Environment (build 1.6.0_24-b07-334-10M3326)
Java HotSpot(TM) 64-Bit Server VM (build 19.1-b02-334, mixed mode)
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1.5.4 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-actors-1.3.1.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 the
AKKA_HOME variable:
$ cd /Users/jboner/tools/akka-actors-1.3.1
$ export AKKA_HOME=‘pwd‘
$ echo $AKKA_HOME
/Users/jboner/tools/akka-actors-1.3.1
The distribution looks like this:
$ ls -1
config
doc
lib
src
• In the config directory we have the Akka conf files.
• In the doc directory we have the documentation, API, doc JARs, and also the source files for the tutorials.
• 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-1.3.1.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 core distribution has seven
modules:
• akka-actor-1.3.1.jar – Standard Actors
• akka-typed-actor-1.3.1.jar – Typed Actors
• akka-remote-1.3.1.jar – Remote Actors
• akka-stm-1.3.1.jar – STM (Software Transactional Memory), transactors and transactional datastructures
• akka-http-1.3.1.jar – Akka Mist for continuation-based asynchronous HTTP and also Jersey integration
• akka-slf4j-1.3.1.jar – SLF4J Event Handler Listener for logging with SLF4J
• akka-testkit-1.3.1.jar – Toolkit for testing Actors
We also have Akka Modules containing add-on modules outside the core of Akka. You can download the Akka
Modules distribution from http://akka.io/downloads/. 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-1.3.1.jar – Akka microkernel for running a bare-bones mini application server (embeds Jetty etc.)
• akka-amqp-1.3.1.jar – AMQP integration
• akka-camel-1.3.1.jar – Apache Camel Actors integration (it’s the best way to have your Akka
application communicate with the rest of the world)
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• akka-camel-typed-1.3.1.jar – Apache Camel Typed Actors integration
• akka-scalaz-1.3.1.jar – Support for the Scalaz library
• akka-spring-1.3.1.jar – Spring framework integration
• akka-osgi-dependencies-bundle-1.3.1.jar – OSGi support
1.5.5 Downloading and installing the Scala IDE for Eclipse
If you want to use Eclipse for coding your Akka tutorial, you need to install the Scala plugin for Eclipse. This
plugin comes with its own version of Scala, so if you don’t plan to run the example from the command line, you
don’t need to download the Scala distribution (and you can skip the next section).
You can install this plugin using the regular update mechanism. First choose a version of the IDE from
http://download.scala-ide.org. We recommend you choose 2.0.x, which comes with Scala 2.9. Copy the corresponding URL and then choose Help/Install New Software and paste the URL you just copied. You
should see something similar to the following image.
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Make sure you select both the JDT Weaving for Scala and the Scala IDE for Eclipse plugins.
The other plugin is optional, and contains the source code of the plugin itself.
Once the installation is finished, you need to restart Eclipse. The first time the plugin starts it will open a diagnostics window and offer to fix several settings, such as the delay for content assist (code-completion) or the shown
completion proposal types.
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Accept the recommended settings, and follow the instructions if you need to increase the heap size of Eclipse.
Check that the installation succeeded by creating a new Scala project (File/New>Scala Project), and
typing some code. You should have content-assist, hyperlinking to definitions, instant error reporting, and so on.
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You are ready to code now!
1.5.6 Downloading and installing Scala
To build and run the tutorial sample from the command line, you have to install the Scala distribution. If you
prefer to use Eclipse to build and run the sample then you can skip this section and jump to the next one.
Scala can be downloaded from http://www.scala-lang.org/downloads. Browse there and download the Scala 2.9.1
release. If you pick the tgz or zip distribution 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-2.9.1/bin (if that is the directory where you installed Scala) is on
your PATH:
$ export PATH=$PATH:scala-2.9.1/bin
You can test your installation by invoking scala:
$ scala -version
Scala code runner version 2.9.1.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).
Some tools require you to set the SCALA_HOME environment variable to the root of the Scala distribution, however
Akka does not require that.
1.5.7 Creating an Akka project in Eclipse
If you have not already done so, now is the time to create an Eclipse project for our tutorial. Use the New Scala
Project wizard and accept the default settings. Once the project is open, we need to add the akka libraries to
the build path. Right click on the project and choose Properties, then click on Java Build Path. Go
to Libraries and click on Add External Jars.., then navigate to the location where you installed akka
and choose akka-actor.jar. You should see something similar to this:
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Using SBT in Eclipse
If you are an SBT user, you can follow the Downloading and installing SBT instruction and additionally install
the sbteclipse plugin. This adds support for generating Eclipse project files from your SBT project. You need
to install the plugin as described in the README of sbteclipse
Then run the eclipse target to generate the Eclipse project:
$ sbt
> eclipse
The options create-src and with-sources are useful:
$ sbt
> eclipse create-src with-sources
• create-src to create the common source directories, e.g. src/main/scala, src/main/test
• with-sources to create source attachments for the library dependencies
Next you need to import this project in Eclipse, by choosing Eclipse/Import.. Existing Projects
into Workspace. Navigate to the directory where you defined your SBT project and choose import:
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Now we have the basis for an Akka Eclipse application, so we can..
1.5.8 Start writing the code
The design we are aiming for is to have one Master actor initiating the computation, creating a set of Worker
actors. 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 prints out the result, shuts down all workers and then
itself.
With this in mind, let’s now create the messages that we want to have flowing in the system.
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1.5.9 Creating the messages
We start by creating a package for our application, let’s call it akka.tutorial.first.scala. We start
by creating case classes for each type of message in our application, so we can place them in a hierarchy, call it
PiMessage. Right click on the package and choose New Scala Class, and enter PiMessage for the name
of the class.
We need three 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
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):
package akka.tutorial.first.scala
sealed trait PiMessage
case object Calculate extends PiMessage
case class Work(start: Int, nrOfElements: Int) extends PiMessage
case class Result(value: Double) extends PiMessage
1.5.10 Creating the worker
Now we can create the worker actor. Create a new class called Worker as before. We need to mix 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(start, nrOfElements) =>
self reply Result(calculatePiFor(start, nrOfElements)) // perform the work
}
}
The Actor trait is defined in akka.actor and you can either import it explicitly, or let Eclipse do it for you
when it cannot resolve the Actor trait. The quick fix option (Ctrl-F1) will offer two options:
Choose the Akka Actor and move on.
As you can see we have now created an Actor with a receive method as a handler for the Work message. In
this handler we invoke the calculatePiFor(..) method, wrap the result in a Result message and send 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 for future use.
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The only thing missing in our Worker actor is the implementation on the calculatePiFor(..) method.
While there are many ways we can implement this algorithm in Scala, in this introductory tutorial we have chosen
an imperative style using a for comprehension and an accumulator:
def calculatePiFor(start: Int, nrOfElements: Int): Double = {
var acc = 0.0
for (i <- start until (start + nrOfElements))
acc += 4.0 * (1 - (i % 2) * 2) / (2 * i + 1)
acc
}
1.5.11 Creating the master
Now create a new class for the master actor. 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. First we need to add some imports:
import
import
import
import
akka.actor.{Actor, PoisonPill}
akka.routing.{Routing, CyclicIterator}
Routing._
akka.dispatch.Dispatchers
import java.util.concurrent.CountDownLatch
and then we can create the workers:
// 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.actorOf
There are two versions of actorOf; one of them taking a actor type and the other one an instance of an actor. The
former one (actorOf[MyActor]) is used when the actor class has a no-argument constructor while the second
one (actorOf(new MyActor(..))) is used when the actor class has a constructor that takes arguments.
This is the only way to create an instance of an Actor and the actorOf method ensures this. The latter version is
using call-by-name and lazily creates the actor within the scope of the actorOf method. The actorOf method
instantiates the actor and returns, not an instance to the actor, but an instance to an ActorRef. This reference
is the handle through which you communicate with the actor. It is immutable, serializable and location-aware
meaning that it “remembers” its original actor even if it is sent to other nodes across the network and can be seen
as the equivalent to the Erlang actor’s PID.
The actor’s life-cycle is:
• Created – Actor.actorOf[MyActor] – can not receive messages
• Started – actorRef.start() – can receive messages
• Stopped – actorRef.stop() – can not receive messages
Once the actor has been stopped it is dead and can not be started again.
Now we have a router that is representing all our workers in a single abstraction. If you paid attention to the code
above, you saw 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 have to pass in three integer variables:
• nrOfWorkers – defining how many workers we should start up
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• 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:
class Master(
nrOfWorkers: Int, nrOfMessages: Int, nrOfElements: Int, latch: CountDownLatch)
extends Actor {
var pi: Double = _
var nrOfResults: Int = _
var start: Long = _
// 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()
def receive = { ... }
override def preStart() {
start = System.currentTimeMillis
}
override def postStop() {
// tell the world that the calculation is complete
println(
"\n\tPi estimate: \t\t%s\n\tCalculation time: \t%s millis"
.format(pi, (System.currentTimeMillis - start)))
latch.countDown()
}
}
A couple of things are worth explaining further.
First, we are passing in a java.util.concurrent.CountDownLatch to the Master actor. This latch is
only used for plumbing (in this specific tutorial), to have a simple way of 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 but other abstractions and functions like Channel, Future and ? to
achieve the same thing in a non-blocking way. But for simplicity let’s stick to a CountDownLatch for now.
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 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 it also sends a
Broadcast(PoisonPill) message to the router, which will send out the PoisonPill message to all the
actors it is representing (in our case all the Worker actors). PoisonPill is a special kind of message that tells
the receiver to shut itself down using the normal shutdown method; self.stop. 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 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 that matches the number
of tasks sent out, the Master actor considers itself done and shuts down.
Let’s capture this in code:
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// message handler
def receive = {
case Calculate =>
// schedule work
for (i <- 0 until nrOfMessages) router ! Work(i * nrOfElements, 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()
}
1.5.12 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 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 or using the Eclipse Runner.
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 wait for it to finish:
object Pi extends App {
calculate(nrOfWorkers = 4, nrOfElements = 10000, nrOfMessages = 10000)
... // actors and messages
def calculate(nrOfWorkers: Int, nrOfElements: Int, nrOfMessages: Int) {
// this latch is only plumbing to know when the calculation is completed
val latch = new CountDownLatch(1)
// create the master
val master = actorOf(
new Master(nrOfWorkers, nrOfMessages, nrOfElements, latch)).start()
// start the calculation
master ! Calculate
// wait for master to shut down
latch.await()
}
}
That’s it. Now we are done.
1.5.13 Run it from Eclipse
Eclipse builds your project on every save when Project/Build Automatically is set. If not, bring you
project up to date by clicking Project/Build Project. If there are no compilation errors, you can rightclick in the editor where Pi is defined, and choose Run as.. /Scala application. If everything works
fine, you should see:
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AKKA_HOME is defined as [/Users/jboner/tools/akka-actors-1.3.1]
loading config from [/Users/jboner/tools/akka-actors-1.3.1/config/akka.conf].
Pi estimate:
Calculation time:
3.1435501812459323
858 millis
If you have not defined an the AKKA_HOME environment variable then Akka can’t find the akka.conf configuration file and will print out a Can’t load akka.conf warning. This is ok since it will then just use the
defaults.
You can also define a new Run configuration, by going to Run/Run Configurations. Create a new Scala
application and choose the tutorial project and the main class to be akkatutorial.Pi. You can pass additional command line arguments to the JVM on the Arguments page, for instance to define where akka.conf
is:
Once you finished your run configuration, click Run. You should see the same output in the Console window.
You can use the same configuration for debugging the application, by choosing Run/Debug History or just
Debug As.
1.5.14 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 Eclipse.
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.
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Now we are ready to take on more advanced problems. In the next tutorial we will build on this one, refactor it
into more idiomatic Akka and Scala code, and introduce a few new concepts and abstractions. Whenever you feel
ready, join me in the Getting Started Tutorial: Second Chapter.
Happy hakking.
1.6 Getting Started Tutorial (Java): First Chapter
1.6.1 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.
1.6.2 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:
$ git clone git://github.com/akka/akka.git
Then you can navigate down to the tutorial:
$ cd akka/akka-tutorials/akka-tutorial-first
1.6.3 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.
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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:
$ export JAVA_HOME=..root of java distribution..
$ export PATH=$PATH:$JAVA_HOME/bin
You can test your installation by invoking java:
$ java -version
java version "1.6.0_24"
Java(TM) SE Runtime Environment (build 1.6.0_24-b07-334-10M3326)
Java HotSpot(TM) 64-Bit Server VM (build 19.1-b02-334, mixed mode)
1.6.4 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-actors-1.3.1.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 the
AKKA_HOME variable:
$ cd /Users/jboner/tools/akka-actors-1.3.1
$ export AKKA_HOME=‘pwd‘
$ echo $AKKA_HOME
/Users/jboner/tools/akka-actors-1.3.1
The distribution looks like this:
$ ls -1
config
doc
lib
src
• In the config directory we have the Akka conf files.
• In the doc directory we have the documentation, API, doc JARs, and also the source files for the tutorials.
• 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-1.3.1.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 core distribution has seven
modules:
• akka-actor-1.3.1.jar – Standard Actors
• akka-typed-actor-1.3.1.jar – Typed Actors
• akka-remote-1.3.1.jar – Remote Actors
• akka-stm-1.3.1.jar – STM (Software Transactional Memory), transactors and transactional datastructures
• akka-http-1.3.1.jar – Akka Mist for continuation-based asynchronous HTTP and also Jersey integration
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• akka-slf4j-1.3.1.jar – SLF4J Event Handler Listener for logging with SLF4J
• akka-testkit-1.3.1.jar – Toolkit for testing Actors
We also have Akka Modules containing add-on modules outside the core of Akka. You can download the Akka
Modules distribution from http://akka.io/downloads/. 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-1.3.1.jar – Akka microkernel for running a bare-bones mini application server (embeds Jetty etc.)
• akka-amqp-1.3.1.jar – AMQP integration
• akka-camel-1.3.1.jar – Apache Camel Actors integration (it’s the best way to have your Akka
application communicate with the rest of the world)
• akka-camel-typed-1.3.1.jar – Apache Camel Typed Actors integration
• akka-scalaz-1.3.1.jar – Support for the Scalaz library
• akka-spring-1.3.1.jar – Spring framework integration
• akka-osgi-dependencies-bundle-1.3.1.jar – OSGi support
1.6.5 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.
1.6.6 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:
$ mvn archetype:generate \
-DgroupId=akka.tutorial.first.java \
-DartifactId=akka-tutorial-first-java \
-DarchetypeArtifactId=maven-archetype-quickstart \
-DinteractiveMode=false
Now we have the basis for our Maven-based Akka project. Let’s step into the project directory:
$ cd akka-tutorial-first-java
Here is the layout that Maven created:
akka-tutorial-first-jboner
|-- pom.xml
‘-- src
|-- main
|
‘-- java
|
‘-- akka
|
‘-- tutorial
|
‘-- first
|
‘-- java
|
‘-- App.java
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://repo.akka.io/releases and Typesafe
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provides http://repo.typesafe.com/typesafe/releases/ that proxies several other repositories, including akka.io. It
should now look something like this:
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4
<modelVersion>4.0.0</modelVersion>
<name>akka-tutorial-first-java</name>
<groupId>akka.tutorial.first.java</groupId>
<artifactId>akka-tutorial-first-java</artifactId>
<packaging>jar</packaging>
<version>1.0-SNAPSHOT</version>
<url>http://akka.io</url>
<dependencies>
<dependency>
<groupId>se.scalablesolutions.akka</groupId>
<artifactId>akka-actor</artifactId>
<version>1.3.1</version>
</dependency>
</dependencies>
<repositories>
<repository>
<id>typesafe</id>
<name>Typesafe Repository</name>
<url>http://repo.typesafe.com/typesafe/releases/</url>
</repository>
</repositories>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>2.3.2</version>
<configuration>
<source>1.6</source>
<target>1.6</target>
</configuration>
</plugin>
</plugins>
</build>
</project>
1.6.7 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:
package akka.tutorial.first.java;
import static akka.actor.Actors.actorOf;
import static akka.actor.Actors.poisonPill;
import static java.util.Arrays.asList;
import akka.actor.ActorRef;
import akka.actor.UntypedActor;
import akka.actor.UntypedActorFactory;
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import
import
import
import
akka.routing.CyclicIterator;
akka.routing.InfiniteIterator;
akka.routing.Routing.Broadcast;
akka.routing.UntypedLoadBalancer;
import java.util.concurrent.CountDownLatch;
If
you
are
using
Maven
in
this
tutorial
then
src/main/java/akka/tutorial/first/java directory.
create
the
file
in
the
If you are using the command line tools then create the file wherever you want.
I 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.
1.6.8 Creating the messages
The design we are aiming for is to have one Master actor initiating the computation, creating a set of Worker
actors. 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 prints out the result, shuts down all workers and then
itself.
With this in mind, let’s now create the messages that we want to have flowing in the system. We need three
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
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:
public class Pi {
static class Calculate {}
static class Work {
private final int start;
private final int nrOfElements;
public Work(int start, int nrOfElements) {
this.start = start;
this.nrOfElements = nrOfElements;
}
public int getStart() { return start; }
public int getNrOfElements() { return nrOfElements; }
}
static class Result {
private final double value;
public Result(double value) {
this.value = value;
}
public double getValue() { return value; }
}
}
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1.6.9 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:
static class Worker extends UntypedActor {
// message handler
public void onReceive(Object message) {
if (message instanceof Work) {
Work work = (Work) message;
// perform the work
double result = calculatePiFor(work.getStart(), work.getNrOfElements());
// reply with the result
getContext().replyUnsafe(new Result(result));
} else throw new IllegalArgumentException("Unknown message [" + message + "]");
}
}
As you can see we have now created an UntypedActor with a onReceive method as a handler for the
Work message. 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().replyUnsafe(..). 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:
// define the work
private double calculatePiFor(int start, int nrOfElements) {
double acc = 0.0;
for (int i = start * nrOfElements; i <= ((start + 1) * nrOfElements - 1); i++) {
acc += 4.0 * (1 - (i % 2) * 2) / (2 * i + 1);
}
return acc;
}
1.6.10 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:
static class Master extends UntypedActor {
...
static class PiRouter extends UntypedLoadBalancer {
private final InfiniteIterator<ActorRef> workers;
public PiRouter(ActorRef[] workers) {
this.workers = new CyclicIterator<ActorRef>(asList(workers));
}
public InfiniteIterator<ActorRef> seq() {
return workers;
}
}
public Master(...) {
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...
// create the workers
final ActorRef[] workers = new ActorRef[nrOfWorkers];
for (int i = 0; i < nrOfWorkers; i++) {
workers[i] = actorOf(Worker.class).start();
}
// wrap them with a load-balancing router
ActorRef router = actorOf(new UntypedActorFactory() {
public UntypedActor create() {
return new PiRouter(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 Actors object but is usually
imported:
import static akka.actor.Actors.actorOf;
One thing to note is that we used two different versions of the actorOf method. For creating the Worker actor
we just pass in the class but to create the PiRouter actor we can’t do that since the constructor in the PiRouter
class takes arguments, instead we need to use the UntypedActorFactory which unfortunately is a bit more
verbose.
actorOf is the only way to create an instance of an Actor, this is enforced by Akka runtime. The actorOf
method instantiates the actor and returns, not an instance to the actor, but an instance to an ActorRef. This
reference is the handle through which you communicate with the actor. It is immutable, serializable and locationaware meaning that it “remembers” its original actor even if it is sent to other nodes across the network and can
be seen as the equivalent to the Erlang actor’s PID.
The actor’s life-cycle is:
• Created – Actor.actorOf[MyActor] – can not receive messages
• Started – actorRef.start() – can receive messages
• Stopped – actorRef.stop() – can not receive messages
Once the actor has been stopped it is dead and can not be started again.
Now we have a router that is representing all our workers in a single abstraction. If you paid attention to the code
above, you saw 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 have to pass in 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:
static class Master extends UntypedActor {
private final int nrOfMessages;
private final int nrOfElements;
private final CountDownLatch latch;
private double pi;
private int nrOfResults;
private long start;
private ActorRef router;
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static class PiRouter extends UntypedLoadBalancer {
private final InfiniteIterator<ActorRef> workers;
public PiRouter(ActorRef[] workers) {
this.workers = new CyclicIterator<ActorRef>(asList(workers));
}
public InfiniteIterator<ActorRef> seq() {
return workers;
}
}
public Master(
int nrOfWorkers, int nrOfMessages, int nrOfElements, CountDownLatch latch) {
this.nrOfMessages = nrOfMessages;
this.nrOfElements = nrOfElements;
this.latch = latch;
// create the workers
final ActorRef[] workers = new ActorRef[nrOfWorkers];
for (int i = 0; i < nrOfWorkers; i++) {
workers[i] = actorOf(Worker.class).start();
}
// wrap them with a load-balancing router
router = actorOf(new UntypedActorFactory() {
public UntypedActor create() {
return new PiRouter(workers);
}
}).start();
}
// message handler
public void onReceive(Object message) { ... }
@Override
public void preStart() {
start = System.currentTimeMillis();
}
@Override
public void postStop() {
// tell the world that the calculation is complete
System.out.println(String.format(
"\n\tPi estimate: \t\t%s\n\tCalculation time: \t%s millis",
pi, (System.currentTimeMillis() - start)));
latch.countDown();
}
}
A couple of things are worth explaining further.
First, we are passing in a java.util.concurrent.CountDownLatch to the Master actor. This latch
is only used for plumbing (in this specific tutorial), to have a simple way of 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 but other abstractions and functions like Channel, Future and
sendRequestReplyFuture to achieve the same thing in a non-blocking way. But for simplicity let’s stick to
a CountDownLatch for now.
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 we also invoke
latch.countDown() to tell the outside world that we are done.
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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 it also sends a new
Broadcast(poisonPill()) message to the router, which will send out the PoisonPill message to all
the actors it is representing (in our case all the Worker actors). PoisonPill is a special kind of message
that tells the receiver to shut itself down using the normal shutdown method; getContext().stop(), and is
created through the poisonPill() method. 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 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 that matches the number
of tasks sent out, the Master actor considers itself done and shuts down.
Let’s capture this in code:
// message handler
public void onReceive(Object message) {
if (message instanceof Calculate) {
// schedule work
for (int start = 0; start < nrOfMessages; start++) {
router.tell(new Work(start, nrOfElements), getContext());
}
// send a PoisonPill to all workers telling them to shut down themselves
router.tell(new Broadcast(poisonPill()));
// send a PoisonPill to the router, telling him to shut himself down
router.tell(poisonPill());
} else if (message instanceof Result) {
// handle result from the worker
Result result = (Result) message;
pi += result.getValue();
nrOfResults += 1;
if (nrOfResults == nrOfMessages) getContext().stop();
} else throw new IllegalArgumentException("Unknown message [" + message + "]");
}
1.6.11 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:
public class Pi {
public static void main(String[] args) throws Exception {
Pi pi = new Pi();
pi.calculate(4, 10000, 10000);
}
public void calculate(final int nrOfWorkers, final int nrOfElements, final int nrOfMessages)
throws Exception {
// this latch is only plumbing to know when the calculation is completed
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final CountDownLatch latch = new CountDownLatch(1);
// create the master
ActorRef master = actorOf(new UntypedActorFactory() {
public UntypedActor create() {
return new Master(nrOfWorkers, nrOfMessages, nrOfElements, latch);
}
}).start();
// start the calculation
master.tell(new Calculate());
// wait for master to shut down
latch.await();
}
}
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:
package akka.tutorial.first.java;
import static akka.actor.Actors.actorOf;
import static akka.actor.Actors.poisonPill;
import static java.util.Arrays.asList;
import
import
import
import
import
import
import
akka.actor.ActorRef;
akka.actor.UntypedActor;
akka.actor.UntypedActorFactory;
akka.routing.CyclicIterator;
akka.routing.InfiniteIterator;
akka.routing.Routing.Broadcast;
akka.routing.UntypedLoadBalancer;
import java.util.concurrent.CountDownLatch;
public class Pi {
public static void main(String[] args) throws Exception {
Pi pi = new Pi();
pi.calculate(4, 10000, 10000);
}
// ====================
// ===== Messages =====
// ====================
static class Calculate {}
static class Work {
private final int start;
private final int nrOfElements;
public Work(int start, int nrOfElements) {
this.start = start;
this.nrOfElements = nrOfElements;
}
public int getStart() { return start; }
public int getNrOfElements() { return nrOfElements; }
}
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static class Result {
private final double value;
public Result(double value) {
this.value = value;
}
public double getValue() { return value; }
}
// ==================
// ===== Worker =====
// ==================
static class Worker extends UntypedActor {
// define the work
private double calculatePiFor(int start, int nrOfElements) {
double acc = 0.0;
for (int i = start * nrOfElements; i <= ((start + 1) * nrOfElements - 1); i++) {
acc += 4.0 * (1 - (i % 2) * 2) / (2 * i + 1);
}
return acc;
}
// message handler
public void onReceive(Object message) {
if (message instanceof Work) {
Work work = (Work) message;
// perform the work
double result = calculatePiFor(work.getStart(), work.getNrOfElements())
// reply with the result
getContext().replyUnsafe(new Result(result));
} else throw new IllegalArgumentException("Unknown message [" + message + "]");
}
}
// ==================
// ===== Master =====
// ==================
static class Master extends UntypedActor {
private final int nrOfMessages;
private final int nrOfElements;
private final CountDownLatch latch;
private double pi;
private int nrOfResults;
private long start;
private ActorRef router;
static class PiRouter extends UntypedLoadBalancer {
private final InfiniteIterator<ActorRef> workers;
public PiRouter(ActorRef[] workers) {
this.workers = new CyclicIterator<ActorRef>(asList(workers));
}
public InfiniteIterator<ActorRef> seq() {
return workers;
}
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}
public Master(
int nrOfWorkers, int nrOfMessages, int nrOfElements, CountDownLatch latch) {
this.nrOfMessages = nrOfMessages;
this.nrOfElements = nrOfElements;
this.latch = latch;
// create the workers
final ActorRef[] workers = new ActorRef[nrOfWorkers];
for (int i = 0; i < nrOfWorkers; i++) {
workers[i] = actorOf(Worker.class).start();
}
// wrap them with a load-balancing router
router = actorOf(new UntypedActorFactory() {
public UntypedActor create() {
return new PiRouter(workers);
}
}).start();
}
// message handler
public void onReceive(Object message) {
if (message instanceof Calculate) {
// schedule work
for (int start = 0; start < nrOfMessages; start++) {
router.tell(new Work(start, nrOfElements), getContext());
}
// send a PoisonPill to all workers telling them to shut down themselves
router.tell(new Broadcast(poisonPill()));
// send a PoisonPill to the router, telling him to shut himself down
router.tell(poisonPill());
} else if (message instanceof Result) {
// handle result from the worker
Result result = (Result) message;
pi += result.getValue();
nrOfResults += 1;
if (nrOfResults == nrOfMessages) getContext().stop();
} else throw new IllegalArgumentException("Unknown message [" + message + "]");
}
@Override
public void preStart() {
start = System.currentTimeMillis();
}
@Override
public void postStop() {
// tell the world that the calculation is complete
System.out.println(String.format(
"\n\tPi estimate: \t\t%s\n\tCalculation time: \t%s millis",
pi, (System.currentTimeMillis() - start)));
latch.countDown();
}
}
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// ==================
// ===== Run it =====
// ==================
public void calculate(final int nrOfWorkers, final int nrOfElements, final int nrOfMessages)
throws Exception {
// this latch is only plumbing to know when the calculation is completed
final CountDownLatch latch = new CountDownLatch(1);
// create the master
ActorRef master = actorOf(new UntypedActorFactory() {
public UntypedActor create() {
return new Master(nrOfWorkers, nrOfMessages, nrOfElements, latch);
}
}).start();
// start the calculation
master.tell(new Calculate());
// wait for master to shut down
latch.await();
}
}
1.6.12 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-1.3.1.jar and the scala-library.jar JAR files, so let’s add them to the compiler
classpath when we compile the source:
$ javac -cp lib/scala-library.jar:lib/akka/akka-actor-1.3.1.jar tutorial/akka/tutorial/first/java/
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-1.3.1.jar and the scala-library.jar JAR files to the classpath as
well as the classes we compiled ourselves:
$ java \
-cp lib/scala-library.jar:lib/akka/akka-actor-1.3.1.jar:tutorial \
akka.tutorial.java.first.Pi
AKKA_HOME is defined as [/Users/jboner/tools/akka-actors-1.3.1]
loading config from [/Users/jboner/tools/akka-actors-1.3.1/config/akka.conf].
Pi estimate:
Calculation time:
3.1435501812459323
822 millis
Yippee! It is working.
If you have not defined the AKKA_HOME environment variable then Akka can’t find the akka.conf configuration file and will print out a Can’t load akka.conf warning. This is ok since it will then just use the
defaults.
1.6.13 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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$ mvn compile
When this in done we can run our application directly inside Maven:
$ mvn exec:java -Dexec.mainClass="akka.tutorial.first.java.Pi"
...
Pi estimate:
3.1435501812459323
Calculation time:
939 millis
Yippee! It is working.
If you have not defined an the AKKA_HOME environment variable then Akka can’t find the akka.conf configuration file and will print out a Can’t load akka.conf warning. This is ok since it will then just use the
defaults.
1.6.14 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.
Now we are ready to take on more advanced problems. In the next tutorial we will build on this one, refactor it
into more idiomatic Akka and Scala code, and introduce a few new concepts and abstractions. Whenever you feel
ready, join me in the Getting Started Tutorial: Second Chapter.
Happy hakking.
1.7 Use-case and Deployment Scenarios
1.7.1 How can I use and deploy Akka?
Akka can be used in two different ways:
• As a library: used as a regular JAR on the classpath and/or in a web app, to be put into WEB-INF/lib
• As a microkernel: stand-alone microkernel, embedding a servlet container along with many other services
Using Akka as library
This is most likely what you want if you are building Web applications. There are several ways you can use Akka
in Library mode by adding more and more modules to the stack.
Actors as services
The simplest way you can use Akka is to use the actors as services in your Web application. All that’s needed
to do that is to put the Akka charts as well as its dependency jars into WEB-INF/lib. You also need to put the
akka.conf config file in the $AKKA_HOME/config directory. Now you can create your Actors as regular
services referenced from your Web application. You should also be able to use the Remoting service, e.g. be able
to make certain Actors remote on other hosts. Please note that remoting service does not speak HTTP over port
80, but a custom protocol over the port is specified in akka.conf.
1.7. Use-case and Deployment Scenarios
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Using Akka as a stand alone microkernel
Akka can also be run as a stand-alone microkernel. It implements a full enterprise stack. See the Add-on Modules
for more information.
Using the Akka sbt plugin to package your application
The Akka sbt plugin can create a full Akka microkernel deployment for your sbt project.
To use the plugin, first add a plugin
project/plugins/Plugins.scala with:
definition
to
your
SBT
project
by
creating
import sbt._
class Plugins(info: ProjectInfo) extends PluginDefinition(info) {
val akkaRepo = "Akka Repo" at "http://repo.akka.io/releases"
val akkaPlugin = "se.scalablesolutions.akka" % "akka-sbt-plugin" % "1.3.1"
}
Then mix the AkkaKernelProject trait into your project definition. For example:
class MyProject(info: ProjectInfo) extends DefaultProject(info) with AkkaKernelProject
This will automatically add all the Akka dependencies needed for a microkernel deployment (download them with
sbt update).
Place your config files in src/main/config.
To build a microkernel deployment use the dist task:
sbt dist
1.8 Examples of use-cases for Akka
There is a great discussion on use-cases for Akka with some good write-ups by production users here
1.8.1 Here are some of the areas where Akka is being deployed into production
Transaction processing (Online Gaming, Finance/Banking, Trading, Statistics, Betting, Social
Media, Telecom)
Scale up, scale out, fault-tolerance / HA
Service backend (any industry, any app)
Service REST, SOAP, Cometd, WebSockets etc Act as message hub / integration layer Scale up, scale
out, fault-tolerance / HA
Concurrency/parallelism (any app)
Correct Simple to work with and understand Just add the jars to your existing JVM project (use Scala,
Java, Groovy or JRuby)
Simulation
Master/Worker, Compute Grid, MapReduce etc.
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Batch processing (any industry)
Camel integration to hook up with batch data sources Actors divide and conquer the batch workloads
Communications Hub (Telecom, Web media, Mobile media)
Scale up, scale out, fault-tolerance / HA
Gaming and Betting (MOM, online gaming, betting)
Scale up, scale out, fault-tolerance / HA
Business Intelligence/Data Mining/general purpose crunching
Scale up, scale out, fault-tolerance / HA
Complex Event Stream Processing
Scale up, scale out, fault-tolerance / HA
1.8. Examples of use-cases for Akka
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CHAPTER
TWO
GENERAL
2.1 Akka and the Java Memory Model
Prior to Java 5, the Java Memory Model (JMM) was broken. It was possible to get all kinds of strange results
like unpredictable merged writes made by concurrent executing threads, unexpected reordering of instructions,
and even final fields were not guaranteed to be final. With Java 5 and JSR-133, the Java Memory Model is
clearly specified. This specification makes it possible to write code that performs, but doesn’t cause concurrency
problems. The Java Memory Model is specified in ‘happens before’-rules, e.g.:
• monitor lock rule: a release of a lock happens before every subsequent acquire of the same lock.
• volatile variable rule: a write of a volatile variable happens before every subsequent read of the same
volatile variable
The ‘happens before’-rules clearly specify which visibility guarantees are provided on memory and which reorderings are allowed. Without these rules it would not be possible to write concurrent and performant code in
Java.
2.1.1 Actors and the Java Memory Model
With the Actors implementation in Akka, there are 2 ways multiple threads can execute actions on shared memory
over time:
• if a message is send to an actor (e.g. by another actor). In most cases messages are immutable, but if that
message is not a properly constructed immutable object, without happens before rules, the system still could
be subject to instruction re-orderings and visibility problems (so a possible source of concurrency errors).
• if an actor makes changes to its internal state in one ‘receive’ method and access that state while processing
another message. With the actors model you don’t get any guarantee that the same thread will be executing
the same actor for different messages. Without a happens before relation between these actions, there could
be another source of concurrency errors.
To solve the 2 problems above, Akka adds the following 2 ‘happens before’-rules to the JMM:
• the actor send rule: where the send of the message to an actor happens before the receive of the same
actor.
• the actor subsequent processing rule: where processing of one message happens before processing of the
next message by the same actor.
Both rules only apply for the same actor instance and are not valid if different actors are used.
2.1.2 STM and the Java Memory Model
The Akka STM also provides a happens before rule called:
• the transaction rule: a commit on a transaction happens before every subsequent start of a transaction
where there is at least 1 shared reference.
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How these rules are realized in Akka, is an implementation detail and can change over time (the exact details
could even depend on the used configuration) but they will lift on the other JMM rules like the monitor lock rule
or the volatile variable rule. Essentially this means that you, the Akka user, do not need to worry about adding
synchronization to provide such a happens before relation, because it is the responsibility of Akka. So you have
your hands free to deal with your problems and not that of the framework.
2.2 Configuration
2.2.1 Specifying the configuration file
If you don’t specify a configuration file then Akka uses default values, corresponding to the
akka-reference.conf that you see below. You can specify your own configuration file to override any
property in the reference config. You only have to define the properties that differ from the default configuration.
The location of the config file to use can be specified in various ways:
• Define the -Dakka.config=... system property parameter with a file path to configuration file.
• Put an akka.conf file in the root of the classpath.
• Define the AKKA_HOME environment variable pointing to the root of the Akka distribution. The config
is taken from the AKKA_HOME/config/akka.conf. You can also point to the AKKA_HOME by
specifying the -Dakka.home=... system property parameter.
If several of these ways to specify the config file are used at the same time the precedence is the order as given
above, i.e. you can always redefine the location with the -Dakka.config=... system property.
2.2.2 Defining the configuration file
Here is the reference configuration file:
####################
# Akka Config File #
####################
# This file has all the default settings, so all these could be removed with no visible effect.
# Modify as needed.
akka {
version = "1.3.1"
# Akka version, checked against the runtime version of Akka.
enabled-modules = []
# Comma separated list of the enabled modules. Options: ["remote", "c
time-unit = "seconds"
# Time unit for all timeout properties throughout the config
event-handlers = ["akka.event.EventHandler$DefaultListener"] # event handlers to register at boo
event-handler-level = "INFO" # Options: ERROR, WARNING, INFO, DEBUG
# These boot classes are loaded (and created) automatically when the Akka Microkernel boots up
#
Can be used to bootstrap your application(s)
#
Should be the FQN (Fully Qualified Name) of the boot class which needs to have a default c
# boot = ["sample.camel.Boot",
#
"sample.rest.java.Boot",
#
"sample.rest.scala.Boot",
#
"sample.security.Boot"]
boot = []
actor {
timeout = 5
2.2. Configuration
# Default timeout for Future based invocations
#
- Actor:
? and ask
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serialize-messages = off
throughput = 5
throughput-deadline-time = -1
dispatcher-shutdown-timeout = 1
#
#
#
#
#
#
- UntypedActor: ask
- TypedActor:
methods with non-void return type
Does a deep clone of (non-primitive) messages to ensure i
Default throughput for all ExecutorBasedEventDrivenDispat
Default throughput deadline for all ExecutorBasedEventDri
Using the akka.time-unit, how long dispatchers by default
default-dispatcher {
type = "GlobalExecutorBasedEventDriven" # Must be one of the following, all "Global*" are no
#
- ExecutorBasedEventDriven
#
- ExecutorBasedEventDrivenWorkStealing
#
- GlobalExecutorBasedEventDriven
daemonic = off
# If the Threads created for this dispatcher should be daem
keep-alive-time = 60
# Keep alive time for threads
core-pool-size-factor = 1.0
# No of core threads ... ceil(available processors * factor
max-pool-size-factor = 4.0
# Max no of threads ... ceil(available processors * factor)
executor-bounds = -1
# Makes the Executor bounded, -1 is unbounded
task-queue-size = -1
# Specifies the bounded capacity of the task queue (< 1 ==
task-queue-type = "linked"
# Specifies which type of task queue will be used, can be "
allow-core-timeout = on
# Allow core threads to time out
rejection-policy = "sane"
# sane, abort, caller-runs, discard-oldest, discard
throughput = 5
# Throughput for ExecutorBasedEventDrivenDispatcher, set to
throughput-deadline-time = -1
# Throughput deadline for ExecutorBasedEventDrivenDispatche
mailbox-capacity = -1
# If negative (or zero) then an unbounded mailbox is used (
# If positive then a bounded mailbox is used and the capaci
# NOTE: setting a mailbox to ’blocking’ can be a bit danger
#
could lead to deadlock, use with care
#
# The following are only used for ExecutorBasedEventDriven
# and only if mailbox-capacity > 0
mailbox-push-timeout-time = 10
# Specifies the timeout to add a new message to a mailbox t
#
(in unit defined by the time-unit property)
}
debug {
receive = "false"
autoreceive = "false"
lifecycle = "false"
#
#
#
#
#
enable
to log
enable
(Kill,
enable
function of Actor.loggable(), which is
any received message at DEBUG level
DEBUG logging of all AutoReceiveMessages
PoisonPill and the like)
DEBUG logging of actor lifecycle changes
}
mailbox {
mongodb {
# Any specified collection name will be used as a prefix for collections that use dura
uri = "mongodb://localhost/akka.mailbox"
# Follow Mongo URI Spec - http://www.mongod
# Configurable timeouts for certain ops
timeout {
read = 3000 # number of milliseconds to wait for a read to succeed before timing o
write = 3000 # number of milliseconds to wait for a write to succeed before timing
}
}
}
}
stm {
fair
max-retries
timeout
= on
= 1000
= 5
# Should global transactions be fair or non-fair (non fair yield bet
# Default timeout for blocking transactions and transaction set (in
#
the time-unit property)
write-skew
= true
blocking-allowed = false
interruptible
= false
2.2. Configuration
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speculative
quick-release
propagation
trace-level
=
=
=
=
true
true
"requires"
"none"
}
http {
hostname = "localhost"
port = 9998
#If you are using akka.http.AkkaRestServlet
filters = ["akka.security.AkkaSecurityFilterFactory"] # List with all jersey filters to use
# resource-packages = ["sample.rest.scala",
#
"sample.rest.java",
#
"sample.security"] # List with all resource packages for your Jersey se
resource-packages = []
# The authentication service to use. Need to be overridden (sample now)
# authenticator = "sample.security.BasicAuthenticationService"
authenticator = "N/A"
# Uncomment if you are using the KerberosAuthenticationActor
# kerberos {
#
servicePrincipal = "HTTP/localhost@EXAMPLE.COM"
#
keyTabLocation
= "URL to keytab"
#
kerberosDebug
= "true"
#
realm
= "EXAMPLE.COM"
# }
kerberos {
servicePrincipal = "N/A"
keyTabLocation
= "N/A"
kerberosDebug
= "N/A"
realm
= ""
}
#If you are using akka.http.AkkaMistServlet
mist-dispatcher {
#type = "GlobalExecutorBasedEventDriven" # Uncomment if you want to use a different dispatch
}
connection-close = true
# toggles the addition of the "Connection" response he
root-actor-id = "_httproot"
# the id of the actor to use as the root endpoint
root-actor-builtin = true
# toggles the use of the built-in root endpoint base c
timeout = 1000
# the default timeout for all async requests (in ms)
expired-header-name = "Async-Timeout"
# the name of the response header to use when an async
expired-header-value = "expired"
# the value of the response header to use when an asyn
}
remote {
# secure-cookie = "050E0A0D0D06010A00000900040D060F0C09060B" # generate your own with ’$AKKA_H
secure-cookie = ""
layer = "akka.remote.netty.NettyRemoteSupport"
server {
hostname = "localhost"
#
port = 2552
#
message-frame-size = 1048576 #
require-cookie = off
#
untrusted-mode = off
#
backlog = 4096
#
execution-pool-keepalive = 60#
execution-pool-size
= 16#
2.2. Configuration
The hostname or IP that clients should connect to
The port clients should connect to. Default is 2552 (AKKA)
Increase this if you want to be able to send messages with la
Should the remote server require that it peers share the same
Enable untrusted mode for full security of server managed act
Sets the size of the connection backlog
Length in akka.time-unit how long core threads will be kept a
Size of the core pool of the remote execution unit
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max-channel-memory-size
max-total-memory-size
= 0 # Maximum channel size, 0 for off
= 0 # Maximum total size of all channels, 0 for off
}
client {
buffering {
retry-message-send-on-failure = off # Buffer outbound messages when send failed, if off yo
capacity = -1
# If negative (or zero) then an unbounded mailbox is us
# If positive then a bounded mailbox is used and the ca
}
connection-timeout = 10
# Number in time-unit, timeout for establishing outboun
reconnect-delay = 5
# Number in time-unit, the interval at which to try to
read-timeout = 120
# Number in time-unit, period of inactivity after which
message-frame-size = 1048576
# Size in bytes
reap-futures-delay = 5
# Number in time-unit, the interval of garbage-collecti
reconnection-time-window = 600 # Maximum time window that a client should try to reconnect f
}
}
test {
timefactor = "1.0"
}
# factor by which to scale timeouts during tests, e.g. to account for sh
}
A custom akka.conf might look like this:
# In this file you can override any option defined in the ’akka-reference.conf’ file.
# Copy in all or parts of the ’akka-reference.conf’ file and modify as you please.
akka {
event-handlers = ["akka.event.slf4j.Slf4jEventHandler"]
# Comma separated list of the enabled modules.
enabled-modules = ["camel", "remote"]
# These boot classes are loaded (and created) automatically when the Akka Microkernel boots up
#
Can be used to bootstrap your application(s)
#
Should be the FQN (Fully Qualified Name) of the boot class which needs to have a default c
boot = ["sample.camel.Boot",
"sample.myservice.Boot"]
actor {
throughput = 10
}
# Throughput for ExecutorBasedEventDrivenDispatcher, set to 1 for complete fa
remote {
server {
port = 2562
}
}
# The port clients should connect to. Default is 2552 (AKKA)
}
2.2.3 Specifying files for different modes
You can use different configuration files for different purposes by specifying a mode option, either as
-Dakka.mode=... system property or as AKKA_MODE=... environment variable. For example using DEBUG log level when in development mode. Run with -Dakka.mode=dev and place the following
akka.dev.conf in the root of the classpath.
akka.dev.conf:
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akka {
event-handler-level = "DEBUG"
}
The mode option works in the same way when using configuration files in AKKA_HOME/config/ directory.
The mode option is not used when specifying the configuration file with -Dakka.config=... system property.
2.2.4 Including files
Sometimes it can be useful to include another configuration file, for example if you have one akka.conf with
all environment independent settings and then override some settings for specific modes.
akka.dev.conf:
include "akka.conf"
akka {
event-handler-level = "DEBUG"
}
2.2.5 Showing Configuration Source
If the system property akka.output.config.source is set to anything but null, then the source from which
Akka reads its configuration is printed to the console during application startup.
2.2.6 Summary of System Properties
• akka.home (AKKA_HOME): where Akka searches for configuration
• akka.config: explicit configuration file location
• akka.mode (AKKA_MODE): modify configuration file name for multiple profiles
• akka.output.config.source: whether to print configuration source to console
2.3 Event Handler
There is an Event Handler which takes the place of a logging system in Akka:
akka.event.EventHandler
You can configure which event handlers should be registered at boot time. That is done using the ‘event-handlers’
element in akka.conf. Here you can also define the log level.
akka {
# event handlers to register at boot time (EventHandler$DefaultListener logs to STDOUT)
event-handlers = ["akka.event.EventHandler$DefaultListener"]
event-handler-level = "DEBUG" # Options: ERROR, WARNING, INFO, DEBUG
}
The default one logs to STDOUT and is registered by default. It is not intended to be used for production. There
is also an SLF4J event handler available in the ‘akka-slf4j’ module.
Example of creating a listener from Scala (from Java you just have to create an ‘UntypedActor’ and create a
handler for these messages):
2.3. Event Handler
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val errorHandlerEventListener = Actor.actorOf(new Actor {
self.dispatcher = EventHandler.EventHandlerDispatcher
def receive = {
case EventHandler.Error(cause, instance, message) => ...
case EventHandler.Warning(instance, message) => ...
case EventHandler.Info(instance, message) => ...
case EventHandler.Debug(instance, message) => ...
case genericEvent => ...
}
})
To add the listener:
EventHandler.addListener(errorHandlerEventListener)
To remove the listener:
EventHandler.removeListener(errorHandlerEventListener)
To log an event:
EventHandler.notify(EventHandler.Error(exception, this, message))
EventHandler.notify(EventHandler.Warning(this, message))
EventHandler.notify(EventHandler.Info(this, message))
EventHandler.notify(EventHandler.Debug(this, message))
EventHandler.notify(object)
You can also use one of the direct methods (for a bit better performance):
EventHandler.error(exception, this, message)
EventHandler.error(this, message)
EventHandler.warning(this, message)
EventHandler.info(this, message)
EventHandler.debug(this, message)
The event handler allows you to send an arbitrary object to the handler which you can handle in your event handler
listener. The default listener prints it’s toString String out to STDOUT.
EventHandler.notify(anyRef)
The methods take a call-by-name parameter for the message to avoid object allocation and execution if level is
disabled. The following formatting function will not be evaluated if level is INFO, WARNING, or ERROR.
EventHandler.debug(this, "Processing took %s ms".format(duration))
From Java you need to nest the call in an if statement to achieve the same thing.
if (EventHandler.isDebugEnabled()) {
EventHandler.debug(this, String.format("Processing took %s ms", duration));
}
2.3. Event Handler
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2.4 SLF4J
This module is available in the ‘akka-slf4j.jar’. It has one single dependency; the slf4j-api jar. In runtime you also
need a SLF4J backend, we recommend:
lazy val logback = "ch.qos.logback" % "logback-classic" % "0.9.28" % "runtime"
2.4.1 Event Handler
This module includes a SLF4J Event Handler that works with Akka’s standard Event Handler. You enabled it in
the ‘event-handlers’ element in akka.conf. Here you can also define the log level.
akka {
event-handlers = ["akka.event.slf4j.Slf4jEventHandler"]
event-handler-level = "DEBUG"
}
Read more about how to use the Event Handler.
2.4. SLF4J
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CHAPTER
THREE
COMMON UTILITIES
3.1 Scheduler
Module stability: SOLID
Akka has a little scheduler written using actors. This can be convenient if you want to schedule some periodic
task for maintenance or similar.
It allows you to register a message that you want to be sent to a specific actor at a periodic interval.
3.1.1 Here is an example:
import akka.actor.Scheduler
//Sends messageToBeSent to receiverActor after initialDelayBeforeSending and then after each delay
Scheduler.schedule(receiverActor, messageToBeSent, initialDelayBeforeSending, delayBetweenMessages
//Sends messageToBeSent to receiverActor after delayUntilSend
Scheduler.scheduleOnce(receiverActor, messageToBeSent, delayUntilSend, timeUnit)
3.2 Duration
Module stability: SOLID
Durations are used throughout the Akka library, wherefore this concept is represented by a special data type,
Duration. Values of this type may represent infinite (Duration.Inf, Duration.MinusInf) or finite
durations.
3.2.1 Scala
In Scala durations are constructable using a mini-DSL and support all expected operations:
import akka.util.duration._
// notice the small d
val fivesec = 5.seconds
val threemillis = 3.millis
val diff = fivesec - threemillis
assert (diff < fivesec)
val fourmillis = threemillis * 4 / 3
val n = threemillis / (1 millisecond)
// though you cannot write it the other way around
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Note: You may leave out the dot if the expression is clearly delimited (e.g. within parentheses or in an argument
list), but it is recommended to use it if the time unit is the last token on a line, otherwise semi-colon inference
might go wrong, depending on what starts the next line.
3.2.2 Java
Java provides less syntactic sugar, so you have to spell out the operations as method calls instead:
final Duration fivesec = Duration.create(5, "seconds");
final Duration threemillis = Duration.parse("3 millis");
final Duration diff = fivesec.minus(threemillis);
assert (diff.lt(fivesec));
assert (Duration.Zero().lt(Duration.Inf()));
3.2. Duration
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CHAPTER
FOUR
SCALA API
4.1 Actors (Scala)
Module stability: SOLID
The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates
the developer from having to deal with explicit locking and thread management, making it easier to write correct
concurrent and parallel systems. Actors were defined in the 1973 paper by Carl Hewitt but have been popularized
by the Erlang language, and used for example at Ericsson with great success to build highly concurrent and reliable
telecom systems.
The API of Akka’s Actors is similar to Scala Actors which has borrowed some of its syntax from Erlang.
The Akka 0.9 release introduced a new concept; ActorRef, which requires some refactoring. If you are new to
Akka just read along, but if you have used Akka 0.6.x, 0.7.x and 0.8.x then you might be helped by the 0.8.x =>
0.9.x migration guide
4.1.1 Creating Actors
Actors can be created either by:
• Extending the Actor class and implementing the receive method.
• Create an anonymous actor using one of the actor methods.
Defining an Actor class
Actor classes are implemented by extending the Actor class and implementing the receive method. The
receive method should define a series of case statements (which has the type PartialFunction[Any,
Unit]) that defines which messages your Actor can handle, using standard Scala pattern matching, along with
the implementation of how the messages should be processed.
Here is an example:
import akka.actor.Actor
import akka.event.EventHandler
class MyActor extends Actor {
def receive = {
case "test" => EventHandler.info(this, "received test")
case _ => EventHandler.info(this, "received unknown message")
}
}
Please note that the Akka Actor receive message loop is exhaustive, which is different compared to Erlang and
Scala Actors. This means that you need to provide a pattern match for all messages that it can accept and if you
want to be able to handle unknown messages then you need to have a default case as in the example above.
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Creating Actors
val myActor = Actor.actorOf[MyActor]
myActor.start()
Normally you would want to import the actorOf method like this:
import akka.actor.Actor._
val myActor = actorOf[MyActor]
To avoid prefixing it with Actor every time you use it.
You can also start it in the same statement:
val myActor = actorOf[MyActor].start()
The call to actorOf returns an instance of ActorRef. This is a handle to the Actor instance which you can
use to interact with the Actor. The ActorRef is immutable and has a one to one relationship with the Actor
it represents. The ActorRef is also serializable and network-aware. This means that you can serialize it, send
it over the wire and use it on a remote host and it will still be representing the same Actor on the original node,
across the network.
Creating Actors with non-default constructor
If your Actor has a constructor that takes parameters then you can’t create it using actorOf[TYPE]. Instead
you can use a variant of actorOf that takes a call-by-name block in which you can create the Actor in any way
you like.
Here is an example:
val a = actorOf(new MyActor(..)).start() // allows passing in arguments into the MyActor construct
Creating Actors using anonymous classes
When spawning actors for specific sub-tasks from within an actor, it may be convenient to include the code to be
executed directly in place, using an anonymous class:
def receive = {
case m: DoIt =>
actorOf(new Actor {
def receive = {
case DoIt(msg) =>
val replyMsg = doSomeDangerousWork(msg)
self.reply(replyMsg)
self.stop()
}
def doSomeDangerousWork(msg: Message) = { ... }
}).start() ! m
}
Warning: In this case you need to carefully avoid closing over the containing actor’s reference, i.e. do
not call methods on the enclosing actor from within the anonymous Actor class. This would break the actor
encapsulation and may introduce synchronization bugs and race conditions because the other actor’s code will
be scheduled concurrently to the enclosing actor. Unfortunately there is not yet a way to detect these illegal
accesses at compile time.
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Running a block of code asynchronously
Here we create a light-weight actor-based thread, that can be used to spawn off a task. Code blocks spawned up
like this are always implicitly started, shut down and made eligible for garbage collection. The actor that is created
“under the hood” is not reachable from the outside and there is no way of sending messages to it. It being an actor
is only an implementation detail. It will only run the block in an event-based thread and exit once the block has
run to completion.
spawn {
... // do stuff
}
4.1.2 Actor Internal API
The Actor trait defines only one abstract method, the abovementioned receive. In addition, it offers two
convenience methods become/unbecome for modifying the hotswap behavior stack as described in HotSwap
and the self reference to this actor’s ActorRef object. If the current actor behavior does not match a received
message, unhandled is called, which by default throws an UnhandledMessageException.
The remaining visible methods are user-overridable life-cycle hooks which are described in the following:
def
def
def
def
def
preStart() {}
preRestart(cause: Throwable, message: Option[Any]) {}
freshInstance(): Option[Actor] = None
postRestart(cause: Throwable) {}
postStop() {}
The implementations shown above are the defaults provided by the Actor trait.
Note: There is still the single-argument method preRestart(cause: Throwable), which in fact is
called by the default implementation of the two-argument variant. This method will be removed in version 2.0;
you should add the second (dummy) argument to your actors before upgrading.
Start Hook
Right after starting the actor, its preStart method is invoked. This is guaranteed to happen before the first
message from external sources is queued to the actor’s mailbox.
override def preStart {
// e.g. send initial message to self
self ! GetMeStarted
// or do any other stuff, e.g. registering with other actors
someService ! Register(self)
}
Restart Hooks
A supervised actor, i.e. one which is linked to another actor with a fault handling strategy, will be restarted in case
an exception is thrown while processing a message. This restart involves four of the hooks mentioned above:
1. The old actor is informed by calling preRestart with the exception which caused the restart and the
message which triggered that exception; the latter may be None if the restart was not caused by processing
a message, e.g. when a supervisor does not trap the exception and is restarted in turn by its supervisor. This
method is the best place for cleaning up, preparing hand-over to the fresh actor instance, etc.
2. The old actor’s freshInstance factory method is invoked, which may optionally produce the new actor
instance which will replace this actor. If this method returns None or throws an exception, the initial factory
from the Actor.actorOf call is used to produce the fresh instance.
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3. The new actor’s preStart method is invoked, just as in the normal start-up case.
4. The new actor’s postRestart method is called with the exception which caused the restart.
Warning: The freshInstance hook may be used to propagate (part of) the failed actor’s state to the
fresh instance. This carries the risk of proliferating the cause for the crash which triggered the restart. If you
are tempted to take this route, it is strongly advised to step back and consider other possible approaches, e.g.
distributing the state in question using other means or spawning short-lived worker actors for carrying out
“risky” tasks.
An actor restart replaces only the actual actor object; the contents of the mailbox and the hotswap stack are
unaffected by the restart, so processing of messages will resume after the postRestart hook returns. Any
message sent to an actor while it is being restarted will be queued to its mailbox as usual.
Stop Hook
After stopping an actor, its postStop hook is called, which may be used e.g. for deregistering this actor from
other services. This hook is guaranteed to run after message queuing has been disabled for this actor, i.e. sending
messages would fail with an IllegalActorStateException.
4.1.3 Identifying Actors
Each Actor has two fields:
• self.uuid
• self.id
The difference is that the uuid is generated by the runtime, guaranteed to be unique and can’t be modified. While
the id is modifiable by the user, and defaults to the Actor class name. You can retrieve Actors by both UUID and
ID using the ActorRegistry, see the section further down for details.
4.1.4 Messages and immutability
IMPORTANT: Messages can be any kind of object but have to be immutable. Scala can’t enforce immutability
(yet) so this has to be by convention. Primitives like String, Int, Boolean are always immutable. Apart from these
the recommended approach is to use Scala case classes which are immutable (if you don’t explicitly expose the
state) and works great with pattern matching at the receiver side.
Here is an example:
// define the case class
case class Register(user: User)
// create a new case class message
val message = Register(user)
Other good messages types are scala.Tuple2, scala.List, scala.Map which are all immutable and
great for pattern matching.
4.1.5 Send messages
Messages are sent to an Actor through one of the following methods.
• ! means “fire-and-forget”, e.g. send a message asynchronously and return immediately.
• ? sends a message asynchronously and returns a Future representing a possible reply.
Note: There used to be two more “bang” methods, which are deprecated and will be removed in Akka 2.0:
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• !! was similar to the current (actor ?
timeout handling described below.
msg).as[T]; deprecation followed from the change of
• !!![T] was similar to the current (actor ? msg).mapTo[T], with the same change in the handling
of Future’s timeout as for !!, but additionally the old method could defer possible type cast problems
into seemingly unrelated parts of the code base.
Fire-forget
This is the preferred way of sending messages. No blocking waiting for a message. This gives the best concurrency
and scalability characteristics.
actor ! "Hello"
If invoked from within an Actor, then the sending actor reference will be implicitly passed along with the message
and available to the receiving Actor in its channel: UntypedChannel member field. The target actor can
use this to reply to the original sender, e.g. by using the self.reply(message: Any) method.
If invoked from an instance that is not an Actor there will be no implicit sender passed along with the message
and you will get an IllegalActorStateException when calling self.reply(...).
Send-And-Receive-Future
Using ? will send a message to the receiving Actor asynchronously and will return a Future:
val future = actor ? "Hello"
The receiving actor should reply to this message, which will complete the future with the reply message as value;
if the actor throws an exception while processing the invocation, this exception will also complete the future. If the
actor does not complete the future, it will expire after the timeout period, which is taken from one of the following
three locations in order of precedence:
1. explicitly given timeout as in actor.?("hello")(timeout = 12 millis)
2. implicit argument of type Actor.Timeout, e.g.
implicit val timeout = Actor.Timeout(12 millis)
val future = actor ? "hello"
3. default timeout from akka.conf
See Futures (Scala) for more information on how to await or query a future.
Send-And-Receive-Eventually
The future returned from the ? method can conveniently be passed around or chained with further processing
steps, but sometimes you just need the value, even if that entails waiting for it (but keep in mind that waiting
inside an actor is prone to dead-locks, e.g. if obtaining the result depends on processing another message on this
actor).
For this purpose, there is the method Future.as[T] which waits until either the future is completed or its
timeout expires, whichever comes first. The result is then inspected and returned as Some[T] if it was normally
completed and the answer’s runtime type matches the desired type; if the future contains an exception or the value
cannot be cast to the desired type, it will throw the exception or a ClassCastException (if you want to get
None in the latter case, use Future.asSilently[T]). In case of a timeout, None is returned.
(actor ? msg).as[String] match {
case Some(answer) => ...
case None
=> ...
}
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val resultOption = (actor ? msg).as[String]
if (resultOption.isDefined) ... else ...
for (x <- (actor ? msg).as[Int]) yield { 2 * x }
Forward message
You can forward a message from one actor to another. This means that the original sender address/reference is
maintained even though the message is going through a ‘mediator’. This can be useful when writing actors that
work as routers, load-balancers, replicators etc.
actor.forward(message)
4.1.6 Receive messages
An Actor has to implement the receive method to receive messages:
protected def receive: PartialFunction[Any, Unit]
Note:
Akka has an alias to the PartialFunction[Any, Unit] type called Receive
(akka.actor.Actor.Receive), so you can use this type instead for clarity. But most often you
don’t need to spell it out.
This method should return a PartialFunction, e.g. a ‘match/case’ clause in which the message can be
matched against the different case clauses using Scala pattern matching. Here is an example:
class MyActor extends Actor {
def receive = {
case "Hello" =>
log.info("Received ’Hello’")
case _ =>
throw new RuntimeException("unknown message")
}
}
4.1.7 Reply to messages
Reply using the channel
If you want to have a handle to an object to whom you can reply to the message, you can use the Channel
abstraction. Simply call self.channel and then you can forward that to others, store it away or otherwise
until you want to reply, which you do by channel ! response:
case request =>
val result = process(request)
self.channel ! result
// will throw an exception if there is no sender information
self.channel tryTell result // will return Boolean whether reply succeeded
The Channel trait is contravariant in the expected message type. Since self.channel is subtype of
Channel[Any], you may specialise your return channel to allow the compiler to check your replies:
class MyActor extends Actor {
def doIt(channel: Channel[String], x: Any) = { channel ! x.toString }
def receive = {
case x => doIt(self.channel, x)
}
}
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case request =>
friend forward self.channel
We recommend that you as first choice use the channel abstraction instead of the other ways described in the
following sections.
Reply using the reply and reply_? methods
If you want to send a message back to the original sender of the message you just received then you can use the
reply(..) method.
case request =>
val result = process(request)
self.reply(result)
In this case the result will be send back to the Actor that sent the request.
The reply method throws an IllegalStateException if unable to determine what to reply to, e.g. the
sender is not an actor. You can also use the more forgiving tryReply method which returns true if reply was
sent, and false if unable to determine what to reply to.
case request =>
val result = process(request)
if (self.tryReply(result)) ...// success
else ... // handle failure
Summary of reply semantics and options
• self.reply(...) can be used to reply to an Actor or a Future from within an actor; the current
actor will be passed as reply channel if the current channel supports this.
• self.channel is a reference providing an abstraction for the reply channel; this reference may be passed
to other actors or used by non-actor code.
Note: There used to be two methods for determining the sending Actor or Future for the current invocation:
• self.sender yielded a Option[ActorRef]
• self.senderFuture yielded a Option[CompletableFuture[Any]]
These two concepts have been unified into the channel. If you need to know the nature of the channel, you may
do so using pattern matching:
channel match {
case ref : ActorRef => ...
case f : ActorCompletableFuture => ...
}
4.1.8 Initial receive timeout
A timeout mechanism can be used to receive a message when no initial message is received within a certain
time. To receive this timeout you have to set the receiveTimeout property and declare a case handing the
ReceiveTimeout object.
self.receiveTimeout = Some(30000L) // 30 seconds
def receive = {
case "Hello" =>
log.info("Received ’Hello’")
case ReceiveTimeout =>
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throw new RuntimeException("received timeout")
}
This mechanism also work for hotswapped receive functions. Every time a HotSwap is sent, the receive timeout
is reset and rescheduled.
4.1.9 Starting actors
Actors are started by invoking the start method.
val actor = actorOf[MyActor]
actor.start()
You can create and start the Actor in a one liner like this:
val actor = actorOf[MyActor].start()
When you start the Actor then it will automatically call the def preStart callback method on the Actor
trait. This is an excellent place to add initialization code for the actor.
override def preStart() = {
... // initialization code
}
4.1.10 Stopping actors
Actors are stopped by invoking the stop method.
actor.stop()
When stop is called then a call to the def postStop callback method will take place. The Actor can use this
callback to implement shutdown behavior.
override def postStop() = {
... // clean up resources
}
You can shut down all Actors in the system by invoking:
Actor.registry.shutdownAll()
4.1.11 PoisonPill
You can also send an actor the akka.actor.PoisonPill message, which will stop the actor when the message is processed.
If the sender is a Future (e.g. the message is sent with ?), the Future will be completed with an
akka.actor.ActorKilledException("PoisonPill").
4.1.12 HotSwap
Upgrade
Akka supports hotswapping the Actor’s message loop (e.g. its implementation) at runtime. There are two ways
you can do that:
• Send a HotSwap message to the Actor.
• Invoke the become method from within the Actor.
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Both of these takes a ActorRef => PartialFunction[Any, Unit] that implements the new message
handler. The hotswapped code is kept in a Stack which can be pushed and popped.
To hotswap the Actor body using the HotSwap message:
actor ! HotSwap( self => {
case message => self.reply("hotswapped body")
})
Using the HotSwap message for hotswapping has its limitations. You can not replace it with any code that uses
the Actor’s self reference. If you need to do that the the become method is better.
To hotswap the Actor using become:
def angry: Receive = {
case "foo" => self reply "I am already angry!!!"
case "bar" => become(happy)
}
def happy: Receive = {
case "bar" => self reply "I am already happy :-)"
case "foo" => become(angry)
}
def receive = {
case "foo" => become(angry)
case "bar" => become(happy)
}
The become method is useful for many different things, but a particular nice example of it is in example where it
is used to implement a Finite State Machine (FSM): Dining Hakkers
Here is another little cute example of become and unbecome in action:
case object Swap
class Swapper extends Actor {
def receive = {
case Swap =>
println("Hi")
become {
case Swap =>
println("Ho")
unbecome() // resets the latest ’become’ (just for fun)
}
}
}
val swap = actorOf[Swapper].start()
swap
swap
swap
swap
swap
swap
!
!
!
!
!
!
Swap
Swap
Swap
Swap
Swap
Swap
//
//
//
//
//
//
prints
prints
prints
prints
prints
prints
Hi
Ho
Hi
Ho
Hi
Ho
4.1.13 Encoding Scala Actors nested receives without accidentally leaking
memory: UnnestedReceive
Downgrade
Since the hotswapped code is pushed to a Stack you can downgrade the code as well. There are two ways you can
do that:
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• Send the Actor a RevertHotswap message
• Invoke the unbecome method from within the Actor.
Both of these will pop the Stack and replace the Actor’s implementation with the PartialFunction[Any,
Unit] that is at the top of the Stack.
Revert the Actor body using the RevertHotSwap message:
actor ! RevertHotSwap
Revert the Actor body using the unbecome method:
def receive: Receive = {
case "revert" => unbecome()
}
4.1.14 Killing an Actor
You can kill an actor by sending a Kill message. This will restart the actor through regular supervisor semantics.
Use it like this:
// kill the actor called ’victim’
victim ! Kill
4.1.15 Actor life-cycle
The actor has a well-defined non-circular life-cycle.
NEW (newly created actor) - can’t receive messages (yet)
=> STARTED (when ’start’ is invoked) - can receive messages
=> SHUT DOWN (when ’exit’ or ’stop’ is invoked) - can’t do anything
4.1.16 Extending Actors using PartialFunction chaining
A bit advanced but very useful way of defining a base message handler and then extend that, either through
inheritance or delegation, is to use PartialFunction.orElse chaining.
In generic base Actor:
import akka.actor.Actor.Receive
abstract class GenericActor extends Actor {
// to be defined in subclassing actor
def specificMessageHandler: Receive
// generic message handler
def genericMessageHandler: Receive = {
case event => printf("generic: %s\n", event)
}
def receive = specificMessageHandler orElse genericMessageHandler
}
In subclassing Actor:
class SpecificActor extends GenericActor {
def specificMessageHandler = {
case event: MyMsg => printf("specific: %s\n", event.subject)
}
}
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case class MyMsg(subject: String)
4.2 Typed Actors (Scala)
Module stability: SOLID
The Typed Actors are implemented through Typed Actors. It uses AOP through AspectWerkz to turn regular
POJOs into asynchronous non-blocking Actors with semantics of the Actor Model. Each method dispatch is
turned into a message that is put on a queue to be processed by the Typed Actor sequentially one by one.
If you are using the Spring Framework then take a look at Akka’s Spring integration.
4.2.1 Creating Typed Actors
IMPORTANT: The Typed Actors class must have access modifier ‘public’ (which is default) and can’t be an
inner class (unless it is an inner class in an ‘object’).
Akka turns POJOs with interface and implementation into asynchronous (Typed) Actors. Akka is using AspectWerkz’s Proxy implementation, which is the most performant proxy implementation there exists.
In order to create a Typed Actor you have to subclass the TypedActor base class.
Here is an example.
If you have a POJO with an interface implementation separation like this:
import akka.actor.TypedActor
trait RegistrationService {
def register(user: User, cred: Credentials): Unit
def getUserFor(username: String): User
}
public class RegistrationServiceImpl extends TypedActor with RegistrationService {
def register(user: User, cred: Credentials): Unit = {
... // register user
}
def getUserFor(username: String): User = {
... // fetch user by username
user
}
}
Then you can create an Typed Actor out of it by creating it through the ‘TypedActor’ factory like this:
val service = TypedActor.newInstance(classOf[RegistrationService], classOf[RegistrationServiceImpl
// The last parameter defines the timeout for Future calls
Creating Typed Actors with non-default constructor
To create a typed actor that takes constructor arguments use a variant of ‘newInstance’ or ‘newRemoteInstance’
that takes a call-by-name block in which you can create the Typed Actor in any way you like.
Here is an example:
val service = TypedActor.newInstance(classOf[Service], new ServiceWithConstructorArgs("someString"
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Configuration factory class
Using a configuration object:
import akka.actor.TypedActorConfiguration
import akka.util.Duration
import akka.util.duration._
val config = TypedActorConfiguration()
.timeout(3000 millis)
val service = TypedActor.newInstance(classOf[RegistrationService], classOf[RegistrationServiceImpl
However, often you will not use these factory methods but declaratively define the Typed Actors as part of a
supervisor hierarchy. More on that in the Fault Tolerance Through Supervisor Hierarchies (Scala) section.
4.2.2 Sending messages
Messages are sent simply by invoking methods on the POJO, which is proxy to the “real” POJO now. The
arguments to the method are bundled up atomically into an message and sent to the receiver (the actual POJO
instance).
One-way message send
Methods that return void are turned into ‘fire-and-forget’ semantics by asynchronously firing off the message and
return immediately. In the example above it would be the ‘register’ method, so if this method is invoked then it
returns immediately:
// method invocation returns immediately and method is invoke asynchronously using the Actor Model
service.register(user, creds)
Request-reply message send
Methods that return something (e.g. non-void methods) are turned into ‘send-and-receive-eventually’ semantics
by asynchronously firing off the message and wait on the reply using a Future.
// method invocation is asynchronously dispatched using the Actor Model semantics,
// but it blocks waiting on a Future to be resolved in the background
val user = service.getUser(username)
Generally it is preferred to use fire-forget messages as much as possible since they will never block, e.g. consume
a resource by waiting. But sometimes they are neat to use since they: # Simulates standard Java method dispatch,
which is more intuitive for most Java developers # Are a neat to model request-reply # Are useful when you need
to do things in a defined order
Request-reply-with-future message send
Methods that return a ‘akka.dispatch.Future<TYPE>’ are turned into ‘send-and-receive-with-future’ semantics by
asynchronously firing off the message and returns immediately with a Future. You need to use the ‘future(...)’
method in the TypedActor base class to resolve the Future that the client code is waiting on.
Here is an example:
class MathTypedActorImpl extends TypedActor with MathTypedActor {
def square(x: Int): Future[Integer] = future(x * x)
}
// create the ping actor
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val math = TypedActor.newInstance(classOf[MathTyped], classOf[MathTypedImpl])
// This method will return immediately when called, caller should wait on the Future for the resul
val future = math.square(10)
future.await
val result: Int = future.get
4.2.3 Stopping Typed Actors
Once Typed Actors have been created with one of the TypedActor.newInstance methods they need to be stopped
with TypedActor.stop to free resources allocated by the created Typed Actor (this is not needed when the Typed
Actor is supervised).
// Create Typed Actor
val service = TypedActor.newInstance(classOf[RegistrationService], classOf[RegistrationServiceImpl
// ...
// Free Typed Actor resources
TypedActor.stop(service)
When the Typed Actor defines a shutdown callback method (Fault Tolerance Through Supervisor Hierarchies
(Scala)) it will be invoked on TypedActor.stop.
4.2.4 How to use the TypedActorContext for runtime information access
The ‘akka.actor.TypedActorContext’ class Holds ‘runtime type information’ (RTTI) for the Typed Actor. This
context is a member field in the TypedActor base class and holds for example the current sender reference, the
current sender future etc.
Here is an example how you can use it to in a ‘void’ (e.g. fire-forget) method to implement request-reply by using
the sender reference:
class PingImpl extends TypedActor with Ping {
def hit(count: Int) {
val pong = context.getSender.asInstanceOf[Pong]
pong.hit(count++)
}
}
If the sender, sender future etc. is not available, then these methods will return ‘null’ so you should have a way of
dealing with that scenario.
4.2.5 Messages and immutability
IMPORTANT: Messages can be any kind of object but have to be immutable (there is a workaround, see next
section). Java or Scala can’t enforce immutability (yet) so this has to be by convention. Primitives like String,
int, Long are always immutable. Apart from these you have to create your own immutable objects to send as
messages. If you pass on a reference to an instance that is mutable then this instance can be modified concurrently
by two different Typed Actors and the Actor model is broken leaving you with NO guarantees and most likely
corrupt data.
Akka can help you in this regard. It allows you to turn on an option for serializing all messages, e.g. all parameters
to the Typed Actor effectively making a deep clone/copy of the parameters. This will make sending mutable
messages completely safe. This option is turned on in the ‘$AKKA_HOME/config/akka.conf’ config file like
this:
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akka {
actor {
serialize-messages = on
}
}
# does a deep clone of messages to ensure immutability
This will make a deep clone (using Java serialization) of all parameters.
4.3 ActorRegistry (Scala)
Module stability: SOLID
4.3.1 ActorRegistry: Finding Actors
Actors
can
be
looked
up
by
using
the
akka.actor.Actor.registry:
akka.actor.ActorRegistry. Lookups for actors through this registry can be done by:
• uuid akka.actor.Uuid – this uses the uuid field in the Actor class, returns the actor reference for the actor
with specified uuid, if one exists, otherwise None
• id string – this uses the id field in the Actor class, which can be set by the user (default is the class name),
returns all actor references to actors with specified id
• specific actor class - returns an Array[Actor] with all actors of this exact class
• parameterized type - returns an Array[Actor] with all actors that are a subtype of this specific type
Actors are automatically registered in the ActorRegistry when they are started, removed or stopped. You can
explicitly register and unregister ActorRef’s by using the register and unregister methods. The ActorRegistry contains many convenience methods for looking up typed actors.
Here is a summary of the API for finding actors:
def
def
def
def
def
actors: Array[ActorRef]
actorFor(uuid: akka.actor.Uuid): Option[ActorRef]
actorsFor(id : String): Array[ActorRef]
actorsFor[T <: Actor](implicit manifest: Manifest[T]): Array[ActorRef]
actorsFor[T <: Actor](clazz: Class[T]): Array[ActorRef]
// finding typed actors
def typedActors: Array[AnyRef]
def typedActorFor(uuid: akka.actor.Uuid): Option[AnyRef]
def typedActorsFor(id: String): Array[AnyRef]
def typedActorsFor[T <: AnyRef](implicit manifest: Manifest[T]): Array[AnyRef]
def typedActorsFor[T <: AnyRef](clazz: Class[T]): Array[AnyRef]
Examples of how to use them:
val actor =
val pojo =
Actor.registry.actorFor(uuid)
Actor.registry.typedActorFor(uuid)
val actors = Actor.registry.actorsFor(classOf[...])
val pojos = Actor.registry.typedActorsFor(classOf[...])
val actors =
val pojos =
Actor.registry.actorsFor(id)
Actor.registry.typedActorsFor(id)
val actors = Actor.registry.actorsFor[MyActorType]
val pojos = Actor.registry.typedActorsFor[MyTypedActorImpl]
The ActorRegistry also has a ‘shutdownAll’ and ‘foreach’ methods:
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def foreach(f: (ActorRef) => Unit)
def foreachTypedActor(f: (AnyRef) => Unit)
def shutdownAll()
If you need to know when a new Actor is added or removed from the registry, you can use the subscription API.
You can register an Actor that should be notified when an event happens in the ActorRegistry:
def addListener(listener: ActorRef)
def removeListener(listener: ActorRef)
The messages sent to this Actor are:
case class ActorRegistered(actor: ActorRef)
case class ActorUnregistered(actor: ActorRef)
So your listener Actor needs to be able to handle these two messages. Example:
import
import
import
import
import
akka.actor.Actor
akka.actor.ActorRegistered;
akka.actor.ActorUnregistered;
akka.actor.UntypedActor;
akka.event.EventHandler;
class RegistryListener extends Actor {
def receive = {
case event: ActorRegistered =>
EventHandler.info(this, "Actor registered: %s - %s".format(
event.actor.actorClassName, event.actor.uuid))
case event: ActorUnregistered =>
// ...
}
}
The above actor can be added as listener of registry events:
import akka.actor._
import akka.actor.Actor._
val listener = actorOf[RegistryListener].start()
registry.addListener(listener)
4.4 Futures (Scala)
4.4.1 Introduction
In Akka, a Future is a data structure used to retrieve the result of some concurrent operation. This operation is
usually performed by an Actor or by the Dispatcher directly. This result can be accessed synchronously
(blocking) or asynchronously (non-blocking).
4.4.2 Use with Actors
There are generally two ways of getting a reply from an Actor: the first is by a sent message (actor !
which only works if the original sender was an Actor) and the second is through a Future.
msg),
Using an Actor‘s ? method to send a message will return a Future. To wait for and retrieve the actual result the
simplest method is:
val future = actor ? msg
val result: Any = future.get()
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This will cause the current thread to block and wait for the Actor to ‘complete’ the Future with it’s reply. Due
to the dynamic nature of Akka’s Actors this result will be untyped and will default to Nothing. The safest way
to deal with this is to cast the result to an Any as is shown in the above example. You can also use the expected
result type instead of Any, but if an unexpected type were to be returned you will get a ClassCastException.
For more elegant ways to deal with this and to use the result without blocking, refer to Functional Futures.
4.4.3 Use Directly
A common use case within Akka is to have some computation performed concurrently without needing the extra
utility of an Actor. If you find yourself creating a pool of Actors for the sole reason of performing a calculation
in parallel, there is an easier (and faster) way:
import akka.dispatch.Future
val future = Future {
"Hello" + "World"
}
val result = future.get()
In the above code the block passed to Future will be executed by the default Dispatcher, with the return value
of the block used to complete the Future (in this case, the result would be the string: “HelloWorld”). Unlike
a Future that is returned from an Actor, this Future is properly typed, and we also avoid the overhead of
managing an Actor.
4.4.4 onComplete
The mother of ‘‘Future‘-composition is the onComplete callback, which allows you to get notified asynchronously
when the ‘‘Future‘‘‘gets completed:
val future: Future[Any] = ...
future onComplete {
_.value.get match {
case Left(problem) => handleCompletedWithException(problem)
case Right(result) => handleCompletedWithResult(result)
}
}
//You can also short that down to:
future onComplete { _.value.get.fold(handleCompletedWithException(_), handleCompletedWithResult(_)
//There’s also a callback named ‘‘onResult‘‘ that only deals with results (and the other, ‘‘onExce
future onResult {
case "foo" => logResult("GOT MYSELF A FOO OH YEAH BABY!")
}
4.4.5 Functional Futures
A recent addition to Akka’s Future is several monadic methods that are very similar to the ones used by Scala’s
collections. These allow you to create ‘pipelines’ or ‘streams’ that the result will travel through.
Future is a Monad
The first method for working with Future functionally is map. This method takes a Function which performs
some operation on the result of the Future, and returning a new result. The return value of the map method is
another Future that will contain the new result:
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val f1 = Future {
"Hello" + "World"
}
val f2 = f1 map { x =>
x.length
}
val result = f2.get
In this example we are joining two strings together within a Future. Instead of waiting for this to complete, we
apply our function that calculates the length of the string using the map method. Now we have a second Future
that will eventually contain an Int. When our original Future completes, it will also apply our function and
complete the second Future with it’s result. When we finally get the result, it will contain the number 10. Our
original Future still contains the string “HelloWorld” and is unaffected by the map.
The map method is fine if we are modifying a single Future, but if 2 or more Futures are involved map will
not allow you to combine them together:
val f1 = Future {
"Hello" + "World"
}
val f2 = Future {
3
}
val f3 = f1 map { x =>
f2 map { y =>
x.length * y
}
}
val result = f2.get.get
The get method had to be used twice because f3 is a Future[Future[Int]] instead of the desired
Future[Int]. Instead, the flatMap method should be used:
val f1 = Future {
"Hello" + "World"
}
val f2 = Future {
3
}
val f3 = f1 flatMap { x =>
f2 map { y =>
x.length * y
}
}
val result = f2.get
For Comprehensions
Since Future has a map and flatMap method it can be easily used in a ‘for comprehension’:
val
a
b
c
f = for {
<- Future(10 / 2) // 10 / 2 = 5
<- Future(a + 1) // 5 + 1 = 6
<- Future(a - 1) // 5 - 1 = 4
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} yield b * c
//
6 * 4 = 24
val result = f.get
Something to keep in mind when doing this is even though it looks like parts of the above example can run in
parallel, each step of the for comprehension is run sequentially. This will happen on separate threads for each step
but there isn’t much benefit over running the calculations all within a single Future. The real benefit comes when
the Futures are created first, and then combining them together.
Composing Futures
The example for comprehension above is an example of composing Futures. A common use case for this is
combining the replies of several Actors into a single calculation without resorting to calling get or await to
block for each result. First an example of using get:
val f1 = actor1 ? msg1
val f2 = actor2 ? msg2
val a: Int = f1.get
val b: Int = f2.get
val f3 = actor3 ? (a + b)
val result: String = f3.get
Here we wait for the results from the first 2 Actors before sending that result to the third Actor. We called get
3 times, which caused our little program to block 3 times before getting our final result. Now compare that to this
example:
val f1 = actor1 ? msg1
val f2 = actor2 ? msg2
val f3 = for {
a: Int
<- f1
b: Int
<- f2
c: String <- actor3 ? (a + b)
} yield c
val result = f3.get
Here we have 2 actors processing a single message each. Once the 2 results are available (note that we don’t block
to get these results!), they are being added together and sent to a third Actor, which replies with a string, which
we assign to ‘result’.
This is fine when dealing with a known amount of Actors, but can grow unwieldy if we have more then a
handful. The sequence and traverse helper methods can make it easier to handle more complex use
cases. Both of these methods are ways of turning, for a subclass T of Traversable, T[Future[A]] into
a Future[T[A]]. For example:
// oddActor returns odd numbers sequentially from 1
val listOfFutures: List[Future[Int]] = List.fill(100)(oddActor ? GetNext)
// now we have a Future[List[Int]]
val futureList = Future.sequence(listOfFutures)
// Find the sum of the odd numbers
val oddSum = futureList.map(_.sum).get
To better explain what happened in the example, Future.sequence is taking the List[Future[Int]]
and turning it into a Future[List[Int]]. We can then use map to work with the List[Int] directly, and
we find the sum of the List.
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The traverse method is similar to sequence, but it takes a T[A] and a function T => Future[B] to
return a Future[T[B]], where T is again a subclass of Traversable. For example, to use traverse to sum
the first 100 odd numbers:
val oddSum = Future.traverse((1 to 100).toList)(x => Future(x * 2 - 1)).map(_.sum).get
This is the same result as this example:
val oddSum = Future.sequence((1 to 100).toList.map(x => Future(x * 2 - 1))).map(_.sum).get
But it may be faster to use traverse as it doesn’t have to create an intermediate List[Future[Int]].
Then there’s a method that’s called fold that takes a start-value, a sequence of Future:s and a function from
the type of the start-value and the type of the futures and returns something with the same type as the start-value,
and then applies the function to all elements in the sequence of futures, non-blockingly, the execution will run on
the Thread of the last completing Future in the sequence.
val futures = for(i <- 1 to 1000) yield Future(i * 2) // Create a sequence of Futures
val futureSum = Futures.fold(0)(futures)(_ + _)
That’s all it takes!
If the sequence passed to fold is empty, it will return the start-value, in the case above, that will be 0. In some
cases you don’t have a start-value and you’re able to use the value of the first completing Future in the sequence
as the start-value, you can use reduce, it works like this:
val futures = for(i <- 1 to 1000) yield Future(i * 2) // Create a sequence of Futures
val futureSum = Futures.reduce(futures)(_ + _)
Same as with fold, the execution will be done by the Thread that completes the last of the Futures, you can also
parallize it by chunking your futures into sub-sequences and reduce them, and then reduce the reduced results
again.
This is just a sample of what can be done, but to use more advanced techniques it is easier to take advantage of
Scalaz, which Akka has support for in its akka-scalaz module.
Scalaz
Akka also has a Scalaz module (Add-on Modules) for a more complete support of programming in a functional
style.
4.4.6 Exceptions
Since the result of a Future is created concurrently to the rest of the program, exceptions must be handled
differently. It doesn’t matter if an Actor or the dispatcher is completing the Future, if an Exception is
caught the Future will contain it instead of a valid result. If a Future does contain an Exception, calling
get will cause it to be thrown again so it can be handled properly.
It is also possible to handle an Exception by returning a different result. This is done with the recover
method. For example:
val future = actor ? msg1 recover {
case e: ArithmeticException => 0
}
In this example, if an ArithmeticException was thrown while the Actor processed the message, our
Future would have a result of 0. The failure method works very similarly to the standard try/catch blocks,
so multiple Exceptions can be handled in this manner, and if an Exception is not handled this way it will be
behave as if we hadn’t used the failure method.
You also have the option to register a callback that will be executed if the Future is completed with an exception:
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val f: Future[Any] = ...
f onException {
case npe: NullPointerExcep => doSomething
case 6 => doSomethingElse
case SomeRegex(param) => doSomethingOther
case _ => doAnything
} // Applies the specified partial function to the result of the future when it is completed wit
4.4.7 Timeouts
Waiting forever for a Future to be completed can be dangerous. It could cause your program to block indefinitly
or produce a memory leak. Future has support for a timeout already builtin with a default of 5000 milliseconds
(taken from ‘akka.conf’). A timeout can also be specified when creating a Future:
val future = Future( { doSomething }, 10000 )
This example creates a Future with a 10 second timeout.
If the timeout is reached the Future becomes unusable, even if an attempt is made to complete it. It is possible
to have a Future perform an action on timeout if needed with the onTimeout method:
val future1 = actor ? msg onTimeout { _ =>
println("Timed out!")
}
4.5 Dataflow Concurrency (Scala)
4.5.1 Description
Akka implements Oz-style dataflow concurrency by using a special API for Futures (Scala) that allows single
assignment variables and multiple lightweight (event-based) processes/threads.
Dataflow concurrency is deterministic. This means that it will always behave the same. If you run it once and
it yields output 5 then it will do that every time, run it 10 million times, same result. If it on the other hand
deadlocks the first time you run it, then it will deadlock every single time you run it. Also, there is no difference
between sequential code and concurrent code. These properties makes it very easy to reason about concurrency.
The limitation is that the code needs to be side-effect free, e.g. deterministic. You can’t use exceptions, time,
random etc., but need to treat the part of your program that uses dataflow concurrency as a pure function with
input and output.
The best way to learn how to program with dataflow variables is to read the fantastic book Concepts, Techniques,
and Models of Computer Programming. By Peter Van Roy and Seif Haridi.
The documentation is not as complete as it should be, something we will improve shortly. For now, besides above
listed resources on dataflow concurrency, I recommend you to read the documentation for the GPars implementation, which is heavily influenced by the Akka implementation:
• http://gpars.codehaus.org/Dataflow
• http://www.gpars.org/guide/guide/7.%20Dataflow%20Concurrency.html
4.5.2 Getting Started
Scala’s Delimited Continuations plugin is required to use the Dataflow API. To enable the plugin when using sbt,
your project must inherit the AutoCompilerPlugins trait and contain a bit of configuration as is seen in this
example:
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import sbt._
class MyAkkaProject(info: ProjectInfo) extends DefaultProject(info) with AkkaProject with AutoComp
val continuationsPlugin = compilerPlugin("org.scala-lang.plugins" % "continuations" % "2.9.1")
override def compileOptions = super.compileOptions ++ compileOptions("-P:continuations:enable")
}
4.5.3 Dataflow Variables
Dataflow Variable defines four different operations:
1. Define a Dataflow Variable
val x = Promise[Int]()
2. Wait for Dataflow Variable to be bound (must be contained within a Future.flow block as described in
the next section)
x()
3. Bind Dataflow Variable (must be contained within a Future.flow block as described in the next section)
x << 3
4. Bind Dataflow Variable with a Future (must be contained within a Future.flow block as described in
the next section)
x << y
A Dataflow Variable can only be bound once. Subsequent attempts to bind the variable will be ignored.
4.5.4 Dataflow Delimiter
Dataflow is implemented in Akka using Scala’s Delimited Continuations. To use the Dataflow API the code must
be contained within a Future.flow block. For example:
import Future.flow
val a = Future( ... )
val b = Future( ... )
val c = Promise[Int]()
flow {
c << (a() + b())
}
val result = c.get()
The flow method also returns a Future for the result of the contained expression, so the previous example
could also be written like this:
import Future.flow
val a = Future( ... )
val b = Future( ... )
val c = flow {
a() + b()
}
val result = c.get()
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4.5.5 Examples
Most of these examples are taken from the Oz wikipedia page
To run these examples:
1. Start REPL
$ sbt
> project akka-actor
> console
Welcome to Scala version 2.9.1 (Java HotSpot(TM) 64-Bit Server VM, Java 1.6.0_25).
Type in expressions to have them evaluated.
Type :help for more information.
scala>
2. Paste the examples (below) into the Scala REPL. Note: Do not try to run the Oz version, it is only there for
reference.
3. Have fun.
Simple DataFlowVariable example
This example is from Oz wikipedia page: http://en.wikipedia.org/wiki/Oz_(programming_language). Sort of the
“Hello World” of dataflow concurrency.
Example in Oz:
thread
Z = X+Y
% will wait until both X and Y are bound to a value.
{Browse Z} % shows the value of Z.
end
thread X = 40 end
thread Y = 2 end
Example in Akka:
import akka.dispatch._
import Future.flow
val x, y, z = Promise[Int]()
flow {
z << x() + y()
println("z = " + z())
}
flow { x << 40 }
flow { y << 2 }
Example of using DataFlowVariable with recursion
Using DataFlowVariable and recursion to calculate sum.
Example in Oz:
fun {Ints N Max}
if N == Max then nil
else
{Delay 1000}
N|{Ints N+1 Max}
end
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end
fun {Sum S Stream}
case Stream of nil then S
[] H|T then S|{Sum H+S T} end
end
local X Y in
thread X = {Ints 0 1000} end
thread Y = {Sum 0 X} end
{Browse Y}
end
Example in Akka:
import akka.dispatch._
import Future.flow
def ints(n: Int, max: Int): List[Int] = {
if (n == max) Nil
else n :: ints(n + 1, max)
}
def sum(s: Int, stream: List[Int]): List[Int] = stream match {
case Nil => s :: Nil
case h :: t => s :: sum(h + s, t)
}
val x, y = Promise[List[Int]]()
flow { x << ints(0, 1000) }
flow { y << sum(0, x()) }
flow { println("List of sums: " + y()) }
Example using concurrent Futures
Shows how to have a calculation run in another thread.
Example in Akka:
import akka.dispatch._
import Future.flow
// create four ’Int’ data flow variables
val x, y, z, v = Promise[Int]()
flow {
println("Thread ’main’")
x << 1
println("’x’ set to: " + x())
println("Waiting for ’y’ to be set...")
if (x() > y()) {
z << x
println("’z’ set to ’x’: " + z())
} else {
z << y
println("’z’ set to ’y’: " + z())
}
}
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flow {
y << Future {
println("Thread ’setY’, sleeping")
Thread.sleep(2000)
2
}
println("’y’ set to: " + y())
}
flow {
println("Thread ’setV’")
v << y
println("’v’ set to ’y’: " + v())
}
4.6 Agents (Scala)
Module stability: SOLID
Agents in Akka were inspired by agents in Clojure.
Agents provide asynchronous change of individual locations. Agents are bound to a single storage location for
their lifetime, and only allow mutation of that location (to a new state) to occur as a result of an action. Update
actions are functions that are asynchronously applied to the Agent’s state and whose return value becomes the
Agent’s new state. The state of an Agent should be immutable.
While updates to Agents are asynchronous, the state of an Agent is always immediately available for reading by
any thread (using get or apply) without any messages.
Agents are reactive. The update actions of all Agents get interleaved amongst threads in a thread pool. At any
point in time, at most one send action for each Agent is being executed. Actions dispatched to an agent from
another thread will occur in the order they were sent, potentially interleaved with actions dispatched to the same
agent from other sources.
If an Agent is used within an enclosing transaction, then it will participate in that transaction. Agents are integrated
with the STM - any dispatches made in a transaction are held until that transaction commits, and are discarded if
it is retried or aborted.
4.6.1 Creating and stopping Agents
Agents are created by invoking Agent(value) passing in the Agent’s initial value.
val agent = Agent(5)
An Agent will be running until you invoke close on it. Then it will be eligible for garbage collection (unless
you hold on to it in some way).
agent.close()
4.6.2 Updating Agents
You update an Agent by sending a function that transforms the current value or by sending just a new value. The
Agent will apply the new value or function atomically and asynchronously. The update is done in a fire-forget
manner and you are only guaranteed that it will be applied. There is no guarantee of when the update will be
applied but dispatches to an Agent from a single thread will occur in order. You apply a value or a function by
invoking the send function.
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// send a value
agent send 7
// send a function
agent send (_ + 1)
agent send (_ * 2)
You can also dispatch a function to update the internal state but on its own thread. This does not use the reactive
thread pool and can be used for long-running or blocking operations. You do this with the sendOff method.
Dispatches using either sendOff or send will still be executed in order.
// sendOff a function
agent sendOff (longRunningOrBlockingFunction)
4.6.3 Reading an Agent’s value
Agents can be dereferenced, e.g. you can get an Agent’s value, by invoking the Agent with parenthesis like this:
val result = agent()
Or by using the get method.
val result = agent.get
Reading an Agent’s current value does not involve any message passing and happens immediately. So while
updates to an Agent are asynchronous, reading the state of an Agent is synchronous.
4.6.4 Awaiting an Agent’s value
It is also possible to read the value after all currently queued sends have completed. You can do this with await:
val result = agent.await
You can also get a Future to this value, that will be completed after the currently queued updates have completed:
val future = agent.future
// ...
val result = future.await.result.get
4.6.5 Transactional Agents
If an Agent is used within an enclosing transaction, then it will participate in that transaction. If you send to an
Agent within a transaction then the dispatch to the Agent will be held until that transaction commits, and discarded
if the transaction is aborted.
import akka.agent.Agent
import akka.stm._
def transfer(from:
atomic {
if (from.get <
else {
from send (_
to send (_ +
true
}
}
}
4.6. Agents (Scala)
Agent[Int], to: Agent[Int], amount: Int): Boolean = {
amount) false
- amount)
amount)
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val from = Agent(100)
val to = Agent(20)
val ok = transfer(from, to, 50)
from() // -> 50
to()
// -> 70
4.6.6 Monadic usage
Agents are also monadic, allowing you to compose operations using for-comprehensions. In a monadic usage,
new Agents are created leaving the original Agents untouched. So the old values (Agents) are still available as-is.
They are so-called ‘persistent’.
Example of a monadic usage:
val agent1 = Agent(3)
val agent2 = Agent(5)
// uses foreach
var result = 0
for (value <- agent1) {
result = value + 1
}
// uses map
val agent3 =
for (value <- agent1) yield value + 1
// uses flatMap
val agent4 = for {
value1 <- agent1
value2 <- agent2
} yield value1 + value2
agent1.close()
agent2.close()
agent3.close()
agent4.close()
4.7 Software Transactional Memory (Scala)
Module stability: SOLID
4.7.1 Overview of STM
An STM turns the Java heap into a transactional data set with begin/commit/rollback semantics. Very much like a
regular database. It implements the first three letters in ACID; ACI: * Atomic * Consistent * Isolated
Generally, the STM is not needed very often when working with Akka. Some use-cases (that we can think of) are:
• When you really need composable message flows across many actors updating their internal local state but
need them to do that atomically in one big transaction. Might not be often, but when you do need this then
you are screwed without it.
• When you want to share a datastructure across actors.
• When you need to use the persistence modules.
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Akka’s STM implements the concept in Clojure’s STM view on state in general. Please take the time to read this
excellent document and view this presentation by Rich Hickey (the genius behind Clojure), since it forms the basis
of Akka’s view on STM and state in general.
The STM is based on Transactional References (referred to as Refs). Refs are memory cells, holding an (arbitrary)
immutable value, that implement CAS (Compare-And-Swap) semantics and are managed and enforced by the
STM for coordinated changes across many Refs. They are implemented using the excellent Multiverse STM.
Working with immutable collections can sometimes give bad performance due to extensive copying. Scala provides so-called persistent datastructures which makes working with immutable collections fast. They are immutable but with constant time access and modification. They use structural sharing and an insert or update does
not ruin the old structure, hence “persistent”. Makes working with immutable composite types fast. The persistent
datastructures currently consist of a Map and Vector.
4.7.2 Simple example
Here is a simple example of an incremental counter using STM. This shows creating a Ref, a transactional
reference, and then modifying it within a transaction, which is delimited by atomic.
import akka.stm._
val ref = Ref(0)
def counter = atomic {
ref alter (_ + 1)
}
counter
// -> 1
counter
// -> 2
4.7.3 Ref
Refs (transactional references) are mutable references to values and through the STM allow the safe sharing of
mutable data. Refs separate identity from value. To ensure safety the value stored in a Ref should be immutable
(they can of course contain refs themselves). The value referenced by a Ref can only be accessed or swapped
within a transaction. If a transaction is not available, the call will be executed in its own transaction (the call will
be atomic). This is a different approach than the Clojure Refs, where a missing transaction results in an error.
Creating a Ref
You can create a Ref with or without an initial value.
import akka.stm._
// giving an initial value
val ref = Ref(0)
// specifying a type but no initial value
val ref = Ref[Int]
Accessing the value of a Ref
Use get to access the value of a Ref. Note that if no initial value has been given then the value is initially null.
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import akka.stm._
val ref = Ref(0)
atomic {
ref.get
}
// -> 0
If there is a chance that the value of a Ref is null then you can use opt, which will create an Option, either
Some(value) or None, or you can provide a default value with getOrElse. You can also check for null using
isNull.
import akka.stm._
val ref = Ref[Int]
atomic {
ref.opt
ref.getOrElse(0)
ref.isNull
}
// -> None
// -> 0
// -> true
Changing the value of a Ref
To set a new value for a Ref you can use set (or equivalently swap), which sets the new value and returns the
old value.
import akka.stm._
val ref = Ref(0)
atomic {
ref.set(5)
}
// -> 0
atomic {
ref.get
}
// -> 5
You can also use alter which accepts a function that takes the old value and creates a new value of the same
type.
import akka.stm._
val ref = Ref(0)
atomic {
ref alter (_ + 5)
}
// -> 5
val inc = (i: Int) => i + 1
atomic {
ref alter inc
}
// -> 6
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Refs in for-comprehensions
Ref is monadic and can be used in for-comprehensions.
import akka.stm._
val ref = Ref(1)
atomic {
for (value <- ref) {
// do something with value
}
}
val anotherRef = Ref(3)
atomic {
for {
value1 <- ref
value2 <- anotherRef
} yield (value1 + value2)
}
// -> Ref(4)
val emptyRef = Ref[Int]
atomic {
for {
value1 <- ref
value2 <- emptyRef
} yield (value1 + value2)
}
// -> Ref[Int]
4.7.4 Transactions
A transaction is delimited using atomic.
atomic {
// ...
}
All changes made to transactional objects are isolated from other changes, all make it or non make it (so failure
atomicity) and are consistent. With the AkkaSTM you automatically have the Oracle version of the SERIALIZED
isolation level, lower isolation is not possible. To make it fully serialized, set the writeskew property that checks
if a writeskew problem is allowed to happen.
Retries
A transaction is automatically retried when it runs into some read or write conflict, until the operation completes,
an exception (throwable) is thrown or when there are too many retries. When a read or writeconflict is encountered,
the transaction uses a bounded exponential backoff to prevent cause more contention and give other transactions
some room to complete.
If you are using non transactional resources in an atomic block, there could be problems because a transaction can
be retried. If you are using print statements or logging, it could be that they are called more than once. So you
need to be prepared to deal with this. One of the possible solutions is to work with a deferred or compensating
task that is executed after the transaction aborts or commits.
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Unexpected retries
It can happen for the first few executions that you get a few failures of execution that lead to unexpected retries,
even though there is not any read or writeconflict. The cause of this is that speculative transaction configuration/selection is used. There are transactions optimized for a single transactional object, for 1..n and for n to
unlimited. So based on the execution of the transaction, the system learns; it begins with a cheap one and upgrades to more expensive ones. Once it has learned, it will reuse this knowledge. It can be activated/deactivated
using the speculative property on the TransactionFactory. In most cases it is best use the default value (enabled)
so you get more out of performance.
Coordinated transactions and Transactors
If you need coordinated transactions across actors or threads then see Transactors (Scala).
Configuring transactions
It’s possible to configure transactions.
The atomic method can take an implicit or explicit
TransactionFactory, which can determine properties of the transaction. A default transaction factory is
used if none is specified explicitly or there is no implicit TransactionFactory in scope.
Configuring transactions with an implicit TransactionFactory:
import akka.stm._
implicit val txFactory = TransactionFactory(readonly = true)
atomic {
// read only transaction
}
Configuring transactions with an explicit TransactionFactory:
import akka.stm._
val txFactory = TransactionFactory(readonly = true)
atomic(txFactory) {
// read only transaction
}
The following settings are possible on a TransactionFactory:
• familyName - Family name for transactions. Useful for debugging.
• readonly - Sets transaction as readonly. Readonly transactions are cheaper.
• maxRetries - The maximum number of times a transaction will retry.
• timeout - The maximum time a transaction will block for.
• trackReads - Whether all reads should be tracked. Needed for blocking operations.
• writeSkew - Whether writeskew is allowed. Disable with care.
• blockingAllowed - Whether explicit retries are allowed.
• interruptible - Whether a blocking transaction can be interrupted.
• speculative - Whether speculative configuration should be enabled.
• quickRelease - Whether locks should be released as quickly as possible (before whole commit).
• propagation - For controlling how nested transactions behave.
• traceLevel - Transaction trace level.
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You can also specify the default values for some of these options in akka.conf. Here they are with their default
values:
stm {
fair
max-retries
timeout
write-skew
blocking-allowed
interruptible
speculative
quick-release
propagation
trace-level
= on
= 1000
= 5
=
=
=
=
=
=
=
# Should global transactions be fair or non-fair (non fair yield bette
# Default timeout for blocking transactions and transaction set (in un
#
the time-unit property)
true
false
false
true
true
"requires"
"none"
}
You can also determine at which level a transaction factory is shared or not shared, which affects the way in which
the STM can optimise transactions.
Here is a shared transaction factory for all instances of an actor.
import akka.actor._
import akka.stm._
object MyActor {
implicit val txFactory = TransactionFactory(readonly = true)
}
class MyActor extends Actor {
import MyActor.txFactory
def receive = {
case message: String =>
atomic {
// read only transaction
}
}
}
Here’s a similar example with an individual transaction factory for each instance of an actor.
import akka.actor._
import akka.stm._
class MyActor extends Actor {
implicit val txFactory = TransactionFactory(readonly = true)
def receive = {
case message: String =>
atomic {
// read only transaction
}
}
}
Transaction lifecycle listeners
It’s possible to have code that will only run on the successful commit of a transaction, or when a transaction aborts.
You can do this by adding deferred or compensating blocks to a transaction.
import akka.stm._
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atomic {
deferred {
// executes when transaction commits
}
compensating {
// executes when transaction aborts
}
}
Blocking transactions
You can block in a transaction until a condition is met by using an explicit retry. To use retry you also need
to configure the transaction to allow explicit retries.
Here is an example of using retry to block until an account has enough money for a withdrawal. This is also an
example of using actors and STM together.
import
import
import
import
akka.stm._
akka.actor._
akka.util.duration._
akka.event.EventHandler
type Account = Ref[Double]
case class Transfer(from: Account, to: Account, amount: Double)
class Transferer extends Actor {
implicit val txFactory = TransactionFactory(blockingAllowed = true, trackReads = true, timeout =
def receive = {
case Transfer(from, to, amount) =>
atomic {
if (from.get < amount) {
EventHandler.info(this, "not enough money - retrying")
retry
}
EventHandler.info(this, "transferring")
from alter (_ - amount)
to alter (_ + amount)
}
}
}
val account1 = Ref(100.0)
val account2 = Ref(100.0)
val transferer = Actor.actorOf(new Transferer).start()
transferer ! Transfer(account1, account2, 500.0)
// INFO Transferer: not enough money - retrying
atomic { account1 alter (_ + 2000) }
// INFO Transferer: transferring
atomic { account1.get }
// -> 1600.0
atomic { account2.get }
// -> 600.0
transferer.stop()
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Alternative blocking transactions
You can also have two alternative blocking transactions, one of which can succeed first, with either-orElse.
import
import
import
import
akka.stm._
akka.actor._
akka.util.duration._
akka.event.EventHandler
case class Branch(left: Ref[Int], right: Ref[Int], amount: Int)
class Brancher extends Actor {
implicit val txFactory = TransactionFactory(blockingAllowed = true, trackReads = true, timeout =
def receive = {
case Branch(left, right, amount) =>
atomic {
either {
if (left.get < amount) {
EventHandler.info(this, "not enough on left - retrying")
retry
}
log.info("going left")
} orElse {
if (right.get < amount) {
EventHandler.info(this, "not enough on right - retrying")
retry
}
log.info("going right")
}
}
}
}
val ref1 = Ref(0)
val ref2 = Ref(0)
val brancher = Actor.actorOf(new Brancher).start()
brancher ! Branch(ref1, ref2, 1)
// INFO Brancher: not enough on left - retrying
// INFO Brancher: not enough on right - retrying
atomic { ref2 alter (_ + 1) }
// INFO Brancher: not enough on left - retrying
// INFO Brancher: going right
brancher.stop()
4.7.5 Transactional datastructures
Akka provides two datastructures that are managed by the STM.
• TransactionalMap
• TransactionalVector
TransactionalMap and TransactionalVector look like regular mutable datastructures, they even implement the
standard Scala ‘Map’ and ‘RandomAccessSeq’ interfaces, but they are implemented using persistent datastructures
and managed references under the hood. Therefore they are safe to use in a concurrent environment. Underlying
TransactionalMap is HashMap, an immutable Map but with near constant time access and modification operations.
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Similarly TransactionalVector uses a persistent Vector. See the Persistent Datastructures section below for more
details.
Like managed references, TransactionalMap and TransactionalVector can only be modified inside the scope of an
STM transaction.
IMPORTANT: There have been some problems reported when using transactional datastructures with ‘lazy’ initialization. Avoid that.
Here is how you create these transactional datastructures:
import akka.stm._
// assuming something like
case class User(name: String)
case class Address(location: String)
// using initial values
val map = TransactionalMap("bill" -> User("bill"))
val vector = TransactionalVector(Address("somewhere"))
// specifying types
val map = TransactionalMap[String, User]
val vector = TransactionalVector[Address]
TransactionalMap and TransactionalVector wrap persistent datastructures with transactional references and provide a standard Scala interface. This makes them convenient to use.
Here is an example of using a Ref and a HashMap directly:
import akka.stm._
import scala.collection.immutable.HashMap
case class User(name: String)
val ref = Ref(HashMap[String, User]())
atomic {
val users = ref.get
val newUsers = users + ("bill" -> User("bill")) // creates a new HashMap
ref.swap(newUsers)
}
atomic {
ref.get.apply("bill")
}
// -> User("bill")
Here is the same example using TransactionalMap:
import akka.stm._
case class User(name: String)
val users = TransactionalMap[String, User]
atomic {
users += "bill" -> User("bill")
}
atomic {
users("bill")
}
// -> User("bill")
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4.7.6 Persistent datastructures
Akka’s STM should only be used with immutable data. This can be costly if you have large datastructures and are
using a naive copy-on-write. In order to make working with immutable datastructures fast enough Scala provides
what are called Persistent Datastructures. There are currently two different ones: * HashMap (scaladoc) * Vector
(scaladoc)
They are immutable and each update creates a completely new version but they are using clever structural sharing
in order to make them almost as fast, for both read and update, as regular mutable datastructures.
This illustration is taken from Rich Hickey’s presentation. Copyright Rich Hickey 2009.
4.7.7 Ants simulation sample
One fun and very enlightening visual demo of STM, actors and transactional references is the Ant simulation
sample. I encourage you to run it and read through the code since it’s a good example of using actors with STM.
4.8 Transactors (Scala)
Module stability: SOLID
4.8.1 Why Transactors?
Actors are excellent for solving problems where you have many independent processes that can work in isolation
and only interact with other Actors through message passing. This model fits many problems. But the actor model
is unfortunately a terrible model for implementing truly shared state. E.g. when you need to have consensus and a
stable view of state across many components. The classic example is the bank account where clients can deposit
and withdraw, in which each operation needs to be atomic. For detailed discussion on the topic see this JavaOne
presentation.
STM on the other hand is excellent for problems where you need consensus and a stable view of the state by
providing compositional transactional shared state. Some of the really nice traits of STM are that transactions
compose, and it raises the abstraction level from lock-based concurrency.
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Akka’s Transactors combine Actors and STM to provide the best of the Actor model (concurrency and asynchronous event-based programming) and STM (compositional transactional shared state) by providing transactional, compositional, asynchronous, event-based message flows.
If you need Durability then you should not use one of the in-memory data structures but one of the persistent ones.
Generally, the STM is not needed very often when working with Akka. Some use-cases (that we can think of) are:
• When you really need composable message flows across many actors updating their internal local state but
need them to do that atomically in one big transaction. Might not often, but when you do need this then you
are screwed without it.
• When you want to share a datastructure across actors.
• When you need to use the persistence modules.
Actors and STM
You can combine Actors and STM in several ways. An Actor may use STM internally so that particular changes
are guaranteed to be atomic. Actors may also share transactional datastructures as the STM provides safe shared
state across threads.
It’s also possible to coordinate transactions across Actors or threads so that either the transactions in a set all
commit successfully or they all fail. This is the focus of Transactors and the explicit support for coordinated
transactions in this section.
4.8.2 Coordinated transactions
Akka provides an explicit mechanism for coordinating transactions across Actors. Under the hood it uses a
CountDownCommitBarrier, similar to a CountDownLatch.
Here is an example of coordinating two simple counter Actors so that they both increment together in coordinated
transactions. If one of them was to fail to increment, the other would also fail.
import akka.transactor.Coordinated
import akka.stm.Ref
import akka.actor.{Actor, ActorRef}
case class Increment(friend: Option[ActorRef] = None)
case object GetCount
class Counter extends Actor {
val count = Ref(0)
def receive = {
case coordinated @ Coordinated(Increment(friend)) => {
friend foreach (_ ! coordinated(Increment()))
coordinated atomic {
count alter (_ + 1)
}
}
case GetCount => self.reply(count.get)
}
}
val counter1 = Actor.actorOf[Counter].start()
val counter2 = Actor.actorOf[Counter].start()
counter1 ! Coordinated(Increment(Some(counter2)))
...
(counter1 ? GetCount).as[Int] // Some(1)
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counter1.stop()
counter2.stop()
To start a new coordinated transaction that you will also participate in, just create a Coordinated object:
val coordinated = Coordinated()
To start a coordinated transaction that you won’t participate in yourself you can create a Coordinated object
with a message and send it directly to an actor. The recipient of the message will be the first member of the
coordination set:
actor ! Coordinated(Message)
To receive a coordinated message in an actor simply match it in a case statement:
def receive = {
case coordinated @ Coordinated(Message) => ...
}
To include another actor in the same coordinated transaction that you’ve created or received, use the apply method
on that object. This will increment the number of parties involved by one and create a new Coordinated object
to be sent.
actor ! coordinated(Message)
To enter the coordinated transaction use the atomic method of the coordinated object:
coordinated atomic {
// do something in transaction ...
}
The coordinated transaction will wait for the other transactions before committing. If any of the coordinated
transactions fail then they all fail.
4.8.3 Transactor
Transactors are actors that provide a general pattern for coordinating transactions, using the explicit coordination
described above.
Here’s an example of a simple transactor that will join a coordinated transaction:
import akka.transactor.Transactor
import akka.stm.Ref
case object Increment
class Counter extends Transactor {
val count = Ref(0)
override def atomically = {
case Increment => count alter (_ + 1)
}
}
You could send this Counter transactor a Coordinated(Increment) message. If you were to send it just an
Increment message it will create its own Coordinated (but in this particular case wouldn’t be coordinating
transactions with any other transactors).
To coordinate with other transactors override the coordinate method. The coordinate method maps a
message to a set of SendTo objects, pairs of ActorRef and a message. You can use the include and sendTo
methods to easily coordinate with other transactors. The include method will send on the same message that
was received to other transactors. The sendTo method allows you to specify both the actor to send to, and the
message to send.
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Example of coordinating an increment:
import akka.transactor.Transactor
import akka.stm.Ref
import akka.actor.ActorRef
case object Increment
class FriendlyCounter(friend: ActorRef) extends Transactor {
val count = Ref(0)
override def coordinate = {
case Increment => include(friend)
}
override def atomically = {
case Increment => count alter (_ + 1)
}
}
Using include to include more than one transactor:
override def coordinate = {
case Message => include(actor1, actor2, actor3)
}
Using sendTo to coordinate transactions but pass-on a different message than the one that was received:
override def coordinate = {
case Message => sendTo(someActor -> SomeOtherMessage)
case SomeMessage => sendTo(actor1 -> Message1, actor2 -> Message2)
}
To execute directly before or after the coordinated transaction, override the before and after methods. These
methods also expect partial functions like the receive method. They do not execute within the transaction.
To completely bypass coordinated transactions override the normally method. Any message matched by
normally will not be matched by the other methods, and will not be involved in coordinated transactions.
In this method you can implement normal actor behavior, or use the normal STM atomic for local transactions.
4.8.4 Coordinating Typed Actors
It’s also possible to use coordinated transactions with typed actors. You can explicitly pass around Coordinated
objects, or use built-in support with the @Coordinated annotation and the Coordination.coordinate
method.
To specify a method should use coordinated transactions add the @Coordinated annotation. Note: the
@Coordinated annotation only works with methods that return Unit (one-way methods).
trait Counter {
@Coordinated def increment()
def get: Int
}
To coordinate transactions use a coordinate block:
coordinate {
counter1.increment()
counter2.increment()
}
Here’s an example of using @Coordinated with a TypedActor to coordinate increments.
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import
import
import
import
akka.actor.TypedActor
akka.stm.Ref
akka.transactor.annotation.Coordinated
akka.transactor.Coordination._
trait Counter {
@Coordinated def increment()
def get: Int
}
class
val
def
def
}
CounterImpl extends TypedActor with Counter {
ref = Ref(0)
increment() { ref alter (_ + 1) }
get = ref.get
...
val counter1 = TypedActor.newInstance(classOf[Counter], classOf[CounterImpl])
val counter2 = TypedActor.newInstance(classOf[Counter], classOf[CounterImpl])
coordinate {
counter1.increment()
counter2.increment()
}
TypedActor.stop(counter1)
TypedActor.stop(counter2)
The coordinate block will wait for the transactions to complete. If you do not want to wait then you can
specify this explicitly:
coordinate(wait = false) {
counter1.increment()
counter2.increment()
}
4.9 Remote Actors (Scala)
Module stability: SOLID
Akka supports starting and interacting with Actors and Typed Actors on remote nodes using a very efficient and
scalable NIO implementation built upon JBoss Netty and Google Protocol Buffers .
The usage is completely transparent with local actors, both in regards to sending messages and error handling and
propagation as well as supervision, linking and restarts. You can send references to other Actors as part of the
message.
You can find a runnable sample here.
4.9.1 Starting up the remote service
Starting remote service in user code as a library
Here is how to start up the RemoteNode and specify the hostname and port programmatically:
import akka.actor.Actor._
remote.start("localhost", 2552)
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// Specify the classloader to use to load the remote class (actor)
remote.start("localhost", 2552, classLoader)
Here is how to start up the RemoteNode and specify the hostname and port in the ‘akka.conf’ configuration file
(see the section below for details):
import akka.actor.Actor._
remote.start()
// Specify the classloader to use to load the remote class (actor)
remote.start(classLoader)
Starting remote service as part of the stand-alone Kernel
You simply need to make sure that the service is turned on in the external ‘akka.conf’ configuration file.
akka {
remote {
server {
service = on
hostname = "localhost"
port = 2552
connection-timeout = 1000 # in millis
}
}
}
Stopping a RemoteNode or RemoteServer
If you invoke ‘shutdown’ on the server then the connection will be closed.
import akka.actor.Actor._
remote.shutdown()
Connecting and shutting down a client explicitly
Normally you should not have to start and stop the client connection explicitly since that is handled by Akka on a
demand basis. But if you for some reason want to do that then you can do it like this:
import akka.actor.Actor._
import java.net.InetSocketAddress
remote.shutdownClientConnection(new InetSocketAddress("localhost", 6666)) //Returns true if succes
remote.restartClientConnection(new InetSocketAddress("localhost", 6666)) //Returns true if success
Remote Client message frame size configuration
You can define the max message frame size for the remote messages:
akka {
remote {
client {
message-frame-size = 1048576
}
}
}
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Remote Client reconnect configuration
The Remote Client automatically performs reconnection upon connection failure.
You can configure it like this:
akka {
remote {
client {
reconnect-delay = 5
# in seconds (5 sec default)
read-timeout = 10
# in seconds (10 sec default)
reconnection-time-window = 600 # the maximum time window that a client should try to reconne
}
}
}
The RemoteClient is automatically trying to reconnect to the server if the connection is broken. By default it has
a reconnection window of 10 minutes (600 seconds).
If it has not been able to reconnect during this period of time then it is shut down and further attempts to use it
will yield a ‘RemoteClientException’. The ‘RemoteClientException’ contains the message as well as a reference
to the RemoteClient that is not yet connect in order for you to retrieve it an do an explicit connect if needed.
You can also register a listener that will listen for example the ‘RemoteClientStopped’ event, retrieve the ‘RemoteClient’ from it and reconnect explicitly.
See the section on RemoteClient listener and events below for details.
Remote Client message buffering and send retry on failure
The Remote Client implements message buffering on network failure. This feature has zero overhead (even turned
on) in the successful scenario and a queue append operation in case of unsuccessful send. So it is really really fast.
The default behavior is that the remote client will maintain a transaction log of all messages that it has failed to
send due to network problems (not other problems like serialization errors etc.). The client will try to resend these
messages upon first successful reconnect and the message ordering is maintained. This means that the remote
client will swallow all exceptions due to network failure and instead queue remote messages in the transaction
log. The failures will however be reported through the remote client life-cycle events as well as the regular Akka
event handler. You can turn this behavior on and off in the configuration file. It gives ‘at-least-once’ semantics,
use a message id/counter for discarding potential duplicates (or use idempotent messages).
akka {
remote {
client {
buffering {
retry-message-send-on-failure = on
capacity = -1
# If negative (or zero) then an unbounded mailbox is us
# If positive then a bounded mailbox is used and the ca
}
}
}
}
If you choose a capacity higher than 0, then a bounded queue will be used and if the limit of the queue is reached
then a ‘RemoteClientMessageBufferException’ will be thrown.
4.9.2 Running Remote Server in untrusted mode
You can run the remote server in untrusted mode. This means that the server will not allow any client-managed
remote actors or any life-cycle messages and methods. This is useful if you want to let untrusted clients use servermanaged actors in a safe way. This can optionally be combined with the secure cookie authentication mechanism
described below as well as the SSL support for remote actor communication.
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If the client is trying to perform one of these unsafe actions then a ‘java.lang.SecurityException’ is thrown on the
server as well as transferred to the client and thrown there as well.
Here is how you turn it on:
akka {
remote {
server {
untrusted-mode = on # the default is ’off’
}
}
}
The messages that it prevents are all that extends ‘LifeCycleMessage’: * class HotSwap(..) * class RevertHotSwap..) * class Restart(..) * class Exit(..) * class Link(..) * class Unlink(..) * class UnlinkAndStop(..)
* class ReceiveTimeout..)
It also prevents the client from invoking any life-cycle and side-effecting methods, such as: * start * stop * link *
unlink * spawnLink * etc.
4.9.3 Using secure cookie for remote client authentication
Akka is using a similar scheme for remote client node authentication as Erlang; using secure cookies. In order to
use this authentication mechanism you have to do two things:
• Enable secure cookie authentication in the remote server
• Use the same secure cookie on all the trusted peer nodes
Enabling secure cookie authentication
The first one is done by enabling the secure cookie authentication in the remote server section in the configuration
file:
akka {
remote {
server {
require-cookie = on
}
}
}
Now if you have try to connect to a server with a client then it will first try to authenticate the client by comparing
the secure cookie for the two nodes. If they are the same then it allows the client to connect and use the server
freely but if they are not the same then it will throw a ‘java.lang.SecurityException’ and not allow the client to
connect.
Generating and using the secure cookie
The secure cookie can be any string value but in order to ensure that it is secure it is best to randomly generate it. This can be done by invoking the ‘generate_config_with_secure_cookie.sh’ script which resides in the
‘$AKKA_HOME/scripts’ folder. This script will generate and print out a complete ‘akka.conf’ configuration file
with the generated secure cookie defined that you can either use as-is or cut and paste the ‘secure-cookie’ snippet.
Here is an example of its generated output:
# This config imports the Akka reference configuration.
include "akka-reference.conf"
# In this file you can override any option defined in the ’akka-reference.conf’ file.
# Copy in all or parts of the ’akka-reference.conf’ file and modify as you please.
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akka {
remote {
secure-cookie = "000E02050F0300040C050C0D060A040306090B0C"
}
}
The simplest way to use it is to have it create your ‘akka.conf’ file like this:
cd $AKKA_HOME
./scripts/generate_config_with_secure_cookie.sh > ./config/akka.conf
Now it is good to make sure that the configuration file is only accessible by the owner of the file. On Unix-style
file system this can be done like this:
chmod 400 ./config/akka.conf
Running this script requires having ‘scala’ on the path (and will take a couple of seconds to run since it is using
Scala and has to boot up the JVM to run).
You can also generate the secure cookie by using the ‘Crypt’ object and its ‘generateSecureCookie’ method.
import akka.util.Crypt
val secureCookie = Crypt.generateSecureCookie
The secure cookie is a cryptographically secure randomly generated byte array turned into a SHA-1 hash.
4.9.4 Client-managed Remote Actors
DEPRECATED AS OF 1.1
The client creates the remote actor and “moves it” to the server.
When you define an actor as being remote it is instantiated as on the remote host and your local actor becomes a
proxy, it works as a handle to the remote actor. The real execution is always happening on the remote node.
Actors can be made remote by calling remote.actorOf[MyActor](host, port)
Here is an example:
import akka.actor.Actor
class MyActor extends Actor {
def receive = {
case "hello" => self.reply("world")
}
}
val remoteActor = Actor.remote.actorOf[MyActor]("192.68.23.769", 2552)
An Actor can also start remote child Actors through one of the ‘spawn/link’ methods. These will start, link and
make the Actor remote atomically.
...
self.spawnRemote[MyActor](hostname, port, timeout)
self.spawnLinkRemote[MyActor](hostname, port, timeout)
...
4.9.5 Server-managed Remote Actors
Here it is the server that creates the remote actor and the client can ask for a handle to this actor.
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Server side setup
The API for server managed remote actors is really simple. 2 methods only:
class HelloWorldActor extends Actor {
def receive = {
case "Hello" => self.reply("World")
}
}
remote.start("localhost", 2552) //Start the server
remote.register("hello-service", actorOf[HelloWorldActor]) //Register the actor with the specified
Actors created like this are automatically started.
You can also register an actor by its UUID rather than ID or handle. This is done by prefixing the handle with the
“uuid:” protocol.
remote.register("uuid:" + actor.uuid, actor)
remote.unregister("uuid:" + actor.uuid)
Session bound server side setup
Session bound server managed remote actors work by creating and starting a new actor for every client that
connects. Actors are stopped automatically when the client disconnects. The client side is the same as regular
server managed remote actors. Use the function registerPerSession instead of register.
Session bound actors are useful if you need to keep state per session, e.g. username. They are also useful if you
need to perform some cleanup when a client disconnects by overriding the postStop method as described here
class HelloWorldActor extends Actor {
def receive = {
case "Hello" => self.reply("World")
}
}
remote.start("localhost", 2552)
remote.registerPerSession("hello-service", actorOf[HelloWorldActor])
Note that the second argument in registerPerSession is an implicit function. It will be called to create an actor
every time a session is established.
Client side usage
val actor = remote.actorFor("hello-service", "localhost", 2552)
remote.start("localhost", 2553);
val result = (actor ? "Hello").as[String]
There are many variations on the ‘remote#actorFor’ method. Here are some of them:
...
...
...
...
...
= remote.actorFor(className, hostname, port)
= remote.actorFor(className, timeout, hostname, port)
= remote.actorFor(uuid, className, hostname, port)
= remote.actorFor(uuid, className, timeout, hostname, port)
// etc
All of these also have variations where you can pass in an explicit ‘ClassLoader’ which can be used when deserializing messages sent from the remote actor.
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Running sample
Here is a complete running sample (also available here):
Paste in the code below into two sbt concole shells. Then run:
• ServerInitiatedRemoteActorServer.run() in one shell
• ServerInitiatedRemoteActorClient.run() in the other shell
import akka.actor.Actor
import Actor._
import akka.event.EventHandler
class HelloWorldActor extends Actor {
def receive = {
case "Hello" => self.reply("World")
}
}
object ServerInitiatedRemoteActorServer {
def run() {
remote.start("localhost", 2552)
remote.register("hello-service", actorOf[HelloWorldActor])
}
def main(args: Array[String]) { run() }
}
object ServerInitiatedRemoteActorClient {
def run() {
val actor = remote.actorFor("hello-service", "localhost", 2552)
val result = (actor ? "Hello").as[AnyRef]
EventHandler.info("Result from Remote Actor: %s", result)
}
def main(args: Array[String]) { run() }
}
4.9.6 Automatic remote ‘sender’ reference management
The sender of a remote message will be reachable with a reply through the remote server on the node that the actor
is residing, automatically. Please note that firewalled clients won’t work right now. [2011-01-05]
4.9.7 Identifying remote actors
The ‘id’ field in the ‘Actor’ class is of importance since it is used as identifier for the remote actor. If you want to
create a brand new actor every time you instantiate a remote actor then you have to set the ‘id’ field to a unique
‘String’ for each instance. If you want to reuse the same remote actor instance for each new remote actor (of the
same class) you create then you don’t have to do anything since the ‘id’ field by default is equal to the name of the
actor class.
Here is an example of overriding the ‘id’ field:
import akka.actor.newUuid
class MyActor extends Actor {
self.id = newUuid.toString
def receive = {
case "hello" => self.reply("world")
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}
}
val actor = remote.actorOf[MyActor]("192.68.23.769", 2552)
4.9.8 Client-managed Remote Typed Actors
DEPRECATED AS OF 1.1
You can define the Typed Actor to be a remote service by adding the ‘RemoteAddress’ configuration element in
the declarative supervisor configuration:
new Component(
Foo.class,
new LifeCycle(new Permanent(), 1000),
1000,
new RemoteAddress("localhost", 2552))
You can also define an Typed Actor to be remote programmatically when creating it explicitly:
TypedActorFactory factory = new TypedActorFactory();
POJO pojo = (POJO) factory.newRemoteInstance(POJO.class, 1000, "localhost", 2552)
... // use pojo as usual
4.9.9 Server-managed Remote Typed Actors
WARNING: Remote TypedActors do not work with overloaded methods on your TypedActor, refrain from using
overloading.
Server side setup
The API for server managed remote typed actors is nearly the same as for untyped actor
class RegistrationServiceImpl extends TypedActor with RegistrationService {
def registerUser(user: User): Unit = {
... // register user
}
}
remote.start("localhost", 2552)
val typedActor = TypedActor.newInstance(classOf[RegistrationService], classOf[RegistrationServiceI
remote.registerTypedActor("user-service", typedActor)
Actors created like this are automatically started.
Session bound server side setup
Session bound server managed remote actors work by creating and starting a new actor for every client that
connects. Actors are stopped automatically when the client disconnects. The client side is the same as regular
server managed remote actors. Use the function registerTypedPerSessionActor instead of registerTypedActor.
Session bound actors are useful if you need to keep state per session, e.g. username. They are also useful if you
need to perform some cleanup when a client disconnects.
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class RegistrationServiceImpl extends TypedActor with RegistrationService {
def registerUser(user: User): Unit = {
... // register user
}
}
remote.start("localhost", 2552)
remote.registerTypedPerSessionActor("user-service",
TypedActor.newInstance(classOf[RegistrationService],
classOf[RegistrationServiceImpl], 2000))
Note that the second argument in registerTypedPerSessionActor is an implicit function. It will be called to create
an actor every time a session is established.
Client side usage
val actor = remote.typedActorFor(classOf[RegistrationService], "user-service", 5000L, "localhost",
actor.registerUser(...)
There are variations on the ‘remote#typedActorFor’ method. Here are some of them:
... = remote.typedActorFor(interfaceClazz, serviceIdOrClassName, hostname, port)
... = remote.typedActorFor(interfaceClazz, serviceIdOrClassName, timeout, hostname, port)
... = remote.typedActorFor(interfaceClazz, serviceIdOrClassName, timeout, hostname, port, classLoa
4.9.10 Data Compression Configuration
Akka uses compression to minimize the size of the data sent over the wire. Currently it only supports ‘zlib’
compression but more will come later.
You can configure it like this:
akka {
remote {
compression-scheme = "zlib" # Options: "zlib" (lzf to come), leave out for no compression
zlib-compression-level = 6 # Options: 0-9 (1 being fastest and 9 being the most compressed),
}
}
4.9.11 Code provisioning
Akka does currently not support automatic code provisioning but requires you to have the remote actor class files
available on both the “client” the “server” nodes. This is something that will be addressed soon. Until then, sorry
for the inconvenience.
4.9.12 Subscribe to Remote Client events
Akka has a subscription API for the client event. You can register an Actor as a listener and this actor will have to
be able to process these events:
sealed trait RemoteClientLifeCycleEvent
case class RemoteClientError(
@BeanProperty cause: Throwable,
@BeanProperty client: RemoteClientModule,
@BeanProperty remoteAddress: InetSocketAddress) extends RemoteClientLifeCycleEvent
case class RemoteClientDisconnected(
@BeanProperty client: RemoteClientModule,
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@BeanProperty remoteAddress: InetSocketAddress) extends RemoteClientLifeCycleEvent
case class RemoteClientConnected(
@BeanProperty client: RemoteClientModule,
@BeanProperty remoteAddress: InetSocketAddress) extends RemoteClientLifeCycleEvent
case class RemoteClientStarted(
@BeanProperty client: RemoteClientModule,
@BeanProperty remoteAddress: InetSocketAddress) extends RemoteClientLifeCycleEvent
case class RemoteClientShutdown(
@BeanProperty client: RemoteClientModule,
@BeanProperty remoteAddress: InetSocketAddress) extends RemoteClientLifeCycleEvent
case class RemoteClientWriteFailed(
@BeanProperty request: AnyRef,
@BeanProperty cause: Throwable,
@BeanProperty client: RemoteClientModule,
@BeanProperty remoteAddress: InetSocketAddress) extends RemoteClientLifeCycleEvent
So a simple listener actor can look like this:
import akka.actor.Actor
import akka.actor.Actor._
import akka.remoteinterface._
val listener = actorOf(new Actor {
def receive = {
case RemoteClientError(cause, client, address) => //... act upon
case RemoteClientDisconnected(client, address) => //... act upon
case RemoteClientConnected(client, address)
=> //... act upon
case RemoteClientStarted(client, address)
=> //... act upon
case RemoteClientShutdown(client, address)
=> //... act upon
case RemoteClientWriteFailed(request, cause, client, address) =>
case _ => // ignore other
}
}).start()
error
disconnection
connection
client shutdown
client shutdown
//... act upon write failure
Registration and de-registration can be done like this:
remote.addListener(listener)
...
remote.removeListener(listener)
4.9.13 Subscribe to Remote Server events
Akka has a subscription API for the ‘RemoteServer’. You can register an Actor as a listener and this actor will
have to be able to process these events:
sealed trait RemoteServerLifeCycleEvent
case class RemoteServerStarted(
@BeanProperty val server: RemoteServerModule) extends RemoteServerLifeCycleEvent
case class RemoteServerShutdown(
@BeanProperty val server: RemoteServerModule) extends RemoteServerLifeCycleEvent
case class RemoteServerError(
@BeanProperty val cause: Throwable,
@BeanProperty val server: RemoteServerModule) extends RemoteServerLifeCycleEvent
case class RemoteServerClientConnected(
@BeanProperty val server: RemoteServerModule,
@BeanProperty val clientAddress: Option[InetSocketAddress]) extends RemoteServerLifeCycleEvent
case class RemoteServerClientDisconnected(
@BeanProperty val server: RemoteServerModule,
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@BeanProperty val clientAddress: Option[InetSocketAddress]) extends RemoteServerLifeCycleEvent
case class RemoteServerClientClosed(
@BeanProperty val server: RemoteServerModule,
@BeanProperty val clientAddress: Option[InetSocketAddress]) extends RemoteServerLifeCycleEvent
case class RemoteServerWriteFailed(
@BeanProperty request: AnyRef,
@BeanProperty cause: Throwable,
@BeanProperty server: RemoteServerModule,
@BeanProperty clientAddress: Option[InetSocketAddress]) extends RemoteServerLifeCycleEvent
So a simple listener actor can look like this:
import akka.actor.Actor
import akka.actor.Actor._
import akka.remoteinterface._
val listener = actorOf(new Actor {
def receive = {
case RemoteServerStarted(server)
=> //...
case RemoteServerShutdown(server)
=> //...
case RemoteServerError(cause, server)
=> //...
case RemoteServerClientConnected(server, clientAddress)
=> //...
case RemoteServerClientDisconnected(server, clientAddress) => //...
case RemoteServerClientClosed(server, clientAddress)
=> //...
case RemoteServerWriteFailed(request, cause, server, clientAddress)
}
}).start()
act upon
act upon
act upon
act upon
act upon
act upon
=> //...
server start
server shutdown
server error
client connection
client disconnect
client connection
act upon server w
Registration and de-registration can be done like this:
remote.addListener(listener)
...
remote.removeListener(listener)
4.9.14 Message Serialization
All messages that are sent to remote actors needs to be serialized to binary format to be able to travel
over the wire to the remote node. This is done by letting your messages extend one of the traits in the
‘akka.serialization.Serializable’ object. If the messages don’t implement any specific serialization trait then the
runtime will try to use standard Java serialization.
Here are some examples, but full documentation can be found in the Serialization (Scala).
Scala JSON
case class MyMessage(id: String, value: Tuple2[String, Int]) extends Serializable.ScalaJSON[MyMess
Protobuf
Protobuf message specification needs to be compiled with ‘protoc’ compiler.
message ProtobufPOJO {
required uint64 id = 1;
required string name = 2;
required bool status = 3;
}
Using the generated message builder to send the message to a remote actor:
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val resultFuture = actor ? ProtobufPOJO.newBuilder
.setId(11)
.setStatus(true)
.setName("Coltrane")
.build
4.10 Serialization (Scala)
Module stability: SOLID
4.10.1 Serialization of ActorRef
An Actor can be serialized in two different ways:
• Serializable RemoteActorRef - Serialized to an immutable, network-aware Actor reference that can be freely
shared across the network. They “remember” and stay mapped to their original Actor instance and host node,
and will always work as expected.
• Serializable LocalActorRef - Serialized by doing a deep copy of both the ActorRef and the Actor instance
itself. Can be used to physically move an Actor from one node to another and continue the execution there.
Both of these can be sent as messages over the network and/or store them to disk, in a persistent storage backend
etc.
Actor serialization in Akka is implemented through a type class Format[T <: Actor] which publishes the
fromBinary and toBinary methods for serialization. Here’s the complete definition of the type class:
/**
* Type class definition for Actor Serialization
*/
trait FromBinary[T <: Actor] {
def fromBinary(bytes: Array[Byte], act: T): T
}
trait ToBinary[T <: Actor] {
def toBinary(t: T): Array[Byte]
}
// client needs to implement Format[] for the respective actor
trait Format[T <: Actor] extends FromBinary[T] with ToBinary[T]
Deep serialization of an Actor and ActorRef
You can serialize the whole actor deeply, e.g. both the ActorRef and then instance of its Actor. This can be
useful if you want to move an actor from one node to another, or if you want to store away an actor, with its state,
into a database.
Here is an example of how to serialize an Actor.
Step 1: Define the actor
class MyActor extends Actor {
var count = 0
def receive = {
case "hello" =>
count = count + 1
self.reply("world " + count)
}
}
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Step 2: Implement the type class for the actor. ProtobufProtocol.Counter is something you need to define yourself,
as explained in the Protobuf section.
import akka.serialization.{Serializer, Format}
import akka.actor.Actor
import akka.actor.Actor._
object BinaryFormatMyActor {
implicit object MyActorFormat extends Format[MyActor] {
def fromBinary(bytes: Array[Byte], act: MyActor) = {
val p = Serializer.Protobuf.fromBinary(bytes, Some(classOf[ProtobufProtocol.Counter])).asIns
act.count = p.getCount
act
}
def toBinary(ac: MyActor) =
ProtobufProtocol.Counter.newBuilder.setCount(ac.count).build.toByteArray
}
}
Step 3: Import the type class module definition and serialize / de-serialize
it("should be able to serialize and de-serialize a stateful actor") {
import akka.serialization.ActorSerialization._
import BinaryFormatMyActor._
val actor1 = actorOf[MyActor].start()
(actor1 ? "hello").as[String].getOrElse("_") should equal("world 1")
(actor1 ? "hello").as[String].getOrElse("_") should equal("world 2")
val bytes = toBinary(actor1)
val actor2 = fromBinary(bytes)
actor2.start()
(actor2 ? "hello").as[String].getOrElse("_") should equal("world 3")
}
Helper Type Class for Stateless Actors
If your actor is stateless, then you can use the helper trait that Akka provides to serialize / de-serialize. Here’s the
definition:
trait StatelessActorFormat[T <: Actor] extends Format[T] {
def fromBinary(bytes: Array[Byte], act: T) = act
def toBinary(ac: T) = Array.empty[Byte]
}
Then you use it as follows:
class MyStatelessActor extends Actor {
def receive = {
case "hello" =>
self.reply("world")
}
}
Just create an object for the helper trait for your actor:
object BinaryFormatMyStatelessActor {
implicit object MyStatelessActorFormat extends StatelessActorFormat[MyStatelessActor]
}
and use it for serialization:
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it("should be able to serialize and de-serialize a stateless actor") {
import akka.serialization.ActorSerialization._
import BinaryFormatMyStatelessActor._
val actor1 = actorOf[MyStatelessActor].start()
(actor1 ? "hello").as[String].getOrElse("_") should equal("world")
(actor1 ? "hello").as[String].getOrElse("_") should equal("world")
val bytes = toBinary(actor1)
val actor2 = fromBinary(bytes)
actor2.start()
(actor2 ? "hello").as[String].getOrElse("_") should equal("world")
}
Helper Type Class for actors with external serializer
Use the trait SerializerBasedActorFormat for specifying serializers.
trait
val
def
def
}
SerializerBasedActorFormat[T <: Actor] extends Format[T] {
serializer: Serializer
fromBinary(bytes: Array[Byte], act: T) = serializer.fromBinary(bytes, Some(act.self.actorCla
toBinary(ac: T) = serializer.toBinary(ac)
For a Java serializable actor:
class MyJavaSerializableActor extends Actor with scala.Serializable {
var count = 0
def receive = {
case "hello" =>
count = count + 1
self.reply("world " + count)
}
}
Create a module for the type class ..
import akka.serialization.{SerializerBasedActorFormat, Serializer}
object BinaryFormatMyJavaSerializableActor {
implicit object MyJavaSerializableActorFormat extends SerializerBasedActorFormat[MyJavaSerializa
val serializer = Serializer.Java
}
}
and serialize / de-serialize ..
it("should be able to serialize and de-serialize a stateful actor with a given serializer") {
import akka.actor.Actor._
import akka.serialization.ActorSerialization._
import BinaryFormatMyJavaSerializableActor._
val actor1 = actorOf[MyJavaSerializableActor].start()
(actor1 ? "hello").as[String].getOrElse("_") should equal("world 1")
(actor1 ? "hello").as[String].getOrElse("_") should equal("world 2")
val bytes = toBinary(actor1)
val actor2 = fromBinary(bytes)
actor2.start()
(actor2 ? "hello").as[String].getOrElse("_") should equal("world 3")
}
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4.10.2 Serialization of a RemoteActorRef
You can serialize an ActorRef to an immutable, network-aware Actor reference that can be freely shared across
the network, a reference that “remembers” and stay mapped to its original Actor instance and host node, and will
always work as expected.
The RemoteActorRef serialization is based upon Protobuf (Google Protocol Buffers) and you don’t need to
do anything to use it, it works on any ActorRef.
Currently Akka will not autodetect an ActorRef as part of your message and serialize it for you automatically,
so you have to do that manually or as part of your custom serialization mechanisms.
Here is an example of how to serialize an Actor.
import akka.serialization.RemoteActorSerialization._
val actor1 = actorOf[MyActor]
val bytes = toRemoteActorRefProtocol(actor1).toByteArray
To
deserialize
the
ActorRef
to
a
RemoteActorRef
you
need
to
use
the
fromBinaryToRemoteActorRef(bytes: Array[Byte]) method on the ActorRef companion object:
import akka.serialization.RemoteActorSerialization._
val actor2 = fromBinaryToRemoteActorRef(bytes)
You can also pass in a class loader to load the ActorRef class and dependencies from:
import akka.serialization.RemoteActorSerialization._
val actor2 = fromBinaryToRemoteActorRef(bytes, classLoader)
4.10.3 Deep serialization of a TypedActor
Serialization of typed actors works almost the same way as untyped actors. You can serialize the whole actor
deeply, e.g. both the ‘proxied ActorRef’ and the instance of its TypedActor.
Here is the example from above implemented as a TypedActor.
Step 1: Define the actor
import akka.actor.TypedActor
trait MyTypedActor {
def requestReply(s: String) : String
def oneWay() : Unit
}
class MyTypedActorImpl extends TypedActor with MyTypedActor {
var count = 0
override def requestReply(message: String) : String = {
count = count + 1
"world " + count
}
override def oneWay() {
count = count + 1
}
}
Step 2: Implement the type class for the actor
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import akka.serialization.{Serializer, Format}
class MyTypedActorFormat extends Format[MyTypedActorImpl] {
def fromBinary(bytes: Array[Byte], act: MyTypedActorImpl) = {
val p = Serializer.Protobuf.fromBinary(bytes, Some(classOf[ProtobufProtocol.Counter])).asInsta
act.count = p.getCount
act
}
def toBinary(ac: MyTypedActorImpl) = ProtobufProtocol.Counter.newBuilder.setCount(ac.count).buil
}
Step 3: Import the type class module definition and serialize / de-serialize
import akka.serialization.TypedActorSerialization._
val typedActor1 = TypedActor.newInstance(classOf[MyTypedActor], classOf[MyTypedActorImpl], 1000)
val f = new MyTypedActorFormat
val bytes = toBinaryJ(typedActor1, f)
val typedActor2: MyTypedActor = fromBinaryJ(bytes, f)
typedActor2.requestReply("hello")
//type hint needed
4.10.4 Serialization of a remote typed ActorRef
To deserialize the TypedActor to a RemoteTypedActorRef (an aspectwerkz proxy to a RemoteActorRef)
you need to use the fromBinaryToRemoteTypedActorRef(bytes: Array[Byte]) method on
RemoteTypedActorSerialization object:
import akka.serialization.RemoteTypedActorSerialization._
val typedActor = fromBinaryToRemoteTypedActorRef(bytes)
// you can also pass in a class loader
val typedActor2 = fromBinaryToRemoteTypedActorRef(bytes, classLoader)
4.10.5 Compression
Akka has a helper class for doing compression of binary data. This can be useful for example when storing data
in one of the backing storages. It currently supports LZF which is a very fast compression algorithm suited for
runtime dynamic compression.
Here is an example of how it can be used:
import akka.serialization.Compression
val bytes: Array[Byte] = ...
val compressBytes = Compression.LZF.compress(bytes)
val uncompressBytes = Compression.LZF.uncompress(compressBytes)
4.10.6 Using the Serializable trait and Serializer class for custom serialization
If you are sending messages to a remote Actor and these messages implement one of the predefined interfaces/traits
in the akka.serialization.Serializable.* object, then Akka will transparently detect which serialization format it should use as wire protocol and will automatically serialize and deserialize the message according
to this protocol.
Each serialization interface/trait in
• akka.serialization.Serializable.*
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has a matching serializer in
• akka.serialization.Serializer.*
Note however that if you are using one of the Serializable interfaces then you don’t have to do anything else in
regard to sending remote messages.
The ones currently supported are (besides the default which is regular Java serialization):
• ScalaJSON (Scala only)
• JavaJSON (Java but some Scala structures)
• Protobuf (Scala and Java)
Apart from the above, Akka also supports Scala object serialization through SJSON that implements APIs similar
to akka.serialization.Serializer.*. See the section on SJSON below for details.
4.10.7 Protobuf
Akka supports using Google Protocol Buffers to serialize your objects. Protobuf is a very efficient network serialization protocol which is also used internally by Akka. The remote actors understand Protobuf messages so if you
just send them as they are they will be correctly serialized and unserialized.
Here is an example.
Let’s say you have this Protobuf message specification that you want to use as message between remote actors.
First you need to compiled it with ‘protoc’ compiler.
message ProtobufPOJO {
required uint64 id = 1;
required string name = 2;
required bool status = 3;
}
When you compile the spec you will among other things get a message builder. You then use this builder to create
the messages to send over the wire:
val resultFuture = remoteActor ? ProtobufPOJO.newBuilder
.setId(11)
.setStatus(true)
.setName("Coltrane")
.build
The remote Actor can then receive the Protobuf message typed as-is:
class MyRemoteActor extends Actor {
def receive = {
case pojo: ProtobufPOJO =>
val id = pojo.getId
val status = pojo.getStatus
val name = pojo.getName
...
}
}
4.10.8 JSON: Scala
Use the akka.serialization.Serializable.ScalaJSON base class with its toJSON method. Akka’s
Scala JSON is based upon the SJSON library.
For your POJOs to be able to serialize themselves you have to extend the ScalaJSON[] trait as follows. JSON
serialization is based on a type class protocol which you need to define for your own abstraction. The instance of
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the type class is defined as an implicit object which is used for serialization and de-serialization. You also need to
implement the methods in terms of the APIs which sjson publishes.
import
import
import
import
akka.serialization._
akka.serialization.Serializable.ScalaJSON
akka.serialization.JsonSerialization._
akka.serialization.DefaultProtocol._
case class MyMessage(val id: String, val value: Tuple2[String, Int]) extends ScalaJSON[MyMessage]
// type class instance
implicit val MyMessageFormat: sjson.json.Format[MyMessage] =
asProduct2("id", "value")(MyMessage)(MyMessage.unapply(_).get)
def
def
def
def
toJSON: String = JsValue.toJson(tojson(this))
toBytes: Array[Byte] = tobinary(this)
fromBytes(bytes: Array[Byte]) = frombinary[MyMessage](bytes)
fromJSON(js: String) = fromjson[MyMessage](Js(js))
}
// sample test case
it("should be able to serialize and de-serialize MyMessage") {
val s = MyMessage("Target", ("cooker", 120))
s.fromBytes(s.toBytes) should equal(s)
s.fromJSON(s.toJSON) should equal(s)
}
Use akka.serialization.Serializer.ScalaJSON to do generic JSON serialization, e.g. serialize object that does not
extend ScalaJSON using the JSON serializer. Serialization using Serializer can be done in two ways :1. Type class based serialization (recommended)
2. Reflection based serialization
We will discuss both of these techniques in this section. For more details refer to the discussion in the next section
SJSON: Scala.
Serializer API using type classes
Here are the steps that you need to follow:
1. Define your class
case class MyMessage(val id: String, val value: Tuple2[String, Int])
2. Define the type class instance
import akka.serialization.DefaultProtocol._
implicit val MyMessageFormat: sjson.json.Format[MyMessage] =
asProduct2("id", "value")(MyMessage)(MyMessage.unapply(_).get)
3. Serialize
import akka.serialization.Serializer.ScalaJSON
import akka.serialization.JsonSerialization._
val o = MyMessage("dg", ("akka", 100))
fromjson[MyMessage](tojson(o)) should equal(o)
frombinary[MyMessage](tobinary(o)) should equal(o)
Serializer API using reflection
You can also use the Serializer abstraction to serialize using the reflection based serialization API of sjson. But
we recommend using the type class based one, because reflection based serialization has limitations due to type
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erasure. Here’s an example of reflection based serialization:
import scala.reflect.BeanInfo
import akka.serialization.Serializer
@BeanInfo case class Foo(name: String) {
def this() = this(null) // default constructor is necessary for deserialization
}
val foo = Foo("bar")
val json = Serializer.ScalaJSON.toBinary(foo)
val fooCopy = Serializer.ScalaJSON.fromBinary(json) // returns a JsObject as an AnyRef
val fooCopy2 = Serializer.ScalaJSON.fromJSON(new String(json)) // can also take a string as input
val fooCopy3 = Serializer.ScalaJSON.fromBinary[Foo](json).asInstanceOf[Foo]
Classes without a @BeanInfo annotation cannot be serialized as JSON. So if you see something like that:
scala> Serializer.ScalaJSON.out(bar)
Serializer.ScalaJSON.out(bar)
java.lang.UnsupportedOperationException: Class class Bar not supported for conversion
at sjson.json.JsBean$class.toJSON(JsBean.scala:210)
at sjson.json.Serializer$SJSON$.toJSON(Serializer.scala:107)
at sjson.json.Serializer$SJSON$class.out(Serializer.scala:37)
at sjson.json.Serializer$SJSON$.out(Serializer.scala:107)
at akka.serialization.Serializer$ScalaJSON...
it means, that you haven’t got a @BeanInfo annotation on your class.
You may also see this exception when trying to serialize a case class without any attributes, like this:
@BeanInfo case class Empty() // cannot be serialized
4.10.9 SJSON: Scala
SJSON supports serialization of Scala objects into JSON. It implements support for built in Scala structures like
List, Map or String as well as custom objects. SJSON is available as an Apache 2 licensed project on Github here.
Example: I have a Scala object as ..
val addr = Address("Market Street", "San Francisco", "956871")
where Address is a custom class defined by the user. Using SJSON, I can store it as JSON and retrieve as plain old
Scala object. Here’s the simple assertion that validates the invariant. Note that during de-serialziation, the class
name is specified. Hence what it gives back is an instance of Address.
val serializer = sjson.json.Serializer.SJSON
addr should equal(
serializer.in[Address](serializer.out(addr)))
Note, that the class needs to have a default constructor. Otherwise the deserialization into the specified class will
fail.
There are situations, particularly when writing generic persistence libraries in Akka, when the exact class is not
known during de-serialization. Using SJSON I can get it as AnyRef or Nothing ..
serializer.in[AnyRef](serializer.out(addr))
or just as ..
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serializer.in(serializer.out(addr))
What you get back from is a JsValue, an abstraction of the JSON object model. For details of JsValueimplementation, refer to dispatch-json that SJSON uses as the underlying JSON parser implementation. Once I have the
JsValue model, I can use use extractors to get back individual attributes ..
val serializer = sjson.json.Serializer.SJSON
val a = serializer.in[AnyRef](serializer.out(addr))
// use extractors
val c = ’city ? str
val c(_city) = a
_city should equal("San Francisco")
val s = ’street ? str
val s(_street) = a
_street should equal("Market Street")
val z = ’zip ? str
val z(_zip) = a
_zip should equal("956871")
Serialization of Embedded Objects
SJSON supports serialization of Scala objects that have other embedded objects. Suppose you have the following
Scala classes .. Here Contact has an embedded Address Map ..
@BeanInfo
case class Contact(name: String,
@(JSONTypeHint @field)(value = classOf[Address])
addresses: Map[String, Address]) {
override def toString = "name = " + name + " addresses = " +
addresses.map(a => a._1 + ":" + a._2.toString).mkString(",")
}
@BeanInfo
case class Address(street: String, city: String, zip: String) {
override def toString = "address = " + street + "/" + city + "/" + zip
}
With SJSON, I can do the following:
val a1 = Address("Market Street", "San Francisco", "956871")
val a2 = Address("Monroe Street", "Denver", "80231")
val a3 = Address("North Street", "Atlanta", "987671")
val c = Contact("Bob", Map("residence" -> a1, "office" -> a2, "club" -> a3))
val co = serializer.out(c)
val serializer = sjson.json.Serializer.SJSON
// with class specified
c should equal(serializer.in[Contact](co))
// no class specified
val a = serializer.in[AnyRef](co)
// extract name
val n = ’name ? str
val n(_name) = a
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"Bob" should equal(_name)
// extract addresses
val addrs = ’addresses ? obj
val addrs(_addresses) = a
// extract residence from addresses
val res = ’residence ? obj
val res(_raddr) = _addresses
// make an Address bean out of _raddr
val address = JsBean.fromJSON(_raddr, Some(classOf[Address]))
a1 should equal(address)
object r { def ># [T](f: JsF[T]) = f(a.asInstanceOf[JsValue]) }
// still better: chain ’em up
"Market Street" should equal(
(r ># { (’addresses ? obj) andThen (’residence ? obj) andThen (’street ? str) }))
Changing property names during serialization
@BeanInfo
case class Book(id: Number,
title: String, @(JSONProperty @getter)(value = "ISBN") isbn: String) {
override def toString = "id = " + id + " title = " + title + " isbn = " + isbn
}
When this will be serialized out, the property name will be changed.
val b = new Book(100, "A Beautiful Mind", "012-456372")
val jsBook = Js(JsBean.toJSON(b))
val expected_book_map = Map(
JsString("id") -> JsNumber(100),
JsString("title") -> JsString("A Beautiful Mind"),
JsString("ISBN") -> JsString("012-456372")
)
Serialization with ignore properties
When serializing objects, some of the properties can be ignored declaratively. Consider the following class declaration:
@BeanInfo
case class Journal(id: BigDecimal,
title: String,
author: String,
@(JSONProperty @getter)(ignore = true) issn: String) {
override def toString =
"Journal: " + id + "/" + title + "/" + author +
(issn match {
case null => ""
case _ => "/" + issn
})
}
The annotation @JSONProperty can be used to selectively ignore fields. When I serialize a Journal object out and
then back in, the content of issn field will be null.
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val serializer = sjson.json.Serializer.SJSON
it("should ignore issn field") {
val j = Journal(100, "IEEE Computer", "Alex Payne", "012-456372")
serializer.in[Journal](serializer.out(j)).asInstanceOf[Journal].issn should equal(null)
}
Similarly, we can ignore properties of an object only if they are null and not ignore otherwise. Just specify the
annotation @JSONProperty as @JSONProperty {val ignoreIfNull = true}.
Serialization with Type Hints for Generic Data Members
Consider the following Scala class:
@BeanInfo
case class Contact(name: String,
@(JSONTypeHint @field)(value = classOf[Address])
addresses: Map[String, Address]) {
override def toString = "name = " + name + " addresses = " +
addresses.map(a => a._1 + ":" + a._2.toString).mkString(",")
}
Because of erasure, you need to add the type hint declaratively through the annotation @JSONTypeHint that
SJSON will pick up during serialization. Now we can say:
val serializer = sjson.json.Serializer.SJSON
val c = Contact("Bob", Map("residence" -> a1, "office" -> a2, "club" -> a3))
val co = serializer.out(c)
it("should give an instance of Contact") {
c should equal(serializer.in[Contact](co))
}
With optional generic data members, we need to provide the hint to SJSON through another annotation @OptionTypeHint.
@BeanInfo
case class ContactWithOptionalAddr(name: String,
@(JSONTypeHint @field)(value = classOf[Address])
@(OptionTypeHint @field)(value = classOf[Map[_,_]])
addresses: Option[Map[String, Address]]) {
override def toString = "name = " + name + " " +
(addresses match {
case None => ""
case Some(ad) => " addresses = " + ad.map(a => a._1 + ":" + a._2.toString).mkString(",")
})
}
Serialization works ok with optional members annotated as above.
val serializer = sjson.json.Serializer.SJSON
describe("Bean with optional bean member serialization") {
it("should serialize with Option defined") {
val c = new ContactWithOptionalAddr("Debasish Ghosh",
Some(Map("primary" -> new Address("10 Market Street", "San Francisco, CA", "94111"),
"secondary" -> new Address("3300 Tamarac Drive", "Denver, CO", "98301"))))
c should equal(
serializer.in[ContactWithOptionalAddr](serializer.out(c)))
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}
}
You can also specify a custom ClassLoader while using the SJSON serializer:
object SJSON {
val classLoader = //.. specify a custom classloader
}
import SJSON._
serializer.out(..)
//..
Fighting Type Erasure
Because of type erasure, it’s not always possible to infer the correct type during de-serialization of objects. Consider the following example:
abstract class A
@BeanInfo case class B(param1: String) extends A
@BeanInfo case class C(param1: String, param2: String) extends A
@BeanInfo case class D(@(JSONTypeHint @field)(value = classOf[A])param1: List[A])
and the serialization code like the following:
object TestSerialize{
def main(args: Array[String]) {
val test1 = new D(List(B("hello1")))
val json = sjson.json.Serializer.SJSON.out(test1)
val res = sjson.json.Serializer.SJSON.in[D](json)
val res1: D = res.asInstanceOf[D]
println(res1)
} q
}
Note that the type hint on class D says A, but the actual instances that have been put into the object before
serialization is one of the derived classes (B). During de-serialization, we have no idea of what can be inside D.
The serializer.in API will fail since all hint it has is for A, which is abstract. In such cases, we need to handle the
de-serialization by using extractors over the underlying data structure that we use for storing JSON objects, which
is JsValue. Here’s an example:
val serializer = sjson.json.Serializer.SJSON
val test1 = new D(List(B("hello1")))
val json = serializer.out(test1)
// create a JsValue from the string
val js = Js(new String(json))
// extract the named list argument
val m = (Symbol("param1") ? list)
val m(_m) = js
// extract the string within
val s = (Symbol("param1") ? str)
// form a list of B’s
val result = _m.map{ e =>
val s(_s) = e
B(_s)
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}
// form a D
println("result = " + D(result))
The above snippet de-serializes correctly using extractors defined on JsValue. For more details on JsValue and the
extractors, please refer to dispatch-json .
NOTE: Serialization with SJSON is based on bean introspection. In the current version of Scala (2.8.0.Beta1 and
2.7.7) there is a bug where bean introspection does not work properly for classes enclosed within another class.
Please ensure that the beans are the top level classes in your application. They can be within objects though. A
ticket has been filed in the Scala Tracker and also fixed in the trunk. Here’s the ticket .
Type class based Serialization
If type erasure hits you, reflection based serialization may not be the right option. In fact the last section shows
some of the scenarios which may not be possible to handle using reflection based serialization of sjson. sjson also
supports type class based serialization where you can provide a custom protocol for serialization as part of the
type class implementation.
Here’s a sample session at the REPL which shows the default serialization protocol of sjson:
scala> import sjson.json._
import sjson.json._
scala> import DefaultProtocol._
import DefaultProtocol._
scala> val str = "debasish"
str: java.lang.String = debasish
scala> import JsonSerialization._
import JsonSerialization._
scala> tojson(str)
res0: dispatch.json.JsValue = "debasish"
scala> fromjson[String](res0)
res1: String = debasish
You can use serialization of generic data types using the default protocol as well:
scala> val list = List(10, 12, 14, 18)
list: List[Int] = List(10, 12, 14, 18)
scala> tojson(list)
res2: dispatch.json.JsValue = [10, 12, 14, 18]
scala> fromjson[List[Int]](res2)
res3: List[Int] = List(10, 12, 14, 18)
You can also define your own custom protocol, which as to be an implementation of the following type class:
trait Writes[T] {
def writes(o: T): JsValue
}
trait Reads[T] {
def reads(json: JsValue): T
}
trait Format[T] extends Writes[T] with Reads[T]
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Consider a case class and a custom protocol to serialize it into JSON. Here’s the type class implementation:
object Protocols {
case class Person(lastName: String, firstName: String, age: Int)
object PersonProtocol extends DefaultProtocol {
import dispatch.json._
import JsonSerialization._
implicit object PersonFormat extends Format[Person] {
def reads(json: JsValue): Person = json match {
case JsObject(m) =>
Person(fromjson[String](m(JsString("lastName"))),
fromjson[String](m(JsString("firstName"))), fromjson[Int](m(JsString("age"))))
case _ => throw new RuntimeException("JsObject expected")
}
def writes(p: Person): JsValue =
JsObject(List(
(tojson("lastName").asInstanceOf[JsString], tojson(p.lastName)),
(tojson("firstName").asInstanceOf[JsString], tojson(p.firstName)),
(tojson("age").asInstanceOf[JsString], tojson(p.age)) ))
}
}
}
and the serialization in action in the REPL:
scala> import sjson.json._
import sjson.json._
scala> import Protocols._
import Protocols._
scala> import PersonProtocol._
import PersonProtocol._
scala> val p = Person("ghosh", "debasish", 20)
p: sjson.json.Protocols.Person = Person(ghosh,debasish,20)
scala> import JsonSerialization._
import JsonSerialization._
scala> tojson[Person](p)
res1: dispatch.json.JsValue = {"lastName" : "ghosh", "firstName" : "debasish", "age" : 20}
scala> fromjson[Person](res1)
res2: sjson.json.Protocols.Person = Person(ghosh,debasish,20)
There are other nifty ways to implement case class serialization using sjson. For more details, have a look at the
wiki for sjson.
4.10.10 JSON: Java
Use the akka.serialization.Serializable.JavaJSON base class with its toJSONmethod. Akka’s
Java JSON is based upon the Jackson library.
For your POJOs to be able to serialize themselves you have to extend the JavaJSON base class.
import akka.serialization.Serializable.JavaJSON;
import akka.serialization.SerializerFactory;
class MyMessage extends JavaJSON {
private String name = null;
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public MyMessage(String name) {
this.name = name;
}
public String getName() {
return name;
}
}
MyMessage message = new MyMessage("json");
String json = message.toJSON();
SerializerFactory factory = new SerializerFactory();
MyMessage messageCopy = factory.getJavaJSON().in(json);
Use the akka.serialization.SerializerFactory.getJavaJSON to do generic JSON serialization, e.g. serialize object
that does not extend JavaJSON using the JSON serializer.
Foo foo = new Foo();
SerializerFactory factory = new SerializerFactory();
String json = factory.getJavaJSON().out(foo);
Foo fooCopy = factory.getJavaJSON().in(json, Foo.class);
4.11 Fault Tolerance Through Supervisor Hierarchies (Scala)
Module stability: SOLID
The “let it crash” approach to fault/error handling, implemented by linking actors, is very different to what Java
and most non-concurrency oriented languages/frameworks have adopted. It’s a way of dealing with failure that is
designed for concurrent and distributed systems.
4.11.1 Concurrency
Throwing an exception in concurrent code (let’s assume we are using non-linked actors), will just simply blow up
the thread that currently executes the actor.
• There is no way to find out that things went wrong (apart from inspecting the stack trace).
• There is nothing you can do about it.
Here actors provide a clean way of getting notification of the error and do something about it.
Linking actors also allow you to create sets of actors where you can be sure that either:
• All are dead
• None are dead
This is very useful when you have thousands of concurrent actors. Some actors might have implicit dependencies
and together implement a service, computation, user session etc.
It encourages non-defensive programming. Don’t try to prevent things from go wrong, because they will, whether
you want it or not. Instead; expect failure as a natural state in the life-cycle of your app, crash early and let
someone else (that sees the whole picture), deal with it.
4.11.2 Distributed actors
You can’t build a fault-tolerant system with just one single box - you need at least two. Also, you (usually)
need to know if one box is down and/or the service you are talking to on the other box is down. Here actor
supervision/linking is a critical tool for not only monitoring the health of remote services, but to actually manage
the service, do something about the problem if the actor or node is down. Such as restarting actors on the same
node or on another node.
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In short, it is a very different way of thinking, but a way that is very useful (if not critical) to building fault-tolerant
highly concurrent and distributed applications, which is as valid if you are writing applications for the JVM or the
Erlang VM (the origin of the idea of “let-it-crash” and actor supervision).
4.11.3 Supervision
Supervisor hierarchies originate from Erlang’s OTP framework.
A supervisor is responsible for starting, stopping and monitoring its child processes. The basic idea of a supervisor
is that it should keep its child processes alive by restarting them when necessary. This makes for a completely
different view on how to write fault-tolerant servers. Instead of trying all things possible to prevent an error from
happening, this approach embraces failure. It shifts the view to look at errors as something natural and something
that will happen, instead of trying to prevent it; embraces it. Just “Let It Crash”, since the components will be
reset to a stable state and restarted upon failure.
Akka has two different restart strategies; All-For-One and One-For-One. Best explained using some pictures
(referenced from erlang.org ):
OneForOne
The OneForOne fault handler will restart only
age:http://www.erlang.org/doc/design_principles/sup4.gif
the
component
that
has
crashed.
im-
AllForOne
The AllForOne fault handler will restart all the components that the supervisor is managing, including the one
that have crashed. This strategy should be used when you have a certain set of components that are coupled in some way that if one is crashing they all need to be reset to a stable state before continuing. image:http://www.erlang.org/doc/design_principles/sup5.gif
Restart callbacks
There are two different callbacks that the Typed Actor and Actor can hook in to:
• Pre restart
• Post restart
These are called prior to and after the restart upon failure and can be used to clean up and reset/reinitialize state
upon restart. This is important in order to reset the component failure and leave the component in a fresh and
stable state before consuming further messages.
Defining a supervisor’s restart strategy
Both the Typed Actor supervisor configuration and the Actor supervisor configuration take a ‘FaultHandlingStrategy’ instance which defines the fault management. The different strategies are:
• AllForOne
• OneForOne
These have the semantics outlined in the section above.
Here is an example of how to define a restart strategy:
AllForOneStrategy( //FaultHandlingStrategy; AllForOneStrategy or OneForOneStrategy
List(classOf[Exception]), //What exceptions will be handled
3,
// maximum number of restart retries
5000
// within time in millis
)
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Defining actor life-cycle
The other common configuration element is the “LifeCycle’ which defines the life-cycle. The supervised actor
can define one of two different life-cycle configurations:
• Permanent: which means that the actor will always be restarted.
• Temporary: which means that the actor will not be restarted, but it will be shut down through the regular
shutdown process so the ‘postStop’ callback function will called.
Here is an example of how to define the life-cycle:
Permanent // means that the component will always be restarted
Temporary // means that it will not be restarted, but it will be shut
// down through the regular shutdown process so the ’postStop’ hook will called
4.11.4 Supervising Actors
Declarative supervisor configuration
The Actor’s supervision can be declaratively defined by creating a “Supervisor’ factory object. Here is an example:
val supervisor = Supervisor(
SupervisorConfig(
AllForOneStrategy(List(classOf[Exception]), 3, 1000),
Supervise(
actorOf[MyActor1],
Permanent) ::
Supervise(
actorOf[MyActor2],
Permanent) ::
Nil))
Supervisors created like this are implicitly instantiated and started.
To configure a handler function for when the actor underlying the supervisor receives a MaximumNumberOfRestartsWithinTimeRangeReached message, you can specify a function of type (ActorRef, MaximumNumberOfRestartsWithinTimeRangeReached) => Unit when creating the SupervisorConfig. This handler will
be called with the ActorRef of the supervisor and the MaximumNumberOfRestartsWithinTimeRangeReached
message.
val handler = {
(supervisor:ActorRef,max:MaximumNumberOfRestartsWithinTimeRangeReached) => EventHandler.notify(s
}
val supervisor = Supervisor(
SupervisorConfig(
AllForOneStrategy(List(classOf[Exception]), 3, 1000),
Supervise(
actorOf[MyActor1],
Permanent) ::
Supervise(
actorOf[MyActor2],
Permanent) ::
Nil), handler)
You can link and unlink actors from a declaratively defined supervisor using the ‘link’ and ‘unlink’ methods:
val supervisor = Supervisor(...)
supervisor.link(..)
supervisor.unlink(..)
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You can also create declarative supervisors through the ‘SupervisorFactory’ factory object. Use this factory instead
of the ‘Supervisor’ factory object if you want to control instantiation and starting of the Supervisor, if not then it
is easier and better to use the ‘Supervisor’ factory object.
Example usage:
val factory = SupervisorFactory(
SupervisorConfig(
OneForOneStrategy(List(classOf[Exception]), 3, 10),
Supervise(
myFirstActor,
Permanent) ::
Supervise(
mySecondActor,
Permanent) ::
Nil))
Then create a new instance our Supervisor and start it up explicitly.
val supervisor = factory.newInstance
supervisor.start // start up all managed servers
Declaratively define actors as remote services
You can declaratively define an actor to be available as a remote actor by specifying true for registerAsRemoteService.
Here is an example:
val supervisor = Supervisor(
SupervisorConfig(
AllForOneStrategy(List(classOf[Exception]), 3, 1000),
Supervise(
actorOf[MyActor1],
Permanent,
**true**)
:: Nil))
Programmatic linking and supervision of Actors
Actors can at runtime create, spawn, link and supervise other actors. Linking and unlinking is done using one of
the ‘link’ and ‘unlink’ methods available in the ‘ActorRef’ (therefore prefixed with ‘self’ in these examples).
Here is the API and how to use it from within an ‘Actor’:
// link and unlink actors
self.link(actorRef)
self.unlink(actorRef)
// starts and links Actors atomically
self.startLink(actorRef)
// spawns (creates and starts) actors
self.spawn[MyActor]
self.spawnRemote[MyActor]
// spawns and links Actors atomically
self.spawnLink[MyActor]
self.spawnLinkRemote[MyActor]
A child actor can tell the supervising actor to unlink him by sending him the ‘Unlink(this)’ message. When the
supervisor receives the message he will unlink and shut down the child. The supervisor for an actor is available in
the ‘supervisor: Option[Actor]’ method in the ‘ActorRef’ class. Here is how it can be used.
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if (supervisor.isDefined) supervisor.get ! Unlink(self)
// Or shorter using ’foreach’:
supervisor.foreach(_ ! Unlink(self))
The supervising actor’s side of things
If a linked Actor is failing and throws an exception then an “Exit(deadActor, cause)’ message will be sent to the
supervisor (however you should never try to catch this message in your own message handler, it is managed by
the runtime).
The supervising Actor also needs to define a fault handler that defines the restart strategy the Actor should accommodate when it traps an “Exit’ message. This is done by setting the “faultHandler’ field.
protected var faultHandler: FaultHandlingStrategy
The different options are:
• AllForOneStrategy(trapExit, maxNrOfRetries, withinTimeRange)
– trapExit is a List or Array of classes inheriting from Throwable, they signal which types of exceptions
this actor will handle
• OneForOneStrategy(trapExit, maxNrOfRetries, withinTimeRange)
– trapExit is a List or Array of classes inheriting from Throwable, they signal which types of exceptions
this actor will handle
Here is an example:
self.faultHandler = AllForOneStrategy(List(classOf[Throwable]), 3, 1000)
Putting all this together it can look something like this:
class MySupervisor extends Actor {
self.faultHandler = OneForOneStrategy(List(classOf[Throwable]), 5, 5000)
def receive = {
case Register(actor) =>
self.link(actor)
}
}
You can also link an actor from outside the supervisor like this:
val supervisor = Actor.registry.actorsFor(classOf[MySupervisor]).head
supervisor.link(actor)
The supervised actor’s side of things
The supervised actor needs to define a life-cycle. This is done by setting the lifeCycle field as follows:
self.lifeCycle = Permanent // Permanent or Temporary or UndefinedLifeCycle
In the supervised Actor you can override the “preRestart’ and “postRestart’ callback methods to add hooks into
the restart process. These methods take the reason for the failure, e.g. the exception that caused termination and
restart of the actor as argument. It is in these methods that you have to add code to do cleanup before termination
and initialization after restart. Here is an example:
class FaultTolerantService extends Actor {
override def preRestart(reason: Throwable) {
... // clean up before restart
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}
override def postRestart(reason: Throwable) {
... // reinit stable state after restart
}
}
Reply to initial senders
Supervised actors have the option to reply to the initial sender within preRestart, postRestart and postStop. A
reply within these methods is possible after receive has thrown an exception. When receive returns normally it is
expected that any necessary reply has already been done within receive. Here’s an example.
class FaultTolerantService extends Actor {
def receive = {
case msg => {
// do something that may throw an exception
// ...
self.reply("ok")
}
}
override def preRestart(reason: scala.Throwable) {
self.tryReply(reason.getMessage)
}
override def postStop() {
self.tryReply("stopped by supervisor")
}
}
• A reply within preRestart or postRestart must be a try reply via self.tryReply because an self.reply will throw
an exception when the actor is restarted without having failed. This can be the case in context of AllForOne
restart strategies.
• A reply within postStop must be a reply via self.tryReply because an self.reply will throw an exception when
the actor has been stopped by the application (and not by a supervisor) after successful execution of receive
(or no execution at all).
Handling too many actor restarts within a specific time limit
If you remember, when you define the ‘RestartStrategy’ you also defined maximum number of restart retries
within time in millis.
AllForOneStrategy( //Restart policy, AllForOneStrategy or OneForOneStrategy
List(classOf[Exception]), //What kinds of exception it will handle
3,
// maximum number of restart retries
5000
// within time in millis
)
Now, what happens if this limit is reached?
What will happen is that the failing actor will send a system message to its supervisor called ‘MaximumNumberOfRestartsWithinTimeRangeReached’ with the following signature:
case class MaximumNumberOfRestartsWithinTimeRangeReached(
victim: ActorRef, maxNrOfRetries: Int, withinTimeRange: Int, lastExceptionCausingRestart: Throwa
If you want to be able to take action upon this event (highly recommended) then you have to create a message
handle for it in the supervisor.
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Here is an example:
val supervisor = actorOf(new Actor{
self.faultHandler = OneForOneStrategy(List(classOf[Throwable]), 5, 5000)
protected def receive = {
case MaximumNumberOfRestartsWithinTimeRangeReached(
victimActorRef, maxNrOfRetries, withinTimeRange, lastExceptionCausingRestart) =>
... // handle the error situation
}
}).start()
You will also get this log warning similar to this:
WAR [20100715-14:05:25.821] actor: Maximum number of restarts [5] within time range [5000] reached
WAR [20100715-14:05:25.821] actor:
Will *not* restart actor [Actor[akka.actor.SupervisorHierar
WAR [20100715-14:05:25.821] actor:
Last exception causing restart was [akka.actor.SupervisorHi
If you don’t define a message handler for this message then you don’t get an error but the message is simply not
sent to the supervisor. Instead you will get a log warning.
4.11.5 Supervising Typed Actors
Declarative supervisor configuration
To configure Typed Actors for supervision you have to consult the “TypedActorConfigurator’ and its “configure’
method. This method takes a “RestartStrategy’ and an array of “Component’ definitions defining the Typed Actors
and their “LifeCycle’. Finally you call the “supervise’ method to start everything up. The configuration elements
reside in the “akka.config.JavaConfig’ class and need to be imported statically.
Here is an example:
import akka.config.Supervision._
val manager = new TypedActorConfigurator
manager.configure(
AllForOneStrategy(List(classOf[Exception]), 3, 1000),
List(
SuperviseTypedActor(
Foo.class,
FooImpl.class,
Permanent,
1000),
new SuperviseTypedActor(
Bar.class,
BarImpl.class,
Permanent,
1000)
)).supervise
Then you can retrieve the Typed Actor as follows:
Foo foo = manager.getInstance(classOf[Foo])
Restart callbacks
Programatic linking and supervision of TypedActors
TypedActors can be linked and unlinked just like actors - in fact the linking is done on the underlying actor:
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TypedActor.link(supervisor, supervised)
TypedActor.unlink(supervisor, supervised)
If the parent TypedActor (supervisor) wants to be able to do handle failing child TypedActors, e.g. be able restart
the linked TypedActor according to a given fault handling scheme then it has to set its ‘trapExit’ flag to an array
of Exceptions that it wants to be able to trap:
TypedActor.faultHandler(supervisor, AllForOneStrategy(Array(classOf[IOException]), 3, 2000))
For convenience there is an overloaded link that takes trapExit and faultHandler for the supervisor as arguments.
Here is an example:
import akka.actor.TypedActor._
val foo = newInstance(classOf[Foo], 1000)
val bar = newInstance(classOf[Bar], 1000)
link(foo, bar, new AllForOneStrategy(Array(classOf[IOException]), 3, 2000))
// alternative: chaining
bar = faultHandler(foo, new AllForOneStrategy(Array(classOf[IOException]), 3, 2000))
.newInstance(Bar.class, 1000)
link(foo, bar
4.12 Dispatchers (Scala)
Module stability: SOLID
The Dispatcher is an important piece that allows you to configure the right semantics and parameters for optimal
performance, throughput and scalability. Different Actors have different needs.
Akka supports dispatchers for both event-driven lightweight threads, allowing creation of millions of threads on a
single workstation, and thread-based Actors, where each dispatcher is bound to a dedicated OS thread.
The event-based Actors currently consume ~600 bytes per Actor which means that you can create more than 6.5
million Actors on 4 GB RAM.
4.12.1 Default dispatcher
For most scenarios the default settings are the best. Here we have one single event-based dispatcher for all Actors
created. The dispatcher used is this one:
Dispatchers.globalExecutorBasedEventDrivenDispatcher
But if you feel that you are starting to contend on the single dispatcher (the ‘Executor’ and its queue) or want
to group a specific set of Actors for a dedicated dispatcher for better flexibility and configurability then you can
override the defaults and define your own dispatcher. See below for details on which ones are available and how
they can be configured.
4.12.2 Setting the dispatcher
Normally you set the dispatcher from within the Actor itself. The dispatcher is defined by the ‘dispatcher: MessageDispatcher’ member field in ‘ActorRef’.
class MyActor extends Actor {
self.dispatcher = ... // set the dispatcher
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...
}
You can also set the dispatcher for an Actor before it has been started:
actorRef.dispatcher = dispatcher
4.12.3 Types of dispatchers
There are six different types of message dispatchers:
• Thread-based
• Event-based
• Priority event-based
• Work-stealing
Factory methods for all of these, including global versions of some of them, are in the ‘akka.dispatch.Dispatchers’
object.
Let’s now walk through the different dispatchers in more detail.
Thread-based
The ‘ThreadBasedDispatcher’ binds a dedicated OS thread to each specific Actor. The messages are posted to
a ‘LinkedBlockingQueue’ which feeds the messages to the dispatcher one by one. A ‘ThreadBasedDispatcher’
cannot be shared between actors. This dispatcher has worse performance and scalability than the event-based
dispatcher but works great for creating “daemon” Actors that consumes a low frequency of messages and are
allowed to go off and do their own thing for a longer period of time. Another advantage with this dispatcher is that
Actors do not block threads for each other.
It would normally by used from within the actor like this:
class MyActor extends Actor {
self.dispatcher = Dispatchers.newThreadBasedDispatcher(self)
...
}
Event-based
The ‘ExecutorBasedEventDrivenDispatcher’ binds a set of Actors to a thread pool backed up by a ‘BlockingQueue’. This dispatcher is highly configurable and supports a fluent configuration API to configure the ‘BlockingQueue’ (type of queue, max items etc.) as well as the thread pool.
The event-driven dispatchers must be shared between multiple Actors. One best practice is to let each top-level
Actor, e.g. the Actors you define in the declarative supervisor config, to get their own dispatcher but reuse the
dispatcher for each new Actor that the top-level Actor creates. But you can also share dispatcher between multiple
top-level Actors. This is very use-case specific and needs to be tried out on a case by case basis. The important
thing is that Akka tries to provide you with the freedom you need to design and implement your system in the
most efficient way in regards to performance, throughput and latency.
It comes with many different predefined BlockingQueue configurations: * Bounded LinkedBlockingQueue *
Unbounded LinkedBlockingQueue * Bounded ArrayBlockingQueue * Unbounded ArrayBlockingQueue * SynchronousQueue
You can also set the rejection policy that should be used, e.g. what should be done if the dispatcher (e.g. the Actor)
can’t keep up and the mailbox is growing up to the limit defined. You can choose between four different rejection
policies:
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• java.util.concurrent.ThreadPoolExecutor.CallerRuns - will run the message processing in the caller’s thread
as a way to slow him down and balance producer/consumer
• java.util.concurrent.ThreadPoolExecutor.AbortPolicy - rejected messages by throwing a ‘RejectedExecutionException’
• java.util.concurrent.ThreadPoolExecutor.DiscardPolicy - discards the message (throws it away)
• java.util.concurrent.ThreadPoolExecutor.DiscardOldestPolicy - discards the oldest message in the mailbox
(throws it away)
You cane read more about these policies here.
Here is an example:
import akka.actor.Actor
import akka.dispatch.Dispatchers
class MyActor extends Actor {
self.dispatcher = Dispatchers.newExecutorBasedEventDrivenDispatcher(name)
.withNewThreadPoolWithLinkedBlockingQueueWithCapacity(100)
.setCorePoolSize(16)
.setMaxPoolSize(128)
.setKeepAliveTimeInMillis(60000)
.build
...
}
This ‘ExecutorBasedEventDrivenDispatcher’ allows you to define the ‘throughput’ it should have. This defines
the number of messages for a specific Actor the dispatcher should process in one single sweep. Setting this to a
higher number will increase throughput but lower fairness, and vice versa. If you don’t specify it explicitly then it
uses the default value defined in the ‘akka.conf’ configuration file:
actor {
throughput = 5
}
If you don’t define a the ‘throughput’ option in the configuration file then the default value of ‘5’ will be used.
Browse the ScalaDoc or look at the code for all the options available.
Priority event-based
Sometimes it’s useful to be able to specify priority order of messages, that is done by using PriorityExecutorBasedEventDrivenDispatcher and supply a java.util.Comparator[MessageInvocation] or use a
akka.dispatch.PriorityGenerator (recommended):
Creating a PriorityExecutorBasedEventDrivenDispatcher using PriorityGenerator:
import akka.dispatch._
import akka.actor._
val gen = PriorityGenerator { // Create a new PriorityGenerator, lower prio means more important
case ’highpriority => 0
// ’highpriority messages should be treated first if possible
case ’lowpriority => 100 // ’lowpriority messages should be treated last if possible
case otherwise
=> 50
// We default to 50
}
val a = Actor.actorOf( // We create a new Actor that just prints out what it processes
new Actor {
def receive = {
case x => println(x)
}
})
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// We create a new Priority dispatcher and seed it with the priority generator
a.dispatcher = new PriorityExecutorBasedEventDrivenDispatcher("foo", gen)
a.start // Start the Actor
a.dispatcher.suspend(a) // Suspending the actor so it doesn’t start to treat the messages before
a
a
a
a
a
a
a
!
!
!
!
!
!
!
’lowpriority
’lowpriority
’highpriority
’pigdog
’pigdog2
’pigdog3
’highpriority
a.dispatcher.resume(a) // Resuming the actor so it will start treating its messages
Prints:
‘highpriority ‘highpriority ‘pigdog ‘pigdog2 ‘pigdog3 ‘lowpriority ‘lowpriority
Work-stealing event-based
The ‘ExecutorBasedEventDrivenWorkStealingDispatcher’ is a variation of the ‘ExecutorBasedEventDrivenDispatcher’ in which Actors of the same type can be set up to share this dispatcher and during execution time the
different actors will steal messages from other actors if they have less messages to process. This can be a great
way to improve throughput at the cost of a little higher latency.
Normally the way you use it is to create an Actor companion object to hold the dispatcher and then set in in the
Actor explicitly.
object MyActor {
val dispatcher = Dispatchers.newExecutorBasedEventDrivenWorkStealingDispatcher(name).build
}
class MyActor extends Actor {
self.dispatcher = MyActor.dispatcher
...
}
Here is an article with some more information: Load Balancing Actors with Work Stealing Techniques Here is
another article discussing this particular dispatcher: Flexible load balancing with Akka in Scala
4.12.4 Making the Actor mailbox bounded
Global configuration
You can make the Actor mailbox bounded by a capacity in two ways. Either you define it in the configuration file
under ‘default-dispatcher’. This will set it globally.
actor {
default-dispatcher {
mailbox-capacity = -1
# If negative (or zero) then an unbounded mailbox is used (de
# If positive then a bounded mailbox is used and the capacity
}
}
Per-instance based configuration
You can also do it on a specific dispatcher instance.
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For the ‘ExecutorBasedEventDrivenDispatcher’ and the ‘ExecutorBasedWorkStealingDispatcher’ you can do it
through their constructor
class MyActor extends Actor {
val mailboxCapacity = BoundedMailbox(capacity = 100)
self.dispatcher = Dispatchers.newExecutorBasedEventDrivenDispatcher(name, throughput, mailboxCap
...
}
For the ‘ThreadBasedDispatcher’, it is non-shareable between actors, and associates a dedicated Thread with the
actor. Making it bounded (by specifying a capacity) is optional, but if you do, you need to provide a pushTimeout
(default is 10 seconds). When trying to send a message to the Actor it will throw a MessageQueueAppendFailedException if the message cannot be added to the mailbox within the time specified by the pushTimeout.
class MyActor extends Actor {
import akka.util.duration._
self.dispatcher = Dispatchers.newThreadBasedDispatcher(self, mailboxCapacity = 100,
pushTimeOut = 10 seconds)
...
}
4.13 Routing (Scala)
Akka-core includes some building blocks to build more complex message flow handlers, they are listed and explained below:
4.13.1 Dispatcher
A Dispatcher is an actor that routes incoming messages to outbound actors.
To use it you can either create a Dispatcher through the dispatcherActor() factory method
import akka.actor.Actor._
import akka.actor.Actor
import akka.routing.Routing._
//Our message types
case object Ping
case object Pong
//Two actors, one named Pinger
//The actor(pf) method creates
val pinger = actorOf(new Actor
val ponger = actorOf(new Actor
and one named Ponger
an anonymous actor and starts it
{ def receive = { case x => println("Pinger: " + x) } }).start()
{ def receive = { case x => println("Ponger: " + x) } }).start()
//A dispatcher that dispatches Ping messages to the pinger
//and Pong messages to the ponger
val d = dispatcherActor {
case Ping => pinger
case Pong => ponger
}
d ! Ping //Prints "Pinger: Ping"
d ! Pong //Prints "Ponger: Pong"
Or by mixing in akka.patterns.Dispatcher:
import akka.actor.Actor
import akka.actor.Actor._
import akka.routing.Dispatcher
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//Our message types
case object Ping
case object Pong
class MyDispatcher extends Actor with Dispatcher {
//Our pinger and ponger actors
val pinger = actorOf(new Actor { def receive = { case x => println("Pinger: " + x) } }).start()
val ponger = actorOf(new Actor { def receive = { case x => println("Ponger: " + x) } }).start()
//When we get a ping, we dispatch to the pinger
//When we get a pong, we dispatch to the ponger
def routes = {
case Ping => pinger
case Pong => ponger
}
}
//Create an instance of our dispatcher, and start it
val d = actorOf[MyDispatcher].start()
d ! Ping //Prints "Pinger: Ping"
d ! Pong //Prints "Ponger: Pong"
4.13.2 LoadBalancer
A LoadBalancer is an actor that forwards messages it receives to a boundless sequence of destination actors.
Example using the loadBalancerActor() factory method:
import
import
import
import
akka.actor.Actor._
akka.actor.Actor
akka.routing.Routing._
akka.routing.CyclicIterator
//Our message types
case object Ping
case object Pong
//Two actors, one named Pinger and one named Ponger
//The actor(pf) method creates an anonymous actor and starts it
val pinger = actorOf(new Actor { def receive = { case x => println("Pinger: " + x) } }).start()
val ponger = actorOf(new Actor { def receive = { case x => println("Ponger: " + x) } }).start()
//A load balancer that given a sequence of actors dispatches them accordingly
//a CyclicIterator works in a round-robin-fashion
val d = loadBalancerActor( new CyclicIterator( List(pinger,ponger) ) )
d
d
d
d
!
!
!
!
Pong
Pong
Ping
Ping
//Prints
//Prints
//Prints
//Prints
"Pinger:
"Ponger:
"Pinger:
"Ponger:
Pong"
Pong"
Ping"
Ping"
Or by mixing in akka.routing.LoadBalancer
import akka.actor._
import akka.actor.Actor._
import akka.routing.{ LoadBalancer, CyclicIterator }
//Our message types
case object Ping
case object Pong
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//A load balancer that balances between a pinger and a ponger
class MyLoadBalancer extends Actor with LoadBalancer {
val pinger = actorOf(new Actor { def receive = { case x => println("Pinger: " + x) } }).start()
val ponger = actorOf(new Actor { def receive = { case x => println("Ponger: " + x) } }).start()
val seq = new CyclicIterator[ActorRef](List(pinger,ponger))
}
//Create an instance of our loadbalancer, and start it
val d = actorOf[MyLoadBalancer].start()
d
d
d
d
!
!
!
!
Pong
Pong
Ping
Ping
//Prints
//Prints
//Prints
//Prints
"Pinger:
"Ponger:
"Pinger:
"Ponger:
Pong"
Pong"
Ping"
Ping"
Also, instead of using the CyclicIterator, you can create your own message distribution algorithms, there’s already
one that dispatches depending on target mailbox size, effectively dispatching to the one that’s got fewest messages
to process right now.
Example http://pastie.org/984889
You can also send a ‘Routing.Broadcast(msg)’ message to the router to have it be broadcasted out to all the actors
it represents.
router ! Routing.Broadcast(PoisonPill)
4.13.3 Actor Pool
An actor pool is similar to the load balancer is that it routes incoming messages to other actors. It has different
semantics however when it comes to how those actors are managed and selected for dispatch. Therein lies the
difference. The pool manages, from start to shutdown, the lifecycle of all delegated actors. The number of actors
in a pool can be fixed or grow and shrink over time. Also, messages can be routed to more than one actor in the
pool if so desired. This is a useful little feature for accounting for expected failure - especially with remoting where you can invoke the same request of multiple actors and just take the first, best response.
The actor pool is built around three concepts: capacity, filtering and selection.
Selection
All pools require a Selector to be mixed-in. This trait controls how and how many actors in the pool will receive
the incoming message. Define selectionCount to some positive number greater than one to route to multiple actors.
Currently two are provided:
• SmallestMailboxSelector - Using the exact same logic as the iterator of the same name, the pooled actor
with the fewest number of pending messages will be chosen.
• RoundRobinSelector - Performs a very simple index-based selection, wrapping around the end of the list,
very much like the CyclicIterator does.
Partial Fills
When selecting more than one pooled actor, its possible that in order to fulfill the requested amount, the selection
set must contain duplicates. By setting partialFill to true, you instruct the selector to return only unique actors
from the pool.
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Capacity
As you’d expect, capacity traits determine how the pool is funded with actors. There are two types of strategies
that can be employed:
• FixedCapacityStrategy - When you mix this into your actor pool, you define a pool size and when the pool
is started, it will have that number of actors within to which messages will be delegated.
• BoundedCapacityStrategy - When you mix this into your actor pool, you define upper and lower bounds,
and when the pool is started, it will have the minimum number of actors in place to handle messages. You
must also mix-in a Capacitor and a Filter when using this strategy (see below).
The BoundedCapacityStrategy requires additional logic to function. Specifically it requires a Capacitor and a
Filter. Capacitors are used to determine the pressure that the pool is under and provide a (usually) raw reading of
this information. Currently we provide for the use of either mailbox backlog or active futures count as a means of
evaluating pool pressure. Each expresses itself as a simple number - a reading of the number of actors either with
mailbox sizes over a certain threshold or blocking a thread waiting on a future to complete or expire.
Filtering
A Filter is a trait that modifies the raw pressure reading returned from a Capacitor such that it drives the adjustment
of the pool capacity to a desired end. More simply, if we just used the pressure reading alone, we might only ever
increase the size of the pool (to respond to overload) or we might only have a single mechanism for reducing the
pool size when/if it became necessary. This behavior is fully under your control through the use of Filters. Let’s
take a look at some code to see how this works:
trait BoundedCapacitor
{
def lowerBound:Int
def upperBound:Int
def capacity(delegates:Seq[ActorRef]):Int =
{
val current = delegates length
var delta = _eval(delegates)
val proposed = current + delta
if (proposed < lowerBound) delta += (lowerBound - proposed)
else if (proposed > upperBound) delta -= (proposed - upperBound)
delta
}
protected def _eval(delegates:Seq[ActorRef]):Int
}
trait CapacityStrategy
{
import ActorPool._
def pressure(delegates:Seq[ActorRef]):Int
def filter(pressure:Int, capacity:Int):Int
protected def _eval(delegates:Seq[ActorRef]):Int = filter(pressure(delegates), delegates.siz
}
Here we see how the filter function will have the chance to modify the pressure reading to influence the capacity
change. You are free to implement filter() however you like. We provide a Filter trait that evaluates both a rampup
and a backoff subfilter to determine how to use the pressure reading to alter the pool capacity. There are several
subfilters available to use, though again you may create whatever makes the most sense for you pool:
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• BasicRampup - When pressure exceeds current capacity, increase the number of actors in the pool by some
factor (rampupRate) of the current pool size.
• BasicBackoff - When the pressure ratio falls under some predefined amount (backoffThreshold), decrease
the number of actors in the pool by some factor of the current pool size.
• RunningMeanBackoff - This filter tracks the average pressure-to-capacity over the lifetime of the pool
(or since the last time the filter was reset) and will begin to reduce capacity once this mean falls below
some predefined amount. The number of actors that will be stopped is determined by some factor of the
difference between the current capacity and pressure. The idea behind this filter is to reduce the likelihood
of “thrashing” (removing then immediately creating...) pool actors by delaying the backoff until some
quiescent stage of the pool. Put another way, use this subfilter to allow quick rampup to handle load and
more subtle backoff as that decreases over time.
Examples
class TestPool extends Actor with DefaultActorPool
with BoundedCapacityStrategy
with ActiveFuturesPressureCapacitor
with SmallestMailboxSelector
with BasicNoBackoffFilter
{
def receive = _route
def lowerBound = 2
def upperBound = 4
def rampupRate = 0.1
def partialFill = true
def selectionCount = 1
def instance = actorOf(new Actor {def receive = {case n:Int =>
Thread.sleep(n)
counter.incrementAndGet
latch.countDown()}})
}
class TestPool extends Actor with DefaultActorPool
with BoundedCapacityStrategy
with MailboxPressureCapacitor
with SmallestMailboxSelector
with Filter
with RunningMeanBackoff
with BasicRampup
{
def receive = _route
def lowerBound = 1
def upperBound = 5
def pressureThreshold = 1
def partialFill = true
def selectionCount = 1
def rampupRate = 0.1
def backoffRate = 0.50
def backoffThreshold = 0.50
def instance = actorOf(new Actor {def receive = {case n:Int =>
Thread.sleep(n)
latch.countDown()}})
}
Taken from the unit test spec.
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4.14 FSM
Platforms: Scala
Module author: Irmo Manie, Roland Kuhn New in version 1.0.Changed in version 1.2: added Tracing and Logging
Module stability: STABLE
4.14.1 Overview
The FSM (Finite State Machine) is available as a mixin for the akka Actor and is best described in the Erlang
design principles
A FSM can be described as a set of relations of the form:
State(S) x Event(E) -> Actions (A), State(S’)
These relations are interpreted as meaning:
If we are in state S and the event E occurs, we should perform the actions A and make a transition to
the state S’.
4.14.2 A Simple Example
To demonstrate the usage of states we start with a simple FSM without state data. The state can be of any type so
for this example we create the states A, B and C.
sealed trait ExampleState
case object A extends ExampleState
case object B extends ExampleState
case object C extends ExampleState
Now lets create an object representing the FSM and defining the behavior.
import akka.actor.{Actor, FSM}
import akka.event.EventHandler
import akka.util.duration._
case object Move
class ABC extends Actor with FSM[ExampleState, Unit] {
import FSM._
startWith(A, Unit)
when(A) {
case Ev(Move) =>
EventHandler.info(this, "Go to B and move on after 5 seconds")
goto(B) forMax (5 seconds)
}
when(B) {
case Ev(StateTimeout) =>
EventHandler.info(this, "Moving to C")
goto(C)
}
when(C) {
case Ev(Move) =>
EventHandler.info(this, "Stopping")
stop
}
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initialize // this checks validity of the initial state and sets up timeout if needed
}
Each state is described by one or more when(state) blocks; if more than one is given for the same state, they
are tried in the order given until the first is found which matches the incoming event. Events are matched using
either Ev(msg) (if no state data are to be extracted) or Event(msg, data), see below. The statements for
each case are the actions to be taken, where the final expression must describe the transition into the next state.
This can either be stay when no transition is needed or goto(target) for changing into the target state. The
transition may be annotated with additional properties, where this example includes a state timeout of 5 seconds
after the transition into state B: forMax(duration) arranges for a StateTimeout message to be scheduled,
unless some other message is received first. The construction of the FSM is finished by calling the initialize
method as last part of the ABC constructor.
4.14.3 State Data
The FSM can also hold state data associated with the internal state of the state machine. The state data can be of
any type but to demonstrate let’s look at a lock with a String as state data holding the entered unlock code. First
we need two states for the lock:
sealed trait LockState
case object Locked extends LockState
case object Open extends LockState
Now we can create a lock FSM that takes LockState as a state and a String as state data:
import akka.actor.{Actor, FSM}
class Lock(code: String) extends Actor with FSM[LockState, String] {
import FSM._
val emptyCode = ""
startWith(Locked, emptyCode)
when(Locked) {
// receive a digit and the code that we have so far
case Event(digit: Char, soFar) => {
// add the digit to what we have
soFar + digit match {
case incomplete if incomplete.length < code.length =>
// not enough digits yet so stay using the incomplete code as the new state data
stay using incomplete
case ‘code‘ =>
// code matched the one from the lock so go to Open state and reset the state data
goto(Open) using emptyCode forMax (1 seconds)
case wrong =>
// wrong code, stay Locked and reset the state data
stay using emptyCode
}
}
}
when(Open) {
case Ev(StateTimeout, _) => {
// after the timeout, go back to Locked state
goto(Locked)
}
}
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initialize
}
This very simple example shows how the complete state of the FSM is encoded in the (State, Data) pair and
only explicitly updated during transitions. This encapsulation is what makes state machines a powerful abstraction,
e.g. for handling socket states in a network server application.
4.14.4 Reference
This section describes the DSL in a more formal way, refer to Examples for more sample material.
The FSM Trait and Object
The FSM trait may only be mixed into an Actor. Instead of extending Actor, the self type approach was
chosen in order to make it obvious that an actor is actually created. Importing all members of the FSM object is
recommended to receive useful implicits and directly access the symbols like StateTimeout. This import is
usually placed inside the state machine definition:
class MyFSM extends Actor with FSM[State, Data] {
import FSM._
...
}
The FSM trait takes two type parameters:
1. the supertype of all state names, usually a sealed trait with case objects extending it,
2. the type of the state data which are tracked by the FSM module itself.
Note: The state data together with the state name describe the internal state of the state machine; if you stick to
this scheme and do not add mutable fields to the FSM class you have the advantage of making all changes of the
internal state explicit in a few well-known places.
Defining Timeouts
The FSM module uses Duration for all timing configuration. Several methods, like when and startWith take
a FSM.Timeout, which is an alias for Option[Duration]. There is an implicit conversion available in the
FSM object which makes this transparent, just import it into your FSM body.
Defining States
A state is defined by one or more invocations of the method
when(<name>[, stateTimeout = <timeout>])(stateFunction).
The given name must be an object which is type-compatible with the first type parameter given to the FSM trait.
This object is used as a hash key, so you must ensure that it properly implements equals and hashCode; in
particular it must not be mutable. The easiest fit for these requirements are case objects.
If the stateTimeout parameter is given, then all transitions into this state, including staying, receive this timeout by default. Initiating the transition with an explicit timeout may be used to override this default, see Initiating
Transitions for more information. The state timeout of any state may be changed during action processing with
setStateTimeout(state, duration). This enables runtime configuration e.g. via external message.
The stateFunction argument is a PartialFunction[Event, State], which is conveniently given
using the partial function literal syntax as demonstrated below:
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when(Idle) {
case Ev(Start(msg)) => // convenience extractor when state data not needed
goto(Timer) using (msg, self.channel)
}
when(Timer, stateTimeout = 12 seconds) {
case Event(StateTimeout, (msg, channel)) =>
channel ! msg
goto(Idle)
}
The Event(msg, data) case class may be used directly in the pattern as shown in state Idle, or you may use
the extractor Ev(msg) when the state data are not needed.
Defining the Initial State
Each FSM needs a starting point, which is declared using
startWith(state, data[, timeout])
The optionally given timeout argument overrides any specification given for the desired initial state. If you want
to cancel a default timeout, use Duration.Inf.
Unhandled Events
If a state doesn’t handle a received event a warning is logged. If you want to do something else in this case you
can specify that with whenUnhandled(stateFunction):
whenUnhandled {
case Event(x : X, data) =>
EventHandler.info(this, "Received unhandled event: " + x)
stay
case Ev(msg) =>
EventHandler.warn(this, "Received unknown event: " + x)
goto(Error)
}
IMPORTANT: This handler is not stacked, meaning that each invocation of whenUnhandled replaces the
previously installed handler.
Initiating Transitions
The result of any stateFunction must be a definition of the next state unless terminating the FSM, which is
described in Termination from Inside. The state definition can either be the current state, as described by the stay
directive, or it is a different state as given by goto(state). The resulting object allows further qualification by
way of the modifiers described in the following:
forMax(duration) This modifier sets a state timeout on the next state. This means that a timer is started
which upon expiry sends a StateTimeout message to the FSM. This timer is canceled upon reception of
any other message in the meantime; you can rely on the fact that the StateTimeout message will not be
processed after an intervening message.
This modifier can also be used to override any default timeout which is specified for the target state. If you
want to cancel the default timeout, use Duration.Inf.
using(data) This modifier replaces the old state data with the new data given. If you follow the advice above,
this is the only place where internal state data are ever modified.
replying(msg) This modifier sends a reply to the currently processed message and otherwise does not modify
the state transition.
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All modifier can be chained to achieve a nice and concise description:
when(State) {
case Ev(msg) =>
goto(Processing) using (msg) forMax (5 seconds) replying (WillDo)
}
The parentheses are not actually needed in all cases, but they visually distinguish between modifiers and their
arguments and therefore make the code even more pleasant to read for foreigners.
Note: Please note that the return statement may not be used in when blocks or similar; this is a Scala
restriction. Either refactor your code using if () ... else ... or move it into a method definition.
Monitoring Transitions
Transitions occur “between states” conceptually, which means after any actions you have put into the event handling block; this is obvious since the next state is only defined by the value returned by the event handling logic.
You do not need to worry about the exact order with respect to setting the internal state variable, as everything
within the FSM actor is running single-threaded anyway.
Internal Monitoring
Up to this point, the FSM DSL has been centered on states and events. The dual view is to describe it as a series
of transitions. This is enabled by the method
onTransition(handler)
which associates actions with a transition instead of with a state and event. The handler is a partial function which
takes a pair of states as input; no resulting state is needed as it is not possible to modify the transition in progress.
onTransition {
case Idle -> Active => setTimer("timeout")
case Active -> _ => cancelTimer("timeout")
case x -> Idle => EventHandler.info("entering Idle from "+x)
}
The convenience extractor -> enables decomposition of the pair of states with a clear visual reminder of the
transition’s direction. As usual in pattern matches, an underscore may be used for irrelevant parts; alternatively
you could bind the unconstrained state to a variable, e.g. for logging as shown in the last case.
It is also possible to pass a function object accepting two states to onTransition, in case your transition
handling logic is implemented as a method:
onTransition(handler _)
private def handler(from: State, to: State) {
...
}
The handlers registered with this method are stacked, so you can intersperse onTransition blocks with when
blocks as suits your design. It should be noted, however, that all handlers will be invoked for each transition,
not only the first matching one. This is designed specifically so you can put all transition handling for a certain
aspect into one place without having to worry about earlier declarations shadowing later ones; the actions are still
executed in declaration order, though.
Note: This kind of internal monitoring may be used to structure your FSM according to transitions, so that for
example the cancellation of a timer upon leaving a certain state cannot be forgot when adding new target states.
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External Monitoring
External actors may be registered to be notified of state transitions by sending a message SubscribeTransitionCallBack(actorRef).
The named actor will be sent a
CurrentState(self, stateName) message immediately and will receive Transition(actorRef,
oldState, newState) messages whenever a new state is reached. External monitors may be unregistered
by sending UnsubscribeTransitionCallBack(actorRef) to the FSM actor.
Registering a not-running listener generates a warning and fails gracefully. Stopping a listener without unregistering will remove the listener from the subscription list upon the next transition.
Timers
Besides state timeouts, FSM manages timers identified by String names. You may set a timer using
setTimer(name, msg, interval, repeat)
where msg is the message object which will be sent after the duration interval has elapsed. If repeat is
true, then the timer is scheduled at fixed rate given by the interval parameter. Timers may be canceled using
cancelTimer(name)
which is guaranteed to work immediately, meaning that the scheduled message will not be processed after this call
even if the timer already fired and queued it. The status of any timer may be inquired with
timerActive_?(name)
These named timers complement state timeouts because they are not affected by intervening reception of other
messages.
Termination from Inside
The FSM is stopped by specifying the result state as
stop([reason[, data]])
The reason must be one of Normal (which is the default), Shutdown or Failure(reason), and the second
argument may be given to change the state data which is available during termination handling.
Note: It should be noted that stop does not abort the actions and stop the FSM immediately. The stop action
must be returned from the event handler in the same way as a state transition (but note that the return statement
may not be used within a when block).
when(A) {
case Ev(Stop) =>
doCleanup()
stop()
}
You can use onTermination(handler) to specify custom code that is executed when the FSM is stopped.
The handler is a partial function which takes a StopEvent(reason, stateName, stateData) as argument:
onTermination {
case StopEvent(Normal, s, d)
=> ...
case StopEvent(Shutdown, _, _)
=> ...
case StopEvent(Failure(cause), s, d) => ...
}
As for the whenUnhandled case, this handler is not stacked, so each invocation of onTermination replaces
the previously installed handler.
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Termination from Outside
When an ActorRef associated to a FSM is stopped using the stop method, its postStop hook will be
executed. The default implementation by the FSM trait is to execute the onTermination handler if that is
prepared to handle a StopEvent(Shutdown, ...).
Warning: In case you override postStop and want to have your onTermination handler called, do not
forget to call super.postStop.
4.14.5 Testing and Debugging Finite State Machines
During development and for trouble shooting FSMs need care just as any other actor. There are specialized tools
available as described in Testing Finite State Machines and in the following.
Event Tracing
The setting akka.actor.debug.fsm in akka.conf enables logging of an event trace by LoggingFSM
instances:
class MyFSM extends Actor with LoggingFSM[X, Z] {
...
}
This FSM will log at DEBUG level:
• all processed events, including StateTimeout and scheduled timer messages
• every setting and cancellation of named timers
• all state transitions
Life cycle changes and special messages can be logged as described for Actors.
Rolling Event Log
The LoggingFSM trait adds one more feature to the FSM: a rolling event log which may be used during debugging (for tracing how the FSM entered a certain failure state) or for other creative uses:
class MyFSM extends Actor with LoggingFSM[X, Z] {
override def logDepth = 12
onTermination {
case StopEvent(Failure(_), state, data) =>
EventHandler.warning(this, "Failure in state "+state+" with data "+data+"\n"+
"Events leading up to this point:\n\t"+getLog.mkString("\n\t"))
}
...
}
The logDepth defaults to zero, which turns off the event log.
Warning: The log buffer is allocated during actor creation, which is why the configuration is done using a
virtual method call. If you want to override with a val, make sure that its initialization happens before the
initializer of LoggingFSM runs, and do not change the value returned by logDepth after the buffer has
been allocated.
The contents of the event log are available using method getLog, which returns an IndexedSeq[LogEntry]
where the oldest entry is at index zero.
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4.14.6 Examples
A bigger FSM example contrasted with Actor’s become/unbecome can be found in the sources:
• Dining Hakkers using FSM
• Dining Hakkers using become
4.15 HTTP
Module stability: SOLID
4.15.1 When using Akkas embedded servlet container
Akka supports the JSR for REST called JAX-RS (JSR-311). It allows you to create interaction with your actors
through HTTP + REST
You can deploy your REST services directly into the Akka kernel. All you have to do is to drop the JAR with
your application containing the REST services into the ‘$AKKA_HOME/deploy’ directory and specify in your
akka.conf what resource packages to scan for (more on that below) and optionally define a “boot class” (if you
need to create any actors or do any config). WAR deployment is coming soon.
4.15.2 Boot configuration class
The boot class is needed for Akka to bootstrap the application and should contain the initial supervisor configuration of any actors in the module.
The boot class should be a regular POJO with a default constructor in which the initial configuration is done. The
boot class then needs to be defined in the ‘$AKKA_HOME/config/akka.conf’ config file like this:
akka {
boot = ["sample.java.Boot", "sample.scala.Boot"]
# FQN to the class doing initial actor
# supervisor bootstrap, should be defined in
...
}
After you’ve placed your service-jar into the $AKKA_HOME/deploy directory, you’ll need to tell Akka where to
look for your services, and you do that by specifying what packages you want Akka to scan for services, and that’s
done in akka.conf in the http-section:
akka {
http {
...
resource-packages = ["com.bar","com.foo.bar"] # List with all resource packages for your Jerse
...
}
4.15.3 When deploying in another servlet container:
If you deploy Akka in another JEE container, don’t forget to create an Akka initialization and cleanup hook:
package com.my //<--- your own package
import akka.util.AkkaLoader
import akka.remote.BootableRemoteActorService
import akka.actor.BootableActorLoaderService
import javax.servlet.{ServletContextListener, ServletContextEvent}
/**
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* This class can be added to web.xml mappings as a listener to start and postStop Akka.
*<web-app>
* ...
* <listener>
<listener-class>com.my.Initializer</listener-class>
*
* </listener>
* ...
*</web-app>
*/
class Initializer extends ServletContextListener {
lazy val loader = new AkkaLoader
def contextDestroyed(e: ServletContextEvent): Unit = loader.shutdown
def contextInitialized(e: ServletContextEvent): Unit =
loader.boot(true, new BootableActorLoaderService with BootableRemoteActorService) //<--- Impo
//
loader.boot(true, new BootableActorLoaderService {}) // If you don’t need akka-remote
}
For Java users, it’s currently only possible to use BootableActorLoaderService, but you’ll need to use:
akka.actor.DefaultBootableActorLoaderService
Then you just declare it in your web.xml:
<web-app>
...
<listener>
<listener-class>your.package.Initializer</listener-class>
</listener>
...
</web-app>
Also, you need to map the servlet that will handle your Jersey/JAX-RS calls, you use Jerseys ServletContainer
servlet.
<web-app>
...
<servlet>
<servlet-name>Akka</servlet-name>
<servlet-class>com.sun.jersey.spi.container.servlet.ServletContainer</servlet-class>
<!-- And you want to configure your services -->
<init-param>
<param-name>com.sun.jersey.config.property.resourceConfigClass</param-name>
<param-value>com.sun.jersey.api.core.PackagesResourceConfig</param-value>
</init-param>
<init-param>
<param-name>com.sun.jersey.config.property.packages</param-name>
<param-value>your.resource.package.here;and.another.here;and.so.on</param-value>
</init-param>
</servlet>
<servlet-mapping>
<url-pattern>*</url-pattern>
<servlet-name>Akka</servlet-name>
</servlet-mapping>
...
</web-app>
4.15.4 Adapting your own Akka Initializer for the Servlet Container
If you want to use akka-camel or any other modules that have their own “Bootable“‘s you’ll need to write your
own Initializer, which is _ultra_ simple, see below for an example on how to include Akka-camel.
package com.my //<--- your own package
import akka.remote.BootableRemoteActorService
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import akka.actor.BootableActorLoaderService
import akka.camel.CamelService
import javax.servlet.{ServletContextListener, ServletContextEvent}
/**
* This class can be added to web.xml mappings as a listener to start and postStop Akka.
*<web-app>
* ...
* <listener>
<listener-class>com.my.Initializer</listener-class>
*
</listener>
*
* ...
*</web-app>
*/
class Initializer extends ServletContextListener {
lazy val loader = new AkkaLoader
def contextDestroyed(e: ServletContextEvent): Unit = loader.shutdown
def contextInitialized(e: ServletContextEvent): Unit =
loader.boot(true, new BootableActorLoaderService with BootableRemoteActorService with CamelSe
}
4.15.5 Java API: Typed Actors
Sample module for REST services with Actors in Java
4.15.6 Scala API: Actors
Sample module for REST services with Actors in Scala
4.15.7 Using Akka with the Pinky REST/MVC framework
Pinky has a slick Akka integration. Read more here
4.15.8 jetty-run in SBT
If you want to use jetty-run in SBT you need to exclude the version of Jetty that is bundled in akka-http:
override def ivyXML =
<dependencies>
<dependency org="se.scalablesolutions.akka" name="akka-http" rev="AKKA_VERSION_GOES_HERE">
<exclude module="jetty"/>
</dependency>
</dependencies>
4.15.9 Mist - Lightweight Asynchronous HTTP
The Mist layer was developed to provide a direct connection between the servlet container and Akka actors with the
goal of handling the incoming HTTP request as quickly as possible in an asynchronous manner. The motivation
came from the simple desire to treat REST calls as completable futures, that is, effectively passing the request
along an actor message chain to be resumed at the earliest possible time. The primary constraint was to not block
any existing threads and secondarily, not create additional ones. Mist is very simple and works both with Jetty
Continuations as well as with Servlet API 3.0 (tested using Jetty-8.0.0.M1). When the servlet handles a request, a
message is created typed to represent the method (e.g. Get, Post, etc.), the request is suspended and the message
is sent (fire-and-forget) to the root endpoint actor. That’s it. There are no POJOs required to host the service
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endpoints and the request is treated as any other. The message can be resumed (completed) using a number of
helper methods that set the proper HTTP response status code.
Complete runnable example can be found here: https://github.com/buka/akka-mist-sample
Endpoints
Endpoints are actors that handle request messages. Minimally there must be an instance of the RootEndpoint and
then at least one more (to implement your services).
Preparations
In order to use Mist you have to register the MistServlet in web.xml or do the analogous for the embedded server
if running in Akka Microkernel:
<servlet>
<servlet-name>akkaMistServlet</servlet-name>
<servlet-class>akka.http.AkkaMistServlet</servlet-class>
<!-- <async-supported>true</async-supported> Enable this for Servlet 3.0 support -->
</servlet>
<servlet-mapping>
<servlet-name>akkaMistServlet</servlet-name>
<url-pattern>/*</url-pattern>
</servlet-mapping>
Then you also have to add the following dependencies to your SBT build definition:
val jettyWebapp = "org.eclipse.jetty" % "jetty-webapp" % "8.0.0.M2" % "test"
val javaxServlet30 = "org.mortbay.jetty" % "servlet-api" % "3.0.20100224" % "provided"
Attention: You have to use SBT 0.7.5.RC0 or higher in order to be able to work with that Jetty version.
An Example
Startup
In this example, we’ll use the built-in RootEndpoint class and implement our own service from that. Here the
services are started in the boot loader and attached to the top level supervisor.
class Boot {
val factory = SupervisorFactory(
SupervisorConfig(
OneForOneStrategy(List(classOf[Exception]), 3, 100),
//
// in this particular case, just boot the built-in default root endpoint
//
Supervise(
actorOf[RootEndpoint],
Permanent) ::
Supervise(
actorOf[SimpleAkkaAsyncHttpService],
Permanent)
:: Nil))
factory.newInstance.start
}
Defining the Endpoint The service is an actor that mixes in the Endpoint trait. Here the dispatcher is taken from
the Akka configuration file which allows for custom tuning of these actors, though naturally, any dispatcher can
be used.
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URI Handling
Rather than use traditional annotations to pair HTTP request and class methods, Mist uses hook and provide
functions. This offers a great deal of flexibility in how a given endpoint responds to a URI. A hook function is
simply a filter, returning a Boolean to indicate whether or not the endpoint will handle the URI. This can be as
simple as a straight match or as fancy as you need. If a hook for a given URI returns true, the matching provide
function is called to obtain an actor to which the message can be delivered. Notice in the example below, in one
case, the same actor is returned and in the other, a new actor is created and returned. Note that URI hooking is
non-exclusive and a message can be delivered to multiple actors (see next example).
Plumbing
Hook and provider functions are attached to a parent endpoint, in this case the root, by sending it the Endpoint.Attach message. Finally, bind the handleHttpRequest function of the Endpoint trait to the actor’s receive
function and we’re done.
class SimpleAkkaAsyncHttpService extends Actor with Endpoint {
final val ServiceRoot = "/simple/"
final val ProvideSameActor = ServiceRoot + "same"
final val ProvideNewActor = ServiceRoot + "new"
//
// use the configurable dispatcher
//
self.dispatcher = Endpoint.Dispatcher
//
// there are different ways of doing this - in this case, we’ll use a single hook function
// and discriminate in the provider; alternatively we can pair hooks & providers
//
def hook(uri: String): Boolean = ((uri == ProvideSameActor) || (uri == ProvideNewActor))
def provide(uri: String): ActorRef = {
if (uri == ProvideSameActor) same
else actorOf[BoringActor].start()
}
//
// this is where you want attach your endpoint hooks
//
override def preStart() = {
//
// we expect there to be one root and that it’s already been started up
// obviously there are plenty of other ways to obtaining this actor
// the point is that we need to attach something (for starters anyway)
// to the root
//
val root = Actor.registry.actorsFor(classOf[RootEndpoint]).head
root ! Endpoint.Attach(hook, provide)
}
//
// since this actor isn’t doing anything else (i.e. not handling other messages)
// just assign the receive func like so...
// otherwise you could do something like:
// def myrecv = {...}
// def receive = myrecv orElse _recv
//
def receive = handleHttpRequest
//
// this will be our "same" actor provided with ProvideSameActor endpoint is hit
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//
lazy val same = actorOf[BoringActor].start()
}
Handling requests
Messages are handled just as any other that are received by your actor. The servlet requests and response are not
hidden and can be accessed directly as shown below.
/**
* Define a service handler to respond to some HTTP requests
*/
class BoringActor extends Actor {
import java.util.Date
import javax.ws.rs.core.MediaType
var
var
var
var
gets = 0
posts = 0
lastget: Option[Date] = None
lastpost: Option[Date] = None
def receive = {
// handle a get request
case get: Get =>
// the content type of the response.
// similar to @Produces annotation
get.response.setContentType(MediaType.TEXT_HTML)
//
// "work"
//
gets += 1
lastget = Some(new Date)
//
// respond
//
val res = "<p>Gets: "+gets+" Posts: "+posts+"</p><p>Last Get: "+lastget.getOrElse("Never").t
get.OK(res)
// handle a post request
case post:Post =>
// the expected content type of the request
// similar to @Consumes
if (post.request.getContentType startsWith MediaType.APPLICATION_FORM_URLENCODED) {
// the content type of the response.
// similar to @Produces annotation
post.response.setContentType(MediaType.TEXT_HTML)
// "work"
posts += 1
lastpost = Some(new Date)
// respond
val res = "<p>Gets: "+gets+" Posts: "+posts+"</p><p>Last Get: "+lastget.getOrElse("Never")
post.OK(res)
} else {
post.UnsupportedMediaType("Content-Type request header missing or incorrect (was ’" + post
}
}
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case other: RequestMethod =>
other.NotAllowed("Invalid method for this endpoint")
}
}
Timeouts Messages will expire according to the default timeout (specified in akka.conf). Individual messages can
also be updated using the timeout method. One thing that may seem unexpected is that when an expired request
returns to the caller, it will have a status code of OK (200). Mist will add an HTTP header to such responses to
help clients, if applicable. By default, the header will be named “Async-Timeout” with a value of “expired” - both
of which are configurable.
Another Example - multiplexing handlers
As noted above, hook functions are non-exclusive. This means multiple actors can handle the same request if
desired. In this next example, the hook functions are identical (yes, the same one could have been reused) and
new instances of both A and B actors will be created to handle the Post. A third mediator is inserted to coordinate
the results of these actions and respond to the caller.
package sample.mist
import akka.actor._
import akka.actor.Actor._
import akka.http._
import javax.servlet.http.HttpServletResponse
class InterestingService extends Actor with Endpoint {
final val ServiceRoot = "/interesting/"
final val Multi = ServiceRoot + "multi/"
// use the configurable dispatcher
self.dispatcher = Endpoint.Dispatcher
//
// The "multi" endpoint shows forking off multiple actions per request
// It is triggered by POSTing to http://localhost:9998/interesting/multi/{foo}
// Try with/without a header named "Test-Token"
// Try with/without a form parameter named "Data"
def hookMultiActionA(uri: String): Boolean = uri startsWith Multi
def provideMultiActionA(uri: String): ActorRef = actorOf(new ActionAActor(complete)).start()
def hookMultiActionB(uri: String): Boolean = uri startsWith Multi
def provideMultiActionB(uri: String): ActorRef = actorOf(new ActionBActor(complete)).start()
//
// this is where you want attach your endpoint hooks
//
override def preStart() = {
//
// we expect there to be one root and that it’s already been started up
// obviously there are plenty of other ways to obtaining this actor
// the point is that we need to attach something (for starters anyway)
// to the root
//
val root = Actor.registry.actorsFor(classOf[RootEndpoint]).head
root ! Endpoint.Attach(hookMultiActionA, provideMultiActionA)
root ! Endpoint.Attach(hookMultiActionB, provideMultiActionB)
}
//
// since this actor isn’t doing anything else (i.e. not handling other messages)
// just assign the receive func like so...
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// otherwise you could do something like:
// def myrecv = {...}
// def receive = myrecv orElse handleHttpRequest
//
def receive = handleHttpRequest
//
// this guy completes requests after other actions have occurred
//
lazy val complete = actorOf[ActionCompleteActor].start()
}
class ActionAActor(complete:ActorRef) extends Actor {
import javax.ws.rs.core.MediaType
def receive = {
// handle a post request
case post: Post =>
// the expected content type of the request
// similar to @Consumes
if (post.request.getContentType startsWith MediaType.APPLICATION_FORM_URLENCODED) {
// the content type of the response.
// similar to @Produces annotation
post.response.setContentType(MediaType.TEXT_HTML)
// get the resource name
val name = post.request.getRequestURI.substring("/interesting/multi/".length)
if (name.length % 2 == 0) post.response.getWriter.write("<p>Action A verified request.</p>
else post.response.getWriter.write("<p>Action A could not verify request.</p>")
// notify the next actor to coordinate the response
complete ! post
} else post.UnsupportedMediaType("Content-Type request header missing or incorrect (was ’" +
}
}
}
class ActionBActor(complete:ActorRef) extends Actor {
import javax.ws.rs.core.MediaType
def receive = {
// handle a post request
case post: Post =>
// the expected content type of
// similar to @Consumes
if (post.request.getContentType
// pull some headers and form
def default(any: Any): String
the request
startsWith MediaType.APPLICATION_FORM_URLENCODED) {
params
= ""
val token = post.getHeaderOrElse("Test-Token", default)
val data = post.getParameterOrElse("Data", default)
val (resp, status) = (token, data) match {
case ("", _) => ("No token provided", HttpServletResponse.SC_FORBIDDEN)
case (_, "") => ("No data", HttpServletResponse.SC_ACCEPTED)
case _ => ("Data accepted", HttpServletResponse.SC_OK)
}
// update the response body
post.response.getWriter.write(resp)
// notify the next actor to coordinate the response
complete ! (post, status)
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} else post.UnsupportedMediaType("Content-Type request header missing or incorrect (was ’" +
}
case other: RequestMethod =>
other.NotAllowed("Invalid method for this endpoint")
}
}
class ActionCompleteActor extends Actor {
import collection.mutable.HashMap
val requests = HashMap.empty[Int, Int]
def receive = {
case req: RequestMethod =>
if (requests contains req.hashCode) complete(req)
else requests += (req.hashCode -> 0)
case t: Tuple2[RequestMethod, Int] =>
if (requests contains t._1.hashCode) complete(t._1)
else requests += (t._1.hashCode -> t._2)
}
def complete(req: RequestMethod) = requests.remove(req.hashCode) match {
case Some(HttpServletResponse.SC_FORBIDDEN) => req.Forbidden("")
case Some(HttpServletResponse.SC_ACCEPTED) => req.Accepted("")
case Some(_) => req.OK("")
case _ => {}
}
}
Examples
Using the Akka Mist module with OAuth
https://gist.github.com/759501
Using the Akka Mist module with the Facebook Graph API and WebGL
Example project using Akka Mist with the Facebook Graph API and WebGL https://github.com/buka/fbgl1
4.16 ZeroMQ
Module stability: IN PROGRESS
Akka provides a ZeroMQ module which abstracts a ZeroMQ connection and therefore allows interaction between
Akka actors to take place over ZeroMQ connections. The messages can be of a proprietary format or they can be
defined using Protobuf. In addition, the connection can be fault-tolerant by associating a supervisor with a socket,
and upon a connection failure, reconnection can take place automatically.
The ZeroMQ module for Akka is written against an API introduced in JZMQ, which uses JNI to interact with
the native ZeroMQ library. Instead of using JZMQ, the module uses ZeroMQ binding for Scala that uses the
native ZeroMQ library through JNA. In other words, the only native library that this module requires is the native
ZeroMQ library.
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4.16.1 Connection
ZeroMQ supports multiple connectivity patterns, each aimed to meet a different set of requirements. Currently, this module supports publisher-subscriber connections and connections based on dealers and routers.
For connecting or accepting connections, a socket must be created. Sockets are always created using
akka.zeromq.ZeroMQ.newSocket, for example:
val socket = ZeroMQ.newSocket(SocketParameters(context, SocketType.Pub))
after which the socket is either bound to an address and port or connected, for example:
socket ! Bind("tcp://127.0.0.1:1234")
or:
socket ! Connect("tcp://localhost:1234")
Also, a socket may be created with a listener that handles received messages as well as notifications:
val listener = actorOf(new Actor {
def receive: Receive = {
case Connecting => ...
case _ => ...
}
}).start
val socket = ZeroMQ.newSocket(SocketParameters(context, SocketType.Sub, Some(listener)))
socket ! Connect("tcp://localhost:1234")
The following sub-sections describe the supported connection patterns and how they can be used in an Akka
environment. However, for a comprehensive discussion of connection patterns, please refer to ZeroMQ – The
Guide.
Publisher-subscriber connection
In a publisher-subscriber (pub-sub) connection, the publisher accepts one or more subscribers. Each subscriber
shall subscribe to one or more topics, whereas the publisher publishes messages to a set of topics. Also, a subscriber can subscribe to all available topics. An actor is subscribed to a topic as follows:
val socket = ZeroMQ.newSocket(SocketParameters(context, SocketType.Sub, Some(listener)))
socket ! Connect("tcp://localhost:1234")
socket ! Subscribe("SomeTopic1")
Note that if the given string is empty (see below), the actor is subscribed to all topics. Accordingly, the actor is
unsubscribed as follows:
socket ! Unsubscribe("SomeTopic1")
In an Akka environment, pub-sub connections shall be used when an actor sends messages to one or more actors
that do not interact with the actor that sent the message. The following piece of code creates a publisher actor,
binds the socket, and sends a message to be published:
import akka.zeromq._
val socket = ZeroMQ.newSocket(SocketParameters(context, SocketType.Pub))
socket ! Bind("tcp://127.0.0.1:1234")
socket ! ZMQMessage("hello".getBytes)
In the following code, the subscriber is configured to receive messages for all topics:
import akka.zeromq._
val listener = actorOf(new Actor {
def receive: Receive = {
case message: ZMQMessage => ...
case _ => ...
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}
}).start
val socket = ZMQ.newSocket(SocketParameters(context, SocketType.Sub, Some(listener)))
socket ! Connect("tcp://127.0.0.1:1234")
socket ! Subscribe(Seq())
4.17 HTTP Security
Module stability: IN PROGRESS
Akka supports security for access to RESTful Actors through HTTP Authentication. The security is implemented
as a jersey ResourceFilter which delegates the actual authentication to an authentication actor.
Akka provides authentication via the following authentication schemes:
• Basic Authentication
• Digest Authentication
• Kerberos SPNEGO Authentication
The authentication is performed by implementations of akka.security.AuthenticationActor.
Akka provides a trait for each authentication scheme:
• BasicAuthenticationActor
• DigestAuthenticationActor
• SpnegoAuthenticationActor
4.17.1 Setup
To secure your RESTful actors you need to perform the following steps:
1. configure the resource filter factory ‘akka.security.AkkaSecurityFilterFactory’ in the ‘akka.conf’ like this:
akka {
...
rest {
filters="akka.security.AkkaSecurityFilterFactory"
}
...
}
2. Configure an implementation of an authentication actor in ‘akka.conf’:
akka {
...
rest {
filters= ...
authenticator = "akka.security.samples.BasicAuthenticationService"
}
...
}
3. Start your authentication actor in your ‘Boot’ class. The security package consists of the following parts:
4. Secure your RESTful actors using class or resource level annotations:
• @DenyAll
• @RolesAllowed(listOfRoles)
• @PermitAll
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4.17.2 Security Samples
The akka-samples-security module contains a small sample application with sample implementations for each
authentication scheme. You can start the sample app using the jetty plugin: mvn jetty:run.
The RESTful actor can then be accessed using your browser of choice under:
• permit access only to users having the “chef” role: http://localhost:8080//secureticker/chef
• public access: http://localhost:8080//secureticker/public
You can access the secured resource using any user for basic authentication (which is the default authenticator in
the sample app).
Digest authentication can be directly enabled in the sample app. Kerberos/SPNEGO authentication is a bit more
involved an is described below.
4.17.3 Kerberos/SPNEGO Authentication
Kerberos is a network authentication protocol, (see http://www.ietf.org/rfc/rfc1510.txt). It provides strong authentication for client/server applications. In a kerberos enabled environment a user will need to sign on only once.
Subsequent authentication to applications is handled transparently by kerberos.
Most prominently the kerberos protocol is used to authenticate users in a windows network. When deploying web
applications to a corporate intranet an important feature will be to support the single sign on (SSO), which comes
to make the application kerberos aware.
How does it work (at least for REST actors)?
• When accessing a secured resource the server will check the request for the Authorization header as with
basic or digest authentication.
• If it is not set, the server will respond with a challenge to “Negotiate”. The negotiation is in fact the NEGO
part of the SPNEGO specification
• The browser will then try to acquire a so called service ticket from a ticket granting service, i.e. the kerberos
server
• The browser will send the service ticket to the web application encoded in the header value of the Authorization header
• The web application must validate the ticket based on a shared secret between the web application and the
kerberos server. As a result the web application will know the name of the user
To activate the kerberos/SPNEGO authentication for your REST actor you need to enable the kerberos/SPNEGOauthentication actor in the akka.conf like this:
akka {
...
rest {
filters= ...
authenticator = "akka.security.samples.SpnegoAuthenticationService"
}
...
}
Furthermore you must provide the SpnegoAuthenticator with the following information.
• Service principal name: the name of your web application in the kerberos servers user database. This name
is always has the form HTTP/{server}@{realm}
• Path to the keytab file: this is a kind of certificate for your web application to acquire tickets from the
kerberos server
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akka {
...
rest {
filters= ...
authenticator = "akka.security.samples.SpnegoAuthenticationService"
kerberos {
servicePrincipal = "HTTP/{server}@{realm}"
keyTabLocation
= "URL to keytab"
#
kerberosDebug
= "true"
}
}
...
}
4.17.4 How to setup kerberos on localhost for Ubuntu
This is a short step by step description of howto set up a kerberos server on an ubuntu system.
1. Install the Heimdal Kerberos Server and Client
sudo apt-get install heimdal-clients heimdal-clients-x heimdal-kdc krb5-config
...
2. Set up your kerberos realm. In this example the realm is of course . . . EXAMPLE.COM
eckart@dilbert:~$ sudo kadmin -l
kadmin> init EXAMPLE.COM
Realm max ticket life [unlimited]:
Realm max renewable ticket life [unlimited]:
kadmin> quit
3. Tell your kerberos clients what your realm is and where to find the kerberos server (aka the Key Distribution
Centre or KDC)
Edit the kerberos config file: /etc/krb5.conf and configure . . . . . . the default realm:
[libdefaults]
default_realm = EXAMPLE.COM
. . . where to find the KDC for your realm
[realms]
EXAMPLE.COM = {
kdc = localhost
}
. . . which hostnames or domains map to which realm (a kerberos realm is not a DNS domain):
[domain_realm]
localhost = EXAMPLE.COM
4. Add the principals The user principal:
eckart@dilbert:~$ sudo kadmin -l
kadmin> add zaphod
Max ticket life [1 day]:
Max renewable life [1 week]:
Principal expiration time [never]:
Password expiration time [never]:
Attributes []:
zaphod@EXAMPLE.COM’s Password:
Verifying - zaphod@EXAMPLE.COM’s Password:
kadmin> quit
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The service principal:
eckart@dilbert:~$ sudo kadmin -l
kadmin> add HTTP/localhost@EXAMPLE.COM
Max ticket life [1 day]:
Max renewable life [1 week]:
Principal expiration time [never]:
Password expiration time [never]:
Attributes []:
HTTP/localhost@EXAMPLE.COM’s Password:
Verifying - HTTP/localhost@EXAMPLE.COM’s Password:
kadmin> quit
We can now try to acquire initial tickets for the principals to see if everything worked.
eckart@dilbert:~$ kinit zaphod
zaphod@EXAMPLE.COM’s Password:
If this method returns withour error we have a success. We can additionally list the acquired tickets:
eckart@dilbert:~$ klist
Credentials cache: FILE:/tmp/krb5cc_1000
Principal: zaphod@EXAMPLE.COM
Issued
Oct 24 21:51:59
Expires
Oct 25 06:51:59
Principal
krbtgt/EXAMPLE.COM@EXAMPLE.COM
This seems correct. To remove the ticket cache simply type kdestroy.
5. Create a keytab for your service principal
eckart@dilbert:~$ ktutil -k http.keytab add -p HTTP/localhost@EXAMPLE.COM -V 1 -e aes256-cts-hmacPassword:
Verifying - Password:
eckart@dilbert:~$
This command will create a keytab file for the service principal named http.keytab in the current directory.
You can specify other encryption methods than ‘aes256-cts-hmac-sha1-96’, but this is the e default encryption
method for the heimdal client, so there is no additional configuration needed. You can specify other encryption
types in the krb5.conf.
Note that you might need to install the unlimited strength policy files for java from here:
http://java.sun.com/javase/downloads/index_jdk5.jsp to use the aes256 encryption from your application.
Again we can test if the keytab generation worked with the kinit command:
eckart@dilbert:~$ kinit -t http.keytab HTTP/localhost@EXAMPLE.COM
eckart@dilbert:~$ klist
Credentials cache: FILE:/tmp/krb5cc_1000
Principal: HTTP/localhost@EXAMPLE.COM
Issued
Oct 24 21:59:20
Expires
Oct 25 06:59:20
Principal
krbtgt/EXAMPLE.COM@EXAMPLE.COM
Now point the configuration of the key in ‘akka.conf’ to the correct location and set the correct service principal name. The web application should now startup and produce at least a 401 response with a header
WWW-Authenticate = “Negotiate”. The last step is to configure the browser.
6. Set up Firefox to use Kerberos/SPNEGO This is done by typing about:config. Filter the config entries for network.neg and set the config entries network.negotiate-auth.delegation-uris and
network.negotiate-auth.trusted-uris to localhost. and now . . .
7. Access the RESTful Actor.
8. Have fun . . . but acquire an initial ticket for the user principal first: kinit zaphod
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4.18 Testing Actor Systems
4.18.1 TestKit Example
Ray Roestenburg’s example code from his blog adapted to work with Akka 1.1.
package unit.akka
import
import
import
import
import
import
import
import
org.scalatest.matchers.ShouldMatchers
org.scalatest.{WordSpec, BeforeAndAfterAll}
akka.actor.Actor._
akka.util.duration._
akka.testkit.TestKit
java.util.concurrent.TimeUnit
akka.actor.{ActorRef, Actor}
util.Random
/**
* a Test to show some TestKit examples
*/
class
val
val
val
val
val
val
val
val
TestKitUsageSpec extends WordSpec with BeforeAndAfterAll with ShouldMatchers with TestKit {
echoRef = actorOf(new EchoActor).start()
forwardRef = actorOf(new ForwardingActor(testActor)).start()
filterRef = actorOf(new FilteringActor(testActor)).start()
randomHead = Random.nextInt(6)
randomTail = Random.nextInt(10)
headList = List().padTo(randomHead, "0")
tailList = List().padTo(randomTail, "1")
seqRef = actorOf(new SequencingActor(testActor, headList, tailList)).start()
override protected def afterAll(): scala.Unit = {
stopTestActor
echoRef.stop()
forwardRef.stop()
filterRef.stop()
seqRef.stop()
}
"An EchoActor" should {
"Respond with the same message it receives" in {
within(100 millis) {
echoRef ! "test"
expectMsg("test")
}
}
}
"A ForwardingActor" should {
"Forward a message it receives" in {
within(100 millis) {
forwardRef ! "test"
expectMsg("test")
}
}
}
"A FilteringActor" should {
"Filter all messages, except expected messagetypes it receives" in {
var messages = List[String]()
within(100 millis) {
filterRef ! "test"
expectMsg("test")
filterRef ! 1
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expectNoMsg
filterRef !
filterRef !
filterRef !
filterRef !
filterRef !
"some"
"more"
1
"text"
1
receiveWhile(500 millis) {
case msg: String => messages = msg :: messages
}
}
messages.length should be(3)
messages.reverse should be(List("some", "more", "text"))
}
}
"A SequencingActor" should {
"receive an interesting message at some point " in {
within(100 millis) {
seqRef ! "something"
ignoreMsg {
case msg: String => msg != "something"
}
expectMsg("something")
ignoreMsg {
case msg: String => msg == "1"
}
expectNoMsg
}
}
}
}
/**
* An Actor that echoes everything you send to it
*/
class EchoActor extends Actor {
def receive = {
case msg => {
self.reply(msg)
}
}
}
/**
* An Actor that forwards every message to a next Actor
*/
class ForwardingActor(next: ActorRef) extends Actor {
def receive = {
case msg => {
next ! msg
}
}
}
/**
* An Actor that only forwards certain messages to a next Actor
*/
class FilteringActor(next: ActorRef) extends Actor {
def receive = {
case msg: String => {
next ! msg
}
case _ => None
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}
}
/**
* An actor that sends a sequence of messages with a random head list, an interesting value and a
* The idea is that you would like to test that the interesting value is received and that you can
*/
class SequencingActor(next: ActorRef, head: List[String], tail: List[String]) extends Actor {
def receive = {
case msg => {
head map (next ! _)
next ! msg
tail map (next ! _)
}
}
}
Module author: Roland Kuhn New in version 1.0.Changed in version 1.1: added TestActorRefChanged in
version 1.2: added TestFSMRef As with any piece of software, automated tests are a very important part of the
development cycle. The actor model presents a different view on how units of code are delimited and how they
interact, which has an influence on how to perform tests.
Akka comes with a dedicated module akka-testkit for supporting tests at different levels, which fall into two
clearly distinct categories:
• Testing isolated pieces of code without involving the actor model, meaning without multiple threads; this
implies completely deterministic behavior concerning the ordering of events and no concurrency concerns
and will be called Unit Testing in the following.
• Testing (multiple) encapsulated actors including multi-threaded scheduling; this implies non-deterministic
order of events but shielding from concurrency concerns by the actor model and will be called Integration
Testing in the following.
There are of course variations on the granularity of tests in both categories, where unit testing reaches down to
white-box tests and integration testing can encompass functional tests of complete actor networks. The important
distinction lies in whether concurrency concerns are part of the test or not. The tools offered are described in detail
in the following sections.
Note: Be sure to add the module akka-testkit to your dependencies.
Note: While this document focuses on the Scala API for conducting tests, it does not matter whether an actor
under test is implemented using Scala or Java. However, providing the same functionality as offered by the testkit
in Java would lead to a very much more clunky API, which is why writing the test spec should preferably be done
in Scala. If that is not an option, TestProbe may be used conveniently from Java as well.
4.18.2 Unit Testing with TestActorRef
Testing the business logic inside Actor classes can be divided into two parts: first, each atomic operation must
work in isolation, then sequences of incoming events must be processed correctly, even in the presence of some
possible variability in the ordering of events. The former is the primary use case for single-threaded unit testing,
while the latter can only be verified in integration tests.
Normally, the ActorRef shields the underlying Actor instance from the outside, the only communications
channel is the actor’s mailbox. This restriction is an impediment to unit testing, which led to the inception of the
TestActorRef. This special type of reference is designed specifically for test purposes and allows access to
the actor in two ways: either by obtaining a reference to the underlying actor instance, or by invoking or querying
the actor’s behaviour (receive). Each one warrants its own section below.
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Obtaining a Reference to an Actor
Having access to the actual Actor object allows application of all traditional unit testing techniques on the
contained methods. Obtaining a reference is done like this:
import akka.testkit.TestActorRef
val actorRef = TestActorRef[MyActor]
val actor = actorRef.underlyingActor
Since TestActorRef is generic in the actor type it returns the underlying actor with its proper static type. From
this point on you may bring any unit testing tool to bear on your actor as usual.
Testing Finite State Machines
If your actor under test is a FSM, you may use the special TestFSMRef which offers all features of a normal
TestActorRef and in addition allows access to the internal state:
import akka.testkit.TestFSMRef
import akka.util.duration._
val fsm = TestFSMRef(new Actor with FSM[Int, String] {
startWith(1, "")
when (1) {
case Ev("go") => goto(2) using "go"
}
when (2) {
case Ev("back") => goto(1) using "back"
}
}).start()
assert (fsm.stateName
assert (fsm.stateData
fsm ! "go"
assert (fsm.stateName
assert (fsm.stateData
== 1)
== "")
// being a TestActorRef, this runs also on the CallingThreadDispat
== 2)
== "go")
fsm.setState(stateName = 1)
assert (fsm.stateName == 1)
assert (fsm.timerActive_?("test") == false)
fsm.setTimer("test", 12, 10 millis, true)
assert (fsm.timerActive_?("test") == true)
fsm.cancelTimer("test")
assert (fsm.timerActive_?("test") == false)
Due to a limitation in Scala’s type inference, there is only the factory method shown above, so you
will probably write code like TestFSMRef(new MyFSM) instead of the hypothetical ActorRef-inspired
TestFSMRef[MyFSM]. All methods shown above directly access the FSM state without any synchronization;
this is perfectly alright if the CallingThreadDispatcher is used (which is the default for TestFSMRef)
and no other threads are involved, but it may lead to surprises if you were to actually exercise timer events, because
those are executed on the Scheduler thread.
Testing the Actor’s Behavior
When the dispatcher invokes the processing behavior of an actor on a message, it actually calls apply on the
current behavior registered for the actor. This starts out with the return value of the declared receive method, but
it may also be changed using become and unbecome, both of which have corresponding message equivalents,
meaning that the behavior may be changed from the outside. All of this contributes to the overall actor behavior
and it does not lend itself to easy testing on the Actor itself. Therefore the TestActorRef offers a different
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mode of operation to complement the Actor testing: it supports all operations also valid on normal ActorRef.
Messages sent to the actor are processed synchronously on the current thread and answers may be sent back as
usual. This trick is made possible by the CallingThreadDispatcher described below; this dispatcher is set
implicitly for any actor instantiated into a TestActorRef.
val actorRef = TestActorRef(new MyActor).start()
val result = (actorRef ? Say42).as[Int] // hypothetical message stimulating a ’42’ answer
result must be (Some(42))
As the TestActorRef is a subclass of LocalActorRef with a few special extras, also aspects like linking to
a supervisor and restarting work properly, as long as all actors involved use the CallingThreadDispatcher.
As soon as you add elements which include more sophisticated scheduling you leave the realm of unit testing as
you then need to think about proper synchronization again (in most cases the problem of waiting until the desired
effect had a chance to happen).
One more special aspect which is overridden for single-threaded tests is the receiveTimeout, as including
that would entail asynchronous queuing of ReceiveTimeout messages, violating the synchronous contract.
Warning:
To summarize: TestActorRef overwrites two fields: it sets the dispatcher to
CallingThreadDispatcher.global and it sets the receiveTimeout to zero.
The Way In-Between
If you want to test the actor behavior, including hotswapping, but without involving a dispatcher and without
having the TestActorRef swallow any thrown exceptions, then there is another mode available for you: just
use the TestActorRef as a partial function, the calls to isDefinedAt and apply will be forwarded to the
underlying actor:
val ref = TestActorRef[MyActor]
ref.isDefinedAt(’unknown) must be (false)
intercept[IllegalActorStateException] { ref(RequestReply) }
Use Cases
You may of course mix and match both modi operandi of TestActorRef as suits your test needs:
• one common use case is setting up the actor into a specific internal state before sending the test message
• another is to verify correct internal state transitions after having sent the test message
Feel free to experiment with the possibilities, and if you find useful patterns, don’t hesitate to let the Akka forums
know about them! Who knows, common operations might even be worked into nice DSLs.
4.18.3 Integration Testing with TestKit
When you are reasonably sure that your actor’s business logic is correct, the next step is verifying that it works
correctly within its intended environment (if the individual actors are simple enough, possibly because they use the
FSM module, this might also be the first step). The definition of the environment depends of course very much on
the problem at hand and the level at which you intend to test, ranging for functional/integration tests to full system
tests. The minimal setup consists of the test procedure, which provides the desired stimuli, the actor under test,
and an actor receiving replies. Bigger systems replace the actor under test with a network of actors, apply stimuli
at varying injection points and arrange results to be sent from different emission points, but the basic principle
stays the same in that a single procedure drives the test.
Overview
The TestKit trait contains a collection of tools which makes this common task easy:
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import akka.testkit.TestKit
import org.scalatest.WordSpec
import org.scalatest.matchers.MustMatchers
class MySpec extends WordSpec with MustMatchers with TestKit {
"An Echo actor" must {
"send back messages unchanged" in {
val echo = Actor.actorOf[EchoActor].start()
echo ! "hello world"
expectMsg("hello world")
}
}
}
The TestKit contains an actor named testActor which is implicitly used as sender reference when dispatching messages from the test procedure. This enables replies to be received by this internal actor, whose only
function is to queue them so that interrogation methods like expectMsg can examine them. The testActor
may also be passed to other actors as usual, usually subscribing it as notification listener. There is a whole set
of examination methods, e.g. receiving all consecutive messages matching certain criteria, receiving a whole
sequence of fixed messages or classes, receiving nothing for some time, etc.
Note: The test actor shuts itself down by default after 5 seconds (configurable) of inactivity, relieving you of the
duty of explicitly managing it.
Built-In Assertions
The abovementioned expectMsg is not the only method for formulating assertions concerning received messages. Here is the full list:
• expectMsg[T](d:
Duration, msg:
T): T
The given message object must be received within the specified time; the object will be returned.
• expectMsgPF[T](d:
Duration)(pf:
PartialFunction[Any, T]): T
Within the given time period, a message must be received and the given partial function must be defined for
that message; the result from applying the partial function to the received message is returned. The duration
may be left unspecified (empty parentheses are required in this case) to use the deadline from the innermost
enclosing within block instead.
• expectMsgClass[T](d:
Duration, c:
Class[T]): T
An object which is an instance of the given Class must be received within the allotted time frame; the
object will be returned. Note that this does a conformance check; if you need the class to be equal, have a
look at expectMsgAllClassOf with a single given class argument.
• expectMsgType[T: Manifest](d:
Duration)
An object which is an instance of the given type (after erasure) must be received within the
allotted time frame; the object will be returned.
This method is approximately equivalent to
expectMsgClass(manifest[T].erasure).
• expectMsgAnyOf[T](d:
Duration, obj:
T*):
T
An object must be received within the given time, and it must be equal ( compared with ==) to at least one
of the passed reference objects; the received object will be returned.
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• expectMsgAnyClassOf[T](d:
Duration, obj:
Class[_ <:
T]*):
T
An object must be received within the given time, and it must be an instance of at least one of the supplied
Class objects; the received object will be returned.
• expectMsgAllOf[T](d:
Duration, obj:
T*):
Seq[T]
A number of objects matching the size of the supplied object array must be received within the given
time, and for each of the given objects there must exist at least one among the received ones which equals
(compared with ==) it. The full sequence of received objects is returned.
• expectMsgAllClassOf[T](d:
Duration, c:
Class[_ <:
T]*):
Seq[T]
A number of objects matching the size of the supplied Class array must be received within the given time,
and for each of the given classes there must exist at least one among the received objects whose class equals
(compared with ==) it (this is not a conformance check). The full sequence of received objects is returned.
• expectMsgAllConformingOf[T](d:
Duration, c:
Class[_ <:
T]*):
Seq[T]
A number of objects matching the size of the supplied Class array must be received within the given
time, and for each of the given classes there must exist at least one among the received objects which is an
instance of this class. The full sequence of received objects is returned.
• expectNoMsg(d:
Duration)
No message must be received within the given time. This also fails if a message has been received before
calling this method which has not been removed from the queue using one of the other methods.
• receiveN(n:
Int, d:
Duration):
Seq[AnyRef]
n messages must be received within the given time; the received messages are returned.
In addition to message reception assertions there are also methods which help with message flows:
• receiveOne(d:
Duration):
AnyRef
Tries to receive one message for at most the given time interval and returns null in case of failure. If the
given Duration is zero, the call is non-blocking (polling mode).
• receiveWhile[T](max: Duration, idle:
PartialFunction[Any, T]): Seq[T]
Duration)(pf:
Collect messages as long as
– they are matching the given partial function
– the given time interval is not used up
– the next message is received within the idle timeout
All collected messages are returned. The maximum duration defaults to the time remaining in the innermost
enclosing within block and the idle duration defaults to infinity (thereby disabling the idle timeout feature).
• awaitCond(p:
=> Boolean, max:
Duration, interval:
Duration)
Poll the given condition every interval until it returns true or the max duration is used up. The interval
defaults to 100 ms and the maximum defaults to the time remaining in the innermost enclosing within block.
• ignoreMsg(pf:
PartialFunction[AnyRef, Boolean])
ignoreNoMsg
The internal testActor contains a partial function for ignoring messages: it will only enqueue messages
which do not match the function or for which the function returns false. This function can be set and reset
using the methods given above; each invocation replaces the previous function, they are not composed.
This feature is useful e.g. when testing a logging system, where you want to ignore regular messages and
are only interested in your specific ones.
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Expecting Exceptions
One case which is not handled by the testActor is if an exception is thrown while processing the message sent
to the actor under test. This can be tested by using a Future based invocation:
// assuming ScalaTest ShouldMatchers
evaluating {
(someActor ? badOperation).await.get
} should produce [UnhandledMessageException]
Timing Assertions
Another important part of functional testing concerns timing: certain events must not happen immediately (like a
timer), others need to happen before a deadline. Therefore, all examination methods accept an upper time limit
within the positive or negative result must be obtained. Lower time limits need to be checked external to the
examination, which is facilitated by a new construct for managing time constraints:
within([min, ]max) {
...
}
The block given to within must complete after a Duration which is between min and max, where the former
defaults to zero. The deadline calculated by adding the max parameter to the block’s start time is implicitly
available within the block to all examination methods, if you do not specify it, is is inherited from the innermost
enclosing within block.
It should be noted that if the last message-receiving assertion of the block is expectNoMsg or receiveWhile,
the final check of the within is skipped in order to avoid false positives due to wake-up latencies. This means
that while individual contained assertions still use the maximum time bound, the overall block may take arbitrarily
longer in this case.
class SomeSpec extends WordSpec with MustMatchers with TestKit {
"A Worker" must {
"send timely replies" in {
val worker = actorOf(...)
within (500 millis) {
worker ! "some work"
expectMsg("some result")
expectNoMsg
// will block for the rest of the 500ms
Thread.sleep(1000) // will NOT make this block fail
}
}
}
}
Note: All times are measured using System.nanoTime, meaning that they describe wall time, not CPU time.
Ray Roestenburg has written a great article on using the TestKit: http://roestenburg.agilesquad.com/2011/02/unittesting-akka-actors-with-testkit_12.html. His full example is also available here.
Accounting for Slow Test Systems
The tight timeouts you use during testing on your lightning-fast notebook will invariably lead to spurious test
failures on the heavily loaded Jenkins server (or similar). To account for this situation, all maximum durations are
internally scaled by a factor taken from akka.conf, akka.test.timefactor, which defaults to 1.
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Resolving Conflicts with Implicit ActorRef
The TestKit trait contains an implicit value of type ActorRef to enable the magic reply handling. This value
is named self so that e.g. anonymous actors may be declared within a test class without having to care about
the ambiguous implicit issues which would otherwise arise. If you find yourself in a situation where the implicit
you need comes from a different trait than TestKit and is not named self, then use TestKitLight, which
differs only in not having any implicit members. You would then need to make an implicit available in locally
confined scopes which need it, e.g. different test cases. If this cannot be done, you will need to resort to explicitly
specifying the sender reference:
val actor = actorOf[MyWorker].start()
actor.!(msg)(testActor)
Using Multiple Probe Actors
When the actors under test are supposed to send various messages to different destinations, it may be difficult
distinguishing the message streams arriving at the testActor when using the TestKit as a mixin. Another
approach is to use it for creation of simple probe actors to be inserted in the message flows. To make this more
powerful and convenient, there is a concrete implementation called TestProbe. The functionality is best explained using a small example:
class MyDoubleEcho extends Actor {
var dest1 : ActorRef = _
var dest2 : ActorRef = _
def receive = {
case (d1: ActorRef, d2: ActorRef) =>
dest1 = d1
dest2 = d2
case x =>
dest1 ! x
dest2 ! x
}
}
val probe1 = TestProbe()
val probe2 = TestProbe()
val actor = Actor.actorOf[MyDoubleEcho].start()
actor ! (probe1.ref, probe2.ref)
actor ! "hello"
probe1.expectMsg(50 millis, "hello")
probe2.expectMsg(50 millis, "hello")
Probes may also be equipped with custom assertions to make your test code even more concise and clear:
case class Update(id : Int, value : String)
val probe = new TestProbe {
def expectUpdate(x : Int) = {
expectMsg {
case Update(id, _) if id == x => true
}
reply("ACK")
}
}
You have complete flexibility here in mixing and matching the TestKit facilities with your own checks and
choosing an intuitive name for it. In real life your code will probably be a bit more complicated than the example
given above; just use the power!
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Replying to Messages Received by Probes
The probes keep track of the communications channel for replies, if possible, so they can also reply:
val probe = TestProbe()
val future = probe.ref ? "hello"
probe.expectMsg(0 millis, "hello") // TestActor runs on CallingThreadDispatcher
probe.reply("world")
assert (future.isCompleted && future.as[String] == "world")
Forwarding Messages Received by Probes
Given a destination actor dest which in the nominal actor network would receive a message from actor source.
If you arrange for the message to be sent to a TestProbe probe instead, you can make assertions concerning
volume and timing of the message flow while still keeping the network functioning:
val probe = TestProbe()
val source = Actor.actorOf(new Source(probe)).start()
val dest = Actor.actorOf[Destination].start()
source ! "start"
probe.expectMsg("work")
probe.forward(dest)
The dest actor will receive the same message invocation as if no test probe had intervened.
Caution about Timing Assertions
The behavior of within blocks when using test probes might be perceived as counter-intuitive: you need to
remember that the nicely scoped deadline as described above is local to each probe. Hence, probes do not react to
each other’s deadlines or to the deadline set in an enclosing TestKit instance:
class SomeTest extends TestKit {
val probe = TestProbe()
within(100 millis) {
probe.expectMsg("hallo")
}
// Will hang forever!
}
This test will hang indefinitely, because the expectMsg call does not see any deadline. Currently, the only
option is to use probe.within in the above code to make it work; later versions may include lexically scoped
deadlines using implicit arguments.
4.18.4 CallingThreadDispatcher
The CallingThreadDispatcher serves good purposes in unit testing, as described above, but originally
it was conceived in order to allow contiguous stack traces to be generated in case of an error. As this special
dispatcher runs everything which would normally be queued directly on the current thread, the full history of a
message’s processing chain is recorded on the call stack, so long as all intervening actors run on this dispatcher.
How to use it
Just set the dispatcher as you normally would, either from within the actor
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import akka.testkit.CallingThreadDispatcher
class MyActor extends Actor {
self.dispatcher = CallingThreadDispatcher.global
...
}
or from the client code
val ref = Actor.actorOf[MyActor]
ref.dispatcher = CallingThreadDispatcher.global
ref.start()
As the CallingThreadDispatcher does not have any configurable state, you may always use the (lazily)
preallocated one as shown in the examples.
How it works
When receiving an invocation, the CallingThreadDispatcher checks whether the receiving actor is already
active on the current thread. The simplest example for this situation is an actor which sends a message to itself.
In this case, processing cannot continue immediately as that would violate the actor model, so the invocation
is queued and will be processed when the active invocation on that actor finishes its processing; thus, it will
be processed on the calling thread, but simply after the actor finishes its previous work. In the other case, the
invocation is simply processed immediately on the current thread. Futures scheduled via this dispatcher are also
executed immediately.
This scheme makes the CallingThreadDispatcher work like a general purpose dispatcher for any actors
which never block on external events.
In the presence of multiple threads it may happen that two invocations of an actor running on this dispatcher
happen on two different threads at the same time. In this case, both will be processed directly on their respective
threads, where both compete for the actor’s lock and the loser has to wait. Thus, the actor model is left intact, but
the price is loss of concurrency due to limited scheduling. In a sense this is equivalent to traditional mutex style
concurrency.
The other remaining difficulty is correct handling of suspend and resume: when an actor is suspended, subsequent
invocations will be queued in thread-local queues (the same ones used for queuing in the normal case). The call
to resume, however, is done by one specific thread, and all other threads in the system will probably not be
executing this specific actor, which leads to the problem that the thread-local queues cannot be emptied by their
native threads. Hence, the thread calling resume will collect all currently queued invocations from all threads
into its own queue and process them.
Limitations
If an actor’s behavior blocks on a something which would normally be affected by the calling actor after having
sent the message, this will obviously dead-lock when using this dispatcher. This is a common scenario in actor
tests based on CountDownLatch for synchronization:
val latch = new CountDownLatch(1)
actor ! startWorkAfter(latch)
// actor will call latch.await() before proceeding
doSomeSetupStuff()
latch.countDown()
The example would hang indefinitely within the message processing initiated on the second line and never reach
the fourth line, which would unblock it on a normal dispatcher.
Thus, keep in mind that the CallingThreadDispatcher is not a general-purpose replacement for the normal
dispatchers. On the other hand it may be quite useful to run your actor network on it for testing, because if it runs
without dead-locking chances are very high that it will not dead-lock in production.
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Warning: The above sentence is unfortunately not a strong guarantee, because your code might directly or
indirectly change its behavior when running on a different dispatcher. If you are looking for a tool to help
you debug dead-locks, the CallingThreadDispatcher may help with certain error scenarios, but keep
in mind that it has may give false negatives as well as false positives.
Benefits
To summarize, these are the features with the CallingThreadDispatcher has to offer:
• Deterministic execution of single-threaded tests while retaining nearly full actor semantics
• Full message processing history leading up to the point of failure in exception stack traces
• Exclusion of certain classes of dead-lock scenarios
4.18.5 Tracing Actor Invocations
The testing facilities described up to this point were aiming at formulating assertions about a system’s behavior.
If a test fails, it is usually your job to find the cause, fix it and verify the test again. This process is supported by
debuggers as well as logging, where the Akka toolkit offers the following options:
• Logging of exceptions thrown within Actor instances
This is always on; in contrast to the other logging mechanisms, this logs at ERROR level.
• Logging of message invocations on certain actors
This is enabled by a setting in akka.conf — namely akka.actor.debug.receive — which enables the loggable statement to be applied to an actor’s receive function:
def receive = Actor.loggable(this) { // ‘Actor‘ unnecessary with import Actor._
case msg => ...
}
The first argument to loggable defines the source to be used in the logging events, which should be the
current actor.
If the abovementioned setting is not given in akka.conf, this method will pass through the given
Receive function unmodified, meaning that there is no runtime cost unless actually enabled.
The logging feature is coupled to this specific local mark-up because enabling it uniformly on all actors
is not usually what you need, and it would lead to endless loops if it were applied to EventHandler
listeners.
• Logging of special messages
Actors handle certain special messages automatically, e.g. Kill, PoisonPill, etc. Tracing of these
message invocations is enabled by the setting akka.actor.debug.autoreceive, which enables this
on all actors.
• Logging of the actor lifecycle
Actor creation, start, restart, link, unlink and stop may be traced by enabling the setting
akka.actor.debug.lifecycle; this, too, is enabled uniformly on all actors.
All these messages are logged at DEBUG level. To summarize, you can enable full logging of actor activities using
this configuration fragment:
akka {
event-handler-level = "DEBUG"
actor {
debug {
receive = "true"
autoreceive = "true"
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lifecycle = "true"
}
}
}
4.19 Tutorial: write a scalable, fault-tolerant, network chat server
and client (Scala)
4.19.1 Introduction
Tutorial source code.
Writing correct concurrent, fault-tolerant and scalable applications is too hard. Most of the time it’s because we
are using the wrong tools and the wrong level of abstraction.
Akka is an attempt to change that.
Akka uses the Actor Model together with Software Transactional Memory to raise the abstraction level and provide
a better platform to build correct concurrent and scalable applications.
For fault-tolerance Akka adopts the “Let it crash”, also called “Embrace failure”, model which has been used with
great success in the telecom industry to build applications that self-heal, systems that never stop.
Actors also provides the abstraction for transparent distribution and the basis for truly scalable and fault-tolerant
applications.
Akka is Open Source and available under the Apache 2 License.
In this article we will introduce you to Akka and see how we can utilize it to build a highly concurrent, scalable
and fault-tolerant network server.
But first let’s take a step back and discuss what Actors really are and what they are useful for.
4.19.2 Actors
The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates
the developer from having to deal with explicit locking and thread management. It makes it easier to write correct
concurrent and parallel systems. Actors are really nothing new, they were defined in the 1963 paper by Carl Hewitt
and have been popularized by the Erlang language which emerged in the mid 80s. It has been used by for example
at Ericsson with great success to build highly concurrent and extremely reliable (99.9999999 % availability - 31
ms/year downtime) telecom systems.
Actors encapsulate state and behavior into a lightweight process/thread. In a sense they are like OO objects but
with a major semantic difference; they do not share state with any other Actor. Each Actor has its own view of the
world and can only have impact on other Actors by sending messages to them. Messages are sent asynchronously
and non-blocking in a so-called “fire-and-forget” manner where the Actor sends off a message to some other Actor
and then do not wait for a reply but goes off doing other things or are suspended by the runtime. Each Actor has a
mailbox (ordered message queue) in which incoming messages are processed one by one. Since all processing is
done asynchronously and Actors do not block and consume any resources while waiting for messages, Actors tend
to give very good concurrency and scalability characteristics and are excellent for building event-based systems.
4.19.3 Creating Actors
Akka has both a Scala API (Actors (Scala)) and a Java API (Actors (Java)). In this article we will only look at the
Scala API since that is the most expressive one. The article assumes some basic Scala knowledge, but even if you
don’t know Scala I don’t think it will not be too hard to follow along anyway.
Akka has adopted the same style of writing Actors as Erlang in which each Actor has an explicit message handler
which does pattern matching to match on the incoming messages.
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Actors can be created either by: * Extending the ‘Actor’ class and implementing the ‘receive’ method. * Create
an anonymous Actor using one of the ‘actor’ methods.
Here is a little example before we dive into a more interesting one.
import akka.actor.Actor
class MyActor extends Actor {
def receive = {
case "test" => println("received test")
case _ =>
println("received unknown message")
}
}
val myActor = Actor.actorOf[MyActor]
myActor.start()
From this call we get a handle to the ‘Actor’ called ‘ActorRef’, which we can use to interact with the Actor
The ‘actorOf’ factory method can be imported like this:
import akka.actor.Actor.actorOf
val a = actorOf[MyActor]
From now on we will assume that it is imported like this and can use it directly.
Akka Actors are extremely lightweight. Each Actor consume ~600 bytes, which means that you can create 6.5
million on 4 GB RAM.
Messages are sent using the ‘!’ operator:
myActor ! "test"
4.19.4 Sample application
We will try to write a simple chat/IM system. It is client-server based and uses remote Actors to implement remote
clients. Even if it is not likely that you will ever write a chat system I think that it can be a useful exercise since it
uses patterns and idioms found in many other use-cases and domains.
We will use many of the features of Akka along the way. In particular; Actors, fault-tolerance using Actor
supervision, remote Actors, Software Transactional Memory (STM) and persistence.
4.19.5 Creating an Akka SBT project
First we need to create an SBT project for our tutorial. You do that by stepping into the directory you want to
create your project in and invoking the sbt command answering the questions for setting up your project:
$ sbt
Project does not exist, create new project? (y/N/s) y
Name: Chat
Organization: Hakkers Inc
Version [1.0]:
Scala version [2.9.1]:
sbt version [0.7.6.RC0]:
Add the Akka SBT plugin definition to your SBT project by creating a Plugins.scala file in the
project/plugins directory containing:
import sbt._
class Plugins(info: ProjectInfo) extends PluginDefinition(info) {
val akkaRepo
= "Akka Repo" at "http://repo.akka.io/releases"
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val akkaPlugin = "se.scalablesolutions.akka" % "akka-sbt-plugin" % "1.3.1"
}
Create a project definition project/build/Project.scala file containing:
import sbt._
class
val
val
val
}
ChatProject(info: ProjectInfo) extends DefaultProject(info) with AkkaProject {
akkaRepo = "Akka Repo" at "http://repo.akka.io/releases"
akkaSTM
= akkaModule("stm")
akkaRemote = akkaModule("remote")
Make SBT download the dependencies it needs. That is done by invoking:
> reload
> update
From the SBT project you can generate files for your IDE:
• SbtEclipsify to generate Eclipse project. Detailed instructions are available in Getting Started Tutorial
(Scala with Eclipse): First Chapter.
• sbt-idea to generate IntelliJ IDEA project.
4.19.6 Creating messages
Let’s start by defining the messages that will flow in our system. It is very important that all messages that will
be sent around in the system are immutable. The Actor model relies on the simple fact that no state is shared
between Actors and the only way to guarantee that is to make sure we don’t pass mutable state around as part of
the messages.
In Scala we have something called case classes. These make excellent messages since they are both immutable
and great to pattern match on.
Let’s now start by creating the messages that will flow in our system.
sealed trait Event
case class Login(user: String) extends Event
case class Logout(user: String) extends Event
case class GetChatLog(from: String) extends Event
case class ChatLog(log: List[String]) extends Event
case class ChatMessage(from: String, message: String) extends Event
As you can see with these messages we can log in and out, send a chat message and ask for and get a reply with
all the messages in the chat log so far.
4.19.7 Client: Sending messages
Our client wraps each message send in a function, making it a bit easier to use. Here we assume that we have a
reference to the chat service so we can communicate with it by sending messages. Messages are sent with the ‘!’
operator (pronounced “bang”). This sends a message of asynchronously and do not wait for a reply.
Sometimes however, there is a need for sequential logic, sending a message and wait for the reply before doing
anything else. In Akka we can achieve that using the ‘?’ operator. When sending a message with ‘?’ we get back a
Future. A ‘Future’ is a promise that we will get a result later but with the difference from regular method dispatch
that the OS thread we are running on is put to sleep while waiting and that we can set a time-out for how long
we wait before bailing out, retrying or doing something else. This waiting is achieved with the Future.as[T]
method, which returns a scala.Option which implements the Null Object pattern. It has two subclasses; ‘None’
which means no result and ‘Some(value)’ which means that we got a reply. The ‘Option’ class has a lot of
great methods to work with the case of not getting a defined result. F.e. as you can see below we are using
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the ‘getOrElse’ method which will try to return the result and if there is no result defined invoke the ”...OrElse”
statement.
class ChatClient(val name: String) {
val chat = Actor.remote.actorFor("chat:service", "localhost", 2552)
def
def
def
def
login
=
logout
=
post(message: String) =
chatLog
=
chat ! Login(name)
chat ! Logout(name)
chat ! ChatMessage(name, name + ": " + message)
(chat ? GetChatLog(name)).as[ChatLog]
.getOrElse(throw new Exception("Couldn’t get the chat log from Cha
}
As you can see, we are using the ‘Actor.remote.actorFor’ to lookup the chat server on the remote node. From this
call we will get a handle to the remote instance and can use it as it is local.
4.19.8 Session: Receiving messages
Now we are done with the client side and let’s dig into the server code. We start by creating a user session. The
session is an Actor and is defined by extending the ‘Actor’ trait. This trait has one abstract method that we have to
define; ‘receive’ which implements the message handler for the Actor.
In our example the session has state in the form of a ‘List’ with all the messages sent by the user during the session.
In takes two parameters in its constructor; the user name and a reference to an Actor implementing the persistent
message storage. For both of the messages it responds to, ‘ChatMessage’ and ‘GetChatLog’, it passes them on to
the storage Actor.
If you look closely (in the code below) you will see that when passing on the ‘GetChatLog’ message we are not
using ‘!’ but ‘forward’. This is similar to ‘!’ but with the important difference that it passes the original sender
reference, in this case to the storage Actor. This means that the storage can use this reference to reply to the
original sender (our client) directly.
class Session(user: String, storage: ActorRef) extends Actor {
private val loginTime = System.currentTimeMillis
private var userLog: List[String] = Nil
EventHandler.info(this, "New session for user [%s] has been created at [%s]".format(user, loginT
def receive = {
case msg @ ChatMessage(from, message) =>
userLog ::= message
storage ! msg
case msg @ GetChatLog(_) =>
storage forward msg
}
}
4.19.9 Let it crash: Implementing fault-tolerance
Akka’s approach to fault-tolerance; the “let it crash” model, is implemented by linking Actors. It is very different
to what Java and most non-concurrency oriented languages/frameworks have adopted. It’s a way of dealing with
failure that is designed for concurrent and distributed systems.
If we look at concurrency first. Now let’s assume we are using non-linked Actors. Throwing an exception in
concurrent code, will just simply blow up the thread that currently executes the Actor. There is no way to find
out that things went wrong (apart from see the stack trace in the log). There is nothing you can do about it. Here
linked Actors provide a clean way of both getting notification of the error so you know what happened, as well as
the Actor that crashed, so you can do something about it.
Linking Actors allow you to create sets of Actors where you can be sure that either:
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• All are dead
• All are alive
This is very useful when you have hundreds of thousands of concurrent Actors. Some Actors might have implicit
dependencies and together implement a service, computation, user session etc. for these being able to group them
is very nice.
Akka encourages non-defensive programming. Don’t try to prevent things from go wrong, because they will,
whether you want it or not. Instead; expect failure as a natural state in the life-cycle of your app, crash early and
let someone else (that sees the whole picture), deal with it.
Now let’s look at distributed Actors. As you probably know, you can’t build a fault-tolerant system with just one
single node, but you need at least two. Also, you (usually) need to know if one node is down and/or the service you
are talking to on the other node is down. Here Actor supervision/linking is a critical tool for not only monitoring
the health of remote services, but to actually manage the service, do something about the problem if the Actor or
node is down. This could be restarting him on the same node or on another node.
To sum things up, it is a very different way of thinking but a way that is very useful (if not critical) to building
fault-tolerant highly concurrent and distributed applications.
4.19.10 Supervisor hierarchies
A supervisor is a regular Actor that is responsible for starting, stopping and monitoring its child Actors. The basic
idea of a supervisor is that it should keep its child Actors alive by restarting them when necessary. This makes for
a completely different view on how to write fault-tolerant servers. Instead of trying all things possible to prevent
an error from happening, this approach embraces failure. It shifts the view to look at errors as something natural
and something that will happen and instead of trying to prevent it; embrace it. Just “let it crash” and reset the
service to a stable state through restart.
Akka has two different restart strategies; All-For-One and One-For-One.
• OneForOne: Restart only the component that has crashed.
• AllForOne: Restart all the components that the supervisor is managing, including the one that have crashed.
The latter strategy should be used when you have a certain set of components that are coupled in some way that if
one is crashing they all need to be reset to a stable state before continuing.
4.19.11 Chat server: Supervision, Traits and more
There are two ways you can define an Actor to be a supervisor; declaratively and dynamically. In this example we
use the dynamic approach. There are two things we have to do:
• Define the fault handler by setting the ‘faultHandler’ member field to the strategy we want.
• Define the exceptions we want to “trap”, e.g. which exceptions should be handled according to the fault
handling strategy we have defined. This in done by setting the ‘trapExit’ member field to a ‘List’ with all
exceptions we want to trap.
The last thing we have to do to supervise Actors (in our example the storage Actor) is to ‘link’ the Actor. Invoking
‘link(actor)’ will create a link between the Actor passed as argument into ‘link’ and ourselves. This means that we
will now get a notification if the linked Actor is crashing and if the cause of the crash, the exception, matches one
of the exceptions in our ‘trapExit’ list then the crashed Actor is restarted according the the fault handling strategy
defined in our ‘faultHandler’. We also have the ‘unlink(actor)’ function which disconnects the linked Actor from
the supervisor.
In our example we are using a method called ‘spawnLink(actor)’ which creates, starts and links the Actor in
an atomic operation. The linking and unlinking is done in ‘preStart’ and ‘postStop’ callback methods which are
invoked by the runtime when the Actor is started and shut down (shutting down is done by invoking ‘actor.stop()’).
In these methods we initialize our Actor, by starting and linking the storage Actor and clean up after ourselves by
shutting down all the user session Actors and the storage Actor.
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That is it. Now we have implemented the supervising part of the fault-tolerance for the storage Actor. But before
we dive into the ‘ChatServer’ code there are some more things worth mentioning about its implementation.
It defines an abstract member field holding the ‘ChatStorage’ implementation the server wants to use. We do not
define that in the ‘ChatServer’ directly since we want to decouple it from the actual storage implementation.
The ‘ChatServer’ is a ‘trait’, which is Scala’s version of mixins. A mixin can be seen as an interface with an implementation and is a very powerful tool in Object-Oriented design that makes it possible to design the system into
small, reusable, highly cohesive, loosely coupled parts that can be composed into larger object and components
structures.
I’ll try to show you how we can make use Scala’s mixins to decouple the Actor implementation from the business
logic of managing the user sessions, routing the chat messages and storing them in the persistent storage. Each
of these separate parts of the server logic will be represented by its own trait; giving us four different isolated
mixins; ‘Actor’, ‘SessionManagement’, ‘ChatManagement’ and ‘ChatStorageFactory’ This will give us as loosely
coupled system with high cohesion and reusability. At the end of the article I’ll show you how you can compose
these mixins into a the complete runtime component we like.
/**
* Chat server. Manages sessions and redirects all other messages to the Session for the client.
*/
trait ChatServer extends Actor {
self.faultHandler = OneForOneStrategy(List(classOf[Exception]),5, 5000)
val storage: ActorRef
EventHandler.info(this, "Chat server is starting up...")
// actor message handler
def receive: Receive = sessionManagement orElse chatManagement
// abstract methods to be defined somewhere else
protected def chatManagement: Receive
protected def sessionManagement: Receive
protected def shutdownSessions(): Unit
override def postStop() = {
EventHandler.info(this, "Chat server is shutting down...")
shutdownSessions
self.unlink(storage)
storage.stop()
}
}
If you look at the ‘receive’ message handler function you can see that we have defined it but instead of adding our
logic there we are delegating to two different functions; ‘sessionManagement’ and ‘chatManagement’, chaining
them with ‘orElse’. These two functions are defined as abstract in our ‘ChatServer’ which means that they have to
be provided by some another mixin or class when we instantiate our ‘ChatServer’. Naturally we will put the ‘sessionManagement’ implementation in the ‘SessionManagement’ trait and the ‘chatManagement’ implementation
in the ‘ChatManagement’ trait. First let’s create the ‘SessionManagement’ trait.
Chaining partial functions like this is a great way of composing functionality in Actors. You can for example
put define one default message handle handling generic messages in the base Actor and then let deriving Actors
extend that functionality by defining additional message handlers. There is a section on how that is done here.
4.19.12 Session management
The session management is defined in the ‘SessionManagement’ trait in which we implement the two abstract
methods in the ‘ChatServer’; ‘sessionManagement’ and ‘shutdownSessions’.
The ‘SessionManagement’ trait holds a ‘HashMap’ with all the session Actors mapped by user name as well as a
reference to the storage (to be able to pass it in to each newly created ‘Session’).
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The ‘sessionManagement’ function performs session management by responding to the ‘Login’ and ‘Logout’
messages. For each ‘Login’ message it creates a new ‘Session’ Actor, starts it and puts it in the ‘sessions’ Map and
for each ‘Logout’ message it does the opposite; shuts down the user’s session and removes it from the ‘sessions’
Map.
The ‘shutdownSessions’ function simply shuts all the sessions Actors down. That completes the user session
management.
/**
* Implements user session management.
* <p/>
* Uses self-type annotation (this: Actor =>) to declare that it needs to be mixed in with an Acto
*/
trait SessionManagement { this: Actor =>
val storage: ActorRef // needs someone to provide the ChatStorage
val sessions = new HashMap[String, ActorRef]
protected def sessionManagement: Receive = {
case Login(username) =>
EventHandler.info(this, "User [%s] has logged in".format(username))
val session = actorOf(new Session(username, storage))
session.start()
sessions += (username -> session)
case Logout(username) =>
EventHandler.info(this, "User [%s] has logged out".format(username))
val session = sessions(username)
session.stop()
sessions -= username
}
protected def shutdownSessions =
sessions.foreach { case (_, session) => session.stop() }
}
4.19.13 Chat message management
Chat message management is implemented by the ‘ChatManagement’ trait. It has an abstract ‘HashMap’ session
member field with all the sessions. Since it is abstract it needs to be mixed in with someone that can provide this
reference. If this dependency is not resolved when composing the final component, you will get a compilation
error.
It implements the ‘chatManagement’ function which responds to two different messages; ‘ChatMessage’ and
‘GetChatLog’. It simply gets the session for the user (the sender of the message) and routes the message to this
session. Here we also use the ‘forward’ function to make sure the original sender reference is passed along to
allow the end receiver to reply back directly.
/**
* Implements chat management, e.g. chat message dispatch.
* <p/>
* Uses self-type annotation (this: Actor =>) to declare that it needs to be mixed in with an Acto
*/
trait ChatManagement { this: Actor =>
val sessions: HashMap[String, ActorRef] // needs someone to provide the Session map
protected def chatManagement: Receive = {
case msg @ ChatMessage(from, _) => getSession(from).foreach(_ ! msg)
case msg @ GetChatLog(from) =>
getSession(from).foreach(_ forward msg)
}
private def getSession(from: String) : Option[ActorRef] = {
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if (sessions.contains(from))
Some(sessions(from))
else {
EventHandler.info(this, "Session expired for %s".format(from))
None
}
}
}
Using an Actor as a message broker, as in this example, is a very common pattern with many variations; loadbalancing, master/worker, map/reduce, replication, logging etc. It becomes even more useful with remote Actors
when we can use it to route messages to different nodes.
4.19.14 STM and Transactors
Actors are excellent for solving problems where you have many independent processes that can work in isolation
and only interact with other Actors through message passing. This model fits many problems. But the Actor
model is unfortunately a terrible model for implementing truly shared state. E.g. when you need to have consensus
and a stable view of state across many components. The classic example is the bank account where clients can
deposit and withdraw, in which each operation needs to be atomic. For detailed discussion on the topic see this
presentation.
Software Transactional Memory (STM) on the other hand is excellent for problems where you need consensus
and a stable view of the state by providing compositional transactional shared state. Some of the really nice traits
of STM are that transactions compose and that it raises the abstraction level from lock-based concurrency.
Akka has a STM implementation that is based on the same ideas as found in the Clojure language; Managed
References working with immutable data.
Akka allows you to combine Actors and STM into what we call Transactors (short for Transactional Actors),
these allow you to optionally combine Actors and STM provides IMHO the best of the Actor model (simple
concurrency and asynchronous event-based programming) and STM (compositional transactional shared state) by
providing transactional, compositional, asynchronous, event-based message flows. You don’t need Transactors all
the time but when you do need them then you really need them.
Akka currently provides three different transactional abstractions; ‘Map’, ‘Vector’ and ‘Ref’. They can be shared
between multiple Actors and they are managed by the STM. You are not allowed to modify them outside a
transaction, if you do so, an exception will be thrown.
What you get is transactional memory in which multiple Actors are allowed to read and write to the same memory
concurrently and if there is a clash between two transactions then both of them are aborted and retried. Aborting
a transaction means that the memory is rolled back to the state it were in when the transaction was started.
In database terms STM gives you ‘ACI’ semantics; ‘Atomicity’, ‘Consistency’ and ‘Isolation’. The ‘D’ in ‘ACID’;
‘Durability’, you can’t get with an STM since it is in memory. It possible to implement durable persistence for the
transactional data structures, but in this sample we keep them in memory.
4.19.15 Chat storage: Backed with simple in-memory
To keep it simple we implement the persistent storage, with a in-memory Vector, i.e. it will not be persistent. We
start by creating a ‘ChatStorage’ trait allowing us to have multiple different storage backend. For example one
in-memory and one persistent.
/**
* Abstraction of chat storage holding the chat log.
*/
trait ChatStorage extends Actor
Our ‘MemoryChatStorage’ extends the ‘ChatStorage’ trait. The only state it holds is the ‘chatLog’ which is a
transactional ‘Vector’.
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It responds to two different messages; ‘ChatMessage’ and ‘GetChatLog’. The ‘ChatMessage’ message handler
takes the ‘message’ attribute and appends it to the ‘chatLog’ vector. Here you can see that we are using the ‘atomic
{ ... }’ block to run the vector operation in a transaction. For this in-memory storage it is not important to use a
transactional Vector, since it is not shared between actors, but it illustrates the concept.
The ‘GetChatLog’ message handler retrieves all the messages in the chat log storage inside an atomic block,
iterates over them using the ‘map’ combinator transforming them from ‘Array[Byte] to ‘String’. Then it invokes
the ‘reply(message)’ function that will send the chat log to the original sender; the ‘ChatClient’.
You might remember that the ‘ChatServer’ was supervising the ‘ChatStorage’ actor. When we discussed that we
showed you the supervising Actor’s view. Now is the time for the supervised Actor’s side of things. First, a
supervised Actor need to define a life-cycle in which it declares if it should be seen as a:
• ‘Permanent’: which means that the actor will always be restarted.
• ‘Temporary’: which means that the actor will not be restarted, but it will be shut down through the regular
shutdown process so the ‘postStop’ callback function will called.
We define the ‘MemoryChatStorage’ as ‘Permanent’ by setting the ‘lifeCycle’ member field to ‘Permanent’.
The idea with this crash early style of designing your system is that the services should just crash and then they
should be restarted and reset into a stable state and continue from there. The definition of “stable state” is domain
specific and up to the application developer to define. Akka provides two callback functions; ‘preRestart’ and
‘postRestart’ that are called right before and right after the Actor is restarted. Both of these functions take a
‘Throwable’, the reason for the crash, as argument. In our case we just need to implement the ‘postRestart’ hook
and there re-initialize the ‘chatLog’ member field with a fresh ‘Vector’.
/**
* Memory-backed chat storage implementation.
*/
class MemoryChatStorage extends ChatStorage {
self.lifeCycle = Permanent
private var chatLog = TransactionalVector[Array[Byte]]()
EventHandler.info(this, "Memory-based chat storage is starting up...")
def receive = {
case msg @ ChatMessage(from, message) =>
EventHandler.debug(this, "New chat message [%s]".format(message))
atomic { chatLog + message.getBytes("UTF-8") }
case GetChatLog(_) =>
val messageList = atomic { chatLog.map(bytes => new String(bytes, "UTF-8")).toList }
self.reply(ChatLog(messageList))
}
override def postRestart(reason: Throwable) = chatLog = TransactionalVector()
}
The last thing we need to do in terms of persistence is to create a ‘MemoryChatStorageFactory’ that will take care
of instantiating and resolving the ‘val storage: ChatStorage’ field in the ‘ChatServer’ with a concrete implementation of our persistence Actor.
/**
* Creates and links a MemoryChatStorage.
*/
trait MemoryChatStorageFactory { this: Actor =>
val storage = this.self.spawnLink[MemoryChatStorage] // starts and links ChatStorage
}
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4.19.16 Composing the full Chat Service
We have now created the full functionality for the chat server, all nicely decoupled into isolated and well-defined
traits. Now let’s bring all these traits together and compose the complete concrete ‘ChatService’.
/**
* Class encapsulating the full Chat Service.
* Start service by invoking:
* <pre>
* val chatService = Actor.actorOf[ChatService].start()
* </pre>
*/
class ChatService extends
ChatServer with
SessionManagement with
ChatManagement with
MemoryChatStorageFactory {
override def preStart() = {
remote.start("localhost", 2552);
remote.register("chat:service", self) //Register the actor with the specified service id
}
}
4.19.17 Creating a remote server service
As you can see in the section above, we are overriding the Actor’s ‘start’ method and are starting up a remote
server node by invoking ‘remote.start(“localhost”, 2552)’. This starts up the remote node on address “localhost”
and port 2552 which means that it accepts incoming messages on this address. Then we register the ChatService
actor in the remote node by invoking ‘remote.register(“chat:service”, self)’. This means that the ChatService will
be available to other actors on this specific id, address and port.
That’s it. Were done. Now we have a, very simple, but scalable, fault-tolerant, event-driven, persistent chat server
that can without problem serve a million concurrent users on a regular workstation.
Let’s use it.
4.19.18 Sample client chat session
Now let’s create a simple test runner that logs in posts some messages and logs out.
/**
* Test runner emulating a chat session.
*/
object ClientRunner {
def run = {
val client1 = new ChatClient("jonas")
client1.login
val client2 = new ChatClient("patrik")
client2.login
client1.post("Hi there")
println("CHAT LOG:\n\t" + client1.chatLog.log.mkString("\n\t"))
client2.post("Hello")
println("CHAT LOG:\n\t" + client2.chatLog.log.mkString("\n\t"))
client1.post("Hi again")
println("CHAT LOG:\n\t" + client1.chatLog.log.mkString("\n\t"))
client1.logout
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client2.logout
}
}
4.19.19 Sample code
All this code is available as part of the Akka distribution. It resides in the ‘./akka-samples/akka-sample-chat’
module and have a ‘README’ file explaining how to run it.
Or if you rather browse it online.
4.19.20 Run it
Download and build Akka
1. Check out Akka from http://github.com/akka/akka
2. Set ‘AKKA_HOME’ environment variable to the root of the Akka distribution.
3. Open up a shell and step into the Akka distribution root folder.
4. Build Akka by invoking:
% sbt update
% sbt dist
Run a sample chat session
1. Fire up two shells. For each of them:
• Step down into to the root of the Akka distribution.
• Set ‘export AKKA_HOME=<root of distribution>.
• Run ‘sbt console’ to start up a REPL (interpreter).
2. In the first REPL you get execute:
import sample.chat._
import akka.actor.Actor._
val chatService = actorOf[ChatService].start()
3. In the second REPL you get execute:
import sample.chat._
ClientRunner.run
4. See the chat simulation run.
5. Run it again to see full speed after first initialization.
6. In the client REPL, or in a new REPL, you can also create your own client
import sample.chat._
val myClient = new ChatClient("<your name>")
myClient.login
myClient.post("Can I join?")
println("CHAT LOG:\n\t" + myClient.chatLog.log.mkString("\n\t"))
That’s it. Have fun.
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CHAPTER
FIVE
JAVA API
5.1 Actors (Java)
Module stability: SOLID
The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates
the developer from having to deal with explicit locking and thread management, making it easier to write correct
concurrent and parallel systems. Actors were defined in the 1973 paper by Carl Hewitt but have been popularized
by the Erlang language, and used for example at Ericsson with great success to build highly concurrent and reliable
telecom systems.
5.1.1 Defining an Actor class
Actors in Java are created either by extending the ‘UntypedActor’ class and implementing the ‘onReceive’ method.
This method takes the message as a parameter.
Here is an example:
import akka.actor.UntypedActor;
import akka.event.EventHandler;
public class SampleUntypedActor extends UntypedActor {
public void onReceive(Object message) throws Exception {
if (message instanceof String)
EventHandler.info(this, String.format("Received String message: %s",
message));
else
throw new IllegalArgumentException("Unknown message: " + message);
}
}
Creating Actors
Creating an Actor is done using the ‘akka.actor.Actors.actorOf’ factory method. This method returns a reference
to the UntypedActor’s ActorRef. This ‘ActorRef’ is an immutable serializable reference that you should use to
communicate with the actor, send messages, link to it etc. This reference also functions as the context for the actor
and holds run-time type information such as sender of the last message,
ActorRef myActor = Actors.actorOf(SampleUntypedActor.class);
myActor.start();
Normally you would want to import the ‘actorOf’ method like this:
import static akka.actor.Actors.*;
ActorRef myActor = actorOf(SampleUntypedActor.class);
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To avoid prefix it with ‘Actors’ every time you use it.
You can also create & start the actor in one statement:
ActorRef myActor = actorOf(SampleUntypedActor.class).start();
The call to ‘actorOf’ returns an instance of ‘ActorRef’. This is a handle to the ‘UntypedActor’ instance which you
can use to interact with the Actor, like send messages to it etc. more on this shortly. The ‘ActorRef’ is immutable
and has a one to one relationship with the Actor it represents. The ‘ActorRef’ is also serializable and networkaware. This means that you can serialize it, send it over the wire and use it on a remote host and it will still be
representing the same Actor on the original node, across the network.
Creating Actors with non-default constructor
If your UntypedActor has a constructor that takes parameters then you can’t create it using ‘actorOf(clazz)’.
Instead you can use a variant of ‘actorOf’ that takes an instance of an ‘UntypedActorFactory’ in which you can
create the Actor in any way you like. If you use this method then you to make sure that no one can get a reference
to the actor instance. If they can get a reference it then they can touch state directly in bypass the whole actor
dispatching mechanism and create race conditions which can lead to corrupt data.
Here is an example:
ActorRef actor = actorOf(new UntypedActorFactory() {
public UntypedActor create() {
return new MyUntypedActor("service:name", 5);
}
});
This way of creating the Actor is also great for integrating with Dependency Injection (DI) frameworks like Guice
or Spring.
5.1.2 UntypedActor context
The UntypedActor base class contains almost no member fields or methods to invoke. It only has the ‘onReceive(Object message)’ method, which is defining the Actor’s message handler, and some life-cycle callbacks that
you can choose to implement: ## preStart ## postStop ## preRestart ## postRestart
Most of the API is in the UnypedActorRef a reference for the actor. This reference is available in the ‘getContext()’
method in the UntypedActor (or you can use its alias, the ‘context()’ method, if you prefer. Here, for example,
you find methods to reply to messages, send yourself messages, define timeouts, fault tolerance etc., start and stop
etc.
5.1.3 Identifying Actors
Each ActorRef has two methods: * getContext().getUuid(); * getContext().getId();
The difference is that the ‘uuid’ is generated by the runtime, guaranteed to be unique and can’t be modified. While
the ‘id’ can be set by the user (using ‘getContext().setId(...)’, and defaults to Actor class name. You can retrieve
Actors by both UUID and ID using the ‘ActorRegistry’, see the section further down for details.
5.1.4 Messages and immutability
IMPORTANT: Messages can be any kind of object but have to be immutable. Akka can’t enforce immutability
(yet) so this has to be by convention.
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5.1.5 Send messages
Messages are sent to an Actor through one of the ‘send’ methods. * ‘tell’ means “fire-and-forget”, e.g. send
a message asynchronously and return immediately. * ‘sendRequestReply’ means “send-and-reply-eventually”,
e.g. send a message asynchronously and wait for a reply through a Future. Here you can specify a timeout.
Using timeouts is very important. If no timeout is specified then the actor’s default timeout (set by the ‘getContext().setTimeout(..)’ method in the ‘ActorRef’) is used. This method throws an ‘ActorTimeoutException’ if the
call timed out. * ‘sendRequestReplyFuture’ sends a message asynchronously and returns a ‘Future’.
In all these methods you have the option of passing along your ‘ActorRef’ context variable. Make it a practice of
doing so because it will allow the receiver actors to be able to respond to your message, since the sender reference
is sent along with the message.
Fire-forget
This is the preferred way of sending messages. No blocking waiting for a message. Give best concurrency and
scalability characteristics.
actor.tell("Hello");
Or with the sender reference passed along:
actor.tell("Hello", getContext());
If invoked from within an Actor, then the sending actor reference will be implicitly passed along with the message
and available to the receiving Actor in its ‘getContext().getSender();’ method. He can use this to reply to the
original sender or use the ‘getContext().reply(message);’ method.
If invoked from an instance that is not an Actor there will be no implicit sender passed along the message and you
will get an ‘IllegalStateException’ if you call ‘getContext().reply(..)’.
Send-And-Receive-Eventually
Using ‘sendRequestReply’ will send a message to the receiving Actor asynchronously but it will wait for a reply
on a ‘Future’, blocking the sender Actor until either:
• A reply is received, or
• The Future times out and an ‘ActorTimeoutException’ is thrown.
You can pass an explicit time-out to the ‘sendRequestReply’ method and if none is specified then the default
time-out defined in the sender Actor will be used.
Here are some examples:
UntypedActorRef actorRef = ...
try {
Object result = actorRef.sendRequestReply("Hello", getContext(), 1000);
... // handle reply
} catch(ActorTimeoutException e) {
... // handle timeout
}
Send-And-Receive-Future
Using ‘sendRequestReplyFuture’ will send a message to the receiving Actor asynchronously and will immediately
return a ‘Future’.
Future future = actorRef.sendRequestReplyFuture("Hello", getContext(), 1000);
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The ‘Future’ interface looks like this:
interface Future<T> {
void await();
void awaitBlocking();
boolean isCompleted();
boolean isExpired();
long timeoutInNanos();
Option<T> result();
Option<Throwable> exception();
Future<T> onComplete(Procedure<Future<T>> procedure);
}
So the normal way of working with futures is something like this:
Future future = actorRef.sendRequestReplyFuture("Hello", getContext(), 1000);
future.await();
if (future.isCompleted()) {
Option resultOption = future.result();
if (resultOption.isDefined()) {
Object result = resultOption.get();
...
}
... // whatever
}
The ‘onComplete’ callback can be used to register a callback to get a notification when the Future completes.
Gives you a way to avoid blocking.
Forward message
You can forward a message from one actor to another. This means that the original sender address/reference is
maintained even though the message is going through a ‘mediator’. This can be useful when writing actors that
work as routers, load-balancers, replicators etc. You need to pass along your ActorRef context variable as well.
getContext().forward(message, getContext());
5.1.6 Receive messages
When an actor receives a message it is passed into the ‘onReceive’ method, this is an abstract method on the
‘UntypedActor’ base class that needs to be defined.
Here is an example:
public class SampleUntypedActor extends UntypedActor {
public void onReceive(Object message) throws Exception {
if (message instanceof String)
EventHandler.info(this, String.format("Received String message: %s", message));
else
throw new IllegalArgumentException("Unknown message: " + message);
}
}
5.1.7 Reply to messages
Reply using the channel
If you want to have a handle to an object to whom you can reply to the message, you can use the Channel
abstraction. Simply call getContext().channel() and then you can forward that to others, store it away or otherwise
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until you want to reply, which you do by Channel.tell(msg)
public void onReceive(Object message) throws Exception {
if (message instanceof String) {
String msg = (String)message;
if (msg.equals("Hello")) {
// Reply to original sender of message using the channel
getContext().channel().tryTell(msg + " from " + getContext().getUuid());
}
}
}
We recommend that you as first choice use the channel abstraction instead of the other ways described in the
following sections.
Reply using the ‘replySafe’ and ‘replyUnsafe’ methods
If you want to send a message back to the original sender of the message you just received then you can use the
‘getContext().replyUnsafe(..)’ method.
public void onReceive(Object message) throws Exception {
if (message instanceof String) {
String msg = (String)message;
if (msg.equals("Hello")) {
// Reply to original sender of message using the ’replyUnsafe’ method
getContext().replyUnsafe(msg + " from " + getContext().getUuid());
}
}
}
In this case we will a reply back to the Actor that sent the message.
The ‘replyUnsafe’ method throws an ‘IllegalStateException’ if unable to determine what to reply to, e.g. the
sender has not been passed along with the message when invoking one of ‘send*’ methods. You can also use the
more forgiving ‘replySafe’ method which returns ‘true’ if reply was sent, and ‘false’ if unable to determine what
to reply to.
public void onReceive(Object message) throws Exception {
if (message instanceof String) {
String msg = (String)message;
if (msg.equals("Hello")) {
// Reply to original sender of message using the ’replyUnsafe’ method
if (getContext().replySafe(msg + " from " + getContext().getUuid())) ... // success
else ... // handle failure
}
}
}
Summary of reply semantics and options
• getContext().reply(...) can be used to reply to an Actor or a Future from within an actor;
the current actor will be passed as reply channel if the current channel supports this.
• getContext().channel is a reference providing an abstraction for the reply channel; this reference
may be passed to other actors or used by non-actor code.
Note: There used to be two methods for determining the sending Actor or Future for the current invocation:
• getContext().sender() yielded a Option[ActorRef]
• getContext().senderFuture() yielded a Option[CompletableFuture[Any]]
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These two concepts have been unified into the channel. If you need to know the nature of the channel, you may
do so using instance tests:
if (getContext().channel() instanceof ActorRef) {
...
} else if (getContext().channel() instanceof ActorCompletableFuture) {
...
}
5.1.8 Starting actors
Actors are started by invoking the ‘start’ method.
ActorRef actor = actorOf(SampleUntypedActor.class);
myActor.start();
You can create and start the Actor in a one liner like this:
ActorRef actor = actorOf(SampleUntypedActor.class).start();
When you start the actor then it will automatically call the ‘preStart’ callback method on the ‘UntypedActor’. This
is an excellent place to add initialization code for the actor.
@Override
void preStart() {
... // initialization code
}
5.1.9 Stopping actors
Actors are stopped by invoking the ‘stop’ method.
actor.stop();
When stop is called then a call to the ‘postStop’ callback method will take place. The Actor can use this callback
to implement shutdown behavior.
@Override
void postStop() {
... // clean up resources
}
You can shut down all Actors in the system by invoking:
Actors.registry().shutdownAll();
5.1.10 PoisonPill
You can also send an actor the akka.actor.PoisonPill message, which will stop the actor when
the message is processed.
If the sender is a Future, the Future will be completed with an
akka.actor.ActorKilledException(“PoisonPill”)
Use it like this:
import static akka.actor.Actors.*;
actor.tell(poisonPill());
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5.1.11 Killing an Actor
You can kill an actor by sending a ‘new Kill()’ message. This will restart the actor through regular supervisor
semantics.
Use it like this:
import static akka.actor.Actors.*;
// kill the actor called ’victim’
victim.tell(kill());
5.1.12 Actor life-cycle
The actor has a well-defined non-circular life-cycle.
NEW (newly created actor) - can’t receive messages (yet)
=> STARTED (when ’start’ is invoked) - can receive messages
=> SHUT DOWN (when ’exit’ or ’stop’ is invoked) - can’t do anything
5.2 Typed Actors (Java)
Module stability: SOLID
The Typed Actors are implemented through Typed Actors. It uses AOP through AspectWerkz to turn regular
POJOs into asynchronous non-blocking Actors with semantics of the Actor Model. Each method dispatch is
turned into a message that is put on a queue to be processed by the Typed Actor sequentially one by one.
If you are using the Spring Framework then take a look at Akka’s Spring integration.
5.2.1 Creating Typed Actors
IMPORTANT: The Typed Actors class must have access modifier ‘public’ and can’t be a non-static inner class.
Akka turns POJOs with interface and implementation into asynchronous (Typed) Actors. Akka is using AspectWerkz’s Proxy implementation, which is the most performant proxy implementation there exists.
In order to create a Typed Actor you have to subclass the TypedActor base class.
Here is an example.
If you have a POJO with an interface implementation separation like this:
interface RegistrationService {
void register(User user, Credentials cred);
User getUserFor(String username);
}
import akka.actor.TypedActor;
public class RegistrationServiceImpl extends TypedActor implements RegistrationService {
public void register(User user, Credentials cred) {
... // register user
}
public User getUserFor(String username) {
... // fetch user by username
return user;
}
}
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Then you can create an Typed Actor out of it by creating it through the ‘TypedActor’ factory like this:
RegistrationService service =
(RegistrationService) TypedActor.newInstance(RegistrationService.class, RegistrationServiceImpl.
// The last parameter defines the timeout for Future calls
Creating Typed Actors with non-default constructor
To create a typed actor that takes constructor arguments use a variant of ‘newInstance’ or ‘newRemoteInstance’
that takes an instance of a ‘TypedActorFactory’ in which you can create the TypedActor in any way you like. If
you use this method then make sure that no one can get a reference to the actor instance. Touching actor state
directly is bypassing the whole actor dispatching mechanism and create race conditions which can lead to corrupt
data.
Here is an example:
Service service = TypedActor.newInstance(classOf[Service], new TypedActorFactory() {
public TypedActor create() {
return new ServiceWithConstructorArgsImpl("someString", 500L));
});
Configuration factory class
Using a configuration object:
import static java.util.concurrent.TimeUnit.MILLISECONDS;
import akka.actor.TypedActorConfiguration;
import akka.util.FiniteDuration;
TypedActorConfiguration config = new TypedActorConfiguration()
.timeout(new FiniteDuration(3000, MILLISECONDS));
RegistrationService service = (RegistrationService) TypedActor.newInstance(RegistrationService.cla
However, often you will not use these factory methods but declaratively define the Typed Actors as part of a
supervisor hierarchy. More on that in the Fault Tolerance Through Supervisor Hierarchies (Java) section.
5.2.2 Sending messages
Messages are sent simply by invoking methods on the POJO, which is proxy to the “real” POJO now. The
arguments to the method are bundled up atomically into an message and sent to the receiver (the actual POJO
instance).
One-way message send
Methods that return void are turned into ‘fire-and-forget’ semantics by asynchronously firing off the message and
return immediately. In the example above it would be the ‘register’ method, so if this method is invoked then it
returns immediately:
// method invocation returns immediately and method is invoke asynchronously using the Actor Model
service.register(user, creds);
Request-reply message send
Methods that return something (e.g. non-void methods) are turned into ‘send-and-receive-eventually’ semantics
by asynchronously firing off the message and wait on the reply using a Future.
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// method invocation is asynchronously dispatched using the Actor Model semantics,
// but it blocks waiting on a Future to be resolved in the background
User user = service.getUser(username);
Generally it is preferred to use fire-forget messages as much as possible since they will never block, e.g. consume
a resource by waiting. But sometimes they are neat to use since they:
# Simulates standard Java method dispatch, which is more intuitive for most Java developers # Are a neat to model
request-reply # Are useful when you need to do things in a defined order
The same holds for the ‘request-reply-with-future’ described below.
Request-reply-with-future message send
Methods that return a ‘akka.dispatch.Future<TYPE>’ are turned into ‘send-and-receive-with-future’ semantics by
asynchronously firing off the message and returns immediately with a Future. You need to use the ‘future(...)’
method in the TypedActor base class to resolve the Future that the client code is waiting on.
Here is an example:
public class MathTypedActorImpl extends TypedActor implements MathTypedActor {
public Future<Integer> square(int value) {
return future(value * value);
}
}
MathTypedActor math = TypedActor.actorOf(MathTypedActor .class, MathTypedActorImpl.class);
// This method will return immediately when called, caller should wait on the Future for the resul
Future<Integer> future = math.square(10);
future.await();
Integer result = future.get();
5.2.3 Stopping Typed Actors
Once Typed Actors have been created with one of the TypedActor.newInstance methods they need to be stopped
with TypedActor.stop to free resources allocated by the created Typed Actor (this is not needed when the Typed
Actor is supervised).
// Create Typed Actor
RegistrationService service = (RegistrationService) TypedActor.newInstance(RegistrationService.cla
// ...
// Free Typed Actor resources
TypedActor.stop(service);
When the Typed Actor defines a shutdown callback method (Fault Tolerance Through Supervisor Hierarchies
(Java)) it will be invoked on TypedActor.stop.
5.2.4 How to use the TypedActorContext for runtime information access
The ‘akka.actor.TypedActorContext’ class Holds ‘runtime type information’ (RTTI) for the Typed Actor. This
context is a member field in the TypedActor base class and holds for example the current sender reference, the
current sender future etc.
Here is an example how you can use it to in a ‘void’ (e.g. fire-forget) method to implement request-reply by using
the sender reference:
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class PingImpl implements Ping extends TypedActor {
public void hit(int count) {
Pong pong = (Pong) getContext().getSender();
pong.hit(count++);
}
}
If the sender, sender future etc. is not available, then these methods will return ‘null’ so you should have a way of
dealing with that scenario.
5.2.5 Messages and immutability
IMPORTANT: Messages can be any kind of object but have to be immutable (there is a workaround, see next
section). Java or Scala can’t enforce immutability (yet) so this has to be by convention. Primitives like String,
int, Long are always immutable. Apart from these you have to create your own immutable objects to send as
messages. If you pass on a reference to an instance that is mutable then this instance can be modified concurrently
by two different Typed Actors and the Actor model is broken leaving you with NO guarantees and most likely
corrupt data.
Akka can help you in this regard. It allows you to turn on an option for serializing all messages, e.g. all parameters
to the Typed Actor effectively making a deep clone/copy of the parameters. This will make sending mutable
messages completely safe. This option is turned on in the ‘$AKKA_HOME/config/akka.conf’ config file like
this:
akka {
actor {
serialize-messages = on
}
}
# does a deep clone of messages to ensure immutability
This will make a deep clone (using Java serialization) of all parameters.
5.3 ActorRegistry (Java)
Module stability: SOLID
5.3.1 ActorRegistry: Finding Actors
Actors can be looked up using the ‘akka.actor.Actors.registry()’ object. Through this registry you can look up
actors by:
• uuid com.eaio.uuid.UUID – this uses the uuid field in the Actor class, returns the actor reference for the
actor with specified uuid, if one exists, otherwise None
• id string – this uses the id field in the Actor class, which can be set by the user (default is the class name),
returns all actor references to actors with specified id
• parameterized type - returns a ActorRef[] with all actors that are a subtype of this specific type
• specific actor class - returns a ActorRef[] with all actors of this exact class
Actors are automatically registered in the ActorRegistry when they are started and removed when they are stopped.
But you can explicitly register and unregister ActorRef’s if you need to using the register and unregister
methods.
Here is a summary of the API for finding actors:
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import static akka.actor.Actors.*;
Option<ActorRef> actor = registry().actorFor(uuid);
ActorRef[] actors = registry().actors();
ActorRef[] otherActors = registry().actorsFor(id);
ActorRef[] moreActors = registry().actorsFor(clazz);
You can shut down all Actors in the system by invoking:
registry().shutdownAll();
If you want to know when a new Actor is added to or removed from the registry, you can use the subscription API
on the registry. You can register an Actor that should be notified when an event happens in the ActorRegistry:
void addListener(ActorRef listener);
void removeListener(ActorRef listener);
The messages sent to this Actor are:
public class ActorRegistered {
ActorRef actor();
}
public class ActorUnregistered {
ActorRef actor();
}
So your listener Actor needs to be able to handle these two messages. Example:
import
import
import
import
akka.actor.ActorRegistered;
akka.actor.ActorUnregistered;
akka.actor.UntypedActor;
akka.event.EventHandler;
public class RegistryListener extends UntypedActor {
public void onReceive(Object message) throws Exception {
if (message instanceof ActorRegistered) {
ActorRegistered event = (ActorRegistered) message;
EventHandler.info(this, String.format("Actor registered: %s - %s",
event.actor().actorClassName(), event.actor().getUuid()));
event.actor().actorClassName(), event.actor().getUuid()));
} else if (message instanceof ActorUnregistered) {
// ...
}
}
}
The above actor can be added as listener of registry events:
import static akka.actor.Actors.*;
ActorRef listener = actorOf(RegistryListener.class).start();
registry().addListener(listener);
5.4 Futures (Java)
5.4.1 Introduction
In Akka, a Future is a data structure used to retrieve the result of some concurrent operation. This operation is
usually performed by an Actor or by the Dispatcher directly. This result can be accessed synchronously
(blocking) or asynchronously (non-blocking).
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5.4.2 Use with Actors
There are generally two ways of getting a reply from an UntypedActor: the first is by a sent message
(actorRef.tell(msg);), which only works if the original sender was an UntypedActor) and the second is through a Future.
Using the ActorRef‘s sendRequestReplyFuture method to send a message will return a Future. To wait
for and retrieve the actual result the simplest method is:
Future[Object] future = actorRef.sendRequestReplyFuture[Object](msg);
Object result = future.get(); //Block until result is available, usually bad practice
This will cause the current thread to block and wait for the UntypedActor to ‘complete’ the Future with it’s
reply. Due to the dynamic nature of Akka’s UntypedActors this result can be anything. The safest way to deal
with this is to specify the result to an Object as is shown in the above example. You can also use the expected
result type instead of Any, but if an unexpected type were to be returned you will get a ClassCastException.
For more elegant ways to deal with this and to use the result without blocking, refer to Functional Futures.
5.4.3 Use Directly
A common use case within Akka is to have some computation performed concurrently without needing the extra
utility of an UntypedActor. If you find yourself creating a pool of UntypedActors for the sole reason of
performing a calculation in parallel, there is an easier (and faster) way:
import akka.dispatch.Future;
import static akka.dispatch.Futures.future;
import java.util.concurrent.Callable;
Future<String> f = future(new Callable<String>() {
public String call() {
return "Hello" + "World!";
}
});
String result = f.get(); //Blocks until timeout, default timeout is set in akka.conf, otherwise 5
In the above code the block passed to future will be executed by the default Dispatcher, with the return
value of the block used to complete the Future (in this case, the result would be the string: “HelloWorld”).
Unlike a Future that is returned from an UntypedActor, this Future is properly typed, and we also avoid
the overhead of managing an UntypedActor.
5.4.4 Functional Futures
A recent addition to Akka’s Future is several monadic methods that are very similar to the ones used by Scala‘s
collections. These allow you to create ‘pipelines’ or ‘streams’ that the result will travel through.
Future is a Monad
The first method for working with Future functionally is map. This method takes a Function which performs
some operation on the result of the Future, and returning a new result. The return value of the map method is
another Future that will contain the new result:
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.future;
static akka.japi.Function;
java.util.concurrent.Callable;
Future<String> f1 = future(new Callable<String>() {
public String call() {
return "Hello" + "World";
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}
});
Future<Integer> f2 = f1.map(new Function<String, Integer>() {
public Integer apply(String s) {
return s.length();
}
});
Integer result = f2.get();
In this example we are joining two strings together within a Future. Instead of waiting for f1 to complete, we apply
our function that calculates the length of the string using the map method. Now we have a second Future, f2, that
will eventually contain an Integer. When our original Future, f1, completes, it will also apply our function
and complete the second Future with it’s result. When we finally get the result, it will contain the number 10.
Our original Future still contains the string “HelloWorld” and is unaffected by the map.
Something to note when using these methods: if the Future is still being processed when one of these methods
are called, it will be the completing thread that actually does the work. If the Future is already complete though,
it will be run in our current thread. For example:
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.future;
static akka.japi.Function;
java.util.concurrent.Callable;
Future<String> f1 = future(new Callable<String>() {
public String call() {
Thread.sleep(1000);
return "Hello" + "World";
}
});
Future<Integer> f2 = f1.map(new Function<String, Integer>() {
public Integer apply(String s) {
return s.length();
}
});
Integer result = f2.get();
The original Future will take at least 1 second to execute now, which means it is still being processed at the
time we call map. The function we provide gets stored within the Future and later executed automatically by
the dispatcher when the result is ready.
If we do the opposite:
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.future;
static akka.japi.Function;
java.util.concurrent.Callable;
Future<String> f1 = future(new Callable<String>() {
public String call() {
return "Hello" + "World";
}
});
Thread.sleep(1000);
Future<Integer> f2 = f1.map(new Function<String, Integer>() {
public Integer apply(String s) {
return s.length();
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}
});
Integer result = f2.get();
Our little string has been processed long before our 1 second sleep has finished. Because of this, the dispatcher
has moved onto other messages that need processing and can no longer calculate the length of the string for us,
instead it gets calculated in the current thread just as if we weren’t using a Future.
Normally this works quite well as it means there is very little overhead to running a quick function. If there is
a possibility of the function taking a non-trivial amount of time to process it might be better to have this done
concurrently, and for that we use flatMap:
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.future;
static akka.japi.Function;
java.util.concurrent.Callable;
Future<String> f1 = future(new Callable<String>() {
public String call() {
return "Hello" + "World";
}
});
Future<Integer> f2 = f1.flatMap(new Function<String, Future<Integer>>() {
public Future<Integer> apply(final String s) {
return future(
new Callable<Integer>() {
public Integer call() {
return s.length();
}
});
}
});
Integer result = f2.get();
Now our second Future is executed concurrently as well. This technique can also be used to combine the results
of several Futures into a single calculation, which will be better explained in the following sections.
Composing Futures
It is very often desirable to be able to combine different Futures with eachother, below are some examples on how
that can be done in a non-blocking fashion.
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.sequence;
akka.japi.Function;
java.lang.Iterable;
Iterable<Future<Integer>> listOfFutureInts = ... //Some source generating a sequence of Future<Int
// now we have a Future[Iterable[Int]]
Future<Iterable<Integer>> futureListOfInts = sequence(listOfFutureInts);
// Find the sum of the odd numbers
Long totalSum = futureListOfInts.map(
new Function<LinkedList<Integer>, Long>() {
public Long apply(LinkedList<Integer> ints) {
long sum = 0;
for(Integer i : ints)
sum += i;
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return sum;
}
}).get();
To better explain what happened in the example,
Future.sequence is taking the
Iterable<Future<Integer>> and turning it into a Future<Iterable<Integer>>. We can
then use map to work with the Iterable<Integer> directly, and we aggregate the sum of the Iterable.
The traverse method is similar to sequence, but it takes a sequence of A‘‘s and applies a
function from ‘‘A to Future<B> and returns a Future<Iterable<B>>, enabling parallel map over
the sequence, if you use Futures.future to create the Future.
import
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.traverse;
static akka.dispatch.Futures.future;
java.lang.Iterable;
akka.japi.Function;
Iterable<String> listStrings = ... //Just a sequence of Strings
Future<Iterable<String>> result = traverse(listStrings, new Function<String,Future<String>>(){
public Future<String> apply(final String r) {
return future(new Callable<String>() {
public String call() {
return r.toUpperCase();
}
});
}
});
result.get(); //Returns the sequence of strings as upper case
It’s as simple as that!
Then there’s a method that’s called fold that takes a start-value, a sequence of Future:s and a function from
the type of the start-value, a timeout, and the type of the futures and returns something with the same type as the
start-value, and then applies the function to all elements in the sequence of futures, non-blockingly, the execution
will run on the Thread of the last completing Future in the sequence.
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.fold;
java.lang.Iterable;
akka.japi.Function2;
Iterable<Future<String>> futures = ... //A sequence of Futures, in this case Strings
Future<String> result = fold("", 15000, futures, new Function2<String, String, String>(){ //Start
public String apply(String r, String t) {
return r + t; //Just concatenate
}
});
result.get(); // Will produce a String that says "testtesttesttest"(... and so on).
That’s all it takes!
If the sequence passed to fold is empty, it will return the start-value, in the case above, that will be 0. In some
cases you don’t have a start-value and you’re able to use the value of the first completing Future in the sequence
as the start-value, you can use reduce, it works like this:
import
import
import
import
akka.dispatch.Future;
static akka.dispatch.Futures.reduce;
java.util.Iterable;
akka.japi.Function2;
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Iterable<Future<String>> futures = ... //A sequence of Futures, in this case Strings
Future<String> result = reduce(futures, 15000, new Function2<String, String, String>(){ //Timeout
public String apply(String r, String t) {
return r + t; //Just concatenate
}
});
result.get(); // Will produce a String that says "testtesttesttest"(... and so on).
Same as with fold, the execution will be done by the Thread that completes the last of the Futures, you can also
parallize it by chunking your futures into sub-sequences and reduce them, and then reduce the reduced results
again.
This is just a sample of what can be done.
5.4.5 Exceptions
Since the result of a Future is created concurrently to the rest of the program, exceptions must be handled differently. It doesn’t matter if an UntypedActor or the dispatcher is completing the Future, if an Exception
is caught the Future will contain it instead of a valid result. If a Future does contain an Exception, calling
get will cause it to be thrown again so it can be handled properly.
5.5 Dataflow Concurrency (Java)
5.5.1 Introduction
IMPORTANT: As of Akka 1.1, Akka Future, CompletableFuture and DefaultCompletableFuture have all
the functionality of DataFlowVariables, they also support non-blocking composition and advanced features
like fold and reduce, Akka DataFlowVariable is therefor deprecated and will probably resurface in the
following release as a DSL on top of Futures.
Akka implements Oz-style dataflow concurrency through dataflow (single assignment) variables and lightweight
(event-based) processes/threads.
Dataflow concurrency is deterministic. This means that it will always behave the same. If you run it once and
it yields output 5 then it will do that every time, run it 10 million times, same result. If it on the other hand
deadlocks the first time you run it, then it will deadlock every single time you run it. Also, there is no difference
between sequential code and concurrent code. These properties makes it very easy to reason about concurrency.
The limitation is that the code needs to be side-effect free, e.g. deterministic. You can’t use exceptions, time,
random etc., but need to treat the part of your program that uses dataflow concurrency as a pure function with
input and output.
The best way to learn how to program with dataflow variables is to read the fantastic book Concepts, Techniques,
and Models of Computer Programming. By Peter Van Roy and Seif Haridi.
The documentation is not as complete as it should be, something we will improve shortly. For now, besides above
listed resources on dataflow concurrency, I recommend you to read the documentation for the GPars implementation, which is heavily influenced by the Akka implementation:
• http://gpars.codehaus.org/Dataflow
• http://www.gpars.org/guide/guide/7.%20Dataflow%20Concurrency.html
5.5.2 Dataflow Variables
Dataflow Variable defines three different operations:
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1. Define a Dataflow Variable
import static akka.dataflow.DataFlow.*;
DataFlowVariable<int> x = new DataFlowVariable<int>();
2. Wait for Dataflow Variable to be bound
x.get();
3. Bind Dataflow Variable
x.set(3);
A Dataflow Variable can only be bound once. Subsequent attempts to bind the variable will throw an exception.
You can also shutdown a dataflow variable like this:
x.shutdown();
5.5.3 Threads
You can easily create millions lightweight (event-driven) threads on a regular workstation.
import static akka.dataflow.DataFlow.*;
import akka.japi.Effect;
thread(new Effect() {
public void apply() { ... }
});
You can also set the thread to a reference to be able to control its life-cycle:
import static akka.dataflow.DataFlow.*;
import akka.japi.Effect;
ActorRef t = thread(new Effect() {
public void apply() { ... }
});
... // time passes
t.tell(new Exit()); // shut down the thread
5.5.4 Examples
Most of these examples are taken from the Oz wikipedia page
Simple DataFlowVariable example
This example is from Oz wikipedia page: http://en.wikipedia.org/wiki/Oz_(programming_language). Sort of the
“Hello World” of dataflow concurrency.
Example in Oz:
thread
Z = X+Y
% will wait until both X and Y are bound to a value.
{Browse Z} % shows the value of Z.
end
thread X = 40 end
thread Y = 2 end
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Example in Akka:
import static akka.dataflow.DataFlow.*;
import akka.japi.Effect;
DataFlowVariable<int> x = new DataFlowVariable<int>();
DataFlowVariable<int> y = new DataFlowVariable<int>();
DataFlowVariable<int> z = new DataFlowVariable<int>();
thread(new Effect() {
public void apply() {
z.set(x.get() + y.get());
System.out.println("z = " + z.get());
}
});
thread(new Effect() {
public void apply() {
x.set(40);
}
});
thread(new Effect() {
public void apply() {
y.set(40);
}
});
Example on life-cycle management of DataFlowVariables
Shows how to shutdown dataflow variables and bind threads to values to be able to interact with them (exit etc.).
Example in Akka:
import static akka.dataflow.DataFlow.*;
import akka.japi.Effect;
// create four ’int’ data
DataFlowVariable<int> x =
DataFlowVariable<int> y =
DataFlowVariable<int> z =
DataFlowVariable<int> v =
flow variables
new DataFlowVariable<int>();
new DataFlowVariable<int>();
new DataFlowVariable<int>();
new DataFlowVariable<int>();
ActorRef main = thread(new Effect() {
public void apply() {
System.out.println("Thread ’main’")
if (x.get() > y.get()) {
z.set(x);
System.out.println("’z’ set to ’x’: " + z.get());
} else {
z.set(y);
System.out.println("’z’ set to ’y’: " + z.get());
}
// main completed, shut down the data flow variables
x.shutdown();
y.shutdown();
z.shutdown();
v.shutdown();
}
});
ActorRef setY = thread(new Effect() {
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public void apply() {
System.out.println("Thread ’setY’, sleeping...");
Thread.sleep(5000);
y.set(2);
System.out.println("’y’ set to: " + y.get());
}
});
ActorRef setV = thread(new Effect() {
public void apply() {
System.out.println("Thread ’setV’");
y.set(2);
System.out.println("’v’ set to y: " + v.get());
}
});
// shut down the threads
main.tell(new Exit());
setY.tell(new Exit());
setV.tell(new Exit());
5.6 Software Transactional Memory (Java)
Module stability: SOLID
5.6.1 Overview of STM
An STM turns the Java heap into a transactional data set with begin/commit/rollback semantics. Very much like
a regular database. It implements the first three letters in ACID; ACI: * (failure) Atomicity: all changes during
the execution of a transaction make it, or none make it. This only counts for transactional datastructures. *
Consistency: a transaction gets a consistent of reality (in Akka you get the Oracle version of the SERIALIZED
isolation level). * Isolated: changes made by concurrent execution transactions are not visible to each other.
Generally, the STM is not needed that often when working with Akka. Some use-cases (that we can think of) are:
• When you really need composable message flows across many actors updating their internal local state but
need them to do that atomically in one big transaction. Might not often, but when you do need this then you
are screwed without it.
• When you want to share a datastructure across actors.
• When you need to use the persistence modules.
Akka’s STM implements the concept in Clojure’s STM view on state in general. Please take the time to read this
excellent document and view this presentation by Rich Hickey (the genius behind Clojure), since it forms the basis
of Akka’s view on STM and state in general.
The STM is based on Transactional References (referred to as Refs). Refs are memory cells, holding an (arbitrary)
immutable value, that implement CAS (Compare-And-Swap) semantics and are managed and enforced by the
STM for coordinated changes across many Refs. They are implemented using the excellent Multiverse STM.
Working with immutable collections can sometimes give bad performance due to extensive copying. Scala provides so-called persistent datastructures which makes working with immutable collections fast. They are immutable but with constant time access and modification. The use of structural sharing and an insert or update does
not ruin the old structure, hence “persistent”. Makes working with immutable composite types fast. The persistent
datastructures currently consist of a Map and Vector.
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5.6.2 Simple example
Here is a simple example of an incremental counter using STM. This shows creating a Ref, a transactional
reference, and then modifying it within a transaction, which is delimited by an Atomic anonymous inner class.
import akka.stm.*;
final Ref<Integer> ref = new Ref<Integer>(0);
public int counter() {
return new Atomic<Integer>() {
public Integer atomically() {
int inc = ref.get() + 1;
ref.set(inc);
return inc;
}
}.execute();
}
counter();
// -> 1
counter();
// -> 2
5.6.3 Ref
Refs (transactional references) are mutable references to values and through the STM allow the safe sharing of
mutable data. To ensure safety the value stored in a Ref should be immutable. The value referenced by a Ref can
only be accessed or swapped within a transaction. Refs separate identity from value.
Creating a Ref
You can create a Ref with or without an initial value.
import akka.stm.*;
// giving an initial value
final Ref<Integer> ref = new Ref<Integer>(0);
// specifying a type but no initial value
final Ref<Integer> ref = new Ref<Integer>();
Accessing the value of a Ref
Use get to access the value of a Ref. Note that if no initial value has been given then the value is initially null.
import akka.stm.*;
final Ref<Integer> ref = new Ref<Integer>(0);
Integer value = new Atomic<Integer>() {
public Integer atomically() {
return ref.get();
}
}.execute();
// -> value = 0
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Changing the value of a Ref
To set a new value for a Ref you can use set (or equivalently swap), which sets the new value and returns the
old value.
import akka.stm.*;
final Ref<Integer> ref = new Ref<Integer>(0);
new Atomic() {
public Object atomically() {
return ref.set(5);
}
}.execute();
5.6.4 Transactions
A transaction is delimited using an Atomic anonymous inner class.
new Atomic() {
public Object atomically() {
// ...
}
}.execute();
All changes made to transactional objects are isolated from other changes, all make it or non make it (so failure
atomicity) and are consistent. With the AkkaSTM you automatically have the Oracle version of the SERIALIZED
isolation level, lower isolation is not possible. To make it fully serialized, set the writeskew property that checks
if a writeskew problem is allowed to happen.
Retries
A transaction is automatically retried when it runs into some read or write conflict, until the operation completes,
an exception (throwable) is thrown or when there are too many retries. When a read or writeconflict is encountered,
the transaction uses a bounded exponential backoff to prevent cause more contention and give other transactions
some room to complete.
If you are using non transactional resources in an atomic block, there could be problems because a transaction can
be retried. If you are using print statements or logging, it could be that they are called more than once. So you
need to be prepared to deal with this. One of the possible solutions is to work with a deferred or compensating
task that is executed after the transaction aborts or commits.
Unexpected retries
It can happen for the first few executions that you get a few failures of execution that lead to unexpected retries,
even though there is not any read or writeconflict. The cause of this is that speculative transaction configuration/selection is used. There are transactions optimized for a single transactional object, for 1..n and for n to
unlimited. So based on the execution of the transaction, the system learns; it begins with a cheap one and upgrades to more expensive ones. Once it has learned, it will reuse this knowledge. It can be activated/deactivated
using the speculative property on the TransactionFactoryBuilder. In most cases it is best use the default value
(enabled) so you get more out of performance.
Coordinated transactions and Transactors
If you need coordinated transactions across actors or threads then see Transactors (Java).
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Configuring transactions
It’s possible to configure transactions. The Atomic class can take a TransactionFactory, which can determine properties of the transaction. A default transaction factory is used if none is specified. You can create a
TransactionFactory with a TransactionFactoryBuilder.
Configuring transactions with a TransactionFactory:
import akka.stm.*;
TransactionFactory txFactory = new TransactionFactoryBuilder()
.setReadonly(true)
.build();
new Atomic<Object>(txFactory) {
public Object atomically() {
// read only transaction
return ...;
}
}.execute();
The following settings are possible on a TransactionFactory:
• familyName - Family name for transactions. Useful for debugging because the familyName is shown in
exceptions, logging and in the future also will be used for profiling.
• readonly - Sets transaction as readonly. Readonly transactions are cheaper and can be used to prevent
modification to transactional objects.
• maxRetries - The maximum number of times a transaction will retry.
• timeout - The maximum time a transaction will block for.
• trackReads - Whether all reads should be tracked. Needed for blocking operations. Readtracking makes a
transaction more expensive, but makes subsequent reads cheaper and also lowers the chance of a readconflict.
• writeSkew - Whether writeskew is allowed. Disable with care.
• blockingAllowed - Whether explicit retries are allowed.
• interruptible - Whether a blocking transaction can be interrupted if it is blocked.
• speculative - Whether speculative configuration should be enabled.
• quickRelease - Whether locks should be released as quickly as possible (before whole commit).
• propagation - For controlling how nested transactions behave.
• traceLevel - Transaction trace level.
You can also specify the default values for some of these options in akka.conf. Here they are with their default
values:
stm {
fair
max-retries
timeout
write-skew
blocking-allowed
interruptible
speculative
quick-release
propagation
trace-level
= on
= 1000
= 5
=
=
=
=
=
=
=
# Should global transactions be fair or non-fair (non fair yield bette
# Default timeout for blocking transactions and transaction set (in un
#
the time-unit property)
true
false
false
true
true
"requires"
"none"
}
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Transaction lifecycle listeners
It’s possible to have code that will only run on the successful commit of a transaction, or when a transaction aborts.
You can do this by adding deferred or compensating blocks to a transaction.
import akka.stm.*;
import static akka.stm.StmUtils.deferred;
import static akka.stm.StmUtils.compensating;
new Atomic() {
public Object atomically() {
deferred(new Runnable() {
public void run() {
// executes when transaction commits
}
});
compensating(new Runnable() {
public void run() {
// executes when transaction aborts
}
});
// ...
return something;
}
}.execute();
Blocking transactions
You can block in a transaction until a condition is met by using an explicit retry. To use retry you also need
to configure the transaction to allow explicit retries.
Here is an example of using retry to block until an account has enough money for a withdrawal. This is also an
example of using actors and STM together.
import akka.stm.*;
public class Transfer {
private final Ref<Double> from;
private final Ref<Double> to;
private final double amount;
public Transfer(Ref<Double> from, Ref<Double> to, double amount) {
this.from = from;
this.to = to;
this.amount = amount;
}
public Ref<Double> getFrom() { return from; }
public Ref<Double> getTo() { return to; }
public double getAmount() { return amount; }
}
import
import
import
import
import
import
akka.stm.*;
static akka.stm.StmUtils.retry;
akka.actor.*;
akka.util.FiniteDuration;
java.util.concurrent.TimeUnit;
akka.event.EventHandler;
public class Transferer extends UntypedActor {
TransactionFactory txFactory = new TransactionFactoryBuilder()
.setBlockingAllowed(true)
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.setTrackReads(true)
.setTimeout(new FiniteDuration(60, TimeUnit.SECONDS))
.build();
public void onReceive(Object message) throws Exception {
if (message instanceof Transfer) {
Transfer transfer = (Transfer) message;
final Ref<Double> from = transfer.getFrom();
final Ref<Double> to = transfer.getTo();
final double amount = transfer.getAmount();
new Atomic(txFactory) {
public Object atomically() {
if (from.get() < amount) {
EventHandler.info(this, "not enough money - retrying");
retry();
}
EventHandler.info(this, "transferring");
from.set(from.get() - amount);
to.set(to.get() + amount);
return null;
}
}.execute();
}
}
}
import akka.stm.*;
import akka.actor.*;
public class Main {
public static void main(String...args) throws Exception {
final Ref<Double> account1 = new Ref<Double>(100.0);
final Ref<Double> account2 = new Ref<Double>(100.0);
ActorRef transferer = Actors.actorOf(Transferer.class).start();
transferer.tell(new Transfer(account1, account2, 500.0));
// Transferer: not enough money - retrying
new Atomic() {
public Object atomically() {
return account1.set(account1.get() + 2000);
}
}.execute();
// Transferer: transferring
Thread.sleep(1000);
Double acc1 = new Atomic<Double>() {
public Double atomically() {
return account1.get();
}
}.execute();
Double acc2 = new Atomic<Double>() {
public Double atomically() {
return account2.get();
}
}.execute();
System.out.println("Account 1: " + acc1);
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// Account 1: 1600.0
System.out.println("Account 2: " + acc2);
// Account 2: 600.0
transferer.stop();
}
}
Alternative blocking transactions
You can also have two alternative blocking transactions, one of which can succeed first, with EitherOrElse.
import akka.stm.*;
public class Branch {
private final Ref<Integer> left;
private final Ref<Integer> right;
private final double amount;
public Branch(Ref<Integer> left, Ref<Integer> right, int amount) {
this.left = left;
this.right = right;
this.amount = amount;
}
public Ref<Integer> getLeft() { return left; }
public Ref<Integer> getRight() { return right; }
public double getAmount() { return amount; }
}
import
import
import
import
import
import
akka.actor.*;
akka.stm.*;
static akka.stm.StmUtils.retry;
akka.util.FiniteDuration;
java.util.concurrent.TimeUnit;
akka.event.EventHandler;
public class Brancher extends UntypedActor {
TransactionFactory txFactory = new TransactionFactoryBuilder()
.setBlockingAllowed(true)
.setTrackReads(true)
.setTimeout(new FiniteDuration(60, TimeUnit.SECONDS))
.build();
public void onReceive(Object message) throws Exception {
if (message instanceof Branch) {
Branch branch = (Branch) message;
final Ref<Integer> left = branch.getLeft();
final Ref<Integer> right = branch.getRight();
final double amount = branch.getAmount();
new Atomic<Integer>(txFactory) {
public Integer atomically() {
return new EitherOrElse<Integer>() {
public Integer either() {
if (left.get() < amount) {
EventHandler.info(this, "not enough on left - retrying");
retry();
}
EventHandler.info(this, "going left");
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return left.get();
}
public Integer orElse() {
if (right.get() < amount) {
EventHandler.info(this, "not enough on right - retrying");
retry();
}
EventHandler.info(this, "going right");
return right.get();
}
}.execute();
}
}.execute();
}
}
}
import akka.stm.*;
import akka.actor.*;
public class Main2 {
public static void main(String...args) throws Exception {
final Ref<Integer> left = new Ref<Integer>(100);
final Ref<Integer> right = new Ref<Integer>(100);
ActorRef brancher = Actors.actorOf(Brancher.class).start();
brancher.tell(new Branch(left, right, 500));
// not enough on left - retrying
// not enough on right - retrying
Thread.sleep(1000);
new Atomic() {
public Object atomically() {
return right.set(right.get() + 1000);
}
}.execute();
// going right
brancher.stop();
}
}
5.6.5 Transactional datastructures
Akka provides two datastructures that are managed by the STM.
• TransactionalMap
• TransactionalVector
TransactionalMap and TransactionalVector look like regular mutable datastructures, they even implement the
standard Scala ‘Map’ and ‘RandomAccessSeq’ interfaces, but they are implemented using persistent datastructures
and managed references under the hood. Therefore they are safe to use in a concurrent environment. Underlying
TransactionalMap is HashMap, an immutable Map but with near constant time access and modification operations.
Similarly TransactionalVector uses a persistent Vector. See the Persistent Datastructures section below for more
details.
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Like managed references, TransactionalMap and TransactionalVector can only be modified inside the scope of an
STM transaction.
Here is an example of creating and accessing a TransactionalMap:
import akka.stm.*;
// assuming a User class
final TransactionalMap<String, User> users = new TransactionalMap<String, User>();
// fill users map (in a transaction)
new Atomic() {
public Object atomically() {
users.put("bill", new User("bill"));
users.put("mary", new User("mary"));
users.put("john", new User("john"));
return null;
}
}.execute();
// access users map (in a transaction)
User user = new Atomic<User>() {
public User atomically() {
return users.get("bill").get();
}
}.execute();
Here is an example of creating and accessing a TransactionalVector:
import akka.stm.*;
// assuming an Address class
final TransactionalVector<Address> addresses = new TransactionalVector<Address>();
// fill addresses vector (in a transaction)
new Atomic() {
public Object atomically() {
addresses.add(new Address("somewhere"));
addresses.add(new Address("somewhere else"));
return null;
}
}.execute();
// access addresses vector (in a transaction)
Address address = new Atomic<Address>() {
public Address atomically() {
return addresses.get(0);
}
}.execute();
5.6.6 Persistent datastructures
Akka’s STM should only be used with immutable data. This can be costly if you have large datastructures and are
using a naive copy-on-write. In order to make working with immutable datastructures fast enough Scala provides
what are called Persistent Datastructures. There are currently two different ones:
• HashMap (scaladoc)
• Vector (scaladoc)
They are immutable and each update creates a completely new version but they are using clever structural sharing
in order to make them almost as fast, for both read and update, as regular mutable datastructures.
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This illustration is taken from Rich Hickey’s presentation. Copyright Rich Hickey 2009.
5.7 Transactors (Java)
Module stability: SOLID
5.7.1 Why Transactors?
Actors are excellent for solving problems where you have many independent processes that can work in isolation
and only interact with other Actors through message passing. This model fits many problems. But the actor model
is unfortunately a terrible model for implementing truly shared state. E.g. when you need to have consensus and a
stable view of state across many components. The classic example is the bank account where clients can deposit
and withdraw, in which each operation needs to be atomic. For detailed discussion on the topic see this JavaOne
presentation.
STM on the other hand is excellent for problems where you need consensus and a stable view of the state by
providing compositional transactional shared state. Some of the really nice traits of STM are that transactions
compose, and it raises the abstraction level from lock-based concurrency.
Akka’s Transactors combine Actors and STM to provide the best of the Actor model (concurrency and asynchronous event-based programming) and STM (compositional transactional shared state) by providing transactional, compositional, asynchronous, event-based message flows.
If you need Durability then you should not use one of the in-memory data structures but one of the persistent ones.
Generally, the STM is not needed very often when working with Akka. Some use-cases (that we can think of) are:
• When you really need composable message flows across many actors updating their internal local state but
need them to do that atomically in one big transaction. Might not often, but when you do need this then you
are screwed without it.
• When you want to share a datastructure across actors.
• When you need to use the persistence modules.
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Actors and STM
You can combine Actors and STM in several ways. An Actor may use STM internally so that particular changes
are guaranteed to be atomic. Actors may also share transactional datastructures as the STM provides safe shared
state across threads.
It’s also possible to coordinate transactions across Actors or threads so that either the transactions in a set all
commit successfully or they all fail. This is the focus of Transactors and the explicit support for coordinated
transactions in this section.
5.7.2 Coordinated transactions
Akka provides an explicit mechanism for coordinating transactions across actors. Under the hood it uses a
CountDownCommitBarrier, similar to a CountDownLatch.
Here is an example of coordinating two simple counter UntypedActors so that they both increment together in
coordinated transactions. If one of them was to fail to increment, the other would also fail.
import akka.actor.ActorRef;
public class Increment {
private final ActorRef friend;
public Increment() {
this.friend = null;
}
public Increment(ActorRef friend) {
this.friend = friend;
}
public boolean hasFriend() {
return friend != null;
}
public ActorRef getFriend() {
return friend;
}
}
import
import
import
import
akka.actor.UntypedActor;
akka.stm.Ref;
akka.transactor.Atomically;
akka.transactor.Coordinated;
public class Counter extends UntypedActor {
private Ref<Integer> count = new Ref(0);
private void increment() {
count.set(count.get() + 1);
}
public void onReceive(Object incoming) throws Exception {
if (incoming instanceof Coordinated) {
Coordinated coordinated = (Coordinated) incoming;
Object message = coordinated.getMessage();
if (message instanceof Increment) {
Increment increment = (Increment) message;
if (increment.hasFriend()) {
increment.getFriend().tell(coordinated.coordinate(new Increment()));
}
coordinated.atomic(new Atomically() {
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public void atomically() {
increment();
}
});
}
} else if (incoming.equals("GetCount")) {
getContext().replyUnsafe(count.get());
}
}
}
ActorRef counter1 = actorOf(Counter.class).start();
ActorRef counter2 = actorOf(Counter.class).start();
counter1.tell(new Coordinated(new Increment(counter2)));
To start a new coordinated transaction that you will also participate in, just create a Coordinated object:
Coordinated coordinated = new Coordinated();
To start a coordinated transaction that you won’t participate in yourself you can create a Coordinated object
with a message and send it directly to an actor. The recipient of the message will be the first member of the
coordination set:
actor.tell(new Coordinated(new Message()));
To include another actor in the same coordinated transaction that you’ve created or received, use the coordinate
method on that object. This will increment the number of parties involved by one and create a new Coordinated
object to be sent.
actor.tell(coordinated.coordinate(new Message()));
To enter the coordinated transaction use the atomic method of the coordinated object. This accepts either an
akka.transactor.Atomically object, or an Atomic object the same as used normally in the STM (just
don’t execute it - the coordination will do that).
coordinated.atomic(new Atomically() {
public void atomically() {
// do something in a transaction
}
});
The coordinated transaction will wait for the other transactions before committing. If any of the coordinated
transactions fail then they all fail.
5.7.3 UntypedTransactor
UntypedTransactors are untyped actors that provide a general pattern for coordinating transactions, using the
explicit coordination described above.
Here’s an example of a simple untyped transactor that will join a coordinated transaction:
import akka.transactor.UntypedTransactor;
import akka.stm.Ref;
public class Counter extends UntypedTransactor {
Ref<Integer> count = new Ref<Integer>(0);
@Override
public void atomically(Object message) {
if (message instanceof Increment) {
count.set(count.get() + 1);
}
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}
}
You could send this Counter transactor a Coordinated(Increment) message. If you were to send it just an
Increment message it will create its own Coordinated (but in this particular case wouldn’t be coordinating
transactions with any other transactors).
To coordinate with other transactors override the coordinate method. The coordinate method maps a
message to a set of SendTo objects, pairs of ActorRef and a message. You can use the include and sendTo
methods to easily coordinate with other transactors.
Example of coordinating an increment, similar to the explicitly coordinated example:
import akka.transactor.UntypedTransactor;
import akka.transactor.SendTo;
import akka.stm.Ref;
import java.util.Set;
public class Counter extends UntypedTransactor {
Ref<Integer> count = new Ref<Integer>(0);
@Override
public Set<SendTo> coordinate(Object message) {
if (message instanceof Increment) {
Increment increment = (Increment) message;
if (increment.hasFriend())
return include(increment.getFriend(), new Increment());
}
return nobody();
}
@Override
public void atomically(Object message) {
if (message instanceof Increment) {
count.set(count.get() + 1);
}
}
}
To execute directly before or after the coordinated transaction, override the before and after methods. They
do not execute within the transaction.
To completely bypass coordinated transactions override the normally method. Any message matched by
normally will not be matched by the other methods, and will not be involved in coordinated transactions.
In this method you can implement normal actor behavior, or use the normal STM atomic for local transactions.
5.7.4 Coordinating Typed Actors
It’s also possible to use coordinated transactions with typed actors. You can explicitly pass around Coordinated
objects, or use built-in support with the @Coordinated annotation and the Coordination.coordinate
method.
To specify a method should use coordinated transactions add the @Coordinated annotation. Note: the
@Coordinated annotation will only work with void (one-way) methods.
public interface Counter {
@Coordinated public void increment();
public Integer get();
}
To
coordinate
transactions
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use
a
coordinate
block.
This
accepts
either
an
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akka.transactor.Atomically object, or an Atomic object liked used in the STM (but don’t execute it). The first boolean parameter specifies whether or not to wait for the transactions to complete.
Coordination.coordinate(true, new Atomically() {
public void atomically() {
counter1.increment();
counter2.increment();
}
});
Here’s an example of using @Coordinated with a TypedActor to coordinate increments:
import akka.transactor.annotation.Coordinated;
public interface Counter {
@Coordinated public void increment();
public Integer get();
}
import akka.actor.TypedActor;
import akka.stm.Ref;
public class CounterImpl extends TypedActor implements Counter {
private Ref<Integer> count = new Ref<Integer>(0);
public void increment() {
count.set(count.get() + 1);
}
public Integer get() {
return count.get();
}
}
Counter counter1 = (Counter) TypedActor.newInstance(Counter.class, CounterImpl.class);
Counter counter2 = (Counter) TypedActor.newInstance(Counter.class, CounterImpl.class);
Coordination.coordinate(true, new Atomically() {
public void atomically() {
counter1.increment();
counter2.increment();
}
});
TypedActor.stop(counter1);
TypedActor.stop(counter2);
5.8 Remote Actors (Java)
Module stability: SOLID
Akka supports starting interacting with UntypedActors and TypedActors on remote nodes using a very efficient
and scalable NIO implementation built upon JBoss Netty and Google Protocol Buffers .
The usage is completely transparent with local actors, both in regards to sending messages and error handling and
propagation as well as supervision, linking and restarts. You can send references to other Actors as part of the
message.
WARNING: For security reasons, do not run an Akka node with a Remote Actor port reachable by untrusted
connections unless you have supplied a classloader that restricts access to the JVM.
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5.8.1 Managing the Remote Service
Starting remote service in user code as a library
Here is how to start up the server and specify the hostname and port programmatically:
import static akka.actor.Actors.*;
remote().start("localhost", 2552);
// Specify the classloader to use to load the remote class (actor)
remote().start("localhost", 2552, classLoader);
Here is how to start up the server and specify the hostname and port in the ‘akka.conf’ configuration file (see the
section below for details):
import static akka.actor.Actors.*;
remote().start();
// Specify the classloader to use to load the remote class (actor)
remote().start(classLoader);
Starting remote service as part of the stand-alone Kernel
You simply need to make sure that the service is turned on in the external ‘akka.conf’ configuration file.
akka {
remote {
server {
service = on
hostname = "localhost"
port = 2552
connection-timeout = 1000 # in millis
}
}
}
Stopping the server
import static akka.actor.Actors.*;
remote().shutdown();
Connecting and shutting down a client connection explicitly
Normally you should not have to start and stop the client connection explicitly since that is handled by Akka on a
demand basis. But if you for some reason want to do that then you can do it like this:
import static akka.actor.Actors.*;
import java.net.InetSocketAddress;
remote().shutdownClientConnection(new InetSocketAddress("localhost", 6666)); //Returns true if suc
remote().restartClientConnection(new InetSocketAddress("localhost", 6666)); //Returns true if succ
Client message frame size configuration
You can define the max message frame size for the remote messages:
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akka {
remote {
client {
message-frame-size = 1048576
}
}
}
Client reconnect configuration
The Client automatically performs reconnection upon connection failure.
You can configure it like this:
akka {
remote {
client {
reconnect-delay = 5
# in seconds (5 sec default)
read-timeout = 10
# in seconds (10 sec default)
reconnection-time-window = 600 # the maximum time window that a client should try to reconne
}
}
}
The client will automatically trying to reconnect to the server if the connection is broken. By default it has a
reconnection window of 10 minutes (600 seconds).
If it has not been able to reconnect during this period of time then it is shut down and further attempts to use it
will yield a ‘RemoteClientException’. The ‘RemoteClientException’ contains the message as well as a reference
to the address that is not yet connect in order for you to retrieve it an do an explicit connect if needed.
You can also register a listener that will listen for example the ‘RemoteClientStopped’ event, retrieve the address
that got disconnected and reconnect explicitly.
See the section on client listener and events below for details.
Remote Client message buffering and send retry on failure
The Remote Client implements message buffering on network failure. This feature has zero overhead (even turned
on) in the successful scenario and a queue append operation in case of unsuccessful send. So it is really really fast.
The default behavior is that the remote client will maintain a transaction log of all messages that it has failed to
send due to network problems (not other problems like serialization errors etc.). The client will try to resend these
messages upon first successful reconnect and the message ordering is maintained. This means that the remote
client will swallow all exceptions due to network failure and instead queue remote messages in the transaction
log. The failures will however be reported through the remote client life-cycle events as well as the regular Akka
event handler. You can turn this behavior on and off in the configuration file. It gives ‘at-least-once’ semantics,
use a message id/counter for discarding potential duplicates (or use idempotent messages).
akka {
remote {
client {
buffering {
retry-message-send-on-failure = on
capacity = -1
# If negative (or zero) then an unbounded mailbox is us
# If positive then a bounded mailbox is used and the ca
}
}
}
}
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If you choose a capacity higher than 0, then a bounded queue will be used and if the limit of the queue is reached
then a ‘RemoteClientMessageBufferException’ will be thrown.
5.8.2 Running Remote Server in untrusted mode
You can run the remote server in untrusted mode. This means that the server will not allow any client-managed
remote actors or any life-cycle messages and methods. This is useful if you want to let untrusted clients use servermanaged actors in a safe way. This can optionally be combined with the secure cookie authentication mechanism
described below as well as the SSL support for remote actor communication.
If the client is trying to perform one of these unsafe actions then a ‘java.lang.SecurityException’ is thrown on the
server as well as transferred to the client and thrown there as well.
Here is how you turn it on:
akka {
remote {
server {
untrusted-mode = on # the default is ’off’
}
}
}
The messages that it prevents are all that extends ‘LifeCycleMessage’: * case class HotSwap(..) * case object
RevertHotSwap * case class Restart(..) * case class Exit(..) * case class Link(..) * case class Unlink(..) * case
class UnlinkAndStop(..) * case object ReceiveTimeout
It also prevents the client from invoking any life-cycle and side-effecting methods, such as: * start * stop * link *
unlink * spawnLink * etc.
5.8.3 Using secure cookie for remote client authentication
Akka is using a similar scheme for remote client node authentication as Erlang; using secure cookies. In order to
use this authentication mechanism you have to do two things:
• Enable secure cookie authentication in the remote server
• Use the same secure cookie on all the trusted peer nodes
Enabling secure cookie authentication
The first one is done by enabling the secure cookie authentication in the remote server section in the configuration
file:
akka {
remote {
server {
require-cookie = on
}
}
Now if you have try to connect to a server from a client then it will first try to authenticate the client by comparing
the secure cookie for the two nodes. If they are the same then it allows the client to connect and use the server
freely but if they are not the same then it will throw a ‘java.lang.SecurityException’ and not allow the client to
connect.
Generating and using the secure cookie
The secure cookie can be any string value but in order to ensure that it is secure it is best to randomly generate it. This can be done by invoking the ‘generate_config_with_secure_cookie.sh’ script which resides in the
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‘$AKKA_HOME/scripts’ folder. This script will generate and print out a complete ‘akka.conf’ configuration file
with the generated secure cookie defined that you can either use as-is or cut and paste the ‘secure-cookie’ snippet.
Here is an example of its generated output:
# This config imports the Akka reference configuration.
include "akka-reference.conf"
# In this file you can override any option defined in the ’akka-reference.conf’ file.
# Copy in all or parts of the ’akka-reference.conf’ file and modify as you please.
akka {
remote {
secure-cookie = "000E02050F0300040C050C0D060A040306090B0C"
}
}
The simplest way to use it is to have it create your ‘akka.conf’ file like this:
cd $AKKA_HOME
./scripts/generate_config_with_secure_cookie.sh > ./config/akka.conf
Now it is good to make sure that the configuration file is only accessible by the owner of the file. On Unix-style
file system this can be done like this:
chmod 400 ./config/akka.conf
Running this script requires having ‘scala’ on the path (and will take a couple of seconds to run since it is using
Scala and has to boot up the JVM to run).
You can also generate the secure cookie by using the ‘Crypt’ object and its ‘generateSecureCookie’ method.
import akka.util.Crypt;
String secureCookie = Crypt.generateSecureCookie();
The secure cookie is a cryptographically secure randomly generated byte array turned into a SHA-1 hash.
5.8.4 Client-managed Remote UntypedActor
DEPRECATED AS OF 1.1
The client creates the remote actor and “moves it” to the server.
When you define an actors as being remote it is instantiated as on the remote host and your local actor becomes a
proxy, it works as a handle to the remote actor. The real execution is always happening on the remote node.
Here is an example:
import akka.actor.UntypedActor;
import static akka.actor.Actors.*;
class MyActor extends UntypedActor {
public void onReceive(Object message) throws Exception {
...
}
}
//How to make it client-managed:
remote().actorOf(MyActor.class,"192.68.23.769", 2552);
An UntypedActor can also start remote child Actors through one of the “spawn/link” methods. These will start,
link and make the UntypedActor remote atomically.
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...
getContext().spawnRemote(MyActor.class, hostname, port, timeoutInMsForFutures);
getContext().spawnLinkRemote(MyActor.class, hostname, port, timeoutInMsForFutures);
...
5.8.5 Server-managed Remote UntypedActor
Here it is the server that creates the remote actor and the client can ask for a handle to this actor.
Server side setup
The API for server managed remote actors is really simple. 2 methods only:
import akka.actor.Actors;
import akka.actor.UntypedActor;
class MyActor extends UntypedActor {
public void onReceive(Object message) throws Exception {
...
}
}
Actors.remote().start("localhost", 2552).register("hello-service", Actors.actorOf(HelloWorldActor.
Actors created like this are automatically started.
You can also register an actor by its UUID rather than ID or handle. This is done by prefixing the handle with the
“uuid:” protocol.
server.register("uuid:" + actor.uuid, actor);
server.unregister("uuid:" + actor.uuid);
Session bound server side setup
Session bound server managed remote actors work by creating and starting a new actor for every client that
connects. Actors are stopped automatically when the client disconnects. The client side is the same as regular
server managed remote actors. Use the function registerPerSession instead of register.
Session bound actors are useful if you need to keep state per session, e.g. username. They are also useful if you
need to perform some cleanup when a client disconnects by overriding the postStop method as described here
import static akka.actor.Actors.*;
import akka.japi.Creator;
class HelloWorldActor extends Actor {
...
}
remote().start("localhost", 2552);
remote().registerPerSession("hello-service", new Creator<ActorRef>() {
public ActorRef create() {
return actorOf(HelloWorldActor.class);
}
});
Note that the second argument in registerPerSession is a Creator, it means that the create method will create a new
ActorRef each invocation. It will be called to create an actor every time a session is established.
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Client side usage
import static akka.actor.Actors.*;
ActorRef actor = remote().actorFor("hello-service", "localhost", 2552);
remote().start("localhost", 2553); // needed for replies
Object result = actor.sendRequestReply("Hello");
There are many variations on the ‘remote()#actorFor’ method. Here are some of them:
...
...
...
...
...
= remote().actorFor(className, hostname, port);
= remote().actorFor(className, timeout, hostname, port);
= remote().actorFor(uuid, className, hostname, port);
= remote().actorFor(uuid, className, timeout, hostname, port);
// etc
All of these also have variations where you can pass in an explicit ‘ClassLoader’ which can be used when deserializing messages sent from the remote actor.
5.8.6 Automatic remote ‘sender’ reference management
The sender of a remote message will be reachable with a reply through the remote server on the node that the actor
is residing, automatically. Please note that firewalled clients won’t work right now. [2011-01-05]
5.8.7 Identifying remote actors
The ‘id’ field in the ‘Actor’ class is of importance since it is used as identifier for the remote actor. If you want to
create a brand new actor every time you instantiate a remote actor then you have to set the ‘id’ field to a unique
‘String’ for each instance. If you want to reuse the same remote actor instance for each new remote actor (of the
same class) you create then you don’t have to do anything since the ‘id’ field by default is equal to the name of the
actor class.
Here is an example of overriding the ‘id’ field:
import akka.actor.UntypedActor;
import com.eaio.uuid.UUID;
class MyActor extends UntypedActor {
public MyActor() {
getContext().setId(new UUID().toString());
}
public void onReceive(Object message) throws Exception {
// ...
}
}
5.8.8 Client-managed Remote Typed Actors
DEPRECATED AS OF 1.1
Remote Typed Actors are created through the ‘TypedActor.newRemoteInstance’ factory method.
MyPOJO remoteActor = (MyPOJO) TypedActor.newRemoteInstance(MyPOJO.class, MyPOJOImpl.class, "localh
And if you want to specify the timeout:
MyPOJO remoteActor = (MyPOJO)TypedActor.newRemoteInstance(MyPOJO.class, MyPOJOImpl.class, timeout,
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You can also define the Typed Actor to be a client-managed-remote service by adding the ‘RemoteAddress’
configuration element in the declarative supervisor configuration:
new Component(
Foo.class,
FooImpl.class,
new LifeCycle(new Permanent(), 1000),
1000,
new RemoteAddress("localhost", 2552))
5.8.9 Server-managed Remote Typed Actors
WARNING: Remote TypedActors do not work with overloaded methods on your TypedActor, refrain from using
overloading.
Server side setup
The API for server managed remote typed actors is nearly the same as for untyped actor:
import static akka.actor.Actors.*;
remote().start("localhost", 2552);
RegistrationService typedActor = TypedActor.newInstance(RegistrationService.class, RegistrationSer
remote().registerTypedActor("user-service", typedActor);
Client side usage
import static akka.actor.Actors.*;
RegistrationService actor = remote().typedActorFor(RegistrationService.class, "user-service", 5000
actor.registerUser(...);
There are variations on the ‘remote()#typedActorFor’ method. Here are some of them:
... = remote().typedActorFor(interfaceClazz, serviceIdOrClassName, hostname, port);
... = remote().typedActorFor(interfaceClazz, serviceIdOrClassName, timeout, hostname, port);
... = remote().typedActorFor(interfaceClazz, serviceIdOrClassName, timeout, hostname, port, classL
5.8.10 Data Compression Configuration
Akka uses compression to minimize the size of the data sent over the wire. Currently it only supports ‘zlib’
compression but more will come later.
You can configure it like this:
akka {
remote {
compression-scheme = "zlib" # Options: "zlib" (lzf to come), leave out for no compression
zlib-compression-level = 6 # Options: 0-9 (1 being fastest and 9 being the most compressed),
...
}
}
5.8.11 Code provisioning
Akka does currently not support automatic code provisioning but requires you to have the remote actor class files
available on both the “client” the “server” nodes. This is something that will be addressed soon. Until then, sorry
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for the inconvenience.
5.8.12 Subscribe to Remote Client events
Akka has a subscription API for remote client events. You can register an Actor as a listener and this actor will
have to be able to process these events:
class
class
class
class
class
class
RemoteClientError { Throwable cause; RemoteClientModule client; InetSocketAddress remoteAddr
RemoteClientDisconnected { RemoteClientModule client; InetSocketAddress remoteAddress; }
RemoteClientConnected { RemoteClientModule client; InetSocketAddress remoteAddress; }
RemoteClientStarted { RemoteClientModule client; InetSocketAddress remoteAddress; }
RemoteClientShutdown { RemoteClientModule client; InetSocketAddress remoteAddress; }
RemoteClientWriteFailed { Object message; Throwable cause; RemoteClientModule client; InetSo
So a simple listener actor can look like this:
import akka.actor.UntypedActor;
import akka.remoteinterface.*;
class Listener extends UntypedActor {
public void onReceive(Object message) throws Exception {
if (message instanceof RemoteClientError) {
RemoteClientError event = (RemoteClientError) message;
Throwable cause = event.getCause();
// ...
} else if (message instanceof RemoteClientConnected) {
RemoteClientConnected event = (RemoteClientConnected) message;
// ...
} else if (message instanceof RemoteClientDisconnected) {
RemoteClientDisconnected event = (RemoteClientDisconnected) message;
// ...
} else if (message instanceof RemoteClientStarted) {
RemoteClientStarted event = (RemoteClientStarted) message;
// ...
} else if (message instanceof RemoteClientShutdown) {
RemoteClientShutdown event = (RemoteClientShutdown) message;
// ...
} else if (message instanceof RemoteClientWriteFailed) {
RemoteClientWriteFailed event = (RemoteClientWriteFailed) message;
// ...
}
}
}
Registration and de-registration can be done like this:
ActorRef listener = Actors.actorOf(Listener.class);
...
Actors.remote().addListener(listener);
...
Actors.remote().removeListener(listener);
5.8.13 Subscribe to Remote Server events
Akka has a subscription API for the server events. You can register an Actor as a listener and this actor will have
to be able to process these events:
class RemoteServerStarted { RemoteServerModule server; }
class RemoteServerShutdown { RemoteServerModule server; }
class RemoteServerError { Throwable cause; RemoteServerModule server; }
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class
class
class
class
RemoteServerClientConnected { RemoteServerModule server; Option<InetSocketAddress> clientAdd
RemoteServerClientDisconnected { RemoteServerModule server; Option<InetSocketAddress> client
RemoteServerClientClosed { RemoteServerModule server; Option<InetSocketAddress> clientAddres
RemoteServerWriteFailed { Object request; Throwable cause; RemoteServerModule server; Option
So a simple listener actor can look like this:
import akka.actor.UntypedActor;
import akka.remoteinterface.*;
class Listener extends UntypedActor {
public void onReceive(Object message) throws Exception {
if (message instanceof RemoteClientError) {
RemoteClientError event = (RemoteClientError) message;
Throwable cause = event.getCause();
// ...
} else if (message instanceof RemoteClientConnected) {
RemoteClientConnected event = (RemoteClientConnected) message;
// ...
} else if (message instanceof RemoteClientDisconnected) {
RemoteClientDisconnected event = (RemoteClientDisconnected) message;
// ...
} else if (message instanceof RemoteClientStarted) {
RemoteClientStarted event = (RemoteClientStarted) message;
// ...
} else if (message instanceof RemoteClientShutdown) {
RemoteClientShutdown event = (RemoteClientShutdown) message;
// ...
} else if (message instanceof RemoteClientWriteFailed) {
RemoteClientWriteFailed event = (RemoteClientWriteFailed) message;
// ...
}
}
}
Registration and de-registration can be done like this:
import static akka.actor.Actors.*;
ActorRef listener = actorOf(Listener.class);
...
remote().addListener(listener);
...
remote().removeListener(listener);
5.8.14 Message Serialization
All messages that are sent to remote actors needs to be serialized to binary format to be able to travel
over the wire to the remote node. This is done by letting your messages extend one of the traits in the
‘akka.serialization.Serializable’ object. If the messages don’t implement any specific serialization trait then the
runtime will try to use standard Java serialization.
Here is one example, but full documentation can be found in the Serialization (Java).
Protobuf
Protobuf message specification needs to be compiled with ‘protoc’ compiler.
message ProtobufPOJO {
required uint64 id = 1;
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required string name = 2;
required bool status = 3;
}
Using the generated message builder to send the message to a remote actor:
actor.tell(ProtobufPOJO.newBuilder()
.setId(11)
.setStatus(true)
.setName("Coltrane")
.build());
5.9 Serialization (Java)
Akka serialization module has been documented extensively under the Serialization (Scala) section. In this section
we will point out the different APIs that are available in Akka for Java based serialization of ActorRefs. The Scala
APIs of ActorSerialization has implicit Format objects that set up the type class based serialization. In the Java
API, the Format objects need to be specified explicitly.
5.9.1 Serialization of a Stateless Actor
Step 1: Define the Actor
import akka.actor.UntypedActor;
public class SerializationTestActor extends UntypedActor {
public void onReceive(Object msg) {
getContext().replySafe("got it!");
}
}
Step 2: Define the typeclass instance for the actor
Note how the generated Java classes are accessed using the $class based naming convention of the Scala compiler.
import akka.serialization.StatelessActorFormat;
class SerializationTestActorFormat implements StatelessActorFormat<SerializationTestActor> {
@Override
public SerializationTestActor fromBinary(byte[] bytes, SerializationTestActor act) {
return (SerializationTestActor) StatelessActorFormat$class.fromBinary(this, bytes, act);
}
@Override
public byte[] toBinary(SerializationTestActor ac) {
return StatelessActorFormat$class.toBinary(this, ac);
}
}
Step 3: Serialize and de-serialize
The following JUnit snippet first creates an actor using the default constructor. The actor is, as we saw above a
stateless one. Then it is serialized and de-serialized to get back the original actor. Being stateless, the de-serialized
version behaves in the same way on a message as the original actor.
import
import
import
import
import
akka.actor.ActorRef;
akka.actor.ActorTimeoutException;
akka.actor.Actors;
akka.actor.UntypedActor;
akka.serialization.Format;
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import akka.serialization.StatelessActorFormat;
import static akka.serialization.ActorSerialization.*;
@Test public void mustBeAbleToSerializeAfterCreateActorRefFromClass() {
ActorRef ref = Actors.actorOf(SerializationTestActor.class);
assertNotNull(ref);
ref.start();
try {
Object result = ref.sendRequestReply("Hello");
assertEquals("got it!", result);
} catch (ActorTimeoutException ex) {
fail("actor should not time out");
}
Format<SerializationTestActor> f = new SerializationTestActorFormat();
byte[] bytes = toBinaryJ(ref, f, false);
ActorRef r = fromBinaryJ(bytes, f);
assertNotNull(r);
r.start();
try {
Object result = r.sendRequestReply("Hello");
assertEquals("got it!", result);
} catch (ActorTimeoutException ex) {
fail("actor should not time out");
}
ref.stop();
r.stop();
}
5.9.2 Serialization of a Stateful Actor
Let’s now have a look at how to serialize an actor that carries a state with it. Here the expectation is that the
serialization of the actor will also persist the state information. And after de-serialization we will get back the
state with which it was serialized.
Step 1: Define the Actor
import akka.actor.UntypedActor;
public class MyUntypedActor extends UntypedActor {
int count = 0;
public void onReceive(Object msg) {
if (msg.equals("hello")) {
count = count + 1;
getContext().replyUnsafe("world " + count);
} else if (msg instanceof String) {
count = count + 1;
getContext().replyUnsafe("hello " + msg + " " + count);
} else {
throw new IllegalArgumentException("invalid message type");
}
}
}
Note the actor has a state in the form of an Integer. And every message that the actor receives, it replies with an
addition to the integer member.
Step 2: Define the instance of the typeclass
import akka.actor.UntypedActor;
import akka.serialization.Format;
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import akka.serialization.SerializerFactory;
class MyUntypedActorFormat implements Format<MyUntypedActor> {
@Override
public MyUntypedActor fromBinary(byte[] bytes, MyUntypedActor act) {
ProtobufProtocol.Counter p =
(ProtobufProtocol.Counter) new SerializerFactory().getProtobuf().fromBinary(bytes, ProtobufP
act.count = p.getCount();
return act;
}
@Override
public byte[] toBinary(MyUntypedActor ac) {
return ProtobufProtocol.Counter.newBuilder().setCount(ac.count()).build().toByteArray();
}
}
Note the usage of Protocol Buffers to serialize the state of the actor. ProtobufProtocol.Counter is something you
need to define yourself
Step 3: Serialize and de-serialize
import
import
import
import
akka.actor.ActorRef;
akka.actor.ActorTimeoutException;
akka.actor.Actors;
static akka.serialization.ActorSerialization.*;
@Test public void mustBeAbleToSerializeAStatefulActor() {
ActorRef ref = Actors.actorOf(MyUntypedActor.class);
assertNotNull(ref);
ref.start();
try {
Object result = ref.sendRequestReply("hello");
assertEquals("world 1", result);
result = ref.sendRequestReply("hello");
assertEquals("world 2", result);
} catch (ActorTimeoutException ex) {
fail("actor should not time out");
}
Format<MyUntypedActor> f = new MyUntypedActorFormat();
byte[] bytes = toBinaryJ(ref, f, false);
ActorRef r = fromBinaryJ(bytes, f);
assertNotNull(r);
r.start();
try {
Object result = r.sendRequestReply("hello");
assertEquals("world 3", result);
result = r.sendRequestReply("hello");
assertEquals("world 4", result);
} catch (ActorTimeoutException ex) {
fail("actor should not time out");
}
ref.stop();
r.stop();
}
Note how the de-serialized version starts with the state value with which it was earlier serialized.
5.10 Fault Tolerance Through Supervisor Hierarchies (Java)
Module stability: SOLID
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The “let it crash” approach to fault/error handling, implemented by linking actors, is very different to what Java
and most non-concurrency oriented languages/frameworks have adopted. It’s a way of dealing with failure that is
designed for concurrent and distributed systems.
5.10.1 Concurrency
Throwing an exception in concurrent code (let’s assume we are using non-linked actors), will just simply blow up
the thread that currently executes the actor.
• There is no way to find out that things went wrong (apart from inspecting the stack trace).
• There is nothing you can do about it.
Here actors provide a clean way of getting notification of the error and do something about it.
Linking actors also allow you to create sets of actors where you can be sure that either: - All are dead - None are
dead
This is very useful when you have thousands of concurrent actors. Some actors might have implicit dependencies
and together implement a service, computation, user session etc.
It encourages non-defensive programming. Don’t try to prevent things from go wrong, because they will, whether
you want it or not. Instead; expect failure as a natural state in the life-cycle of your app, crash early and let
someone else (that sees the whole picture), deal with it.
5.10.2 Distributed actors
You can’t build a fault-tolerant system with just one single box - you need at least two. Also, you (usually)
need to know if one box is down and/or the service you are talking to on the other box is down. Here actor
supervision/linking is a critical tool for not only monitoring the health of remote services, but to actually manage
the service, do something about the problem if the actor or node is down. Such as restarting actors on the same
node or on another node.
In short, it is a very different way of thinking, but a way that is very useful (if not critical) to building fault-tolerant
highly concurrent and distributed applications, which is as valid if you are writing applications for the JVM or the
Erlang VM (the origin of the idea of “let-it-crash” and actor supervision).
5.10.3 Supervision
Supervisor hierarchies originate from Erlang’s OTP framework.
A supervisor is responsible for starting, stopping and monitoring its child processes. The basic idea of a supervisor
is that it should keep its child processes alive by restarting them when necessary. This makes for a completely
different view on how to write fault-tolerant servers. Instead of trying all things possible to prevent an error from
happening, this approach embraces failure. It shifts the view to look at errors as something natural and something
that will happen, instead of trying to prevent it; embraces it. Just ‘Let It Crash™’, since the components will be
reset to a stable state and restarted upon failure.
Akka has two different restart strategies; All-For-One and One-For-One. Best explained using some pictures
(referenced from erlang.org ):
OneForOne
The OneForOne fault handler will restart only
age:http://www.erlang.org/doc/design_principles/sup4.gif
the
component
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has
crashed.
im-
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AllForOne
The AllForOne fault handler will restart all the components that the supervisor is managing, including the one
that have crashed. This strategy should be used when you have a certain set of components that are coupled in some way that if one is crashing they all need to be reset to a stable state before continuing. image:http://www.erlang.org/doc/design_principles/sup5.gif
Restart callbacks
There are two different callbacks that an UntypedActor or TypedActor can hook in to:
• Pre restart
• Post restart
These are called prior to and after the restart upon failure and can be used to clean up and reset/reinitialize state
upon restart. This is important in order to reset the component failure and leave the component in a fresh and
stable state before consuming further messages.
Defining a supervisor’s restart strategy
Both the Typed Actor supervisor configuration and the Actor supervisor configuration take a ‘FaultHandlingStrategy’ instance which defines the fault management. The different strategies are:
• AllForOne
• OneForOne
These have the semantics outlined in the section above.
Here is an example of how to define a restart strategy:
new AllForOneStrategy( //Or OneForOneStrategy
new Class[]{ Exception.class }, //List of Exceptions/Throwables to handle
3,
// maximum number of restart retries
5000
// within time in millis
)
Defining actor life-cycle
The other common configuration element is the ‘LifeCycle’ which defines the life-cycle. The supervised actor can
define one of two different life-cycle configurations:
• Permanent: which means that the actor will always be restarted.
• Temporary: which means that the actor will not be restarted, but it will be shut down through the regular
shutdown process so the ‘postStop’ callback function will called.
Here is an example of how to define the life-cycle:
import static akka.config.Supervision.*;
getContext().setLifeCycle(permanent()); //permanent() means that the component will always be rest
getContext().setLifeCycle(temporary()); //temporary() means that the component will not be restart
5.10.4 Supervising Untyped Actor
Declarative supervisor configuration
The Actor’s supervision can be declaratively defined by creating a ‘Supervisor’ factory object. Here is an example:
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import static akka.config.Supervision.*;
import static akka.actor.Actors.*;
Supervisor supervisor = Supervisor.apply(
new SupervisorConfig(
new AllForOneStrategy(new Class[]{Exception.class}, 3, 5000),
new Supervise[] {
new Supervise(
actorOf(MyActor1.class),
permanent()),
new Supervise(
actorOf(MyActor2.class),
permanent())
}));
Supervisors created like this are implicitly instantiated and started.
To configure a handler function for when the actor underlying the supervisor receives a
MaximumNumberOfRestartsWithinTimeRangeReached
message,
you
can
specify
a
Procedure2<ActorRef,MaximumNumberOfRestartsWithinTimeRangeReached> when creating the SupervisorConfig.
This handler will be called with the ActorRef of the supervisor and the MaximumNumberOfRestartsWithinTimeRangeReached message.
import
import
import
import
static akka.config.Supervision.*;
static akka.actor.Actors.*;
akka.event.EventHandler;
akka.japi.Procedure2;
Procedure2<ActorRef, MaximumNumberOfRestartsWithinTimeRangeReached> handler = new Procedure2<Actor
public void apply(ActorRef ref, MaximumNumberOfRestartsWithinTimeRangeReached max) {
EventHandler.error(ref, max);
}
};
Supervisor supervisor = Supervisor.apply(
new SupervisorConfig(
new AllForOneStrategy(new Class[]{Exception.class}, 3, 5000),
new Supervise[] {
new Supervise(
actorOf(MyActor1.class),
permanent()),
new Supervise(
actorOf(MyActor2.class),
permanent())
}, handler));
You can link and unlink actors from a declaratively defined supervisor using the ‘link’ and ‘unlink’ methods:
Supervisor supervisor = Supervisor.apply(...);
supervisor.link(..);
supervisor.unlink(..);
You can also create declarative supervisors through the ‘SupervisorFactory’ factory object. Use this factory instead
of the ‘Supervisor’ factory object if you want to control instantiation and starting of the Supervisor, if not then it
is easier and better to use the ‘Supervisor’ factory object.
Example usage:
import static akka.config.Supervision.*;
import static akka.actor.Actors.*;
SupervisorFactory factory = new SupervisorFactory(
new SupervisorConfig(
new OneForOneStrategy(new Class[]{Exception.class}, 3, 5000),
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new Supervise[] {
new Supervise(
actorOf(MyActor1.class),
permanent()),
new Supervise(
actorOf(MyActor2.class),
temporary())
}));
Then create a new instance our Supervisor and start it up explicitly.
SupervisorFactory supervisor = factory.newInstance();
supervisor.start(); // start up all managed servers
Declaratively define actors as remote services
You can expose your actors as remote services by specifying the registerAsRemote to true in Supervise.
Here is an example:
import static akka.config.Supervision.*;
import static akka.actor.Actors.*;
Supervisor supervisor = Supervisor.apply(
new SupervisorConfig(
new AllForOneStrategy(new Class[]{Exception.class}, 3, 5000),
new Supervise[] {
new Supervise(
actorOf(MyActor1.class),
permanent(),
true)
}));
Programmatic linking and supervision of Untyped Actors
Untyped Actors can at runtime create, spawn, link and supervise other actors. Linking and unlinking is done using
one of the ‘link’ and ‘unlink’ methods available in the ‘ActorRef’ (therefore prefixed with getContext() in these
examples).
Here is the API and how to use it from within an ‘Actor’:
// link and unlink actors
getContext().link(actorRef);
getContext().unlink(actorRef);
// starts and links Actors atomically
getContext().startLink(actorRef);
getContext().startLinkRemote(actorRef);
// spawns (creates and starts) actors
getContext().spawn(MyActor.class);
getContext().spawnRemote(MyActor.class);
// spawns and links Actors atomically
getContext().spawnLink(MyActor.class);
getContext().spawnLinkRemote(MyActor.class);
A child actor can tell the supervising actor to unlink him by sending him the ‘Unlink(this)’ message. When the
supervisor receives the message he will unlink and shut down the child. The supervisor for an actor is available in
the ‘supervisor: Option[Actor]’ method in the ‘ActorRef’ class. Here is how it can be used.
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ActorRef supervisor = getContext().getSupervisor();
if (supervisor != null) supervisor.tell(new Unlink(getContext()))
The supervising actor’s side of things
If a linked Actor is failing and throws an exception then an ‘new Exit(deadActor, cause)’ message will be sent to
the supervisor (however you should never try to catch this message in your own message handler, it is managed
by the runtime).
The supervising Actor also needs to define a fault handler that defines the restart strategy the Actor should accommodate when it traps an ‘Exit’ message. This is done by setting the ‘setFaultHandler’ method.
The different options are:
• AllForOneStrategy(trapExit, maxNrOfRetries, withinTimeRange)
– trapExit is an Array of classes inheriting from Throwable, they signal which types of exceptions this
actor will handle
• OneForOneStrategy(trapExit, maxNrOfRetries, withinTimeRange)
– trapExit is an Array of classes inheriting from Throwable, they signal which types of exceptions this
actor will handle
Here is an example:
getContext().setFaultHandler(new AllForOneStrategy(new Class[]{MyException.class, IOException.clas
Putting all this together it can look something like this:
class MySupervisor extends UntypedActor {
public MySupervisor() {
getContext().setFaultHandler(new AllForOneStrategy(new Class[]{MyException.class, IOException.
}
public void onReceive(Object message) throws Exception {
if (message instanceof Register) {
Register event = (Register)message;
UntypedActorRef actor = event.getActor();
context.link(actor);
} else throw new IllegalArgumentException("Unknown message: " + message);
}
}
You can also link an actor from outside the supervisor like this:
UntypedActor supervisor = Actors.registry().actorsFor(MySupervisor.class])[0];
supervisor.link(actorRef);
The supervised actor’s side of things
The supervised actor needs to define a life-cycle. This is done by setting the lifeCycle field as follows:
import static akka.config.Supervision.*;
getContext().setLifeCycle(permanent()); // Permanent or Temporary
Default is ‘Permanent’ so if you don’t set the life-cycle then that is what you get.
In the supervised Actor you can override the ‘preRestart’ and ‘postRestart’ callback methods to add hooks into
the restart process. These methods take the reason for the failure, e.g. the exception that caused termination and
restart of the actor as argument. It is in these methods that you have to add code to do cleanup before termination
and initialization after restart. Here is an example:
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class FaultTolerantService extends UntypedActor {
@Override
public void preRestart(Throwable reason) {
... // clean up before restart
}
@Override
public void postRestart(Throwable reason) {
... // reinit stable state after restart
}
}
Reply to initial senders
Supervised actors have the option to reply to the initial sender within preRestart, postRestart and postStop. A
reply within these methods is possible after receive has thrown an exception. When receive returns normally it is
expected that any necessary reply has already been done within receive. Here’s an example.
public class FaultTolerantService extends UntypedActor {
public void onReceive(Object msg) {
// do something that may throw an exception
// ...
getContext().replySafe("ok");
}
@Override
public void preRestart(Throwable reason) {
getContext().replySafe(reason.getMessage());
}
@Override
public void postStop() {
getContext().replySafe("stopped by supervisor");
}
}
• A reply within preRestart or postRestart must be a safe reply via getContext().replySafe() because a getContext().replyUnsafe() will throw an exception when the actor is restarted without having failed. This can
be the case in context of AllForOne restart strategies.
• A reply within postStop must be a safe reply via getContext().replySafe() because a getContext().replyUnsafe() will throw an exception when the actor has been stopped by the application (and not by
a supervisor) after successful execution of receive (or no execution at all).
Handling too many actor restarts within a specific time limit
If you remember, when you define the ‘RestartStrategy’ you also defined maximum number of restart retries
within time in millis.
new AllForOneStrategy( // FaultHandlingStrategy policy (AllForOneStrategy or OneForOneStrategy)
new Class[]{MyException.class, IOException.class}, //What types of errors will be handled
3,
// maximum number of restart retries
5000
// within time in millis
);
Now, what happens if this limit is reached?
What will happen is that the failing actor will send a system message to its supervisor called ‘MaximumNumberOfRestartsWithinTimeRangeReached’ with the following these properties:
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• victim: ActorRef
• maxNrOfRetries: int
• withinTimeRange: int
• lastExceptionCausingRestart: Throwable
If you want to be able to take action upon this event (highly recommended) then you have to create a message
handle for it in the supervisor.
Here is an example:
public class SampleUntypedActorSupervisor extends UntypedActor {
...
public void onReceive(Object message) throws Exception {
if (message instanceof MaximumNumberOfRestartsWithinTimeRangeReached) {
MaximumNumberOfRestartsWithinTimeRangeReached event = (MaximumNumberOfRestartsWithinTimeRan
... = event.getVictim();
... = event.getMaxNrOfRetries();
... = event.getWithinTimeRange();
... = event.getLastExceptionCausingRestart();
} else throw new IllegalArgumentException("Unknown message: " + message);
}
}
You will also get this log warning similar to this:
WAR [20100715-14:05:25.821] actor: Maximum number of restarts [5] within time range [5000] reached
WAR [20100715-14:05:25.821] actor:
Will *not* restart actor [Actor[akka.actor.SupervisorHierar
WAR [20100715-14:05:25.821] actor:
Last exception causing restart was [akka.actor.SupervisorHi
If you don’t define a message handler for this message then you don’t get an error but the message is simply not
sent to the supervisor. Instead you will get a log warning.
5.10.5 Supervising Typed Actors
Declarative supervisor configuration
To configure Typed Actors for supervision you have to consult the ‘TypedActorConfigurator’ and its ‘configure’
method. This method takes a ‘RestartStrategy’ and an array of ‘Component’ definitions defining the Typed Actors
and their ‘LifeCycle’. Finally you call the ‘supervise’ method to start everything up. The Java configuration
elements reside in the ‘akka.config.JavaConfig’ class and need to be imported statically.
Here is an example:
import static akka.config.Supervision.*;
import static akka.config.SupervisorConfig.*;
TypedActorConfigurator manager = new TypedActorConfigurator();
manager.configure(
new AllForOneStrategy(new Class[]{Exception.class}, 3, 1000),
new SuperviseTypedActor[] {
new SuperviseTypedActor(
Foo.class,
FooImpl.class,
temporary(),
1000),
new SuperviseTypedActor(
Bar.class,
BarImpl.class,
permanent(),
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1000)
}).supervise();
Then you can retrieve the Typed Actor as follows:
Foo foo = (Foo) manager.getInstance(Foo.class);
Restart callbacks
In the supervised TypedActor you can override the ‘preRestart’ and ‘postRestart’ callback methods to add hooks
into the restart process. These methods take the reason for the failure, e.g. the exception that caused termination
and restart of the actor as argument. It is in these methods that you have to add code to do cleanup before
termination and initialization after restart. Here is an example:
class FaultTolerantService extends TypedActor {
@Override
public void preRestart(Throwable reason) {
... // clean up before restart
}
@Override
public void postRestart(Throwable reason) {
... // reinit stable state after restart
}
}
Programatic linking and supervision of TypedActors
TypedActors can be linked and unlinked just like UntypedActors:
TypedActor.link(supervisor, supervised);
TypedActor.unlink(supervisor, supervised);
If the parent TypedActor (supervisor) wants to be able to do handle failing child TypedActors, e.g. be able restart
the linked TypedActor according to a given fault handling scheme then it has to set its ‘trapExit’ flag to an array
of Exceptions that it wants to be able to trap:
TypedActor.faultHandler(supervisor, new AllForOneStrategy(new Class[]{IOException.class}, 3, 2000)
For convenience there is an overloaded link that takes trapExit and faultHandler for the supervisor as arguments.
Here is an example:
import static akka.actor.TypedActor.*;
import static akka.config.Supervision.*;
foo = newInstance(Foo.class, FooImpl.class, 1000);
bar = newInstance(Bar.class, BarImpl.class, 1000);
link(foo, bar, new AllForOneStrategy(new Class[]{IOException.class}, 3, 2000));
// alternative: chaining
bar = faultHandler(foo, new AllForOneStrategy(new Class[]{IOException.class}, 3, 2000)).newInstanc
link(foo, bar);
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5.11 Dispatchers (Java)
Module stability: SOLID
The Dispatcher is an important piece that allows you to configure the right semantics and parameters for optimal
performance, throughput and scalability. Different Actors have different needs.
Akka supports dispatchers for both event-driven lightweight threads, allowing creation of millions threads on a
single workstation, and thread-based Actors, where each dispatcher is bound to a dedicated OS thread.
The event-based Actors currently consume ~600 bytes per Actor which means that you can create more than 6.5
million Actors on 4 GB RAM.
5.11.1 Default dispatcher
For most scenarios the default settings are the best. Here we have one single event-based dispatcher for all Actors
created. The dispatcher used is globalExecutorBasedEventDrivenDispatcher in akka.dispatch.Dispatchers.
But if you feel that you are starting to contend on the single dispatcher (the ‘Executor’ and its queue) or want
to group a specific set of Actors for a dedicated dispatcher for better flexibility and configurability then you can
override the defaults and define your own dispatcher. See below for details on which ones are available and how
they can be configured.
5.11.2 Setting the dispatcher
Normally you set the dispatcher from within the Actor itself. The dispatcher is defined by the ‘dispatcher: MessageDispatcher’ member field in ‘ActorRef’.
class MyActor extends UntypedActor {
public MyActor() {
getContext().setDispatcher(..); // set the dispatcher
}
...
}
You can also set the dispatcher for an Actor before it has been started:
actorRef.setDispatcher(dispatcher);
5.11.3 Types of dispatchers
There are six different types of message dispatchers:
• Thread-based
• Event-based
• Priority event-based
• Work-stealing event-based
Factory methods for all of these, including global versions of some of them, are in the ‘akka.dispatch.Dispatchers’
object.
Let’s now walk through the different dispatchers in more detail.
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Thread-based
The ‘ThreadBasedDispatcher’ binds a dedicated OS thread to each specific Actor. The messages are posted to
a ‘LinkedBlockingQueue’ which feeds the messages to the dispatcher one by one. A ‘ThreadBasedDispatcher’
cannot be shared between actors. This dispatcher has worse performance and scalability than the event-based
dispatcher but works great for creating “daemon” Actors that consumes a low frequency of messages and are
allowed to go off and do their own thing for a longer period of time. Another advantage with this dispatcher is that
Actors do not block threads for each other.
Dispatcher dispatcher = Dispatchers.newThreadBasedDispatcher(actorRef);
It would normally by used from within the actor like this:
class MyActor extends UntypedActor {
public MyActor() {
getContext().setDispatcher(Dispatchers.newThreadBasedDispatcher(getContext()));
}
...
}
Event-based
The ‘ExecutorBasedEventDrivenDispatcher’ binds a set of Actors to a thread pool backed up by a ‘BlockingQueue’. This dispatcher is highly configurable and supports a fluent configuration API to configure the ‘BlockingQueue’ (type of queue, max items etc.) as well as the thread pool.
The event-driven dispatchers must be shared between multiple Typed Actors and/or Actors. One best practice
is to let each top-level Actor, e.g. the Actors you define in the declarative supervisor config, to get their own
dispatcher but reuse the dispatcher for each new Actor that the top-level Actor creates. But you can also share
dispatcher between multiple top-level Actors. This is very use-case specific and needs to be tried out on a case
by case basis. The important thing is that Akka tries to provide you with the freedom you need to design and
implement your system in the most efficient way in regards to performance, throughput and latency.
It comes with many different predefined BlockingQueue configurations:
• Bounded LinkedBlockingQueue
• Unbounded LinkedBlockingQueue
• Bounded ArrayBlockingQueue
• Unbounded ArrayBlockingQueue
• SynchronousQueue
You can also set the rejection policy that should be used, e.g. what should be done if the dispatcher (e.g. the Actor)
can’t keep up and the mailbox is growing up to the limit defined. You can choose between four different rejection
policies:
• java.util.concurrent.ThreadPoolExecutor.CallerRuns - will run the message processing in the caller’s thread
as a way to slow him down and balance producer/consumer
• java.util.concurrent.ThreadPoolExecutor.AbortPolicy - rejected messages by throwing a ‘RejectedExecutionException’
• java.util.concurrent.ThreadPoolExecutor.DiscardPolicy - discards the message (throws it away)
• java.util.concurrent.ThreadPoolExecutor.DiscardOldestPolicy - discards the oldest message in the mailbox
(throws it away)
You cane read more about these policies here.
Here is an example:
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import akka.actor.Actor;
import akka.dispatch.Dispatchers;
class MyActor extends UntypedActor {
public MyActor() {
getContext().setDispatcher(Dispatchers.newExecutorBasedEventDrivenDispatcher(name)
.withNewThreadPoolWithLinkedBlockingQueueWithCapacity(100)
.setCorePoolSize(16)
.setMaxPoolSize(128)
.setKeepAliveTimeInMillis(60000)
.build());
}
...
}
This ‘ExecutorBasedEventDrivenDispatcher’ allows you to define the ‘throughput’ it should have. This defines
the number of messages for a specific Actor the dispatcher should process in one single sweep. Setting this to a
higher number will increase throughput but lower fairness, and vice versa. If you don’t specify it explicitly then it
uses the default value defined in the ‘akka.conf’ configuration file:
actor {
throughput = 5
}
If you don’t define a the ‘throughput’ option in the configuration file then the default value of ‘5’ will be used.
Browse the Scaladoc API or look at the code for all the options available.
Priority event-based
Sometimes it’s useful to be able to specify priority order of messages, that is done by using PriorityExecutorBasedEventDrivenDispatcher and supply a java.util.Comparator[MessageInvocation] or use a
akka.dispatch.PriorityGenerator (recommended):
Creating a PriorityExecutorBasedEventDrivenDispatcher using PriorityGenerator:
package some.pkg;
import akka.actor.*;
import akka.dispatch.*;
public class Main {
// A simple Actor that just prints the messages it processes
public static class MyActor extends UntypedActor {
public void onReceive(Object message) throws Exception {
System.out.println(message);
}
}
public static void main(String[] args) {
// Create a new PriorityGenerator, lower prio means more important
PriorityGenerator gen = new PriorityGenerator() {
public int gen(Object message) {
if (message.equals("highpriority")) return 0;
// "highpriority" messages should be
else if (message.equals("lowpriority")) return 100; // "lowpriority" messages should be
else return 50; // We default to 50
}
};
// We create an instance of the actor that will print out the messages it processes
ActorRef ref = Actors.actorOf(MyActor.class);
// We create a new Priority dispatcher and seed it with the priority generator
ref.setDispatcher(new PriorityExecutorBasedEventDrivenDispatcher("foo", gen));
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ref.start(); // Start the actor
ref.getDispatcher().suspend(ref); // Suspending the actor so it doesn’t start to treat the mes
ref.tell("lowpriority");
ref.tell("lowpriority");
ref.tell("highpriority");
ref.tell("pigdog");
ref.tell("pigdog2");
ref.tell("pigdog3");
ref.tell("highpriority");
ref.getDispatcher().resume(ref); // Resuming the actor so it will start treating its messages
}
}
Prints:
highpriority highpriority pigdog pigdog2 pigdog3 lowpriority lowpriority
Work-stealing event-based
The ‘ExecutorBasedEventDrivenWorkStealingDispatcher’ is a variation of the ‘ExecutorBasedEventDrivenDispatcher’ in which Actors of the same type can be set up to share this dispatcher and during execution time the
different actors will steal messages from other actors if they have less messages to process. This can be a great
way to improve throughput at the cost of a little higher latency.
Normally the way you use it is to define a static field to hold the dispatcher and then set in in the Actor explicitly.
class MyActor extends UntypedActor {
public static MessageDispatcher dispatcher = Dispatchers.newExecutorBasedEventDrivenWorkStealing
public MyActor() {
getContext().setDispatcher(dispatcher);
}
...
}
Here is an article with some more information: Load Balancing Actors with Work Stealing Techniques Here is
another article discussing this particular dispatcher: Flexible load balancing with Akka in Scala
5.11.4 Making the Actor mailbox bounded
Global configuration
You can make the Actor mailbox bounded by a capacity in two ways. Either you define it in the configuration file
under ‘default-dispatcher’. This will set it globally.
actor {
default-dispatcher {
mailbox-capacity = -1
# If negative (or zero) then an unbounded mailbox is used (de
# If positive then a bounded mailbox is used and the capacity
}
}
Per-instance based configuration
You can also do it on a specific dispatcher instance.
For the ‘ExecutorBasedEventDrivenDispatcher’ and the ‘ExecutorBasedWorkStealingDispatcher’ you can do it
through their constructor
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class MyActor extends UntypedActor {
public MyActor() {
int capacity = 100;
Duration pushTimeout = new FiniteDuration(10, TimeUnit.SECONDS);
MailboxType mailboxCapacity = new BoundedMailbox(false, capacity, pushTimeout);
MessageDispatcher dispatcher =
Dispatchers.newExecutorBasedEventDrivenDispatcher(name, throughput, mailboxCapacity).build
getContext().setDispatcher(dispatcher);
}
...
}
For the ‘ThreadBasedDispatcher’, it is non-shareable between actors, and associates a dedicated Thread with the
actor. Making it bounded (by specifying a capacity) is optional, but if you do, you need to provide a pushTimeout
(default is 10 seconds). When trying to send a message to the Actor it will throw a MessageQueueAppendFailedException if the message cannot be added to the mailbox within the time specified by the pushTimeout.
class MyActor extends UntypedActor {
public MyActor() {
int mailboxCapacity = 100;
Duration pushTimeout = new FiniteDuration(10, TimeUnit.SECONDS);
getContext().setDispatcher(Dispatchers.newThreadBasedDispatcher(getContext(), mailboxCapacity,
}
...
}
5.12 Routing (Java)
5.12.1 UntypedDispatcher
An UntypedDispatcher is an actor that routes incoming messages to outbound actors.
import static akka.actor.Actors.*;
import akka.actor.*;
import akka.routing.*;
//A Pinger is an UntypedActor that prints "Pinger: <message>"
class Pinger extends UntypedActor {
public void onReceive(Object message) throws Exception {
System.out.println("Pinger: " + message);
}
}
//A Ponger is an UntypedActor that prints "Ponger: <message>"
class Ponger extends UntypedActor {
public void onReceive(Object message) throws Exception {
System.out.println("Ponger: " + message);
}
}
public class MyDispatcher extends UntypedDispatcher {
private ActorRef pinger = actorOf(Pinger.class).start();
private ActorRef ponger = actorOf(Ponger.class).start();
//Route Ping-messages to the pinger, and Pong-messages to the ponger
public ActorRef route(Object message) {
if("Ping".equals(message)) return pinger;
else if("Pong".equals(message)) return ponger;
else throw new IllegalArgumentException("I do not understand " + message);
}
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}
ActorRef dispatcher = actorOf(MyDispatcher.class).start();
dispatcher.tell("Ping"); //Prints "Pinger: Ping"
dispatcher.tell("Pong"); //Prints "Ponger: Pong"
5.12.2 UntypedLoadBalancer
An UntypedLoadBalancer is an actor that forwards messages it receives to a boundless sequence of destination
actors.
import
import
import
import
static akka.actor.Actors.*;
akka.actor.*;
akka.routing.*;
static java.util.Arrays.asList;
//A Pinger is an UntypedActor that prints "Pinger: <message>"
class Pinger extends UntypedActor {
public void onReceive(Object message) throws Exception {
System.out.println("Pinger: " + message);
}
}
//A Ponger is an UntypedActor that prints "Ponger: <message>"
class Ponger extends UntypedActor {
public void onReceive(Object message) throws Exception {
System.out.println("Ponger: " + message);
}
}
//Our load balancer, sends messages to a pinger, then a ponger, rinse and repeat.
public class MyLoadBalancer extends UntypedLoadBalancer {
private InfiniteIterator<ActorRef> actors = new CyclicIterator<ActorRef>(asList(
actorOf(Pinger.class).start(),
actorOf(Ponger.class).start()
));
public InfiniteIterator<ActorRef> seq() {
return actors;
}
}
ActorRef dispatcher = actorOf(MyLoadBalancer.class).start();
dispatcher.tell("Pong"); //Prints "Pinger: Pong"
dispatcher.tell("Ping"); //Prints "Ponger: Ping"
dispatcher.tell("Ping"); //Prints "Pinger: Ping"
dispatcher.tell("Pong"); //Prints "Ponger: Pong
You can also send a ‘new Routing.Broadcast(msg)’ message to the router to have it be broadcasted out to all the
actors it represents.
router.tell(new Routing.Broadcast(new PoisonPill()));
5.13 Guice Integration
Module stability: STABLE
All Typed Actors support dependency injection using Guice annotations (such as ‘@Inject’ etc.). The ‘TypedActorManager’ class understands Guice and will do the wiring for you.
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5.13.1 External Guice modules
You can also plug in external Guice modules and have not-actors wired up as part of the configuration. Here is an
example:
import static akka.config.Supervision.*;
import static akka.config.SupervisorConfig.*;
TypedActorConfigurator manager = new TypedActorConfigurator();
manager.configure(
new AllForOneStrategy(new Class[]{Exception.class}, 3, 1000),
new SuperviseTypedActor[] {
new SuperviseTypedActor(
Foo.class,
FooImpl.class,
temporary(),
1000),
new SuperviseTypedActor(
Bar.class,
BarImpl.class,
permanent(),
1000)
})
.addExternalGuiceModule(new AbstractModule() {
protected void configure() {
bind(Ext.class).to(ExtImpl.class).in(Scopes.SINGLETON);
}})
.configure()
.inject()
.supervise();
5.13.2 Retrieve the external Guice dependency
The external dependency can be retrieved like this:
Ext ext = manager.getExternalDependency(Ext.class);
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CHAPTER
SIX
INFORMATION FOR DEVELOPERS
6.1 Building Akka
This page describes how to build and run Akka from the latest source code.
• Get the source code
• SBT - Simple Build Tool
• Building Akka
– Fetching dependencies
– Building
– Publish to local Ivy repository
– Publish to local Maven repository
– SBT interactive mode
– SBT batch mode
• Building Akka Modules
• Dependencies
6.1.1 Get the source code
Akka uses Git and is hosted at Github.
You first need Git installed on your machine. You can then clone the source repositories:
• Akka repository from http://github.com/akka/akka
• Akka Modules repository from http://github.com/akka/akka-modules
For example:
git clone git://github.com/akka/akka.git
git clone git://github.com/akka/akka-modules.git
If you have already cloned the repositories previously then you can update the code with git pull:
git pull origin master
6.1.2 SBT - Simple Build Tool
Akka is using the excellent SBT build system. So the first thing you have to do is to download and install SBT.
You can read more about how to do that in the SBT setup documentation.
The SBT commands that you’ll need to build Akka are all included below. If you want to find out more about
SBT and using it for your own projects do read the SBT documentation.
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The Akka SBT build file is project/build/AkkaProject.scala with some properties defined in
project/build.properties.
6.1.3 Building Akka
First make sure that you are in the akka code directory:
cd akka
Fetching dependencies
SBT does not fetch dependencies automatically. You need to manually do this with the update command:
sbt update
Once finished, all the dependencies for Akka will be in the lib_managed directory under each module: akkaactor, akka-stm, and so on.
Note: you only need to run update the first time you are building the code, or when the dependencies have changed.
Building
To compile all the Akka core modules use the compile command:
sbt compile
You can run all tests with the test command:
sbt test
If compiling and testing are successful then you have everything working for the latest Akka development version.
Publish to local Ivy repository
If you want to deploy the artifacts to your local Ivy repository (for example, to use from an SBT project) use the
publish-local command:
sbt publish-local
Publish to local Maven repository
If you want to deploy the artifacts to your local Maven repository use:
sbt publish-local publish
SBT interactive mode
Note that in the examples above we are calling sbt compile and sbt test and so on. SBT also has an
interactive mode. If you just run sbt you enter the interactive SBT prompt and can enter the commands directly.
This saves starting up a new JVM instance for each command and can be much faster and more convenient.
For example, building Akka as above is more commonly done like this:
% sbt
[info] Building project akka 1.3.1 against Scala 2.9.1
[info]
using AkkaParentProject with sbt 0.7.6 and Scala 2.7.7
> update
[info]
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[info] == akka-actor / update ==
...
[success] Successful.
[info]
[info] Total time ...
> compile
...
> test
...
SBT batch mode
It’s also possible to combine commands in a single call. For example, updating, testing, and publishing Akka to
the local Ivy repository can be done with:
sbt update test publish-local
6.1.4 Building Akka Modules
See the Akka Modules documentation.
6.1.5 Dependencies
If you are managing dependencies by hand you can find the dependencies for each module by looking in the
lib_managed directories. For example, this will list all compile dependencies (providing you have the source
code and have run sbt update):
cd akka
ls -1 */lib_managed/compile
You
can
also
look
at
the
Ivy
dependency
resolution
information
that
is
created
on
sbt update
and
found
in
~/.ivy2/cache.
For
example,
the
.ivy2/cache/se.scalablesolutions.akka-akka-remote-compile.xml file contains the
resolution information for the akka-remote module compile dependencies. If you open this file in a web browser
you will get an easy to navigate view of dependencies.
6.2 Developer Guidelines
6.2.1 Code Style
The Akka code style follows this document .
Here is a code style settings file for IntelliJ IDEA: Download
Please follow the code style. Look at the code around you and mimic.
6.2.2 Testing
All code that is checked in should have tests. All testing is done with ScalaTest and ScalaCheck.
• Name tests as Test.scala if they do not depend on any external stuff. That keeps surefire happy.
• Name tests as Spec.scala if they have external dependencies.
There is a testing standard that should be followed: Ticket001Spec
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Actor TestKit
There is a useful test kit for testing actors: akka.util.TestKit. It enables assertions concerning replies received and
their timing, there is more documentation in the Testing Actor Systems module.
NetworkFailureTest
You can use the ‘NetworkFailureTest’ trait to test network failure. See the ‘RemoteErrorHandlingNetworkTest’
test. Your tests needs to end with ‘NetworkTest’. They are disabled by default. To run them you need to enable a
flag.
Example:
project akka-remote
set akka.test.network true
test-only akka.actor.remote.RemoteErrorHandlingNetworkTest
It uses ‘ipfw’ for network management. Mac OSX comes with it installed but if you are on another platform you
might need to install it yourself. Here is a port:
http://info.iet.unipi.it/~luigi/dummynet
6.3 Documentation Guidelines
The Akka documentation uses reStructuredText as its markup language and is built using Sphinx.
6.3.1 Sphinx
More to come...
6.3.2 reStructuredText
More to come...
Sections
Section headings are very flexible in reST. We use the following convention in the Akka documentation:
• # (over and under) for module headings
• = for sections
• - for subsections
• ^ for subsubsections
• ~ for subsubsubsections
Cross-referencing
Sections that may be cross-referenced across the documentation should be marked with a reference. To
mark a section use .. _ref-name: before the section heading. The section can then be linked with
:ref:‘ref-name‘. These are unique references across the entire documentation.
For example:
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.. _akka-module:
#############
Akka Module
#############
This is the module documentation.
.. _akka-section:
Akka Section
============
Akka Subsection
--------------Here is a reference to "akka section": :ref:‘akka-section‘ which will have the
name "Akka Section".
6.3.3 Build the documentation
First install Sphinx. See below.
Building
cd akka-docs
make html
open _build/html/index.html
make pdf
open _build/latex/Akka.pdf
Installing Sphinx on OS X
Install Homebrew
Install Python and pip:
brew install python
/usr/local/share/python/easy_install pip
Add the Homebrew Python path to your $PATH:
/usr/local/Cellar/python/2.7.1/bin
More information in case of trouble: https://github.com/mxcl/homebrew/wiki/Homebrew-and-Python
Install sphinx:
pip install sphinx
Add sphinx_build to your $PATH:
/usr/local/share/python
Install BasicTeX package from: http://www.tug.org/mactex/morepackages.html
Add texlive bin to $PATH:
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/usr/local/texlive/2010basic/bin/universal-darwin
Add missing tex packages:
sudo
sudo
sudo
sudo
sudo
sudo
sudo
tlmgr
tlmgr
tlmgr
tlmgr
tlmgr
tlmgr
tlmgr
update --self
install titlesec
install framed
install threeparttable
install wrapfig
install helvetic
install courier
Link the akka pygments style:
cd /usr/local/Cellar/python/2.7.1/lib/python2.7/site-packages/pygments/styles
ln -s /path/to/akka/akka-docs/themes/akka/pygments/akka.py akka.py
6.4 Team
Name
Jonas Bonér
Viktor Klang
Debasish Ghosh
Ross McDonald
Eckhart Hertzler
Mikael Högqvist
Tim Perrett
Jeanfrancois Arcand
Martin Krasser
Jan Van Besien
Michael Kober
Peter Vlugter
Peter Veentjer
Irmo Manie
Heiko Seeberger
Hiram Chirino
Scott Clasen
Roland Kuhn
Patrik Nordwall
Derek Williams
6.4. Team
Role
Founder, Despot, Committer
Bad cop, Committer
Committer
Alumni
Alumni
Alumni
Alumni
Alumni
Committer
Alumni
Alumni
Committer
Committer
Committer
Committer
Committer
Committer
Committer
Committer
Committer
Email
jonas AT jonasboner DOT com
viktor DOT klang AT gmail DOT com
dghosh AT acm DOT org
rossajmcd AT gmail DOT com
jfarcand AT apache DOT org
krasserm AT googlemail DOT com
patrik DOT nordwall AT gmail DOT com
derek AT nebvin DOT ca
242
CHAPTER
SEVEN
PROJECT INFORMATION
7.1 Migration Guides
7.1.1 Migration Guide 1.0.x to 1.1.x
Akka has now moved to Scala 2.9.x
Akka Actor
• is now dependency free, with the exception of the dependency on the scala-library.jar
• does not bundle any logging anymore, but you can subscribe to events within Akka by registering an event
handler on akka.event.EventHandler or by specifying the FQN of an Actor in the akka.conf under akka.eventhandlers; there is an akka-slf4j module which still provides the Logging trait and a default SLF4J
logger adapter.
Don’t forget to add a SLF4J backend though, we recommend:
lazy val logback = "ch.qos.logback" % "logback-classic" % "0.9.28" % "runtime"
• If you used HawtDispatcher and want to continue using it, you need to include
akka-dispatcher-extras.jar from Akka Modules, in your akka.conf you need to specify:
akka.dispatch.HawtDispatcherConfigurator instead of HawtDispatcher
• FSM: the onTransition method changed from Function1 to PartialFunction; there is an implicit conversion
for the precise types in place, but it may be necessary to add an underscore if you are passing an etaexpansion (using a method as function value).
Akka Typed Actor
• All methods starting with get* are deprecated and will be removed in post 1.1 release.
Akka Remote
• UnparsebleException has been renamed to CannotInstantiateRemoteExceptionDueToRemoteProtocol
classname, message)
Akka HTTP
• akka.servlet.Initializer has been moved to akka-kernel to be able to have akka-http not depend
on akka-remote. If you don’t want to use the class for kernel, just create your own version of
akka.servlet.Initializer, it’s just a couple of lines of code and there are instructions in the
HTTP docs.
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• akka.http.ListWriter has been removed in full, if you use it and want to keep using it, here’s the code:
ListWriter.
• Jersey-server is now a “provided” dependency for akka-http, so you’ll need to add the dependency to
your project, it’s built against Jersey 1.3
Akka Testkit
• The TestKit moved into the akka-testkit subproject and correspondingly into the akka.testkit package.
7.1.2 Migration guide from 0.10.x to 1.0.x
Akka & Akka Modules separated into two different repositories and distributions
Akka is split up into two different parts: * Akka - Reflects all the sections under ‘Scala API’ and ‘Java API’ in the
navigation bar. * Akka Modules - Reflects all the sections under ‘Add-on modules’ in the navigation bar.
Download the release you need (Akka core or Akka Modules) from http://akka.io/downloads and unzip it.
Changed Akka URI
http:*akkasource.org changed to http:*akka.io
Reflects XSDs, Maven repositories, ScalaDoc etc.
Removed ‘se.scalablesolutions’ prefix
We have removed some boilerplate by shortening the Akka package from se.scalablesolutions.akka to just akka
so just do a search-replace in your project, we apologize for the inconvenience, but we did it for our users.
Akka-core is no more
Akka-core has been split into akka-actor, akka-stm, akka-typed-actor & akka-remote this means that you need to
update any deps you have on akka-core.
Config
Turning on/off modules
All the ‘service = on’ elements for turning modules on and off have been replaced by a top-level list of the enabled
services.
Services available for turning on/off are: * “remote” * “http” * “camel”
All services are OFF by default. Enable the ones you are using.
akka {
enabled-modules = [] # Comma separated list of the enabled modules. Options: ["remote", "camel"
}
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Renames
• ‘rest’ section - has been renamed to ‘http’ to align with the module name ‘akka-http’.
• ‘storage’ section - has been renamed to ‘persistence’ to align with the module name ‘akka-persistence’.
akka {
http {
..
}
persistence {
..
}
}
Important changes from RC2-RC3
akka.config.SupervisionSupervise
Scala
def apply(actorRef: ActorRef, lifeCycle: LifeCycle, registerAsRemoteService: Boolean = false)
• boolean instead of remoteAddress, registers that actor with it’s id as service name on the local server
akka.actor.Actors now is the API for Java to interact with Actors, Remoting and ActorRegistry:
Java
import static akka.actor.Actors.*; // <-- The important part
actorOf();
remote().actorOf();
registry().actorsFor("foo");
*akka.actor.Actor now is the API for Scala to interact with Actors, Remoting and ActorRegistry:*
Scala
import akka.actor.Actor._ // <-- The important part
actorOf().method
remote.actorOf()
registry.actorsFor("foo")
object UntypedActor has been deleted and replaced with akka.actor.Actors/akka.actor.Actor (Java/Scala)
• UntypedActor.actorOf -> Actors.actorOf (Java) or Actor.actorOf (Scala)
object ActorRegistry has been deleted and replaced with akka.actor.Actors.registry()/akka.actor.Actor.registry
(Java/Scala)
• ActorRegistry. -> Actors.registry(). (Java) or Actor.registry. (Scala)
object RemoteClient has been deleted and replaced with akka.actor.Actors.remote()/akka.actor.Actor.remote
(Java/Scala)
• RemoteClient -> Actors.remote() (Java) or Actor.remote (Scala)
object RemoteServer has been deleted and replaced with akka.actor.Actors.remote()/akka.actor.Actor.remote
(Java/Scala)
• RemoteServer - deleted -> Actors.remote() (Java) or Actor.remote (Scala)
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classes RemoteActor, RemoteUntypedActor and RemoteUntypedConsumerActors has been deleted and
replaced with akka.actor.Actors.remote().actorOf(x, host port)/akka.actor.Actor.remote.actorOf(x, host,
port)
• RemoteActor, RemoteUntypedActor - deleted, use: remote().actorOf(YourActor.class, host, port) (Java) or
remote.actorOf[YourActor](host, port)
Remoted spring-actors now default to spring id as service-name, use “service-name” attribute on “remote”tag to override
Listeners for RemoteServer and RemoteClient are now registered on Actors.remote().addListener (Java) or
Actor.remote.addListener (Scala), this means that all listeners get all remote events, both remote server evens and
remote client events, so adjust your code accordingly.
ActorRef.startLinkRemote has been removed since one specified on creation wether the actor is clientmanaged or not.
Important change from RC3 to RC4
The Akka-Spring namespace has changed from akkasource.org and scalablesolutions.se to http:*akka.io/schema
and http:*akka.io/akka-<version>.xsd
Module akka-actor
The Actor.init callback has been renamed to “preStart” to align with the general callback naming and is more clear
about when it’s called.
The Actor.shutdown callback has been renamed to “postStop” to align with the general callback naming and is
more clear about when it’s called.
The Actor.initTransactionalState callback has been removed, logic should be moved to preStart and be wrapped
in an atomic block
se.scalablesolutions.akka.config.ScalaConfig and se.scalablesolutions.akka.config.JavaConfig have been
merged into akka.config.Supervision
RemoteAddress has moved from se.scalablesolutions.akka.config.ScalaConfig to akka.config
The ActorRef.lifeCycle has changed signature from Option[LifeCycle] to LifeCycle, this means you need to
change code that looks like this: self.lifeCycle = Some(LifeCycle(Permanent)) to self.lifeCycle = Permanent
The equivalent to self.lifeCycle = None is self.lifeCycle = UndefinedLifeCycle LifeCycle(Permanent)
becomes Permanent new LifeCycle(permanent()) becomes permanent() (need to do: import static
se.scalablesolutions.akka.config.Supervision.*; first)
JavaConfig.Component and ScalaConfig.Component have been consolidated and renamed as Supervision.SuperviseTypedActor
self.trapExit has been moved into the FaultHandlingStrategy, and ActorRef.faultHandler has switched type
from Option[FaultHandlingStrategy] to FaultHandlingStrategy:
Scala
import akka.config.Supervision._
self.faultHandler = OneForOneStrategy(List(classOf[Exception]), 3, 5000)
Java
import static akka.Supervision.*;
getContext().setFaultHandler(new OneForOneStrategy(new Class[] { Exception.class },50,1000))
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RestartStrategy, AllForOne, OneForOne have been replaced with AllForOneStrategy and OneForOneStrategy in se.scalablesolutions.akka.config.Supervision
Scala
import akka.config.Supervision._
SupervisorConfig(
OneForOneStrategy(List(classOf[Exception]), 3, 5000),
Supervise(pingpong1,Permanent) :: Nil
)
Java
import static akka.Supervision.*;
new SupervisorConfig(
new OneForOneStrategy(new Class[] { Exception.class },50,1000),
new Server[] { new Supervise(pingpong1, permanent()) }
)
*We have removed the following factory methods:*
Actor.actor { case foo => bar } Actor.transactor { case foo => bar } Actor.temporaryActor { case foo => bar
} Actor.init {} receive { case foo => bar }
They started the actor and no config was possible, it was inconsistent and irreparable.
replace with your own factories, or:
Scala
actorOf( new Actor { def receive = { case foo => bar } } ).start
actorOf( new Actor { self.lifeCycle = Temporary; def receive = { case foo => bar } } ).start
ReceiveTimeout is now rescheduled after every message, before there was only an initial timeout. To stop
rescheduling of ReceiveTimeout, set receiveTimeout = None
HotSwap
HotSwap does no longer use behavior stacking by default, but that is an option to both “become” and HotSwap.
HotSwap now takes for Scala a Function from ActorRef to a Receive, the ActorRef passed in is the reference to
self, so you can do self.reply() etc.
Module akka-stm
The STM stuff is now in its own module. This means that there is no support for transactions or transactors in
akka-actor.
Local and global
The local/global distinction has been dropped. This means that if the following general import was being used:
Scala
import akka.stm.local._
this is now just:
Scala
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import akka.stm._
Coordinated is the new global
There is a new explicit mechanism for coordinated transactions. See the Scala Transactors and Java Transactors documentation for more information. Coordinated transactions and transactors are found in the
akka.transactor package now. The usage of transactors has changed.
Agents
Agent is now in the akka-stm module and has moved to the akka.agent package. The implementation has been
reworked and is now closer to Clojure agents. There is not much difference in general usage, the main changes
involve interaction with the STM.
While updates to Agents are asynchronous, the state of an Agent is always immediately available for reading
by any thread. Agents are integrated with the STM - any dispatches made in a transaction are held until that
transaction commits, and are discarded if it is retried or aborted. There is a new sendOff method for longrunning or blocking update functions.
Module akka-camel
Access to the CamelService managed by CamelServiceManager has changed:
• Method service renamed to mandatoryService (Scala)
• Method service now returns Option[CamelService] (Scala)
• Introduced method getMandatoryService() (Java)
• Introduced method getService() (Java)
Scala
import se.scalablesolutions.akka.camel.CamelServiceManager._
import se.scalablesolutions.akka.camel.CamelService
val o: Option[CamelService] = service
val s: CamelService = mandatoryService
Java
import se.scalablesolutions.akka.camel.CamelService;
import se.scalablesolutions.akka.japi.Option;
import static se.scalablesolutions.akka.camel.CamelServiceManager.*;
Option<CamelService> o = getService();
CamelService s = getMandatoryService();
Access to the CamelContext and ProducerTemplate managed by CamelContextManager has changed:
• Method context renamed to mandatoryContext (Scala)
• Method template renamed to mandatoryTemplate (Scala)
• Method service now returns Option[CamelContext] (Scala)
• Method template now returns Option[ProducerTemplate] (Scala)
• Introduced method getMandatoryContext() (Java)
• Introduced method getContext() (Java)
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• Introduced method getMandatoryTemplate() (Java)
• Introduced method getTemplate() (Java)
Scala
import org.apache.camel.CamelContext
import org.apache.camel.ProducerTemplate
import se.scalablesolutions.akka.camel.CamelContextManager._
val co: Option[CamelContext] = context
val to: Option[ProducerTemplate] = template
val c: CamelContext = mandatoryContext
val t: ProducerTemplate = mandatoryTemplate
Java
import org.apache.camel.CamelContext;
import org.apache.camel.ProducerTemplate;
import se.scalablesolutions.akka.japi.Option;
import static se.scalablesolutions.akka.camel.CamelContextManager.*;
Option<CamelContext> co = getContext();
Option<ProducerTemplate> to = getTemplate();
CamelContext c = getMandatoryContext();
ProducerTemplate t = getMandatoryTemplate();
The following methods have been renamed on class se.scalablesolutions.akka.camel.Message:
• bodyAs(Class) has been renamed to getBodyAs(Class)
• headerAs(String, Class) has been renamed to getHeaderAs(String, Class)
The API for waiting for consumer endpoint activation and de-activation has been changed
• CamelService.expectEndpointActivationCount
vice.awaitEndpointActivation
has
been
removed
and
replaced
by
CamelSer-
• CamelService.expectEndpointDeactivationCount has been removed and replaced by CamelService.awaitEndpointDeactivation
Scala
import se.scalablesolutions.akka.actor.Actor
import se.scalablesolutions.akka.camel.CamelServiceManager._
val s = startCamelService
val actor = Actor.actorOf[SampleConsumer]
// wait for 1 consumer being activated
s.awaitEndpointActivation(1) {
actor.start
}
// wait for 1 consumer being de-activated
s.awaitEndpointDeactivation(1) {
actor.stop
}
s.stop
Java
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import
import
import
import
import
import
java.util.concurrent.TimeUnit;
se.scalablesolutions.akka.actor.ActorRef;
se.scalablesolutions.akka.actor.Actors;
se.scalablesolutions.akka.camel.CamelService;
se.scalablesolutions.akka.japi.SideEffect;
static se.scalablesolutions.akka.camel.CamelServiceManager.*;
CamelService s = startCamelService();
final ActorRef actor = Actors.actorOf(SampleUntypedConsumer.class);
// wait for 1 consumer being activated
s.awaitEndpointActivation(1, new SideEffect() {
public void apply() {
actor.start();
}
});
// wait for 1 consumer being de-activated
s.awaitEndpointDeactivation(1, new SideEffect() {
public void apply() {
actor.stop();
}
});
s.stop();
Module Akka-Http
Atmosphere support has been removed. If you were using akka.comet.AkkaServlet for Jersey support only, you
can switch that to: akka.http.AkkaRestServlet and it should work just like before.
Atmosphere has been removed because we have a new async http support in the form of Akka Mist, a very thin
bridge between Servlet3.0/JettyContinuations and Actors, enabling Http-as-messages, read more about it here:
http://doc.akka.io/http#Mist%20-%20Lightweight%20Asynchronous%20HTTP
If you really need Atmosphere support, you can add it yourself by following the steps listed at the start of:
http://doc.akka.io/comet
Module akka-spring
The Akka XML schema URI has changed to http://repo.akka.io/schema/akka
<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:akka="http://repo.akka.io/schema/akka"
xsi:schemaLocation="
http://www.springframework.org/schema/beans
http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
http://repo.akka.io/schema/akka
http://repo.akka.io/akka-1.0.xsd">
<!-- ... -->
</beans>
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7.1.3 Migration Guide 0.9.x to 0.10.x
Module akka-camel
The following list summarizes the breaking changes since Akka 0.9.1.
• CamelService moved from package
se.scalablesolutions.akka.camel.
se.scalablesolutions.akka.camel.service
one
level
up
to
• CamelService.newInstance removed. For starting and stopping a CamelService, applications should use
– CamelServiceManager.startCamelService and
– CamelServiceManager.stopCamelService.
• Existing def receive = produce method definitions from Producer implementations must be removed (resolves compile error: method receive needs override modifier).
• The Producer.async method and the related Sync trait have been removed. This is now fully covered by
Camel’s asynchronous routing engine.
• @consume annotation can not placed any longer on actors (i.e. on type-level), only on typed actor methods.
Consumer actors must mixin the Consumer trait.
• @consume annotation moved to package se.scalablesolutions.akka.camel.
Logging
We’ve switched to Logback (SLF4J compatible) for the logging, if you’re having trouble seeing your log output
you’ll need to make sure that there’s a logback.xml available on the classpath or you’ll need to specify the location
of the logback.xml file via the system property, ex: -Dlogback.configurationFile=/path/to/logback.xml
Configuration
• The configuration is now JSON-style (see below).
• Now you can define the time-unit to be used throughout the config file:
akka {
version = "0.10"
time-unit = "seconds"
actor {
timeout = 5
throughput = 5
}
...
# default timeout time unit for all timeout properties throughout the
# default timeout for future based invocations
# default throughput for ExecutorBasedEventDrivenDispatcher
}
RemoteClient events
All events now has a reference to the RemoteClient instance instead of ‘hostname’ and ‘port’. This is more
flexible. Enables simpler reconnecting etc.
7.1.4 Migration Guide 0.8.x to 0.9.x
This document describes between the 0.8.x and the 0.9 release.
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Background for the new ActorRef
In the work towards 0.9 release we have now done a major change to how Actors are created. In short we have
separated identity and value, created an ‘ActorRef’ that holds the actual Actor instance. This allows us to do many
great things such as for example:
• Create serializable, immutable, network-aware Actor references that can be freely shared across the network.
They “remember” their origin and will always work as expected.
• Not only kill and restart the same supervised Actor instance when it has crashed (as we do now), but
dereference it, throw it away and make it eligible for garbage collection.
• etc. much more
These work very much like the ‘PID’ (process id) in Erlang.
These changes means that there is no difference in defining Actors. You still use the old Actor trait, all methods
are there etc. But you can’t just new this Actor up and send messages to it since all its public API methods are
gone. They now reside in a new class; ‘ActorRef’ and use need to use instances of this class to interact with the
Actor (sending messages etc.).
Here is a short migration guide with the things that you have to change. It is a big conceptual change but in
practice you don’t have to change much.
Creating Actors with default constructor
From:
val a = new MyActor
a ! msg
To:
import Actor._
val a = actorOf[MyActor]
a ! msg
You can also start it in the same statement:
val a = actorOf[MyActor].start
Creating Actors with non-default constructor
From:
val a = new MyActor(..)
a ! msg
To:
import Actor._
val a = actorOf(new MyActor(..))
a ! msg
Use of ‘self’ ActorRef API
Where you have used ‘this’ to refer to the Actor from within itself now use ‘self’:
self ! MessageToMe
7.1. Migration Guides
252
Akka Documentation, Release 1.3.1
Now the Actor trait only has the callbacks you can implement: * receive * postRestart/preRestart * init/shutdown
It has no state at all.
All API has been moved to ActorRef. The Actor is given its ActorRef through the ‘self’ member variable. Here
you find functions like: * !, !!, !!! and forward * link, unlink, startLink, spawnLink etc * makeTransactional,
makeRemote etc. * start, stop * etc.
Here you also find fields like * dispatcher = ... * id = ... * lifeCycle = ... * faultHandler = ... * trapExit = ... * etc.
This means that to use them you have to prefix them with ‘self’, like this:
self ! Message
However, for convenience you can import these functions and fields like below, which will allow you do drop the
‘self’ prefix:
class MyActor extends Actor {
import self._
id = ...
dispatcher = ...
spawnLink[OtherActor]
...
}
Serialization
If you want to serialize it yourself, here is how to do it:
val actorRef1 = actorOf[MyActor]
val bytes = actorRef1.toBinary
val actorRef2 = ActorRef.fromBinary(bytes)
If you are also using Protobuf then you can use the methods that work with Protobuf’s Messages directly.
val actorRef1 = actorOf[MyActor]
val protobufMessage = actorRef1.toProtocol
val actorRef2 = ActorRef.fromProtocol(protobufMessage)
Camel
Some methods of the se.scalablesolutions.akka.camel.Message class have been deprecated in 0.9. These are
package se.scalablesolutions.akka.camel
case class Message(...) {
// ...
@deprecated def bodyAs[T](clazz: Class[T]): T
@deprecated def setBodyAs[T](clazz: Class[T]): Message
// ...
}
They will be removed in 1.0. Instead use
package se.scalablesolutions.akka.camel
case class Message(...) {
// ...
def bodyAs[T](implicit m: Manifest[T]): T =
7.1. Migration Guides
253
Akka Documentation, Release 1.3.1
def setBodyAs[T](implicit m: Manifest[T]): Message
// ...
}
Usage example: .. code-block:: scala
val m = Message(1.4) val b = m.bodyAs[String]
7.1.5 Migration Guide 0.7.x to 0.8.x
This is a case-by-case migration guide from Akka 0.7.x (on Scala 2.7.7) to Akka 0.8.x (on Scala
2.8.x)
Cases:
Actor.send is removed and replaced in full with Actor.!
myActor send "test"
becomes
myActor ! "test"
Actor.! now has it’s implicit sender defaulted to None
def !(message: Any)(implicit sender: Option[Actor] = None)
“import Actor.Sender.Self” has been removed because it’s not needed anymore
Remove
import Actor.Sender.Self
Actor.spawn now uses manifests instead of concrete class types
val someActor = spawn(classOf[MyActor])
becomes
val someActor = spawn[MyActor]
Actor.spawnRemote now uses manifests instead of concrete class types
val someActor = spawnRemote(classOf[MyActor],"somehost",1337)
becomes
val someActor = spawnRemote[MyActor]("somehost",1337)
7.1. Migration Guides
254
Akka Documentation, Release 1.3.1
Actor.spawnLink now uses manifests instead of concrete class types
val someActor = spawnLink(classOf[MyActor])
becomes
val someActor = spawnLink[MyActor]
Actor.spawnLinkRemote now uses manifests instead of concrete class types
val someActor = spawnLinkRemote(classOf[MyActor],"somehost",1337)
becomes
val someActor = spawnLinkRemote[MyActor]("somehost",1337)
Transaction.atomic and friends are moved into Transaction.Local._ and Transaction.Global._
We now make a difference between transaction management that are local within a thread and global across many
threads (and actors).
7.2 Release Notes
7.2.1 Release 1.3
This release, aside for the new 0MQ module, includes several important bugfixes.
• FIX - Fix use of AlreadyCompletedFuture with monadic methods (map/flatMap/etc...)
• FIX - Make sure the Mongo based durable mailbox gets published
• ADD - Make sure akka-zeromq module is code reviewed and enabled in AkkaBuild
• FIX - Avoid Class.getSimpleName in EventHandler
• FIX - Remove Netty compression
• FIX - Remote client terminates connection during remote typed actor operation
• FIX - Actor pool start with min amount of actor (preFill)
• FIX - def broadcast should do forward and not !
• FIX - toRemoteActorRefProtocol regression
• FIX - Possible leak of ActorRefs when using actor pool
• FIX - Adding sane rejected execution handler as default
7.2.2 Release 1.2
This release, while containing several substantial improvements, focuses on paving the way for the upcoming
2.0 release. A selection of changes is presented in the following, for the full list of tickets closed during the
development cycle please refer to the issue tracker.
• Actor:
– unified Channel abstraction for Promise & Actor
– reintegrate invocation tracing (to be enabled per class and globally)
7.2. Release Notes
255
Akka Documentation, Release 1.3.1
– make last message available during preRestart
– experimental freshInstance life-cycle hook for priming the new instance during restart
– new textual primitives tell (!) and ask (?, formerly !!!)
– timeout for ask Futures taken from implicit argument (currently with fallback to deprecated
ActorRef.timeout
• durable mailboxes:
– beanstalk, file, mongo, redis
• Future:
– onTimeout callback
– select dispatcher for execution by implicit argument
– add safer cast methods as[T]: T and mapTo[T]: Future[T]
• TestKit:
– add TestProbe (can receive, reply and forward messages, supports all TestKit assertions)
– add TestKit.awaitCond
– support global time-factor for all timing assertions (for running on busy CI servers)
• FSM:
– add TestFSMRef
– add LoggingFSM (transition tracing, rolling event log)
• updated dependencies:
– Scala 2.9.1
– Jackson 1.8.0
– Netty 3.2.5
– Protobuf 2.4.1
– ScalaTest 1.6.1
• various fixes, small improvements and documentation updates
• several deprecations in preparation for 2.0
Method
Actor.preRestart(cause)
ActorRef.sendOneWay
ActorRef.sendOneWaySafe
ActorRef.sendRequestReply
ActorRef.sendRequestReplyFuture
ActorRef.replyUnsafe
ActorRef.replySafe
ActorRef.mailboxSize
ActorRef.!!
ActorRef.!!!
ActorRef.reply_?
Future.receive
Future.collect
Future.failure
MessageDispatcher.pendingFutures
RemoteClientModule.*Listener(s)
TestKit.expectMsg(pf)
TestKit.receiveWhile(pf)
7.2. Release Notes
Replacement
Actor.preRestart(cause, lastMsg)
ActorRef.tell
ActorRef.tryTell
ActorRef.ask(...).get()
ActorRef.ask(...).get()
ActorRef.reply
ActorRef.tryReply
ActorRef.dispatcher.mailboxSize(actorRef)
ActorRef.?(...).as[T]
ActorRef.?
ActorRef.tryReply
Future.onResult
Future.map
Future.recover
MessageDispatcher.tasks
EventHandler.<X>
TestKit.expectMsgPF
TestKit.receiveWhile()(pf)
256
Akka Documentation, Release 1.3.1
Trivia
This release contains changes to 221 files, with 16844 insertions and 4010 deletions (diff between two points,
does not count multiple changes to the same line). The authorship of the corresponding commits is distributed
as shown below; the listing should not be taken too seriously, though, it has just been done using git log
--shortstat and summing up the numbers, so it certainly misses details like who originally authored changes
which were then back-ported from the master branch (do not fear, you will be correctly attributed when the stats
for 2.0 are made).
Commits
90
78
41
31
7
4
1
2
Insertions
12315
3823
9834
1819
238
86
17
15
Deletions
245
200
130
131
22
25
5
5
Author
Viktor Klang
Roland Kuhn
Patrik Nordwall
Peter Vlugter
Derek Williams
Peter Veentjer
Debasish Ghosh
Jonas Bonér
Note: Release notes of previous releases consisted of ticket or change listings in no particular order
7.2.3 Release 1.1
• ADD - #647 Extract an akka-camel-typed module out of akka-camel for optional typed actor support (Martin
Krasser)
• ADD - #654 Allow consumer actors to acknowledge in-only message exchanges (Martin Krasser)
• ADD - #669 Support self.reply in preRestart and postStop after exception in receive (Martin Krasser)
• ADD - #682 Support for fault-tolerant Producer actors (Martin Krasser)
• ADD - Move TestKit to akka-testkit and add CallingThreadDispatcher (Roland Kuhn)
• ADD - Remote Client message buffering transaction log for buffering messages failed to send due to network problems. Flushes the buffer on reconnect. (Jonas Bonér)
• ADD - Added trait simulate network problems/errors to be used for remote actor testing (Jonas Bonér)
• ADD - Add future and await methods to Agent (Peter Vlugter)
• ADD - #586 Allow explicit reconnect for RemoteClient (Viktor Klang)
• ADD - #587 Dead letter sink queue for messages sent through RemoteClient that didn’t get sent due to
connection failure (Viktor Klang)
• ADD - #598 actor.id when using akka-spring should be the id of the spring bean (Viktor Klang)
• ADD - #652 Reap expired futures from ActiveRemoteClientHandler (Viktor Klang)
• ADD - #656 Squeeze more out of EBEDD? (Viktor Klang)
• ADD - #715 EventHandler.error should be usable without Throwable (Viktor Klang)
• ADD - #717 Add ExecutionHandler to NettyRemoteServer for more performance and scalability (Viktor
Klang)
• ADD - #497 Optimize remote sends done in local scope (Viktor Klang)
• ADD - #633 Add support for Scalaz in akka-modules (Derek Williams)
• ADD - #677 Add map, flatMap, foreach, and filter to Future (Derek Williams)
• ADD - #661 Optimized Future’s internals (Derek Williams)
• ADD - #685 Optimize execution of Futures (Derek Williams)
7.2. Release Notes
257
Akka Documentation, Release 1.3.1
• ADD - #711 Make Future.completeWith work with an uncompleted Future (Derek Williams)
• UPD - #667 Upgrade to Camel 2.7.0 (Martin Krasser)
• UPD - Updated HawtDispatch to 1.1 (Hiram Chirino)
• UPD - #688 Update Akka 1.1-SNAPSHOT to Scala 2.9.0-RC1 (Viktor Klang)
• UPD - #718 Add HawtDispatcher to akka-modules (Viktor Klang)
• UPD - #698 Deprecate client-managed actors (Viktor Klang)
• UPD - #730 Update Akka and Akka Modules to SBT 0.7.6-RC0 (Viktor Klang)
• UPD - #663 Update to latest scalatest (Derek Williams)
• FIX - Misc cleanup, API changes and refactorings (Jonas Bonér)
• FIX - #675 preStart() is called twice when creating new instance of TypedActor (Debasish Ghosh)
• FIX - #704 Write docs for Java Serialization (Debasish Ghosh)
• FIX - #645 Change Futures.awaitAll to not throw FutureTimeoutException but return a List[Option[Any]]
(Viktor Klang)
• FIX - #681 Clean exit using server-managed remote actor via client (Viktor Klang)
• FIX - #720 Connection loss when sending to a dead remote actor (Viktor Klang)
• FIX - #593 Move Jetty specific stuff (with deps) from akka-http to akka-kernel (Viktor Klang)
• FIX - #638 ActiveRemoteClientHandler - Unexpected exception from downstream in remote client (Viktor
Klang)
• FIX - #655 Remote actors with non-uuid names doesnt work for req./reply-pattern (Viktor Klang)
• FIX - #588 RemoteClient.shutdown does not remove client from Map with clients (Viktor Klang)
• FIX - #672 Remoting breaks if mutual DNS lookup isn’t possible (Viktor Klang)
• FIX - #699 Remote typed actor per-session server won’t start if called method has no result (Viktor Klang)
• FIX - #702 Handle ReadTimeoutException in akka-remote (Viktor Klang)
• FIX - #708 Fall back to Akka classloader if event-handler class cannot be found. (Viktor Klang)
• FIX - #716 Split akka-http and clean-up dependencies (Viktor Klang)
• FIX - #721 Inability to parse/load the Config should do a System.exit(-1) (Viktor Klang)
• FIX - #722 Race condition in Actor hotswapping (Viktor Klang)
• FIX - #723 MessageSerializer CNFE regression (Viktor Klang)
• FIX - #680 Remote TypedActor behavior differs from local one when sending to generic interfaces (Viktor
Klang)
• FIX - #659 Calling await on a Future that is expired and uncompleted should throw an exception (Derek
Williams)
• REM - #626 Update and clean up dependencies (Viktor Klang)
• REM - #623 Remove embedded-repo (Akka + Akka Modules) (Viktor Klang)
• REM - #686 Remove SBinary (Viktor Klang)
7.2.4 Release 1.0-RC6
• FIX - #628 Supervied TypedActors fails to restart (Viktor Klang)
• FIX - #629 Stuck upon actor invocation (Viktor Klang)
7.2. Release Notes
258
Akka Documentation, Release 1.3.1
7.2.5 Release 1.0-RC5
• FIX - Source JARs published to ‘src’ instead of ‘source’ || Odd Moller ||
• FIX - #612 Conflict between Spring autostart=true for Consumer actors and <akka:camel-service> (Martin
Krasser)
• FIX - #613 Change Akka XML schema URI to http://akka.io/schema/akka (Martin Krasser)
• FIX - Spring XSD namespace changed from ‘akkasource.org’ to ‘akka.io’ (Viktor Klang)
• FIX - Checking for remote secure cookie is disabled by default if no akka.conf is loaded (Viktor Klang)
• FIX - Changed Casbah to ScalaToolsRepo for akka-sbt-plugin (Viktor Klang)
• FIX - ActorRef.forward now doesn’t require the sender to be set on the message (Viktor Klang)
7.2.6 Release 1.0-RC3
• ADD - #568 Add autostart attribute to Spring actor configuration (Viktor Klang)
• ADD - #586 Allow explicit reconnect for remote clients (Viktor Klang)
• ADD - #587 Add possibility for dead letter queues for failed remote sends (Viktor Klang)
• ADD - #497 Optimize remote send in local scope (Viktor Klang)
• ADD - Improved Java Actor API: akka.actor.Actors (Viktor Klang)
• ADD - Improved Scala Actor API: akka.actor.Actor (Viktor Klang)
• ADD - #148 Create a testing framework for testing Actors (Roland Kuhn)
• ADD - Support Replica Set/Replica Pair connection modes with MongoDB Persistence || Brendan
McAdams ||
• ADD - User configurable Write Concern settings for MongoDB Persistence || Brendan McAdams ||
• ADD - Support for configuring MongoDB Persistence with MongoDB’s URI Connection String || Brendan
McAdams ||
• ADD - Support for Authentication with MongoDB Persistence || Brendan McAdams ||
• FIX - Misc bug fixes || Team ||
• FIX - #603 Race condition in Remote send (Viktor Klang)
• FIX - #594 Log statement in RemoteClientHandler was wrongly formatted (Viktor Klang)
• FIX - #580 Message uuids must be generated (Viktor Klang)
• FIX - #583 Serialization classloader has a visibility issue (Viktor Klang)
• FIX - #598 By default the bean ID should become the actor id for Spring actor configuration (Viktor Klang)
• FIX - #577 RemoteClientHandler swallows certain exceptions (Viktor Klang)
• FIX - #581 Fix edgecase where an exception could not be deserialized (Viktor Klang)
• FIX - MongoDB write success wasn’t being properly checked; fixed (integrated w/ new write concern
features) || Brendan McAdams ||
• UPD - Improvements to FSM module akka.actor.FSM || Manie & Kuhn ||
• UPD - Changed Akka URI to http://akka.io. Reflects both XSDs, Maven repositories etc. (Jonas Bonér)
• REM - #574 Remote RemoteClient, RemoteServer and RemoteNode (Viktor Klang)
• REM - object UntypedActor, object ActorRegistry, class RemoteActor, class RemoteUntypedActor, class
RemoteUntypedConsumerActor (Viktor Klang)
7.2. Release Notes
259
Akka Documentation, Release 1.3.1
7.2.7 Release 1.0-RC1
• ADD - #477 Added support for Remote Agents (Viktor Klang)
• ADD - #460 Hotswap for Java API (UntypedActor) (Viktor Klang)
• ADD - #471 Added support for TypedActors to return Java Option (Viktor Klang)
• ADD - New design and API for more fluent and intuitive FSM module (Roland Kuhn)
• ADD - Added secure cookie based remote node authentication (Jonas Bonér)
• ADD - Untrusted safe mode for remote server (Jonas Bonér)
• ADD - Refactored config file format - added list of enabled modules etc. (Jonas Bonér)
• ADD - Docs for Dataflow Concurrency (Jonas Bonér)
• ADD - Made remote message frame size configurable (Jonas Bonér)
• ADD - #496 Detect when Remote Client disconnects (Jonas Bonér)
• ADD - #472 Improve API to wait for endpoint activation/deactivation (more ...) (Martin Krasser)
• ADD - #473 Allow consumer actors to customize their own routes (more ...) (Martin Krasser)
• ADD - #504 Add session bound server managed remote actors || Paul Pach ||
• ADD - DSL for FSM (Irmo Manie)
• ADD - Shared unit test for all dispatchers to enforce Actor Model (Viktor Klang)
• ADD - #522 Make stacking optional for become and HotSwap (Viktor Klang)
• ADD - #524 Make frame size configurable for client&server (Bonér & Klang)
• ADD - #526 Add onComplete callback to Future (Viktor Klang)
• ADD - #536 Document Channel-abstraction for later replies (Viktor Klang)
• ADD - #540 Include self-reference as parameter to HotSwap (Viktor Klang)
• ADD - #546 Include Garrick Evans’ Akka-mist into master (Viktor Klang)
• ADD - #438 Support remove operation in PersistentVector (Scott Clasen)
• ADD - #229 Memcached protocol support for Persistence module (Scott Clasen)
• ADD - Amazon SimpleDb support for Persistence module (Scott Clasen)
• FIX - #518 refactor common storage bakend to use bulk puts/gets where possible (Scott Clasen)
• FIX - #532 Prevent persistent datatypes with same uuid from corrupting a TX (Scott Clasen)
• FIX - #464 ThreadPoolBuilder should be rewritten to be an immutable builder (Viktor Klang)
• FIX - #449 Futures.awaitOne now uses onComplete listeners (Viktor Klang)
• FIX - #486 Fixed memory leak caused by Configgy that prevented full unload (Viktor Klang)
• FIX - #488 Fixed race condition in EBEDD restart (Viktor Klang)
• FIX - #492 Fixed race condition in Scheduler (Viktor Klang)
• FIX - #493 Switched to non-https repository for JBoss artifacts (Viktor Klang)
• FIX - #481 Exception when creating an actor now behaves properly when supervised (Viktor Klang)
• FIX - #498 Fixed no-op in supervision DSL (Viktor Klang)
• FIX - #491 reply and reply_? now sets a sender reference (Viktor Klang)
• FIX - #519 NotSerializableError when using Remote Typed Actors (Viktor Klang)
• FIX - #523 Message.toString is called all the time for incomign messages, expensive (Viktor Klang)
• FIX - #537 Make sure top folder is included in sources jar (Viktor Klang)
7.2. Release Notes
260
Akka Documentation, Release 1.3.1
• FIX - #529 Remove Scala version number from Akka artifact ids (Viktor Klang)
• FIX - #533 Can’t set LifeCycle from the Java API (Viktor Klang)
• FIX - #542 Make Future-returning Remote Typed Actor methods use onComplete (Viktor Klang)
• FIX - #479 Do not register listeners when CamelService is turned off by configuration (Martin Krasser)
• FIX - Fixed bug with finding TypedActor by type in ActorRegistry (Jonas Bonér)
• FIX - #515 race condition in FSM StateTimeout Handling (Irmo Manie)
• UPD - Akka package from “se.scalablesolutions.akka” to “akka” (Viktor Klang)
• UPD - Update Netty to 3.2.3.Final (Viktor Klang)
• UPD - #458 Camel to 2.5.0 (Martin Krasser)
• UPD - #458 Spring to 3.0.4.RELEASE (Martin Krasser)
• UPD - #458 Jetty to 7.1.6.v20100715 (Martin Krasser)
• UPD - Update to Scala 2.8.1 (Jonas Bonér)
• UPD - Changed remote server default port to 2552 (AKKA) (Jonas Bonér)
• UPD - Cleaned up and made remote protocol more effifient (Jonas Bonér)
• UPD - #528 RedisPersistentRef should not throw in case of missing key (Debasish Ghosh)
• UPD - #531 Fix RedisStorage add() method in Java API (Debasish Ghosh)
• UPD - #513 Implement snapshot based persistence control in SortedSet (Debasish Ghosh)
• UPD - #547 Update FSM docs (Irmo Manie)
• UPD - #548 Update AMQP docs (Irmo Manie)
• REM - Atmosphere integration, replace with Mist (Klang @ Evans)
• REM - JGroups integration, doesn’t play with cloud services :/ (Viktor Klang)
7.2.8 Release 1.0-MILESTONE1
• ADD - Splitted akka-core up in akka-actor, akka-typed-actor & akka-remote (Jonas Bonér)
• ADD - Added meta-data to network protocol (Jonas Bonér)
• ADD - HotSwap and actor.become now uses a stack of PartialFunctions with API for pushing and popping
the stack (Jonas Bonér)
• ADD - #440 Create typed actors with constructor args (Michael Kober)
• ADD - #322 Abstraction for unification of sender and senderFuture for later reply (Michael Kober)
• ADD - #364 Serialization for TypedActor proxy reference (Michael Kober)
• ADD - #423 Support configuration of Akka via Spring (Michael Kober)
• FIX - #426 UUID wrong for remote proxy for server managed actor (Michael Kober)
• ADD - #378 Support for server initiated remote TypedActor and UntypedActor in Spring config (Michael
Kober)
• ADD - #194 Support for server-managed typed actor ||< Michael Kober ||
• ADD - #447 Allow Camel service to be turned off by configuration (Martin Krasser)
• ADD - #457 JavaAPI improvements for akka-camel (please read the migration guide) (Martin Krasser)
• ADD - #465 Dynamic message routing to actors (more ...) (Martin Krasser)
• FIX - #410 Use log configuration from config directory (Martin Krasser)
7.2. Release Notes
261
Akka Documentation, Release 1.3.1
• FIX - #343 Some problems with persistent structures (Debasish Ghosh)
• FIX - #430 Refactor / re-implement MongoDB adapter so that it conforms to the guidelines followed in
Redis and Cassandra modules (Debasish Ghosh)
• FIX - #436 ScalaJSON serialization does not map Int data types properly when used within a Map (Debasish
Ghosh)
• ADD - #230 Update redisclient to be Redis 2.0 compliant (Debasish Ghosh)
• FIX - #435 Mailbox serialization does not retain messages (Debasish Ghosh)
• ADD - #445 Integrate type class based serialization of sjson into Akka (Debasish Ghosh)
• FIX - #480: Regression multibulk replies redis client (Debasish Ghosh)
• FIX - #415 Publish now generate source and doc jars (Viktor Klang)
• FIX - #420 REST endpoints should be able to be processed in parallel (Viktor Klang)
• FIX - #422 Dispatcher config should work for ThreadPoolBuilder-based dispatchers (Viktor Klang)
• FIX - #401 ActorRegistry should not leak memory (Viktor Klang)
• FIX - #250 Performance optimization for ExecutorBasedEventDrivenDispatcher (Viktor Klang)
• FIX - #419 Rename init and shutdown callbacks to preStart and postStop, and remove initTransactionalState
(Viktor Klang)
• FIX - #346 Make max no of restarts (and within) are now both optional (Viktor Klang)
• FIX - #424 Actors self.supervisor not set by the time init() is called when started by startLink() (Viktor
Klang)
• FIX - #427 spawnLink and startLink now has the same dispatcher semantics (Viktor Klang)
• FIX - #413 Actor shouldn’t process more messages when waiting to be restarted (HawtDispatcher still does)
(Viktor Klang)
• FIX - !! and !!! now do now not block the actor when used in remote actor (Viktor Klang)
• FIX - RemoteClient now reconnects properly (Viktor Klang)
• FIX - Logger.warn now properly works with varargs (Viktor Klang)
• FIX - #450 Removed ActorRef lifeCycle boilerplate: Some(LifeCycle(Permanent)) => Permanent (Viktor
Klang)
• FIX - Moved ActorRef.trapExit into ActorRef.faultHandler and removed Option-boilerplate from faultHandler (Viktor Klang)
• FIX - ThreadBasedDispatcher cheaper for idling actors, also benefits from all that is ExecutorBasedEventDrivenDispatcher (Viktor Klang)
• FIX - Fixing Futures.future, uses Actor.spawn under the hood, specify dispatcher to control where block is
executed (Viktor Klang)
• FIX - #469 Akka “dist” now uses a root folder to avoid loitering if unzipped in a folder (Viktor Klang)
• FIX - Removed ScalaConfig, JavaConfig and rewrote Supervision configuration (Viktor Klang)
• UPD - Jersey to 1.3 (Viktor Klang)
• UPD - Atmosphere to 0.6.2 (Viktor Klang)
• UPD - Netty to 3.2.2.Final (Viktor Klang)
• ADD - Changed config file priority loading and added config modes. (Viktor Klang)
• ADD - #411 Bumped Jetty to v 7 and migrated to it’s eclipse packages (Viktor Klang)
• ADD - #414 Migrate from Grizzly to Jetty for Akka Microkernel (Viktor Klang)
• ADD - #261 Add Java API for ‘routing’ module (Viktor Klang)
7.2. Release Notes
262
Akka Documentation, Release 1.3.1
• ADD - #262 Add Java API for Agent (Viktor Klang)
• ADD - #264 Add Java API for Dataflow (Viktor Klang)
• ADD - Using JerseySimpleBroadcaster instead of JerseyBroadcaster in AkkaBroadcaster (Viktor Klang)
• ADD - #433 Throughput deadline added for ExecutorBasedEventDrivenDispatcher (Viktor Klang)
• ADD - Add possibility to set default cometSupport in akka.conf (Viktor Klang)
• ADD - #451 Added possibility to use akka-http as a standalone REST server (Viktor Klang)
• ADD - #446 Added support for Erlang-style receiveTimeout (Viktor Klang)
• ADD - #462 Added support for suspend/resume of processing individual actors mailbox, should give clearer
restart semantics (Viktor Klang)
• ADD - #466 Actor.spawn now takes an implicit dispatcher to specify who should run the block (Viktor
Klang)
• ADD - #456 Added map to Future and Futures.awaitMap (Viktor Klang)
• REM - #418 Remove Lift sample module and docs (Viktor Klang)
• REM - Removed all Reactor-based dispatchers (Viktor Klang)
• REM - Removed anonymous actor factories (Viktor Klang)
• ADD - Voldemort support for akka-persistence (Scott Clasen)
• ADD - HBase support for akka-persistence (David Greco)
• ADD - CouchDB support for akka-persistence (Yung-Luen Lan & Kahlen)
• ADD - #265 Java API for AMQP module (Irmo Manie)
7.2.9 Release 0.10 - Aug 21 2010
• ADD - Added new Actor type: UntypedActor for Java API (Jonas Bonér)
• ADD - #26 Deep serialization of Actor including its mailbox (Jonas Bonér)
• ADD - Rewritten network protocol. More efficient and cleaner. (Jonas Bonér)
• ADD - Rewritten Java Active Object tests into Scala to be able to run the in SBT. (Jonas Bonér)
• ADD - Added isDefinedAt method to Actor for checking if it can receive a certain message (Jonas Bonér)
• ADD - Added caching of Active Object generated class bytes, huge perf improvement (Jonas Bonér)
• ADD - Added RemoteClient Listener API (Jonas Bonér)
• ADD - Added methods to retrieve children from a Supervisor (Jonas Bonér)
• ADD - Rewritten Supervisor to become more clear and “correct” (Jonas Bonér)
• ADD - Added options to configure a blocking mailbox with custom capacity (Jonas Bonér)
• ADD - Added RemoteClient reconnection time window configuration option (Jonas Bonér)
• ADD - Added ActiveObjectContext with sender reference etc (Jonas Bonér)
• ADD - #293 Changed config format to JSON-style (Jonas Bonér)
• ADD - #302: Incorporate new ReceiveTimeout in Actor serialization (Jonas Bonér)
• ADD - Added Java API docs and made it comparable with Scala API docs. 1-1 mirroring (Jonas Bonér)
• ADD - Renamed Active Object to Typed Actor (Jonas Bonér)
• ADD - Enhanced Typed Actor: remoting, “real” restart upon failure etc. (Jonas Bonér)
• ADD - Typed Actor now inherits Actor and is a full citizen in the Actor world. (Jonas Bonér)
7.2. Release Notes
263
Akka Documentation, Release 1.3.1
• ADD - Added support for remotely shutting down a remote actor (Jonas Bonér)
• ADD - #224 Add support for Camel in typed actors (more ...) (Martin Krasser)
• ADD - #282 Producer trait should implement Actor.receive (more...) (Martin Krasser)
• ADD - #271 Support for bean scope prototype in akka-spring (Johan Rask)
• ADD - Support for DI of values and bean references on target instance in akka-spring (Johan Rask)
• ADD - #287 Method annotated with @postrestart in ActiveObject is not called during restart (Johan Rask)
• ADD - Support for ApplicationContextAware in akka-spring (Johan Rask)
• ADD - #199 Support shutdown hook in TypedActor (Martin Krasser)
• ADD - #266 Access to typed actors from user-defined Camel routes (more ...) (Martin Krasser)
• ADD - #268 Revise akka-camel documentation (more ...) (Martin Krasser)
• ADD - #289 Support for <akka:camel-service> Spring configuration element (more ...) (Martin Krasser)
• ADD - #296 TypedActor lifecycle management (Martin Krasser)
• ADD - #297 Shutdown routes to typed actors (more ...) (Martin Krasser)
• ADD - #314 akka-spring to support typed actor lifecycle management (more ...) (Martin Krasser)
• ADD - #315 akka-spring to support configuration of shutdown callback method (more ...) (Martin Krasser)
• ADD - Fault-tolerant consumer actors and typed consumer actors (more ...) (Martin Krasser)
• ADD - #320 Leverage Camel’s non-blocking routing engine (more ...) (Martin Krasser)
• ADD - #335 Producer trait should allow forwarding of results (Martin Krasser)
• ADD - #339 Redesign of Producer trait (pre/post processing hooks, async in-out) (more ...) (Martin Krasser)
• ADD - Non-blocking, asynchronous routing example for akka-camel (more ...) (Martin Krasser)
• ADD - #333 Allow applications to wait for endpoints being activated (more ...) (Martin Krasser)
• ADD - #356 Support @consume annotations on typed actor implementation class (Martin Krasser)
• ADD - #357 Support untyped Java actors as endpoint consumer (Martin Krasser)
• ADD - #366 CamelService should be a singleton (Martin Krasser)
• ADD - #392 Support untyped Java actors as endpoint producer (Martin Krasser)
• ADD - #393 Redesign CamelService singleton to be a CamelServiceManager (more ...) (Martin Krasser)
• ADD - #295 Refactoring Actor serialization to type classes (Debasish Ghosh)
• ADD - #317 Change documentation for Actor Serialization (Debasish Ghosh)
• ADD - #388 Typeclass serialization of ActorRef/UntypedActor isn’t Java friendly (Debasish Ghosh)
• ADD - #292 Add scheduleOnce to Scheduler (Irmo Manie)
• ADD - #308 Initial receive timeout on actor (Irmo Manie)
• ADD - Redesign of AMQP module (more ...) (Irmo Manie)
• ADD - Added “become(behavior: Option[Receive])” to Actor (Viktor Klang)
• ADD - Added “find[T](f: PartialFunction[ActorRef,T]) : Option[T]” to ActorRegistry (Viktor Klang)
• ADD - #369 Possibility to configure dispatchers in akka.conf (Viktor Klang)
• ADD - #395 Create ability to add listeners to RemoteServer (Viktor Klang)
• ADD - #225 Add possibility to use Scheduler from TypedActor (Viktor Klang)
• ADD - #61 Integrate new persistent datastructures in Scala 2.8 (Peter Vlugter)
• ADD - Expose more of what Multiverse can do (Peter Vlugter)
7.2. Release Notes
264
Akka Documentation, Release 1.3.1
• ADD - #205 STM transaction settings (Peter Vlugter)
• ADD - #206 STM transaction deferred and compensating (Peter Vlugter)
• ADD - #232 Expose blocking transactions (Peter Vlugter)
• ADD - #249 Expose Multiverse Refs for primitives (Peter Vlugter)
• ADD - #390 Expose transaction propagation level in multiverse (Peter Vlugter)
• ADD - Package objects for importing local/global STM (Peter Vlugter)
• ADD - Java API for the STM (Peter Vlugter)
• ADD - #379 Create STM Atomic templates for Java API (Peter Vlugter)
• ADD - #270 SBT plugin for Akka (Peter Vlugter)
• ADD - #198 support for ThreadBasedDispatcher in Spring config (Michael Kober)
• ADD - #377 support HawtDispatcher in Spring config (Michael Kober)
• ADD - #376 support Spring config for untyped actors (Michael Kober)
• ADD - #200 support WorkStealingDispatcher in Spring config (Michael Kober)
• UPD - #336 RabbitMQ 1.8.1 (Irmo Manie)
• UPD - #288 Netty to 3.2.1.Final (Viktor Klang)
• UPD - Atmosphere to 0.6.1 (Viktor Klang)
• UPD - Lift to 2.8.0-2.1-M1 (Viktor Klang)
• UPD - Camel to 2.4.0 (Martin Krasser)
• UPD - Spring to 3.0.3.RELEASE (Martin Krasser)
• UPD - Multiverse to 0.6 (Peter Vlugter)
• FIX - Fixed bug with stm not being enabled by default when no AKKA_HOME is set (Jonas Bonér)
• FIX - Fixed bug in network manifest serialization (Jonas Bonér)
• FIX - Fixed bug Remote Actors (Jonas Bonér)
• FIX - Fixed memory leak in Active Objects (Jonas Bonér)
• FIX - Fixed indeterministic deadlock in Transactor restart (Jonas Bonér)
• FIX - #325 Fixed bug in STM with dead hanging CountDownCommitBarrier (Jonas Bonér)
• FIX - #316: NoSuchElementException during ActiveObject restart (Jonas Bonér)
• FIX - #256: Tests for ActiveObjectContext (Jonas Bonér)
• FIX - Fixed bug in restart of Actors with ‘Temporary’ life-cycle (Jonas Bonér)
• FIX - #280 Tests fail if there is no akka.conf set (Jonas Bonér)
• FIX - #286 unwanted transitive dependencies from Geronimo project (Viktor Klang)
• FIX - Atmosphere comet comment to use stream instead of writer (Viktor Klang)
• FIX - #285 akka.conf is now used as defaults for Akka REST servlet init parameters (Viktor Klang)
• FIX - #321 fixed performance regression in ActorRegistry (Viktor Klang)
• FIX - #286 geronimo servlet 2.4 dep is no longer transitively loaded (Viktor Klang)
• FIX - #334 partial lift sample rewrite to fix breakage (Viktor Klang)
• FIX - Fixed a memory leak in ActorRegistry (Viktor Klang)
• FIX - Fixed a race-condition in Cluster (Viktor Klang)
• FIX - #355 Switched to Array instead of List on ActorRegistry return types (Viktor Klang)
7.2. Release Notes
265
Akka Documentation, Release 1.3.1
• FIX - #352 ActorRegistry.actorsFor(class) now checks isAssignableFrom (Viktor Klang)
• FIX - Fixed a race condition in ActorRegistry.register (Viktor Klang)
• FIX - #337 Switched from Configgy logging to SLF4J, better for OSGi (Viktor Klang)
• FIX - #372 Scheduler now returns Futures to cancel tasks (Viktor Klang)
• FIX - #306 JSON serialization between remote actors is not transparent (Debasish Ghosh)
• FIX - #204 Reduce object creation in STM (Peter Vlugter)
• FIX - #253 Extend Multiverse BasicRef rather than wrap ProgrammaticRef (Peter Vlugter)
• REM - Removed pure POJO-style Typed Actor (old Active Object) (Jonas Bonér)
• REM - Removed Lift as a dependency for Akka-http (Viktor Klang)
• REM - #294 Remove reply and reply_? from Actor (Viktor Klang)
• REM - Removed one field in Actor, should be a minor memory reduction for high actor quantities (Viktor
Klang)
• FIX - #301 DI does not work in akka-spring when specifying an interface (Johan Rask)
• FIX - #328 trapExit should pass through self with Exit to supervisor (Irmo Manie)
• FIX - Fixed warning when deregistering listeners (Martin Krasser)
• FIX - Added camel-jetty-2.4.0.1 to Akka’s embedded-repo. (fixes a concurrency bug in camel-jetty-2.4.0,
to be officially released in Camel 2.5.0) (Martin Krasser)
• FIX - #338 RedisStorageBackend fails when redis closes connection to idle client (Debasish Ghosh)
• FIX - #340 RedisStorage Map.get does not throw exception when disconnected from redis but returns None
(Debasish Ghosh)
7.2.10 Release 0.9 - June 2th 2010
• ADD - Serializable, immutable, network-aware ActorRefs (Jonas Bonér)
• ADD - Optionally JTA-aware STM transactions (Jonas Bonér)
• ADD - Rewritten supervisor management, making use of ActorRef, now really kills the Actor instance and
replaces it (Jonas Bonér)
• ADD - Allow linking and unlinking a declaratively configured Supervisor (Jonas Bonér)
• ADD - Remote protocol rewritten to allow passing along sender reference in all situations (Jonas Bonér)
• ADD - #37 API for JTA usage (Jonas Bonér)
• ADD - Added user accessible ‘sender’ and ‘senderFuture’ references (Jonas Bonér)
• ADD - Sender actor is now passed along for all message send functions (!, !!, !!!, forward) (Jonas Bonér)
• ADD - Subscription API for listening to RemoteClient failures (Jonas Bonér)
• ADD - Implemented link/unlink for ActiveObjects || Jan Kronquist / Michael Kober ||
• ADD - Added alter method to TransactionalRef + added appl(initValue) to Transactional Map/Vector/Ref
(Peter Vlugter)
• ADD - Load dependency JARs in JAR deloyed in kernel’s ,/deploy dir (Jonas Bonér)
• ADD - Allowing using Akka without specifying AKKA_HOME or path to akka.conf config file (Jonas
Bonér)
• ADD - Redisclient now supports PubSub (Debasish Ghosh)
• ADD - Added a sample project under akka-samples for Redis PubSub using Akka actors (Debasish Ghosh)
• ADD - Richer API for Actor.reply (Viktor Klang)
7.2. Release Notes
266
Akka Documentation, Release 1.3.1
• ADD - Added Listeners to Akka patterns (Viktor Klang)
• ADD - #183 Deactivate endpoints of stopped consumer actors (Martin Krasser)
• ADD - Camel Message API improvements (Martin Krasser)
• ADD - #83 Send notification to parent supervisor if all actors supervised by supervisor has been permanently
killed (Jonas Bonér)
• ADD - #121 Make it possible to dynamically create supervisor hierarchies for Active Objects (Michael
Kober)
• ADD - #131 Subscription API for node joining & leaving cluster (Jonas Bonér)
• ADD - #145 Register listener for errors in RemoteClient/RemoteServer (Jonas Bonér)
• ADD - #146 Create an additional distribution with sources (Jonas Bonér)
• ADD - #149 Support loading JARs from META-INF/lib in JARs put into the ./deploy directory (Jonas
Bonér)
• ADD - #166 Implement insertVectorStorageEntriesFor in CassandraStorageBackend (Jonas Bonér)
• ADD - #168 Separate ID from Value in Actor; introduce ActorRef (Jonas Bonér)
• ADD - #174 Create sample module for remote actors (Jonas Bonér)
• ADD - #175 Add new sample module with Peter Vlugter’s Ant demo (Jonas Bonér)
• ADD - #177 Rewrite remote protocol to make use of new ActorRef (Jonas Bonér)
• ADD - #180 Make use of ActorRef indirection for fault-tolerance management (Jonas Bonér)
• ADD - #184 Upgrade to Netty 3.2.0.CR1 (Jonas Bonér)
• ADD - #185 Rewrite Agent and Supervisor to work with new ActorRef (Jonas Bonér)
• ADD - #188 Change the order of how the akka.conf is detected (Jonas Bonér)
• ADD - #189 Reintroduce ‘sender: Option[Actor]’ ref in Actor (Jonas Bonér)
• ADD - #203 Upgrade to Scala 2.8 RC2 (Jonas Bonér)
• ADD - #222 Using Akka without AKKA_HOME or akka.conf (Jonas Bonér)
• ADD - #234 Add support for injection and management of ActiveObjectContext with RTTI such as ‘sender’
and ‘senderFuture’ references etc. (Jonas Bonér)
• ADD - #236 Upgrade SBinary to Scala 2.8 RC2 (Jonas Bonér)
• ADD - #235 Problem with RedisStorage.getVector(..) data structure storage management (Jonas Bonér)
• ADD - #239 Upgrade to Camel 2.3.0 (Martin Krasser)
• ADD - #242 Upgraded to Scala 2.8 RC3 (Jonas Bonér)
• ADD - #243 Upgraded to Protobuf 2.3.0 (Jonas Bonér)
• ADD - Added option to specify class loader when de-serializing messages and RemoteActorRef in RemoteClient (Jonas Bonér)
• ADD - #238 Upgrading to Cassandra 0.6.1 (Jonas Bonér)
• ADD - Upgraded to Jersey 1.2 (Viktor Klang)
• ADD - Upgraded Atmosphere to 0.6-SNAPSHOT, adding WebSocket support (Viktor Klang)
• FIX - Simplified ActiveObject configuration (Michael Kober)
• FIX - #237 Upgrade Mongo Java driver to 1.4 (the latest stable release) (Debasish Ghosh)
• FIX - #165 Implemented updateVectorStorageEntryFor in Mongo persistence module (Debasish Ghosh)
• FIX - #154: Allow ActiveObjects to use the default timeout in config file (Michael Kober)
• FIX - Active Object methods with @inittransactionalstate should be invoked automatically (Michael Kober)
7.2. Release Notes
267
Akka Documentation, Release 1.3.1
• FIX - Nested supervisor hierarchy failure propagation bug fixed (Jonas Bonér)
• FIX - Fixed bug on CommitBarrier transaction registration (Jonas Bonér)
• FIX - Merged many modules to reduce total number of modules (Viktor Klang)
• FIX - Future parameterized (Viktor Klang)
• FIX - #191: Workstealing dispatcher didn’t work with !! (Viktor Klang)
• FIX - #202: Allow applications to disable stream-caching (Martin Krasser)
• FIX - #119 Problem with Cassandra-backed Vector (Jonas Bonér)
• FIX - #147 Problem replying to remote sender when message sent with ! (Jonas Bonér)
• FIX - #171 initial value of Ref can become null if first transaction rolled back (Jonas Bonér)
• FIX - #172 Fix “broken” Protobuf serialization API (Jonas Bonér)
• FIX - #173 Problem with Vector::slice in CassandraStorage (Jonas Bonér)
• FIX - #190 RemoteClient shutdown ends up in endless loop (Jonas Bonér)
• FIX - #211 Problem with getting CommitBarrierOpenException when using Transaction.Global (Jonas
Bonér)
• FIX - #240 Supervised actors not started when starting supervisor (Jonas Bonér)
• FIX - Fixed problem with Transaction.Local not committing to persistent storage (Jonas Bonér)
• FIX - #215: Re-engineered the JAX-RS support (Viktor Klang)
• FIX - Many many bug fixes || Team ||
• REM - Shoal cluster module (Viktor Klang)
7.2.11 Release 0.8.1 - April 6th 2010
• ADD - Redis cluster support (Debasish Ghosh)
• ADD - Reply to remote sender from message set with ! (Jonas Bonér)
• ADD - Load-balancer which prefers actors with few messages in mailbox || Jan Van Besien ||
• ADD - Added developer mailing list: [akka-dev AT googlegroups DOT com] (Jonas Bonér)
• FIX - Separated thread-local from thread-global transaction API (Jonas Bonér)
• FIX - Fixed bug in using STM outside Actors (Jonas Bonér)
• FIX - Fixed bug in anonymous actors (Jonas Bonér)
• FIX - Moved web initializer to new akka-servlet module (Viktor Klang)
7.2.12 Release 0.8 - March 31st 2010
• ADD - Scala 2.8 based (Viktor Klang)
• ADD - Monadic API for Agents (Jonas Bonér)
• ADD - Agents are transactional (Jonas Bonér)
• ADD - Work-stealing dispatcher || Jan Van Besien ||
• ADD - Improved Spring integration (Michael Kober)
• FIX - Various bugfixes || Team ||
• FIX - Improved distribution packaging (Jonas Bonér)
• REMOVE - Actor.send function (Jonas Bonér)
7.2. Release Notes
268
Akka Documentation, Release 1.3.1
7.2.13 Release 0.7 - March 21st 2010
• ADD - Rewritten STM now works generically with fire-forget message flows (Jonas Bonér)
• ADD - Apache Camel integration (Martin Krasser)
• ADD - Spring integration (Michael Kober)
• ADD - Server-managed Remote Actors (Jonas Bonér)
• ADD - Clojure-style Agents (Viktor Klang)
• ADD - Shoal cluster backend (Viktor Klang)
• ADD - Redis-based transactional queue storage backend (Debasish Ghosh)
• ADD - Redis-based transactional sorted set storage backend (Debasish Ghosh)
• ADD - Redis-based atomic INC (index) operation (Debasish Ghosh)
• ADD - Distributed Comet (Viktor Klang)
• ADD - Project moved to SBT (simple-build-tool) || Peter Hausel ||
• ADD - Futures object with utility methods for Future’s (Jonas Bonér)
• ADD - !!! function that returns a Future (Jonas Bonér)
• ADD - Richer ActorRegistry API (Jonas Bonér)
• FIX - Improved event-based dispatcher performance with 40% || Jan Van Besien ||
• FIX - Improved remote client pipeline performance (Viktor Klang)
• FIX - Support several Clusters on the same network (Viktor Klang)
• FIX - Structural package refactoring (Jonas Bonér)
• FIX - Various bugs fixed || Team ||
7.2.14 Release 0.6 - January 5th 2010
• ADD - Clustered Comet using Akka remote actors and clustered membership API (Viktor Klang)
• ADD - Cluster membership API and implementation based on JGroups (Viktor Klang)
• ADD - Security module for HTTP-based authentication and authorization (Viktor Klang)
• ADD - Support for using Scala XML tags in RESTful Actors (scala-jersey) (Viktor Klang)
• ADD - Support for Comet Actors using Atmosphere (Viktor Klang)
• ADD - MongoDB as Akka storage backend (Debasish Ghosh)
• ADD - Redis as Akka storage backend (Debasish Ghosh)
• ADD - Transparent JSON serialization of Scala objects based on SJSON (Debasish Ghosh)
• ADD - Kerberos/SPNEGO support for Security module || Eckhart Hertzler ||
• ADD - Implicit sender for remote actors: Remote actors are able to use reply to answer a request || Mikael
Högqvist ||
• ADD - Support for using the Lift Web framework with Actors || Tim Perrett ||
• ADD - Added CassandraSession API (with socket pooling) wrapping Cassandra’s Thrift API in Scala and
Java APIs (Jonas Bonér)
• ADD - Rewritten STM, now integrated with Multiverse STM (Jonas Bonér)
• ADD - Added STM API for atomic {..} and run {..} orElse {..} (Jonas Bonér)
• ADD - Added STM retry (Jonas Bonér)
7.2. Release Notes
269
Akka Documentation, Release 1.3.1
• ADD - AMQP integration; abstracted as actors in a supervisor hierarchy. Impl AMQP 0.9.1 (Jonas Bonér)
• ADD - Complete rewrite of the persistence transaction management, now based on Unit of Work and Multiverse STM (Jonas Bonér)
• ADD - Monadic API to TransactionalRef (use it in for-comprehension) (Jonas Bonér)
• ADD - Lightweight actor syntax using one of the Actor.actor(..) methods. F.e: ‘val a = actor { case _ => ..
}’ (Jonas Bonér)
• ADD - Rewritten event-based dispatcher which improved perfomance by 10x, now substantially faster than
event-driven Scala Actors (Jonas Bonér)
• ADD - New Scala JSON parser based on sjson (Jonas Bonér)
• ADD - Added zlib compression to remote actors (Jonas Bonér)
• ADD - Added implicit sender reference for fire-forget (‘!’) message sends (Jonas Bonér)
• ADD - Monadic API to TransactionalRef (use it in for-comprehension) (Jonas Bonér)
• ADD - Smoother web app integration; just add akka.conf to the classpath (WEB-INF/classes), no need for
AKKA_HOME or -Dakka.conf=.. (Jonas Bonér)
• ADD - Modularization of distribution into a thin core (actors, remoting and STM) and the rest in submodules
(Jonas Bonér)
• ADD - Added ‘forward’ to Actor, forwards message but keeps original sender address (Jonas Bonér)
• ADD - JSON serialization for Java objects (using Jackson) (Jonas Bonér)
• ADD - JSON serialization for Scala objects (using SJSON) (Jonas Bonér)
• ADD - Added implementation for remote actor reconnect upon failure (Jonas Bonér)
• ADD - Protobuf serialization for Java and Scala objects (Jonas Bonér)
• ADD - SBinary serialization for Scala objects (Jonas Bonér)
• ADD - Protobuf as remote protocol (Jonas Bonér)
• ADD - Updated Cassandra integration and CassandraSession API to v0.4 (Jonas Bonér)
• ADD - CassandraStorage is now works with external Cassandra cluster (Jonas Bonér)
• ADD - ActorRegistry for retrieving Actor instances by class name and by id (Jonas Bonér)
• ADD - SchedulerActor for scheduling periodic tasks (Jonas Bonér)
• ADD - Now start up kernel with ‘java -jar dist/akka-0.6.jar’ (Jonas Bonér)
• ADD - Added Akka user mailing list: akka-user AT googlegroups DOT com]] (Jonas Bonér)
• ADD - Improved and restructured documentation (Jonas Bonér)
• ADD - New URL: http://akkasource.org (Jonas Bonér)
• ADD - New and much improved docs (Jonas Bonér)
• ADD - Enhanced trapping of failures: ‘trapExit = List(classOf[..], classOf[..])’ (Jonas Bonér)
• ADD - Upgraded to Netty 3.2, Protobuf 2.2, ScalaTest 1.0, Jersey 1.1.3, Atmosphere 0.4.1, Cassandra 0.4.1,
Configgy 1.4 (Jonas Bonér)
• FIX - Lowered actor memory footprint; now an actor consumes ~600 bytes, which mean that you can create
6.5 million on 4 GB RAM (Jonas Bonér)
• FIX - Remote actors are now defined by their UUID (not class name) (Jonas Bonér)
• FIX - Fixed dispatcher bugs (Jonas Bonér)
• FIX - Cleaned up Maven scripts and distribution in general (Jonas Bonér)
• FIX - Fixed many many bugs and minor issues (Jonas Bonér)
7.2. Release Notes
270
Akka Documentation, Release 1.3.1
• FIX - Fixed inconsistencies and uglyness in Actors API (Jonas Bonér)
• REMOVE - Removed concurrent mode (Jonas Bonér)
• REMOVE - Removed embedded Cassandra mode (Jonas Bonér)
• REMOVE - Removed the !? method in Actor (synchronous message send, since it’s evil. Use !! with
time-out instead. (Jonas Bonér)
• REMOVE - Removed startup scripts and lib dir (Jonas Bonér)
• REMOVE - Removed the ‘Transient’ life-cycle scope since to close to ‘Temporary’ in semantics. (Jonas
Bonér)
• REMOVE - Removed ‘Transient’ Actors and restart timeout (Jonas Bonér)
7.3 Scaladoc API
7.3.1 Akka Snapshot
Automatically published Scaladoc API for the latest SNAPSHOT version of Akka can be found here:
• Akka - http://doc.akka.io/api/akka/snapshot
• Akka Modules - http://doc.akka.io/api/akka-modules/snapshot
7.3.2 Release Versions
1.3.1 —
• Akka 1.3.1 - http://doc.akka.io/api/akka/1.3.1/
• Akka Modules 1.3.1 - http://doc.akka.io/api/akka-modules/1.3.1/
1.1
• Akka 1.1 - http://doc.akka.io/api/akka/1.1/
• Akka Modules 1.1 - http://doc.akka.io/api/akka-modules/1.1/
1.0
• Akka 1.0 - http://doc.akka.io/api/1.0/
7.4 Documentation for Other Versions
7.4.1 Akka Snapshot
Automatically published documentation for the latest SNAPSHOT version of Akka can be found here:
• Akka - http://doc.akka.io/docs/akka/snapshot/ (or in PDF format)
• Akka Modules - http://doc.akka.io/docs/akka-modules/snapshot/ (or in PDF format)
7.3. Scaladoc API
271
Akka Documentation, Release 1.3.1
7.4.2 Release Versions
1.3.1 —
• Akka 1.3.1 - http://doc.akka.io/docs/akka/1.3.1/ (or in PDF format)
• Akka Modules 1.3.1 - http://doc.akka.io/docs/akka-modules/1.3.1/ (or in PDF format)
1.1
• Akka 1.1 - http://doc.akka.io/docs/akka/1.1/ (or in PDF format)
• Akka Modules 1.1 - http://doc.akka.io/docs/akka-modules/1.1/ (or in PDF format)
1.0
• Akka 1.0 - http://doc.akka.io/docs/akka-1.0/Home.html (or in PDF format)
7.5 Issue Tracking
Akka is using Assembla as issue tracking system.
7.5.1 Browsing
Tickets
You can find the Akka tickets here
You can find the Akka Modules tickets here
Roadmaps
The roadmap for each Akka milestone is here
The roadmap for each Akka Modules milestone is here
7.5.2 Creating tickets
In order to create tickets you need to do the following:
Register here then log in
Register as a Watcher for the Akka project, you won’t be able to open tickets otherwise.
For Akka tickets: Link to create new ticket
For Akka Modules tickets: Link to create new ticket
Thanks a lot for reporting bugs and suggesting features.
7.5.3 Failing test
Please submit a failing test on the following format:
7.5. Issue Tracking
272
Akka Documentation, Release 1.3.1
import org.scalatest.WordSpec
import org.scalatest.matchers.MustMatchers
class Ticket001Spec extends WordSpec with MustMatchers {
"An XXX" should {
"do YYY" in {
1 must be (1)
}
}
}
7.6 Licenses
7.6.1 Akka License
This software is licensed under the Apache 2 license, quoted below.
Copyright 2009-2011 Scalable Solutions AB <http://scalablesolutions.se>
Licensed under the Apache License, Version 2.0 (the "License"); you may not
use this file except in compliance with the License. You may obtain a copy of
the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.
7.6.2 Akka Committer License Agreement
All committers have signed this CLA
Based on: http://www.apache.org/licenses/icla.txt
Scalable Solutions AB
Individual Contributor License Agreement ("Agreement") V2.0
http://www.scalablesolutions.se/licenses/
Thank you for your interest in Akka, a Scalable Solutions AB (the
"Company") Open Source project. In order to clarify the intellectual
property license granted with Contributions from any person or entity,
the Company must have a Contributor License Agreement ("CLA") on file
that has been signed by each Contributor, indicating agreement to the
license terms below. This license is for your protection as a
Contributor as well as the protection of the Company and its users;
it does not change your rights to use your own Contributions for any
other purpose.
Full name: ______________________________________________________
Mailing Address: ________________________________________________
_________________________________________________________________
7.6. Licenses
273
Akka Documentation, Release 1.3.1
_________________________________________________________________
Country:
______________________________________________________
Telephone: ______________________________________________________
Facsimile: ______________________________________________________
E-Mail:
______________________________________________________
You accept and agree to the following terms and conditions for Your
present and future Contributions submitted to the Company. In
return, the Company shall not use Your Contributions in a way that
is contrary to the public benefit or inconsistent with its nonprofit
status and bylaws in effect at the time of the Contribution. Except
for the license granted herein to the Company and recipients of
software distributed by the Company, You reserve all right, title,
and interest in and to Your Contributions.
1. Definitions.
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7.6. Licenses
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Akka Documentation, Release 1.3.1
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7.6. Licenses
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laws of Sweden, applicable to the agreements made there (excluding
the conflict of law rules). This Agreement embodies the entire
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Please sign: __________________________________ Date: ________________
7.6.3 Licenses for Dependency Libraries
Each dependency and its license can be seen in the project build file (the comment on the side of each dependency):
https://github.com/akka/akka/blob/master/project/build/AkkaProject.scala#L127
7.7 Sponsors
7.7.1 YourKit
YourKit is kindly supporting open source projects with its full-featured Java Profiler. YourKit, LLC is the creator
of innovative and intelligent tools for profiling Java and .NET applications. Take a look at YourKit’s leading
software products: YourKit Java Profiler and YourKit .NET Profiler
7.8 Support
Typesafe
7.9 Mailing List
Akka User Google Group
Akka Developer Google Group
7.10 Downloads
http://akka.io/downloads/
7.11 Source Code
Akka uses Git and is hosted at Github.
• Akka: clone the Akka repository from http://github.com/akka/akka
• Akka Modules: clone the Akka Modules repository from http://github.com/akka/akka-modules
7.7. Sponsors
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7.12 Maven Repository
The Akka Maven repository can be found at http://repo.akka.io/releases.
Typesafe provides http://repo.typesafe.com/typesafe/releases/ that proxies several other repositories, including
akka.io. It is convenient to use the Typesafe repository, since it includes all external dependencies of Akka.
It is a “best-effort” service, and if it is unavailable you may need to use the underlying repositories directly.
• http://repo.akka.io/releases
• http://repository.codehaus.org
• http://guiceyfruit.googlecode.com/svn/repo/releases/
• http://repository.jboss.org/nexus/content/groups/public/
• http://download.java.net/maven/2
• http://oss.sonatype.org/content/repositories/releases
• http://download.java.net/maven/glassfish
• http://databinder.net/repo
7.12. Maven Repository
277
CHAPTER
EIGHT
ADDITIONAL INFORMATION
8.1 Durable Mailboxes
8.1.1 Overview
Akka supports a set of durable mailboxes. A durable mailbox is a replacement for the standard actor mailbox that
is durable. What this means in practice is that if there are pending messages in the actor’s mailbox when the node
of the actor resides on crashes, then when you restart the node, the actor will be able to continue processing as if
nothing had happened; with all pending messages still in its mailbox.
IMPORTANT
None of these mailboxes work with blocking message send, e.g. the message send operations that are relying
on futures; !!, !!!, sendRequestReply and sendRequestReplyFuture, ask or ‘‘?‘. If the node
has crashed and then restarted, the thread that was blocked waiting for the reply is gone and there is no way
we can deliver the message.
The durable mailboxes currently supported are:
• FileDurableMailboxStorage – backed by a journaling transaction log on the local file system
• RedisDurableMailboxStorage – backed by Redis
• ZooKeeperDurableMailboxStorage – backed by ZooKeeper
• BeanstalkDurableMailboxStorage – backed by Beanstalkd
• MongoNaiveDurableMailboxStorage – backed by MongoDB
We’ll walk through each one of these in detail in the sections below.
8.1.2 File-based durable mailbox
This mailbox is backed by a journaling transaction log on the local file system. It is the simplest want to use since
it does not require an extra infrastructure piece to administer, but it is usually sufficient and just what you need.
The durable dispatchers and their configuration options reside in the akka.actor.mailbox package.
You configure durable mailboxes through the durable dispatchers, the actor is oblivious to which type of mailbox
it is using. Here is an example:
val dispatcher = DurableEventBasedDispatcher(
"my:dispatcher:name",
FileDurableMailboxStorage)
//Then set the actors dispatcher to this dispatcher
or for a thread-based durable dispatcher.
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self.dispatcher = DurableThreadBasedDispatcher(
self,
FileDurableMailboxStorage)
There
are
2
different
durable
dispatchers,
DurableEventBasedDispatcher
and
DurableThreadBasedDispatcher,
which
are
durable
versions
of
ExecutorBasedEventDrivenDispatcher and ThreadBasedDispatcher.
This gives you an excellent way of creating bulkheads in your application, where groups of actors sharing the
same dispatcher also share the same backing storage.
|more| Read more about that in the Akka Dispatcher Documentation.
You can also configure and tune the file-based durable mailbox.
This
akka.actor.mailbox.file-based section in the akka.conf configuration file.
is
done
in
the
akka {
actor {
mailbox {
file-based {
directory-path = "./_mb"
max-items = 2147483647
max-size = 2147483647
max-items = 2147483647
max-age = 0
max-journal-size = 16777216 # 16 * 1024 * 1024
max-memory-size = 134217728 # 128 * 1024 * 1024
max-journal-overflow = 10
max-journal-size-absolute = 9223372036854775807
discard-old-when-full = on
keep-journal = on
sync-journal = off
}
}
}
}
8.1.3 Redis-based durable mailbox
This mailbox is backed by a Redis queue. Redis Is a very fast NOSQL database that has a wide range of data
structure abstractions, one of them is a queue which is what we are using in this implementation. This means that
you have to start up a Redis server that can host these durable mailboxes. Read more in the Redis documentation
on how to do that.
Here is an example of how you can configure your dispatcher to use this mailbox:
val dispatcher = DurableEventBasedDispatcher(
"my:service",
RedisDurableMailboxStorage)
or for a thread-based durable dispatcher.
self.dispatcher = DurableThreadBasedDispatcher(
self,
RedisDurableMailboxStorage)
You also need to configure the IP and port for the Redis server.
akka.actor.mailbox.redis section in the akka.conf configuration file.
This is done in the
akka {
actor {
mailbox {
redis {
8.1. Durable Mailboxes
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hostname = "127.0.0.1"
port = 6379
}
}
}
}
8.1.4 ZooKeeper-based durable mailbox
This mailbox is backed by ZooKeeper. ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services This means that you have to
start up a ZooKeeper server (for production a ZooKeeper server ensamble) that can host these durable mailboxes.
Read more in the ZooKeeper documentation on how to do that.
Here is an example of how you can configure your dispatcher to use this mailbox:
val dispatcher = DurableEventBasedDispatcher(
"my:service",
ZooKeeperDurableMailboxStorage)
or for a thread-based durable dispatcher.
self.dispatcher = DurableThreadBasedDispatcher(
self,
ZooKeeperDurableMailboxStorage)
You also need to configure ZooKeeper server addresses, timeouts, etc.
This is done in the
akka.actor.mailbox.zookeeper section in the akka.conf configuration file.
akka {
actor {
mailbox {
zookeeper {
server-addresses = "localhost:2181"
session-timeout = 60
connection-timeout = 30
blocking-queue = on
}
}
}
}
8.1.5 Beanstalk-based durable mailbox
This mailbox is backed by Beanstalkd. Beanstalk is a simple, fast work queue. This means that you have to start
up a Beanstalk server that can host these durable mailboxes. Read more in the Beanstalk documentation on how
to do that.
val dispatcher = DurableEventBasedDispatcher(
"my:service",
BeanstalkDurableMailboxStorage)
or for a thread-based durable dispatcher.
self.dispatcher = DurableThreadBasedDispatcher(
self,
BeanstalkDurableMailboxStorage)
You also need to configure the IP, and port, and so on, for the Beanstalk server.
akka.actor.mailbox.beanstalk section in the akka.conf configuration file.
8.1. Durable Mailboxes
This is done in the
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akka {
actor {
mailbox {
beanstalk {
hostname = "127.0.0.1"
port = 11300
reconnect-window = 5
message-submit-delay = 0
message-submit-timeout = 5
message-time-to-live = 120
}
}
}
}
8.2 Add-on Modules
Akka Modules consist of add-on modules outside the core of Akka:
• akka-kernel-1.3.1.jar – Akka microkernel for running a bare-bones mini application server (embeds Jetty etc.)
• akka-amqp-1.3.1.jar – AMQP integration
• akka-camel-1.3.1.jar – Apache Camel Actors integration (it’s the best way to have your Akka
application communicate with the rest of the world)
• akka-camel-typed-1.3.1.jar – Apache Camel Typed Actors integration
• akka-scalaz-1.3.1.jar – Support for the Scalaz library
• akka-spring-1.3.1.jar – Spring framework integration
• akka-osgi-dependencies-bundle-1.3.1.jar – OSGi support
Documentation for Akka Modules is located here.
8.3 Articles & Presentations
8.3.1 Videos
Functional Programming eXchange - March 2011
NE Scala - Feb 2011
JFokus - Feb 2011.
London Scala User Group - Oct 2010
Akka LinkedIn Tech Talk - Sept 2010
Akka talk at Scala Days - March 2010
Devoxx 2010 talk “Akka: Simpler Scalability, Fault-Tolerance, Concurrency” by Viktor Klang
8.3.2 Articles
Scatter-Gather with Akka Dataflow
Actor-Based Continuations with Akka and Swarm
Mimicking Twitter Using an Akka-Based Event-Driven Architecture
8.2. Add-on Modules
281
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Remote Actor Class Loading with Akka
Akka Producer Actors: New Features and Best Practices
Akka Consumer Actors: New Features and Best Practices
Compute Grid with Cloudy Akka
Clustered Actors with Cloudy Akka
Unit testing Akka Actors with the TestKit
Starting with Akka 1.0
Akka Does Async
CQRS with Akka actors and functional domain models
High Level Concurrency with JRuby and Akka Actors
Container-managed actor dispatchers
Even simpler scalability with Akka through RegistryActor
FSM in Akka (in Vietnamese)
Repeater and Idempotent Receiver implementation in Akka
EDA Akka as EventBus
Upgrading examples to Akka master (0.10) and Scala 2.8.0 Final
Testing Akka Remote Actor using Serializable.Protobuf
Flexible load balancing with Akka in Scala
Eventually everything, and actors
Join messages with Akka
Starting with Akka part 2, Intellij IDEA, Test Driven Development
Starting with Akka and Scala
PubSub using Redis and Akka Actors
Akka’s grown-up hump
Akka features for application integration
Load Balancing Actors with Work Stealing Techniques
Domain Services and Bounded Context using Akka - Part 2
Thinking Asynchronous - Domain Modeling using Akka Transactors - Part 1
Introducing Akka – Simpler Scalability, Fault-Tolerance, Concurrency & Remoting through Actors
Using Cassandra with Scala and Akka
No Comet, Hacking with WebSocket and Akka
MongoDB for Akka Persistence
Pluggable Persistent Transactors with Akka
Enterprise scala actors: introducing the Akka framework
8.3.3 Books
Akka and Camel (appendix E of Camel in Action) Ett första steg i Scala (Kapitel “Aktörer och Akka”) (en. “A
first step in Scala”, chapter “Actors and Akka”, book in Swedish)
8.3. Articles & Presentations
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8.3.4 Presentations
Slides from Akka talk at Scala Days 2010, good short intro to Akka
Akka: Simpler Scalability, Fault-Tolerance, Concurrency & Remoting through Actors
http://ccombs.net/storage/presentations/Akka_High_Level_Abstractions.pdf
https://github.com/deanwampler/Presentations/tree/master/akka-intro/
8.3.5 Podcasts
Episode 16 – Scala and Akka an Interview with Jonas Boner
Jonas Boner on the Akka framework, Scala, and highly scalable applications
8.3.6 Interviews
JetBrains/DZone interview: Talking about Akka, Scala and life with Jonas Bonér
Artima interview of Jonas on Akka 1.0
InfoQ interview of Jonas on Akka 1.0
InfoQ interview of Jonas on Akka 0.7
http://jaxenter.com/we-ve-added-tons-of-new-features-since-0-10-33360.html
8.4 Benchmarks
8.4.1 Scalability, Throughput and Latency benchmark
Simple Trading system.
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• Here is the result with some graphs
• Here is the article
• Here is the code
Compares:
• Synchronous Scala solution
• Scala library Actors
– Fire-forget
– Request-reply
• Akka - Request-reply - Fire-forget with default dispatcher - Fire-forget with Hawt dispatcher
8.4.2 Performance benchmark
Benchmarking Akka against:
• Scala Library Actors
• Raw Java concurrency
• Jetlang (Java actors lib) http://github.com/akka/akka-bench
8.5 Here is a list of recipies for all things Akka
• PostStart => Link to Klangism
• Consumer actors best practices
• Producer actors best practices
8.6 External Sample Projects
Here are some external sample projects created by Akka’s users.
8.6.1 Camel in Action - Akka samples
Akka
samples
for
the
upcoming
Camel
in
Action
http://code.google.com/p/camelinaction/source/browse/trunk/appendixE/
book
by
Martin
Krasser.
8.6.2 CQRS impl using Scalaz and Akka
An implementation of CQRS using scalaz for functional domain models and Akka for event sourcing.
https://github.com/debasishg/cqrs-akka
8.6.3 Example of using Comet with Akka Mist
https://github.com/wrwills/AkkaMistComet
8.6.4 Movie store
Code for a book on Scala/Akka. Showcasing Remote Actors. http://github.com/obcode/moviestore_akka
8.5. Here is a list of recipies for all things Akka
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8.6.5 Estimating Pi with Akka
http://www.earldouglas.com/estimating-pi-with-akka
8.6.6 Running Akka on Android
Sample showing Dining Philosophers running in UI on Android. https://github.com/gseitz/DiningAkkaDroids
http://www.vimeo.com/20303656
8.6.7 Remote chat application using Java API
https://github.com/mariofusco/akkachat
8.6.8 Remote chat application using Java API
A sample chat application using the Java API for Akka. Port of the Scala API chat sample application in the Akka
repository. https://github.com/abramsm/akka_chat_java
8.6.9 Sample parallel computing with Akka and Scala API
https://github.com/yannart/ParallelPolynomialIntegral
8.6.10 Akka, Facebook Graph API, WebGL sample
Showcasing Akka Mist HTTP module https://github.com/buka/fbgl1
8.6.11 Akka Mist Sample
https://github.com/buka/akka-mist-sample
8.6.12 Another Akka Mist Sample
https://github.com/nppssk/akka-http-sbt
8.6.13 Bank application
Showcasing Transactors and STM. http://github.com/weiglewilczek/demo-akka
8.6.14 Ant simulation 1
Traveling salesman problem. Inspired by Clojure’s Ant demo. Uses SPDE for GUI. Idiomatic Scala/Akka code.
Good example on how to use Actors and STM http://github.com/pvlugter/ants
8.6.15 Ant simulation 2
Traveling salesman problem. Close to straight port by Clojure’s Ant demo. Uses Swing for GUI. Another nice
example on how to use Actors and STM http://github.com/azzoti/ScalaAkkaAnts
8.6. External Sample Projects
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8.6.16 The santa clause STM example by SPJ using Akka
http://github.com/arjanblokzijl/akka-santa
8.6.17 Akka trading system
http://github.com/patriknw/akka-sample-trading
8.6.18 Snowing version of Game of Life in Akka
https://github.com/mariogleichmann/AkkaSamples/tree/master/src/main/scala/com/mgi/akka/gameoflife
8.6.19 Akka Web (REST/Comet) template project
A sbt-based, scala Akka project that sets up a web project with REST and comet support
http://github.com/mattbowen/akka-web-template
8.6.20 Various samples on how to use Akka
From the May Chciago-Area Scala Enthusiasts Meeting http://github.com/deanwampler/AkkaWebSampleExercise
8.6.21 Absurd concept for a ticket sales & inventory system, using Akka framework
http://github.com/bmjames/ticketfaster
8.6.22 Akka sports book sample: Java API
http://github.com/jrask/akka-activeobjects-application
8.6.23 Sample of using the Finite State Machine (FSM) DSL
http://github.com/ngocdaothanh/lock-fsm-akka
8.6.24 Akka REST, Jetty, SBT template project
Great starting point for building an Akka application. http://github.com/efleming969/akka-template-rest
8.6.25 Samples of various Akka features (in Scala)
http://github.com/efleming969/akka-samples Fork at http://github.com/flintobrien/akka-samples
8.6.26 A sample sbt setup for running the akka-sample-chat
http://github.com/dwhitney/sbt-akka-sample-chat
8.6.27 Akka Benchmark project
Benches Akka against various other actors and concurrency tools http://github.com/akka/akka-bench
8.6. External Sample Projects
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8.6.28 Typed Actor (Java API) sample project
http://github.com/bobo/akka_sample_java
8.6.29 Akka PI calculation sample project
http://github.com/bonnefoa/akkaPi/
8.6.30 Akka Vaadin Ice sample
https://github.com/tomhowe/vaadin-akka-ice-test
8.6.31 Port of Jersey (JAX-RS) samples to Akka
http://github.com/akollegger/akka-jersey-samples
8.6.32 Akka Expect Testing
https://github.com/joda/akka-expect
8.6.33 Akka Java API playground
https://github.com/koevet/akka-java-playground
8.6.34 Family web page build with Scala, Lift, Akka, Redis, and Facebook Connect
http://github.com/derekjw/williamsfamily
8.6.35 An example of queued computation tasks using Akka
http://github.com/derekjw/computation-queue-example
8.6.36 The samples for the New York Scala Enthusiasts Meetup discussing Akka
http://www.meetup.com/New-York-Scala-Enthusiasts/calendar/12315985/ http://github.com/dwhitney/akka_meetup
8.6.37 Container managed thread pools for Akka Dispatchers
https://github.com/remeniuk/akka-cm-dispatcher
8.6.38 “Lock” Finite State Machine demo with Akka
http://github.com/ngocdaothanh/lock-fsm-akka
8.6.39 Template w/ Intellij stuff for random akka playing around (with Bivvy)
http://github.com/b3n00/akka10-template
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8.6.40 Akka chat using Akka Java API by Mario Fusco
https://github.com/mariofusco/akkachat
8.7 Projects using the removed Akka Persistence modules
8.7.1 Akka Terrastore sample
https://github.com/dgreco/akka-terrastore-example
8.7.2 Akka Persistence for Force.com
https://github.com/sclasen/akka-persistence-force
8.7.3 Template for Akka and Redis
http://github.com/andrewmilkowski/template-akka-persistence-redis
8.8 Companies and Open Source projects using Akka
8.8.1 Production Users
These are some of the production Akka users that are able to talk about their use publicly.
CSC
CSC is a global provider of information technology services. The Traffic Management business unit in the Netherlands is a systems integrator for the implementation of Traffic Information and Traffic Enforcement Systems, such
as section control, weigh in motion, travel time and traffic jam detection and national data warehouse for traffic
information. CSC Traffic Management is using Akka for their latest Traffic Information and Traffic Enforcement
Systems.
http://www.csc.com/nl/ds/42449-traffic_management
“Akka has been in use for almost a year now (since 0.7) and has been used successfully for two projects so
far. Akka has enabled us to deliver very flexible, scalable and high performing systems with as little friction as
possible. The Actor model has simplified a lot of concerns in the type of systems that we build and is now part of
our reference architecture. With Akka we deliver systems that meet the most strict performance requirements of
our clients in a near-realtime environment. We have found the Akka framework and it’s support team invaluable.”
Thatcham Motor Insurance Repair Research Centre
Thatcham is a EuroNCAP member. They research efficient, safe, cost effective repair of vehicles, and work with
manufacturers to influence the design of new vehicles Thatcham are using Akka as the implementation for their
distributed modules. All Scala based research software now talks to an Akka based publishing platform. Using
Akka enables Thatcham to ‘free their domain’, and ensures that the platform is cloud enabled and scalable, and
that the team is confident that they are flexible. Akka has been in use, tested under load at Thatcham for almost a
year, with no problems migrating up through the different versions. An old website currently under redesign on a
new Scala powered platform: www.thatcham.org
“We have been in production with Akka for over 18 months with zero downtime. The core is rock solid, never
a problem, performance is great, integration capabilities are diverse and ever growing, and the toolkit is just a
8.7. Projects using the removed Akka Persistence modules
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pleasure to work with. Combine that with the excellent response you get from the devs and users on this list and
you have a winner. Absolutely no regrets on our part for choosing to work with Akka.”
“Scala and Akka are now enabling improvements in the standard of vehicle damage assessment, and in the safety
of vehicle repair across the UK, with Europe, USA, Asia and Australasia to follow. Thatcham (Motor Insurance
Repair Research Centre) are delivering crash specific information with linked detailed repair information for over
7000 methods.
For Thatcham, the technologies enable scalability and elegance when dealing with complicated design constraints.
Because of the complexity of interlinked methods, caching is virtually impossible in most cases, so in steps the
‘actors’ paradigm. Where previously something like JMS would have provided a stable but heavyweight, rigid
solution, Thatcham are now more flexible, and can expand into the cloud in a far simpler, more rewarding way.
Thatcham’s customers, body shop repairers and insurers receive up to date repair information in the form of
crash repair documents of the quality necessary to ensure that every vehicle is repaired back to the original safety
standard. In a market as important as this, availability is key, as is performance. Scala and Akka have delivered
consistently so far.
While recently introduced, growing numbers of UK repairers are receiving up to date repair information from this
service, with the rest to follow shortly. Plans are already in motion to build new clusters to roll the service out
across Europe, USA, Asia and Australasia.
The sheer opportunities opened up to teams by Scala and Akka, in terms of integration, concise expression of
intent and scalability are of huge benefit.”
SVT (Swedish Television)
http://svt.se
“I’m currently working in a project at the Swedish Television where we’re developing a subtitling system with
collaboration capabilities similar to Google Wave. It’s a mission critical system and the design and server implementation is all based on Akka and actors etc. We’ve been running in production for about 6 months and have
been upgrading Akka whenever a new release comes out. We’ve never had a single bug due to Akka, and it’s been
a pure pleasure to work with. I would choose Akka any day of the week!
Our system is highly asynchronous so the actor style of doing things is a perfect fit. I don’t know about how you
feel about concurrency in a big system, but rolling your own abstractions is not a very easy thing to do. When
using Akka you can almost forget about all that. Synchronizing between threads, locking and protecting access to
state etc. Akka is not just about actors, but that’s one of the most pleasurable things to work with. It’s easy to add
new ones and it’s easy to design with actors. You can fire up work actors tied to a specific dispatcher etc. I could
make the list of benefits much longer, but I’m at work right now. I suggest you try it out and see how it fits your
requirements.
We saw a perfect business reason for using Akka. It lets you concentrate on the business logic instead of the low
level things. It’s easy to teach others and the business intent is clear just by reading the code. We didn’t chose
Akka just for fun. It’s a business critical application that’s used in broadcasting. Even live broadcasting. We
wouldn’t have been where we are today in such a short time without using Akka. We’re two developers that have
done great things in such a short amount of time and part of this is due to Akka. As I said, it lets us focus on the
business logic instead of low level things such as concurrency, locking, performance etc.”
Tapad
http://tapad.com
“Tapad is building a real-time ad exchange platform for advertising on mobile and connected devices. Real-time
ad exchanges allows for advertisers (among other things) to target audiences instead of buying fixed set of ad slots
that will be displayed “randomly” to users. To developers without experience in the ad space, this might seem
boring, but real-time ad exchanges present some really interesting technical challenges.
Take for instance the process backing a page view with ads served by a real-time ad exchange auction (somewhat
simplified):
8.8. Companies and Open Source projects using Akka
289
Akka Documentation, Release 1.3.1
1. A user opens a site (or app) which has ads in it.
2. As the page / app loads, the ad serving components fires off a request to the ad exchange (this might just be
due to an image tag on the page).
3. The ad exchange enriches the request with any information about the current user (tracking cookies are
often employed for this) and and display context information (“news article about parenting”, “blog about
food” etc).
4. The ad exchange forwards the enriched request to all bidders registered with the ad exchange.
5. The bidders consider the provided user information and responds with what price they are willing to pay
for this particular ad slot.
6. The ad exchange picks the highest bidder and ensures that the winning bidder’s ad is shown to to user.
Any latency in this process directly influences user experience latency, so this has to happen really fast. All-in-all,
the total time should not exceed about 100ms and most ad exchanges allow bidders to spend about 60ms (including
network time) to return their bids. That leaves the ad exchange with less than 40ms to facilitate the auction. At
Tapad, this happens billions of times per month / tens of thousands of times per second.
Tapad is building bidders which will participate in auctions facilitated by other ad exchanges, but we’re also
building our own. We are using Akka in several ways in several parts of the system. Here are some examples:
Plain old parallelization During an auction in the real-time exchange, it’s obvious that all bidders must receive
the bid requests in parallel. An auctioneer actor sends the bid requests to bidder actors which in turn handles
throttling and eventually IO. We use futures in these requests and the auctioneer discards any responses which
arrive too late.
Inside our bidders, we also rely heavily on parallel execution. In order to determine how much to pay for an ad
slot, several data stores are queried for information pertinent to the current user. In a “traditional” system, we’d
be doing this sequentially, but again, due to the extreme latency constraints, we’re doing this concurrently. Again,
this is done with futures and data that is not available in time, get cut from the decision making (and logged :)).
Maintaining state under concurrent load This is probably the de facto standard use case for the actors model.
Bidders internal to our system are actors backed by a advertiser campaign. A campaign includes, among other
things, budget and “pacing” information. The budget determines how much money to spend for the duration of
the campaign, whereas pacing information might set constraints on how quickly or slowly the money should be
spent. Ad traffic changes from day to day and from hour to hour and our spending algorithms considers past
performance in order to spend the right amount of money at the right time. Needless to say, these algorithms use a
lot of state and this state is in constant flux. A bidder with a high budget may see tens of thousands of bid requests
per second. Luckily, due to round-robin load-balancing and the predictability of randomness under heavy traffic,
the bidder actors do not share state across cluster nodes, they just share their instance count so they know which
fraction of the campaign budget to try to spend.
Pacing is also done for external bidders. Each 3rd party bidder end-point has an actor coordinating requests and
measuring latency and throughput. The actor never blocks itself, but when an incoming bid request is received, it
considers the current performance of the 3rd party system and decides whether to pass on the request and respond
negatively immediately, or forward the request to the 3rd party request executor component (which handles the
IO).
Batch processing We store a lot of data about every single ad request we serve and this is stored in a key-value
data store. Due to the performance characteristics of the data store, it is not feasible to store every single data
point one at at time - it must be batched up and performed in parallel. We don’t need a durable messaging system
for this (losing a couple of hundred data points is no biggie). All our data logging happens asynchronously and
we have a basic load-balanced actors which batches incoming messages and writes on regular intervals (using
Scheduler) or whenever the specified batch size has been reached.
Analytics Needless to say, it’s not feasible / useful to store our traffic information in a relational database. A lot
of analytics and data analysis is done “offline” with map / reduce on top the data store, but this doesn’t work
well for real-time analytics which our customers love. We therefore have metrics actors that receives campaign
bidding and click / impression information in real-time, aggregates this information over configurable periods of
time and flushes it to the database used for customer dashboards for “semi-real-time” display. Five minute history
is considered real-time in this business, but in theory, we could have queried the actors directly for really real-time
data. :)
8.8. Companies and Open Source projects using Akka
290
Akka Documentation, Release 1.3.1
Our Akka journey started as a prototyping project, but Akka has now become a crucial part of our system. All of
the above mentioned components, except the 3rd party bidder integration, have been running in production for a
couple of weeks (on Akka 1.0RC3) and we have not seen any issues at all so far.”
Flowdock
Flowdock delivers Google Wave for the corporate world.
“Flowdock makes working together a breeze. Organize the flow of information, task things over and work together
towards common goals seamlessly on the web - in real time.”
http://flowdock.com/
Travel Budget
http://labs.inevo.pt/travel-budget
Says.US
“says.us is a gathering place for people to connect in real time - whether an informal meeting of people who love
Scala or a chance for people anywhere to speak out about the latest headlines.”
http://says.us/
LShift
• “Diffa is an open source data analysis tool that automatically establishes data differences between two or
more real-time systems.
• Diffa will help you compare local or distributed systems for data consistency, without having to stop them
running or implement manual cross-system comparisons. The interface provides you with simple visual
summary of any consistency breaks and tools to investigate the issues.*
• Diffa is the ideal tool to use to investigate where or when inconsistencies are occurring, or simply to provide
confidence that your systems are running in perfect sync. It can be used operationally as an early warning
system, in deployment for release verification, or in development with other enterprise diagnosis tools to
help troubleshoot faults.”*
http://diffa.lshift.net/
Twimpact
“Real-time twitter trends and user impact”
http://twimpact.com
Rocket Pack Platform
“Rocket Pack Platform is the only fully integrated solution for plugin-free browser game development.”
http://rocketpack.fi/platform/
8.8. Companies and Open Source projects using Akka
291
Akka Documentation, Release 1.3.1
8.8.2 Open Source Projects using Akka
Redis client
A Redis client written Scala, using Akka actors, HawtDispath and non-blocking IO. Supports Redis 2.0+
http://github.com/derekjw/fyrie-redis
Narrator
“Narrator is a a library which can be used to create story driven clustered load-testing packages through a very
readable and understandable api.”
http://github.com/shorrockin/narrator
Kandash
“Kandash is a lightweight kanban web-based board and set of analytics tools.”
http://vasilrem.com/blog/software-development/kandash-project-v-0-3-is-now-available/
http://code.google.com/p/kandash/
Wicket Cassandra Datastore
This project provides an org.apache.wicket.pageStore.IDataStore implementation that writes pages to an Apache
Cassandra cluster using Akka.
http://github.com/gseitz/wicket-cassandra-datastore/
Spray
“spray is a lightweight Scala framework for building RESTful web services on top of Akka actors and Akka Mist.
It sports the following main features:
• Completely asynchronous, non-blocking, actor-based request processing for efficiently handling very high
numbers of concurrent connections
• Powerful, flexible and extensible internal Scala DSL for declaratively defining your web service behavior
• Immutable model of the HTTP protocol, decoupled from the underlying servlet container
• Full testability of your REST services, without the need to fire up containers or actors”
https://github.com/spray/spray/wiki
8.9 Third-party Integrations
8.9.1 The Play! Framework
Dustin Whitney has done an Akka integration module for the Play! framework.
Detailed instructions here: http://github.com/dwhitney/akka/blob/master/README.textile.
There are three screencasts:
• Using Play! with Akka STM: http://vimeo.com/10764693
• Using Play! with Akka Actors: http://vimeo.com/10792173
• Using Play! with Akka Remote Actors: http://vimeo.com/10793443
8.9. Third-party Integrations
292
Akka Documentation, Release 1.3.1
8.9.2 The Pinky REST/MVC Framework
Peter Hausel has done an Akka integration module for the Pinky framework.
Read more here: http://wiki.github.com/pk11/pinky/release-13
8.10 Other Language Bindings
8.10.1 JRuby
High level concurrency using Akka actors and JRuby.
https://github.com/danielribeiro/RubyOnAkka
If you are using STM with JRuby then you need to unwrap the Multiverse control flow exception as follows:
begin
... atomic stuff
rescue NativeException => e
raise e.cause if e.cause.java_class.package.name.include? "org.multiverse"
end
8.10.2 Groovy/Groovy++
https://gist.github.com/620439
8.11 Feature Stability Matrix
Akka is comprised of a number if modules, with different levels of maturity and in different parts of their lifecycle,
the matrix below gives you get current stability level of the modules.
8.11.1 Explanation of the different levels of stability
• Solid - Proven solid in heavy production usage
• Stable - Ready for use in production environment
• In progress - Not enough feedback/use to claim it’s ready for production use
Feature
Actors (Scala)
Actors (Java)
Typed Actors (Scala)
Typed Actors (Java)
STM (Scala)
STM (Java)
Transactors (Scala)
Transactors (Java)
Remote Actors (Scala)
Remote Actors (Java)
Camel
AMQP
HTTP
Integration Guice
Integration Spring
Scheduler
Redis Pub Sub
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Solid
Stable
In progress
Stable
Stable
Solid
8.10. Other Language Bindings
In progress
293
CHAPTER
NINE
LINKS
• Migration Guides
• Downloads
• Source Code
• Scaladoc API
• Documentation for Other Versions
• Akka Modules Documentation
• Issue Tracking
• Support
294
PYTHON MODULE INDEX
a
akka-testkit, 157
f
FSM (Scala), 134
295
INDEX
A
akka-testkit (module), 157
F
FSM (module), 134
296
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