The world of mainstream computing is changing rapidly these days. If you open the hood and look under the covers of your computer, you'll most likely see a dual-core processor there. Or a quad-core, if you're lucky enough.
We all run our software on multi-processors. The code we write today and tomorrow will probably never run on a single processor system. Parallel hardware has become common-place.
Not so with the software though, at least not yet. People still create single-threaded code, although it will never be able to leverage the full power of future hardware. Some experiment with low-level concurrency primitives, like threads, locks or synchronized blocks,
however, it has become obvious that the common shared-memory multithreading causes more troubles than it solves. Low-level concurrency handling is usually hard to get right. And it's not much fun either.
With such a radical change in hardware, software inevitably has to change dramatically too. Higher-level concurrency concepts like map/reduce, fork/join, actors or dataflow will provide natural abstractions for different types of problem domains while leveraging the multi-core hardware underneath.
Meet
GPars - an open-source concurrency library for Java and Groovy that aims to give you multiple high-level abstractions for writing concurrent code in Groovy - map/reduce, fork/join, asynchronous closures, actors, agents, dataflow concurrency and other concepts, which aim to make your Groovy code concurrent with little effort.
With GPars your Groovy or Java code can easily utilize all the available processors on the target system. You can run multiple calculations at the same time, request network resources in parallel,
safely solve hierarchical divide-and-conquer problems, perform functional style map/reduce collection processing or build your applications around the actor model.
The project is open sourced under the
Apache 2 License . If you're working on a commercial, open-source, educational or any other type of software project in Groovy,
download the binaries or integrate them from the maven repository and get going. The way to witting highly concurrent Groovy code is wide open. Enjoy!
This project could not have reached the point where we stand currently, without all the great help and contribution of many individuals,
who have devoted their time, energy and expertise to make GPars a solid product. First, it is the people in the core team,
who should be mentioned:
- Václav Pech
- Dierk Koenig
- Alex Tkachman
- Russel Winder
- Paul King
- Jon Kerridge
Over time, many people have contributed their ideas, provided useful feedback or helped GPars in one way or another.
There are too many people in this group to name them all, but still, let's list at least a few:
- Hamlet d'Arcy
- Hans Dockter
- Guillaume Laforge
- Robert Fischer
- Johannes Link
- Graeme Rocher
- Alex Miller
- Jeff Gortatowsky
- Jiří Kropáček
Great thanks to everyone!
Let's make several assumptions before we really start.
- You know and love Groovy. Otherwise you'd hardly invest your valuable time into studying a Groovy concurrency library.
- If you don't want to use Groovy, you are prepared to pay the inevitable verbosity tax on using GPars from Java
- You target multi-core hardware with your code
- You use or want to use Groovy or Java to write concurrent code.
- You have at least some understanding that in concurrent code some things can happen at any time in any order and often more of them at the same time.
That's about it. Let's roll the ball forward.
Brief overview
GPars aims to bring several useful concurrency abstractions to Java and Groovy developers. It's becoming obvious that dealing
with concurrency on the thread/synchronized/lock level, as provided by the JVM, is way too low level to be safe and comfortable.
Many high-level concepts, like actors or dataflow concurrency have been around for quite some time, since parallel computers
had been in use in computer centers long before multi-core chips hit the hardware mainstream. Now, however, it's the time to
adopt and test these abstractions for the mainstream software industry.
The concepts available in GPars can be categorized into three main groups:
- Code-level helpers - constructs that can be applied to small parts of the code-base such as individual algorithms or data structures without any major changes in the overall project architecture
- Parallel Collections
- Asynchronous Processing
- Fork/Join (Divide/Conquer)
- Architecture-level concepts - constructs that need to be taken into account when designing the project structure
- Actors
- Communicating Sequential Processes
- Dataflow Concurrency
- Shared Mutable State Protection - although about 95 of current use of shared mutable state can be avoided using proper abstractions, good abstractions are still necessary for the remaining 5% use cases, when shared mutable state can't be avoided
- Agents
- Software Transactional Memory (not implemented in GPars yet) would also belong to this group
There are several ways to add GPars to your project. Either download and add all the jar files manually, specify a dependency in Maven, Ivy or Gradle build files or use Grape.
If you're building a Grails or a Griffon application, you can leverage the appropriate plugins to fetch the jar files for you.
Dependency resolution
GPars requires two compulsory dependencies - the
jsr166y and the
extra166y
jar files, which are the artifacts of the
JSR-166 initiative . These must be on the classpath.
<dependency>
<groupId>org.codehaus.jsr166-mirror</groupId>
<artifactId>jsr166y</artifactId>
<version>1.7.0</version>
</dependency>
<dependency>
<groupId>org.codehaus.jsr166-mirror</groupId>
<artifactId>extra166y</artifactId>
<version>1.7.0</version>
</dependency>
GPars defines both of the dependencies in its own descriptor, so both dependencies should be taken care of automatically,
if you use Gradle, Maven, Ivy or other type of automatic dependency resolution tool.
Please visit the
Integration page of the project for details.
Once you got setup, try the following Groovy script to test that your setup is functional. For Java, see below.
import static groovyx.gpars.actor.Actors.actor/**
* A demo showing two cooperating actors. The decryptor decrypts received messages and replies them back.
* The console actor sends a message to decrypt, prints out the reply and terminates both actors.
* The main thread waits on both actors to finish using the join() method to prevent premature exit,
* since both actors use the default actor group, which uses a daemon thread pool.
* @author Dierk Koenig, Vaclav Pech
*/def decryptor = actor {
loop {
react {message ->
if (message instanceof String) reply message.reverse()
else stop()
}
}
}def console = actor {
decryptor.send 'lellarap si yvoorG'
react {
println 'Decrypted message: ' + it
decryptor.send false
}
}[decryptor, console]*.join()
You should get a message "Decrypted message: Groovy is parallel" printed out on the console when you run the code.
GPars - a Java library
Although GPars has been primarily designed for the Groovy programming language, the solid technical foundation plus good performance characteristics
make GPars a good Java library as well. Since most of GPars is written in Java, there is no extra performance penalty Java applications
would pay when using GPars.For details please refer to the Java API section.
To quick-test your integration through Java API, run the following Java actor code:
import groovyx.gpars.MessagingRunnable;
import groovyx.gpars.actor.DynamicDispatchActor;public class StatelessActorDemo {
public static void main(String[] args) throws InterruptedException {
final MyStatelessActor actor = new MyStatelessActor();
actor.start();
actor.send("Hello");
actor.sendAndWait(10);
actor.sendAndContinue(10.0, new MessagingRunnable<String>() {
@Override protected void doRun(final String s) {
System.out.println("Received a reply " + s);
}
});
}
}class MyStatelessActor extends DynamicDispatchActor {
public void onMessage(final String msg) {
System.out.println("Received " + msg);
replyIfExists("Thank you");
} public void onMessage(final Integer msg) {
System.out.println("Received a number " + msg);
replyIfExists("Thank you");
} public void onMessage(final Object msg) {
System.out.println("Received an object " + msg);
replyIfExists("Thank you");
}
}
We follow certain conventions in the code samples. Understanding these may help you read and comprehend GPars code samples better.
- The leftShift operator << has been overloaded on actors, agents and dataflow expressions (both variables and streams) to mean send a message or assign a value.
myActor << 'message'myAgent << {account -> account.add('5 USD')}myDataflowVariable << 120332
- On actors and agents the default call() method has been also overloaded to mean send . So sending a message to an actor or agent may look like a regular method call.
myActor "message"myAgent {house -> house.repair()}
- The rightShift operator >> in GPars has the when bound meaning. So
myDataflowVariable >> {value -> doSomethingWith(value)}
will schedule the closure to run only after
myDataflowVariable is bound to a value, with the value as a parameter.
In samples we tend to statically import frequently used factory methods:
- GParsPool.withPool()
- GParsPool.withExistingPool()
- GParsExecutorsPool.withPool()
- GParsExecutorsPool.withExistingPool()
- Actors.actor()
- Actors.reactor()
- Actors.fairReactor()
- Actors.messageHandler()
- Actors.fairMessageHandler()
- Agent.agent()
- Agent.fairAgent()
- Dataflow.task()
- Dataflow.operator()
It is more a matter of style preferences and personal taste, but we think static imports make the code more compact and readable.
Adding the GPars jar files to your project or defining the appropriate dependencies in pom.xml should be enough to get you started with GPars in your IDE.
GPars DSL recognition
IntelliJ IDEA in both the free
Community Edition and the commercial
Ultimate Edition will recognize the GPars domain specific languages,
complete methods like
eachParallel() ,
reduce() or
callAsync() and validate them. GPars uses the
GroovyDSL
mechanism, which teaches IntelliJ IDEA the DSLs as soon as the GPars jar file is added to the project.
Here you could find basic guide-lines helping you decide on which GPars abstraction to apply to your code at hands.
- You're looking at a collection, which needs to be iterated or processed using one of the many beautiful Groovy collections method, like each() , collect() , find() and such. Proposing that processing each element of the collection is independent of the other items, using GPars parallel collections can be recommended.
- If you have a long-lasting calculation , which may safely run in the background, use the asynchronous invocation support in GPars. You can also benefit, if your long-calculating closures need to be passed around and yet you'd like them not to block the main application thread.
- You need to parallelize an algorithm at hand. You can identify sub-tasks and you're happy to explicitly express the options for parallelization. You create internally sequential tasks, each of which can run concurrently with the others, providing they all have a way to exchange data at some well-defined moments through communication channels with safe semantics. Use GPars dataflow tasks, variables and streams.
- You can't avoid shared mutable state. Multiple threads will be accessing shared data and (some of them) modifying the data. Traditional locking and synchronized approach feels too risky or unfamiliar. Go for agents, which will wrap your data and serialize all access to it.
- You're building a system with high concurrency demands. Tweaking a data structure here or task there won't cut it. You need to build the architecture from the ground up with concurrency in mind. Message-passing might be the way to go.
- Groovy CSP will give you highly deterministic and composable model for concurrent processes.
- If you're trying to solve a complex data-processing problem, consider GPars dataflow operator to build a data flow network.
- Actors will shine if you need to build a general-purpose, highly concurrent and scalable architecture.
Now you may have a better idea of what concepts to use on your current project. Go and check out more details on them in the User Guide.
Again, the new release, this time GPars 0.12, introduces a lot of gradual enhancements and improvements on top of the previous release.
Check out the
JIRA release notesProject changes
See the Breaking Changes listing for the list of breaking changes.
Asynchronous functions
- The asyncFun() method now creates composable asynchronous functions
- The @AsyncFun annotation can be used to create composable asynchronous functions stored in fields in a more declarative way
Parallel collections
- Collections can now repeatedly be made transparently concurrent or sequential using makeConcurrent() and makeSequential() methods
- Renamed makeTransparent() to makeConcurrent()
Fork / Join
- A few new demos illustrating Fork/Join applicability to recursive functions have been added
- Leveraging the new and efficient implementation of the jsr-166y (aka Java 7) Fork/Join library
- The runChildDirectly() method allowing to mix asynchronous and synchronous child task execution
Actors
- Active Objects wrapping actors with an OO facade
- Enhanced DynamicDispatchActor's API for dynamic message handler registration
- Added BlockingActor to allow for non-continuation style actors
- Removed the deprecated actor classes
Dataflow
Agent
Stm
- Initial support for Stm through Multiverse was added
Other
- Switched to the most recent Java 7 Fork/Join library to ensure compatibility with future JDKs
- Raised the Groovy level used for compilation to 1.7
- Created a pdf version of the user guide
- An update to the stand-alone maven-based Java API demo application was added to show GPars integration and use from Java
- Added numerous code examples and demos
- Enhanced project documentation
Renaming hints
- The makeTransparent() method that forces concurrent semantics to iteration methods (each, collect, find, etc.) has been renamed to makeConcurrent()
- Capitalization has changed in the names of dataflow classes DataFlow -> Dataflow e.g. DataFlowVariable is now called DataflowVariable
- The DataFlowPoisson class has been renamed to PoisonPill
Using GPars is very addictive, I guarantee. Once you get hooked you won't be able to code without it.
May the world force you to write code in Java, you will still be able to benefit from most of GPars features.
Java API specifics
Some parts of GPars are irrelevant in Java and it is better to use the underlying Java libraries directly:
- Parallel Collection - use jsr-166y library's Parallel Array directly
- Fork/Join - use jsr-166y library's Fork/Join support directly
- Asynchronous functions - use Java executor services directly
The other parts of GPars can be used from Java just like from Groovy, although most will miss the Groovy DSL capabilities.
GPars Closures in Java API
To overcome the lack of closures as a language element in Java and to avoid forcing users to use Groovy closures directly
through the Java API, a few handy wrapper classes have been provided to help you define callbacks, actor body or dataflow tasks.
- groovyx.gpars.MessagingRunnable - used for single-argument callbacks or actor body
- groovyx.gpars.ReactorMessagingRunnable - used for ReactiveActor body
- groovyx.gpars.DataflowMessagingRunnable - used for dataflow operators' body
These classes can be used in all places GPars API expects a Groovy closure.
Actors
The
DynamicDispatchActor as well as the
ReactiveActor classes can be used just like in Groovy:
import groovyx.gpars.MessagingRunnable;
import groovyx.gpars.actor.DynamicDispatchActor; public class StatelessActorDemo {
public static void main(String[] args) throws InterruptedException {
final MyStatelessActor actor = new MyStatelessActor();
actor.start();
actor.send("Hello");
actor.sendAndWait(10);
actor.sendAndContinue(10.0, new MessagingRunnable<String>() {
@Override protected void doRun(final String s) {
System.out.println("Received a reply " + s);
}
});
}
} class MyStatelessActor extends DynamicDispatchActor {
public void onMessage(final String msg) {
System.out.println("Received " + msg);
replyIfExists("Thank you");
} public void onMessage(final Integer msg) {
System.out.println("Received a number " + msg);
replyIfExists("Thank you");
} public void onMessage(final Object msg) {
System.out.println("Received an object " + msg);
replyIfExists("Thank you");
}
}
Although there are not many differences between Groovy and Java GPars use, notice, the callbacks instantiating the MessagingRunnable class in place for a groovy closure.
import groovy.lang.Closure;
import groovyx.gpars.ReactorMessagingRunnable;
import groovyx.gpars.actor.Actor;
import groovyx.gpars.actor.ReactiveActor;public class ReactorDemo {
public static void main(final String[] args) throws InterruptedException {
final Closure handler = new ReactorMessagingRunnable<Integer, Integer>() {
@Override protected Integer doRun(final Integer integer) {
return integer * 2;
}
};
final Actor actor = new ReactiveActor(handler);
actor.start(); System.out.println("Result: " + actor.sendAndWait(1));
System.out.println("Result: " + actor.sendAndWait(2));
System.out.println("Result: " + actor.sendAndWait(3));
}
}
Convenience factory methods
Obviously, all the essential factory methods to build actors quickly are available where you'd expect them.
import groovy.lang.Closure;
import groovyx.gpars.ReactorMessagingRunnable;
import groovyx.gpars.actor.Actor;
import groovyx.gpars.actor.Actors;public class ReactorDemo {
public static void main(final String[] args) throws InterruptedException {
final Closure handler = new ReactorMessagingRunnable<Integer, Integer>() {
@Override protected Integer doRun(final Integer integer) {
return integer * 2;
}
};
final Actor actor = Actors.reactor(handler); System.out.println("Result: " + actor.sendAndWait(1));
System.out.println("Result: " + actor.sendAndWait(2));
System.out.println("Result: " + actor.sendAndWait(3));
}
}
Agents
import groovyx.gpars.MessagingRunnable;
import groovyx.gpars.agent.Agent; public class AgentDemo {
public static void main(final String[] args) throws InterruptedException {
final Agent counter = new Agent<Integer>(0);
counter.send(10);
System.out.println("Current value: " + counter.getVal());
counter.send(new MessagingRunnable<Integer>() {
@Override protected void doRun(final Integer integer) {
counter.updateValue(integer + 1);
}
});
System.out.println("Current value: " + counter.getVal());
}
}
Dataflow Concurrency
Both
DataflowVariables and
DataflowQueues can be used from Java without any hiccups. Just avoid the handy overloaded operators
and go straight to the methods, like
bind ,
whenBound ,
getVal and other.
You may also continue using dataflow
tasks passing to them instances of
Runnable or
Callable just like groovy
Closure .
import groovyx.gpars.MessagingRunnable;
import groovyx.gpars.dataflow.DataflowVariable;
import groovyx.gpars.group.DefaultPGroup;import java.util.concurrent.Callable;public class DataflowTaskDemo {
public static void main(final String[] args) throws InterruptedException {
final DefaultPGroup group = new DefaultPGroup(10); final DataflowVariable a = new DataflowVariable(); group.task(new Runnable() {
public void run() {
a.bind(10);
}
}); final DataflowVariable result = group.task(new Callable() {
public Object call() throws Exception {
return (Integer)a.getVal() + 10;
}
}); result.whenBound(new MessagingRunnable<Integer>() {
@Override protected void doRun(final Integer integer) {
System.out.println("arguments = " + integer);
}
}); System.out.println("result = " + result.getVal());
}
}
Dataflow operators
The sample below should illustrate the main differences between Groovy and Java API for dataflow operators.
- Use the convenience factory methods accepting list of channels to create operators or selectors
- Use DataflowMessagingRunnable to specify the operator body
- Call getOwningProcessor() to get hold of the operator from within the body in order to e.g. bind output values
import groovyx.gpars.DataflowMessagingRunnable;
import groovyx.gpars.dataflow.Dataflow;
import groovyx.gpars.dataflow.DataflowQueue;
import groovyx.gpars.dataflow.operator.DataflowProcessor;import java.util.Arrays;
import java.util.List;public class DataflowOperatorDemo {
public static void main(final String[] args) throws InterruptedException {
final DataflowQueue stream1 = new DataflowQueue();
final DataflowQueue stream2 = new DataflowQueue();
final DataflowQueue stream3 = new DataflowQueue();
final DataflowQueue stream4 = new DataflowQueue(); final DataflowProcessor op1 = Dataflow.selector(Arrays.asList(stream1), Arrays.asList(stream2), new DataflowMessagingRunnable(1) {
@Override protected void doRun(final Object[] objects) {
getOwningProcessor().bindOutput(2*(Integer)objects[0]);
}
}); final List secondOperatorInput = Arrays.asList(stream2, stream3); final DataflowProcessor op2 = Dataflow.operator(secondOperatorInput, Arrays.asList(stream4), new DataflowMessagingRunnable(2) {
@Override protected void doRun(final Object[] objects) {
getOwningProcessor().bindOutput((Integer) objects[0] + (Integer) objects[1]);
}
}); stream1.bind(1);
stream1.bind(2);
stream1.bind(3);
stream3.bind(100);
stream3.bind(100);
stream3.bind(100);
System.out.println("Result: " + stream4.getVal());
System.out.println("Result: " + stream4.getVal());
System.out.println("Result: " + stream4.getVal());
op1.stop();
op2.stop();
}
}
Performance
In general, GPars overhead is identical irrespective of whether you use it from Groovy or Java and tends to be very low.
GPars actors, for example, can compete head-to-head with other JVM actor options, like Scala actors.
Since Groovy code in general runs slower than Java code, mainly due to dynamic method invocation, you might consider writing
your code in Java to improve performance. Typically numeric operations or frequent fine-grained method calls within a task or actor body
may benefit from a rewrite into Java.
Prerequisites
All the GPars integration rules apply to Java projects just like they do to Groovy projects. You only need to include the groovy distribution jar file in your project and all is clear to march ahead.
You may also want to check out the sample Java Maven project to get tips on how to integrate GPars into a maven-based pure Java application -
Sample Java Maven Project
Focusing on data instead of processes helps a great deal to create robust concurrent programs. You as a programmer
define your data together with functions that should be applied to it and then let the underlying machinery to process the data.
Typically a set of concurrent tasks will be created and then they will be submitted to a thread pool for processing.
In
GPars the
GParsPool and
GParsExecutorsPool classes give you access to low-level data parallelism techniques.
While the
GParsPool class relies on the jsr-166y Fork/Join framework and so offers greater functionality and better performance,
the
GParsExecutorsPool uses good old Java executors and so is easier to setup in a managed or restricted environment.
There are three fundamental domains covered by the GPars low-level data parallelism:
- Processing collections concurrently
- Running functions (closures) asynchronously
- Performing Fork/Join (Divide/Conquer) algorithms
Dealing with data frequently involves manipulating collections. Lists, arrays, sets, maps, iterators, strings and lot of other data types can be viewed as collections of items.
The common pattern to process such collections is to take elements sequentially, one-by-one, and make an action for each of the items in row.
Take, for example, the
min() function, which is supposed to return the smallest element of a collection. When you call the
min() method on a collection of numbers,
the caller thread will create an
accumulator or
so-far-the-smallest-value initialized to the minimum value of the given type, let say to zero. And then the thread will iterate through the elements of the collection
and compare them with the value in the
accumulator . Once all elements have been processed, the minimum value is stored in the
accumulator .
This algorithm, however simple, is
totally wrong on multi-core hardware. Running the
min() function on a dual-core chip can leverage
at most 50% of the computing power of the chip.
On a quad-core it would be only 25%. Correct, this algorithm effectively
wastes 75% of the computing power of the chip.
Tree-like structures proved to be more appropriate for parallel processing. The
min() function in our example doesn't need to iterate through all the elements in row and compare their values with the
accumulator .
What it can do instead is relying on the multi-core nature of your hardware. A
parallel_min() function could, for example, compare pairs (or tuples of certain size) of neighboring values
in the collection and promote the smallest value from the tuple into a next round of comparison. Searching for minimum in different tuples can safely happen in parallel and so tuples in the same round
can be processed by different cores at the same time without races or contention among threads.
Meet Parallel Arrays
The jsr-166y library brings a very convenient abstraction called
Parallel Arrays . GPars leverages the Parallel Arrays implementation
in several ways. The
GParsPool and
GParsExecutorsPool classes provide parallel variants of the common Groovy iteration methods like
each() ,
collect() ,
findAll() and such.
def selfPortraits = images.findAllParallel{it.contains me}.collectParallel {it.resize()}
It also allows for a more functional style map/reduce collection processing.
def smallestSelfPortrait = images.parallel.filter{it.contains me}.map{it.resize()}.min{it.sizeInMB}
Use of
GParsPool - the JSR-166y based concurrent collection processor
Usage of GParsPool
The
GParsPool class enables a ParallelArray-based (from JSR-166y) concurrency DSL for collections and objects.
Examples of use:
//summarize numbers concurrently
GParsPool.withPool {
final AtomicInteger result = new AtomicInteger(0)
[1, 2, 3, 4, 5].eachParallel {result.addAndGet(it)}
assertEquals 15, result
} //multiply numbers asynchronously
GParsPool.withPool {
final List result = [1, 2, 3, 4, 5].collectParallel {it * 2}
assert ([2, 4, 6, 8, 10].equals(result))
}
The passed-in closure takes an instance of a ForkJoinPool as a parameter, which can be then used freely inside the closure.
//check whether all elements within a collection meet certain criteria
GParsPool.withPool(5) {ForkJoinPool pool ->
assert [1, 2, 3, 4, 5].everyParallel {it > 0}
assert ![1, 2, 3, 4, 5].everyParallel {it > 1}
}
The
GParsPool.withPool() method takes optional parameters for number of threads in the created pool and an unhandled exception handler.
withPool(10) {...}
withPool(20, exceptionHandler) {...}
The
GParsPool.withExistingPool() takes an already existing ForkJoinPool instance to reuse.
The DSL is valid only within the associated block of code and only for the thread that has called the
withPool() or
withExistingPool() methods. The
withPool() method returns only after all the worker threads have finished their tasks and the pool has been destroyed, returning back the return value of the associated block of code. The
withExistingPool() method doesn't wait for the pool threads to finish.
Alternatively, the
GParsPool class can be statically imported
import static groovyx.gpars.GParsPool.`*` , which will allow omitting the
GParsPool class name.
withPool {
assert [1, 2, 3, 4, 5].everyParallel {it > 0}
assert ![1, 2, 3, 4, 5].everyParallel {it > 1}
}
The following methods are currently supported on all objects in Groovy:
- eachParallel()
- eachWithIndexParallel()
- collectParallel()
- findAllParallel()
- findAnyParallel
- findParallel()
- everyParallel()
- anyParallel()
- grepParallel()
- groupByParallel()
- foldParallel()
- minParallel()
- maxParallel()
- sumParallel()
- splitParallel()
- countParallel()
- foldParallel()
Meta-class enhancer
As an alternative you can use the
ParallelEnhancer class to enhance meta-classes of any classes or individual instances with the parallel methods.
import groovyx.gpars.ParallelEnhancerdef list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
ParallelEnhancer.enhanceInstance(list)
println list.collectParallel {it * 2 }def animals = ['dog', 'ant', 'cat', 'whale']
ParallelEnhancer.enhanceInstance animals
println (animals.anyParallel {it ==~ /ant/} ? 'Found an ant' : 'No ants found')
println (animals.everyParallel {it.contains('a')} ? 'All animals contain a' : 'Some animals can live without an a')
When using the
ParallelEnhancer class, you're not restricted to a
withPool() block with the use of the GParsPool DSLs. The enhanced classed or instances
remain enhanced till they get garbage collected.
Exception handling
If an exception is thrown while processing any of the passed-in closures, the first exception gets re-thrown from the xxxParallel methods and the algorithm stops as soon as possible.
The exception handling mechanism of GParsPool builds on the one built into the Fork/Join framework. Since Fork/Join algorithms are by nature hierarchical,
once any part of the algorithm fails, there's usually little benefit from continuing the computation, since some branches of the algorithm will never return a result.Bear in mind that the GParsPool implementation doesn't give any guarantees about its behavior after a first unhandled exception occurs,
beyond stopping the algorithm and re-throwing the first detected exception to the caller.
This behavior, after all, is consistent with what the traditional sequential iteration methods do.
Transparently parallel collections
On top of adding new xxxParallel() methods,
GPars can also let you change the semantics of the original iteration methods. For example, you may be passing a collection into a library method, which will process your collection
in a sequential way, let say using the
collect() method. By changing the semantics of the
collect() method on your collection you can effectively parallelize the library sequential code.
GParsPool.withPool { //The selectImportantNames() will process the name collections concurrently
assert ['ALICE', 'JASON'] == selectImportantNames(['Joe', 'Alice', 'Dave', 'Jason'].makeConcurrent())
}/**
* A function implemented using standard sequential collect() and findAll() methods.
*/
def selectImportantNames(names) {
names.collect {it.toUpperCase()}.findAll{it.size() > 4}
}
The
makeSequential() method will reset the collection back to the original sequential semantics.
import static groovyx.gpars.GParsPool.withPooldef list = [1, 2, 3, 4, 5, 6, 7, 8, 9]println 'Sequential: '
list.each { print it + ',' }
println()withPool { println 'Sequential: '
list.each { print it + ',' }
println() list.makeConcurrent() println 'Concurrent: '
list.each { print it + ',' }
println() list.makeSequential() println 'Sequential: '
list.each { print it + ',' }
println()
}println 'Sequential: '
list.each { print it + ',' }
println()
The
asConcurrent() convenience method will allow you to specify code blocks, in which the collection maintains concurrent semantics.
import static groovyx.gpars.GParsPool.withPooldef list = [1, 2, 3, 4, 5, 6, 7, 8, 9]println 'Sequential: '
list.each { print it + ',' }
println()withPool { println 'Sequential: '
list.each { print it + ',' }
println() list.asConcurrent {
println 'Concurrent: '
list.each { print it + ',' }
println()
} println 'Sequential: '
list.each { print it + ',' }
println()
}println 'Sequential: '
list.each { print it + ',' }
println()
Transparent parallelizm, including the
makeConcurrent() ,
makeSequential() and
asConcurrent() methods, is also available in combination with
ParallelEnhancer .
/**
* A function implemented using standard sequential collect() and findAll() methods.
*/
def selectImportantNames(names) {
names.collect {it.toUpperCase()}.findAll{it.size() > 4}
}def names = ['Joe', 'Alice', 'Dave', 'Jason']
ParallelEnhancer.enhanceInstance(names)
//The selectImportantNames() will process the name collections concurrently
assert ['ALICE', 'JASON'] == selectImportantNames(names.makeConcurrent())
import groovyx.gpars.ParallelEnhancerdef list = [1, 2, 3, 4, 5, 6, 7, 8, 9]println 'Sequential: '
list.each { print it + ',' }
println()ParallelEnhancer.enhanceInstance(list)println 'Sequential: '
list.each { print it + ',' }
println()list.asConcurrent {
println 'Concurrent: '
list.each { print it + ',' }
println()}
list.makeSequential()println 'Sequential: '
list.each { print it + ',' }
println()
Avoid side-effects in functions
We have to warn you. Since the closures that are provided to the parallel methods like
eachParallel() or
collectParallel() may be run in parallel, you have to make sure that each of the closures
is written in a thread-safe manner. The closures must hold no internal state, share data nor have side-effects beyond the boundaries the single element that they've been invoked on.
Violations of these rules will open the door for race conditions and deadlocks, the most severe enemies of a modern multi-core programmer.
Don't do this:
def thumbnails = []
images.eachParallel {thumbnails << it.thumbnail} //Concurrently accessing a not-thread-safe collection of thumbnails, don't do this!
At least, you've been warned.
Use of GParsExecutorsPool - the Java Executors' based concurrent collection processor
Usage of GParsExecutorsPool
The
GParsPool class enables a Java Executors-based concurrency DSL for collections and objects.
The
GParsExecutorsPool class can be used as a pure-JDK-based collection parallel processor. Unlike the
GParsPool class,
GParsExecutorsPool doesn't require jsr-166y jar file, but leverages the standard JDK executor services to parallelize closures processing a collections or an object iteratively.
It needs to be states, however, that
GParsPool performs typically much better than
GParsExecutorsPool does.
Examples of use:
//multiply numbers asynchronously
GParsExecutorsPool.withPool {
Collection<Future> result = [1, 2, 3, 4, 5].collectParallel{it * 10}
assertEquals(new HashSet([10, 20, 30, 40, 50]), new HashSet((Collection)result*.get()))
} //multiply numbers asynchronously using an asynchronous closure
GParsExecutorsPool.withPool {
def closure={it * 10}
def asyncClosure=closure.async()
Collection<Future> result = [1, 2, 3, 4, 5].collect(asyncClosure)
assertEquals(new HashSet([10, 20, 30, 40, 50]), new HashSet((Collection)result*.get()))
}
The passed-in closure takes an instance of a ExecutorService as a parameter, which can be then used freely inside the closure.
//find an element meeting specified criteria
GParsExecutorsPool.withPool(5) {ExecutorService service ->
service.submit({performLongCalculation()} as Runnable)
}
The
GParsExecutorsPool.withPool() method takes optional parameters for number of threads in the created pool and a thread factory.
withPool(10) {...}
withPool(20, threadFactory) {...}
The
GParsExecutorsPool.withExistingPool() takes an already existing executor service instance to reuse. The DSL is valid only within the associated block of code and only for the thread that has called the
withPool() or
withExistingPool() method. The
withPool() method returns only after all the worker threads have finished their tasks and the executor service has been destroyed, returning back the return value of the associated block of code. The
withExistingPool() method doesn't wait for the executor service threads to finish.
Alternatively, the
GParsExecutorsPool class can be statically imported
import static groovyx.gpars.GParsExecutorsPool.`*` , which will allow omitting the
GParsExecutorsPool class name.
withPool {
def result = [1, 2, 3, 4, 5].findParallel{Number number -> number > 2}
assert result in [3, 4, 5]
}
The following methods on all objects, which support iterations in Groovy, are currently supported:
- eachParallel()
- eachWithIndexParallel()
- collectParallel()
- findAllParallel()
- findParallel()
- allParallel()
- anyParallel()
- grepParallel()
- groupByParallel()
Meta-class enhancer
As an alternative you can use the
GParsExecutorsPoolEnhancer class to enhance meta-classes for any classes or individual instances with asynchronous methods.
import groovyx.gpars.GParsExecutorsPoolEnhancerdef list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
GParsExecutorsPoolEnhancer.enhanceInstance(list)
println list.collectParallel {it * 2 }def animals = ['dog', 'ant', 'cat', 'whale']
GParsExecutorsPoolEnhancer.enhanceInstance animals
println (animals.anyParallel {it ==~ /ant/} ? 'Found an ant' : 'No ants found')
println (animals.allParallel {it.contains('a')} ? 'All animals contain a' : 'Some animals can live without an a')
When using the
GParsExecutorsPoolEnhancer class, you're not restricted to a
withPool() block with the use of the GParsExecutorsPool DSLs. The enhanced classed or instances
remain enhanced till they get garbage collected.
Exception handling
If exceptions are thrown while processing any of the passed-in closures, an instance of
AsyncException wrapping all the original exceptions gets re-thrown from the xxxParallel methods.
Avoid side-effects in functions
Once again we need to warn you about using closures with side-effects effecting objects beyond the scope of the single currently processed element or closures which keep state. Don't do that! It is dangerous to pass them to any of the
xxxParallel() methods.
The
memoize function enables caching of function's return values. Repeated calls to the memoized function with the same argument values
will, instead of invoking the calculation encoded in the original function, retrieve the result value from an internal transparent cache.
Provided the calculation is considerably slower than retrieving a cached value from the cache, this allows users to trade-off memory for performance.
Checkout out the example, where we attempt to scan multiple websites for particular content:
The memoize functionality of GPars has been contributed to Groovy in version 1.8 and if you run on Groovy 1.8 or later, it is recommended to use the Groovy functionality.
Memoize in GPars is almost identical, except that it searches the memoize caches concurrently using the surrounding thread pool and so may give
performance benefits in some scenarios.
The GPars memoize functionality has been renamed to avoid future conflicts with the memoize functionality in Groovy.
GPars now calls the methods with a preceding letter g , such as gmemoize().
Examples of use
GParsPool.withPool {
def urls = ['http://www.dzone.com', 'http://www.theserverside.com', 'http://www.infoq.com']
Closure download = {url ->
println "Downloading $url"
url.toURL().text.toUpperCase()
}
Closure cachingDownload = download.gmemoize() println 'Groovy sites today: ' + urls.findAllParallel {url -> cachingDownload(url).contains('GROOVY')}
println 'Grails sites today: ' + urls.findAllParallel {url -> cachingDownload(url).contains('GRAILS')}
println 'Griffon sites today: ' + urls.findAllParallel {url -> cachingDownload(url).contains('GRIFFON')}
println 'Gradle sites today: ' + urls.findAllParallel {url -> cachingDownload(url).contains('GRADLE')}
println 'Concurrency sites today: ' + urls.findAllParallel {url -> cachingDownload(url).contains('CONCURRENCY')}
println 'GPars sites today: ' + urls.findAllParallel {url -> cachingDownload(url).contains('GPARS')}
}
Notice closures are enhanced inside the
GParsPool.withPool() blocks with a
memoize() function, which returns a new closure
wrapping the original closure with a cache.
In the example we're calling the
cachingDownload function in several places in the code, however, each unique url gets downloaded only once - the first time
it is needed. The values are then cached and available for subsequent calls. And also to all threads, no matter which thread originally came first with
a download request for the particular url and had to handle the actual calculation/download.
So, to wrap up, memoize shields a function by a cache of past return values. However,
memoize can do even more. In some algorithms
adding a little memory may have dramatic impact on the computational complexity of the calculation. Let's look at a classical example of
Fibonacci numbers.
Fibonacci example
A purely functional, recursive implementation, following closely the definition of Fibonacci numbers is exponentially complex:
Closure fib = {n -> n > 1 ? call(n - 1) + call(n - 2) : n}
Try calling the
fib function with numbers around 30 and you'll see how slow it is.
Now with a little twist and added memoize cache the algorithm magically turns into a linearly complex one:
Closure fib
fib = {n -> n > 1 ? fib(n - 1) + fib(n - 2) : n}.gmemoize()
The extra memory we added cut off all but one recursive branches of the calculation. And all subsequent calls to the same
fib
function will also benefit from the cached values.
Also, see below, how the
memoizeAtMost variant can reduce memory consumption in our example, yet preserve the linear complexity
of the algorithm.
Available variants
memoize
The basic variant, which keeps values in the internal cache for the whole lifetime of the memoized function. Provides the best performance
characteristics of all the variants.
memoizeAtMost
Allows the user to set a hard limit on number of items cached. Once the limit has been reached, all subsequently added values
will eliminate the oldest value from the cache using the LRU (Last Recently Used) strategy.
So for our Fibonacci number example, we could safely reduce the cache size to two items:
Closure fib
fib = {n -> n > 1 ? fib(n - 1) + fib(n - 2) : n}.memoizeAtMost(2)
Setting an upper limit on the cache size may have two purposes:
- Keep the memory footprint of the cache within defined boundaries
- Preserve desired performance characteristics of the function. Too large caches may take longer to retrieve the cached value than it would have taken to calculate the result directly.
memoizeAtLeast
Allows unlimited growth of the internal cache until the JVM's garbage collector decides to step in and evict SoftReferences,
used by our implementation, from the memory. The single parameter value to the
memoizeAtLeast() method specifies the minimum number
of cached items that should be protected from gc eviction. The cache will never shrink below the specified number of entries.
The cache ensures it only protects the most recently used items from eviction using the LRU (Last Recently Used) strategy.
memoizeBetween
Combines memoizeAtLeast and memoizeAtMost and so allowing the cache to grow and shrink in the range between the two parameter values
depending on available memory and the gc activity, yet the cache size will never exceed the upper size limit
to preserve desired performance characteristics of the cache.
The Parallel Collection Map/Reduce DSL gives GPars a more functional flavor. In general, the Map/Reduce DSL may be used for the same purpose as the
xxxParallel() family methods and has very similar semantics.
On the other hand, Map/Reduce can perform considerably faster, if you need to chain multiple methods to process a single collection in multiple steps:
println 'Number of occurrences of the word GROOVY today: ' + urls.parallel
.map {it.toURL().text.toUpperCase()}
.filter {it.contains('GROOVY')}
.map{it.split()}
.map{it.findAll{word -> word.contains 'GROOVY'}.size()}
.sum()
The
xxxParallel() methods have to follow the contract of their non-parallel peers. So a
collectParallel() method must return a legal collection of items, which you can again treat as a Groovy collection.
Internally the parallel collect method builds an efficient parallel structure, called parallel array, performs the required operation concurrently and before returning destroys the Parallel Array building the collection of results to return to you.
A potential call to let say
findAllParallel() on the resulting collection would repeat the whole process of construction and destruction of a Parallel Array instance under the covers.
With Map/Reduce you turn your collection into a Parallel Array and back only once. The Map/Reduce family of methods do not return Groovy collections, but are free to pass along the internal Parallel Arrays directly.
Invoking the
parallel property on a collection will build a Parallel Array for the collection and return a thin wrapper around the Parallel Array instance.
Then you can chain all required methods like:
- map()
- reduce()
- filter()
- size()
- sum()
- min()
- max()
- sort()
- groupBy()
- combine()
Returning back to a plain Groovy collection instance is always just a matter of retrieving the
collection property.
def myNumbers = (1..1000).parallel.filter{it % 2 == 0}.map{Math.sqrt it}.collection
Avoid side-effects in functions
Once again we need to warn you. To avoid nasty surprises, please, keep your closures, which you pass to the Map/Reduce functions, stateless and clean from side-effects.
Availability
This feature is only available when using in the Fork/Join-based
GParsPool , not in
GParsExecutorsPool .
Classical Example
A classical example, inspired by http://github.com/thevery, counting occurencies of words in a string:
import static groovyx.gpars.GParsPool.withPooldef words = "This is just a plain text to count words in"
print count(words)def count(arg) {
withPool {
return arg.parallel
.map{[it, 1]}
.groupBy{it[0]}.getParallel()
.map {it.value=it.value.size();it}
.sort{-it.value}.collection
}
}
The same example, now implemented the more general
combine operation:
def words = "This is just a plain text to count words in"
print count(words)def count(arg) {
withPool {
return arg.parallel
.map{[it, 1]}
.combine(0) {sum, value -> sum + value}.getParallel()
.sort{-it.value}.collection
}
}
Combine
The
combine operation expects on its input a list of tuples (two-element lists) considered to be key-value pairs (such as [key1, value1, key2, value2, key1, value3, key3, value4 … ] )
with potentially repeating keys. When invoked,
combine merges the values for identical keys using the provided accumulator function and produces a map mapping the original (unique) keys to their accumulated values.
E.g. [a, b, c, d, a, e, c, f] will be combined into a : b+e, c : d+f, while the '+' operation on the values needs to be provided by the user as the accumulation closure.
The
accumulation function argument needs to specify a function to use for combining (accumulating) the values belonging to the same key.
An
initial accumulator value needs to be provided as well. Since the
combine method processes items in parallel, the
initial accumulator value will be reused multiple times.
Thus the provided value must allow for reuse. It should be either a
cloneable or
immutable value or a
closure returning a fresh initial accumulator each time requested.
Good combinations of accumulator functions and reusable initial values include:
accumulator = {List acc, value -> acc << value} initialValue = []
accumulator = {List acc, value -> acc << value} initialValue = {-> []}
accumulator = {int sum, int value -> acc + value} initialValue = 0
accumulator = {int sum, int value -> sum + value} initialValue = {-> 0}
accumulator = {ShoppingCart cart, Item value -> cart.addItem(value)} initialValue = {-> new ShoppingCart()}
The return type is a map.
E.g. ['he', 1, 'she', 2, 'he', 2, 'me', 1, 'she, 5, 'he', 1 with the initial value provided a 0 will be combined into
'he' : 4, 'she' : 7, 'he', : 2, 'me' : 1
As an alternative, the efficient tree-based data structures defines in JSR-166y can be used directly. The
parallelArray
property on any collection or object will return a
jsr166y.forkjoin.ParallelArray instance holding the elements of the original collection,
which then can be manipulated through the jsr166y API. Please refer to the jsr166y documentation for the API details.
groovyx.gpars.GParsPool.withPool {
assert 15 == [1, 2, 3, 4, 5].parallelArray.reduce({a, b -> a + b} as Reducer, 0) //summarize
assert 55 == [1, 2, 3, 4, 5].parallelArray.withMapping({it ** 2} as Mapper).reduce({a, b -> a + b} as Reducer, 0) //summarize squares
assert 20 == [1, 2, 3, 4, 5].parallelArray.withFilter({it % 2 == 0} as Predicate) //summarize squares of even numbers
.withMapping({it ** 2} as Mapper)
.reduce({a, b -> a + b} as Reducer, 0) assert 'aa:bb:cc:dd:ee' == 'abcde'.parallelArray //concatenate duplicated characters with separator
.withMapping({it * 2} as Mapper)
.reduce({a, b -> "$a:$b"} as Reducer, "")
Running long-lasting tasks in the background belongs to the activities, the need for which arises quite frequently. Your main
thread of execution wants to initialize a few calculations, downloads, searches or such, however, the results may not be needed
immediately.
GPars gives the developers the tools to schedule the asynchronous activities for processing in the background
and collect the results once they're needed.
Usage of GParsPool and GParsExecutorsPool asynchronous processing facilities
Both
GParsPool and
GParsExecutorsPool provide almost identical services in this domain, although they leverage different
underlying machinery, based on which of the two classes the user chooses.
Closures enhancements
The following methods are added to closures inside the
GPars(Executors)Pool.withPool() blocks:
- async() - Creates an asynchronous variant of the supplied closure, which when invoked returns a future for the potential return value
- callAsync() - Calls a closure in a separate thread supplying the given arguments, returning a future for the potential return value,
Examples:
GParsPool.withPool() {
Closure longLastingCalculation = {calculate()}
Closure fastCalculation = longLastingCalculation.async() //create a new closure, which starts the original closure on a thread pool
Future result=fastCalculation() //returns almost immediately
//do stuff while calculation performs …
println result.get()
}
GParsPool.withPool() {
/**
* The callAsync() method is an asynchronous variant of the default call() method to invoke a closure.
* It will return a Future for the result value.
*/
assert 6 == {it * 2}.call(3)
assert 6 == {it * 2}.callAsync(3).get()
}
Timeouts
The
callTimeoutAsync() methods, taking either a long value or a Duration instance, allow the user to have the calculation cancelled after a given time interval.
{->
while(true) {
Thread.sleep 1000 //Simulate a bit of interesting calculation
if (Thread.currentThread().isInterrupted()) break; //We've been cancelled
}
}.callTimeoutAsync(2000)
In order to allow cancellation, the asynchronously running code must keep checking the
interrupted flag of its own thread and cease the calculation once the flag is set to true.
Executor Service enhancements
The ExecutorService and jsr166y.forkjoin.ForkJoinPool class is enhanced with the << (leftShift) operator to submit tasks to the pool and return
a
Future for the result.
Example:
GParsExecutorsPool.withPool {ExecutorService executorService ->
executorService << {println 'Inside parallel task'}
}
Running functions (closures) in parallel
The
GParsPool and
GParsExecutorsPool classes also provide handy methods
executeAsync() and
executeAsyncAndWait() to easily run multiple closures asynchronously.
Example:
GParsPool.withPool {
assertEquals([10, 20], GParsPool.executeAsyncAndWait({calculateA()}, {calculateB()})) //waits for results
assertEquals([10, 20], GParsPool.executeAsync({calculateA()}, {calculateB()})*.get()) //returns Futures instead and doesn't wait for results to be calculated
}
Functions are to be composed. In fact, composing side-effect-free functions is very easy. Much easier and reliable than composing objects, for example.
Given the same input, functions always return the same result, they never change their behavior unexpectedly nor they break when multiple threads call them at the same time.
Functions in Groovy
We can treat Groovy closures as functions. They take arguments, do their calculation and return a value. Provided you don't let your
closures touch anything outside their scope, your closures are well-behaved pure functions. Functions that you can combine for a better good.
def sum = (0..100000).inject(0, {a, b -> a + b})
For example, by combining a function adding two numbers with the
inject function, which iterates through the whole collection,
you can quickly summarize all items. Then, replacing the
adding function with a
comparison function will immediately give you a combined function calculating maximum.
def max = myNumbers.inject(0, {a, b -> a>b?a:b})
You see, functional programming is popular for a reason.
Are we concurrent yet?
This all works just fine until you realize you're not utilizing the full power of your expensive hardware. The functions are plain sequential.
No parallelism in here. All but one processor core do nothing, they're idle, totally wasted.
Those paying attention would suggest to use the Parallel Collection techniques described earlier and they would certainly be correct.
For our scenario described here, where we process a collection, using those parallel methods would be the best choice.
However, we're now looking for a generic way to create and combine asynchronous functions , which would help us
not only for collection processing but mostly in other more generic cases, like the one right below.
To make things more obvious, here's an example of combining four functions, which are supposed to check whether a particular web page matches the contents of a local file.
We need to download the page, load the file, calculate hashes of both and finally compare the resulting numbers.
Closure download = {String url ->
url.toURL().text
}Closure loadFile = {String fileName ->
… //load the file here
}Closure hash = {s -> s.hashCode()}.asyncFun()Closure compare = {int first, int second ->
first == second
}def result = compare(hash(download('http://www.gpars.org')), hash(loadFile('/coolStuff/gpars/website/index.html')))
println "The result of comparison: " + result
We need to download the page, load up the file, calculate hashes of both and finally compare the resulting numbers.
Each of the functions is responsible for one particular job. One downloads the content, second loads the file, third calculates the hashes
and finally the fourth one will do the comparison. Combining the functions is as simple as nesting their calls.
Making it all asynchronous
The downside of our code is that we don't leverage the independence of the
download() and the
loadFile() functions.
Neither we allow the two hashes to be run concurrently. They could well run in parallel, but our way to combine functions restricts any parallelism.
Obviously not all of the functions can run concurrently. Some functions depend on results of others. They cannot start before the other function finishes.
We need to block them till their parameters are available. The
hash() functions needs a string to work on. The
compare() function needs two numbers to compare.
So we can only parallelize some functions, while blocking parallelism of others. Seems like a challenging task.
Things are bright in the functional world
Luckily, the dependencies between functions are already expressed implicitly in the code. There's no need for us to duplicate the dependency information.
If one functions takes parameters and the parameters need first to be calculated by another function, we implicitly have a dependency here. The
hash() function
depends on the
loadFile() as well as on the
download() functions in our example.
The
inject function in our earlier example depends on the results of the
addition functions invoked gradually on all the elements of the collection.
However difficult it may seem at first, our task is in fact very simple. We only need to teach our functions to return promises of their future results. And we need to teach the other functions
to accept those promises as parameters so that they wait for the real values before they start their work.
And if we convince the functions to release the threads they hold while waiting for the values, we get directly to where the magic can happen.
In the good tradition of
GPars we've made it very straightforward for you to convince any function to believe in other functions' promises. Call the
asyncFun() function on a closure
and you're asynchronous.
withPool {
def maxPromise = numbers.inject(0, {a, b -> a>b?a:b}.asyncFun())
println "Look Ma, I can talk to the user while the math is being done for me!"
println maxPromise.get()
}
The
inject function doesn't really care what objects are being returned from the
addition function, maybe it is just a little surprised that
each call to the
addition function returns so fast, but doesn't moan much, keeps iterating and finally returns the overall result to you.
Now, this is the time you should stand behind what you say and do what you want others to do. Don't frown at the result and just accepts that you got back just a promise.
A
promise to get the result delivered as soon as the calculation is done. The extra heat coming out of your laptop is an indication the calculation
exploits natural parallelism in your functions and makes its best effort to deliver the result to you quickly.
The promise is a good old DataflowVariable , so you may query its status, register notification hooks or make it an input to a Dataflow algorithm.
withPool {
def sumPromise = (0..100000).inject(0, {a, b -> a + b}.asyncFun())
println "Are we done yet? " + sumPromise.bound
sumPromise.whenBound {sum -> println sum}
}
The get() method has also a variant with a timeout parameter, if you want to avoid the risk of waiting indefinitely.
Can things go wrong?
Sure. But you'll get an exception thrown from the result promise
get() method.
try {
sumPromise.get()
} catch (MyCalculationException e) {
println "Guess, things are not ideal today."
}
This is all fine, but what functions can be really combined?
There are no limits. Take any sequential functions you need to combine and you should be able to combine their asynchronous variants as well.
Back to our initial example comparing content of a file with a web page, we simply make all the functions asynchronous by calling
the
asyncFun() method on them and we are ready to set off.
Closure download = {String url ->
url.toURL().text
}.asyncFun() Closure loadFile = {String fileName ->
… //load the file here
}.asyncFun() Closure hash = {s -> s.hashCode()}.asyncFun() Closure compare = {int first, int second ->
first == second
}.asyncFun() def result = compare(hash(download('http://www.gpars.org')), hash(loadFile('/coolStuff/gpars/website/index.html')))
println 'Allowed to do something else now'
println "The result of comparison: " + result.get()
Calling asynchronous functions from within asynchronous functions
Another very valuable characteristics of asynchronous functions is that their result promises can also be composed.
import static groovyx.gpars.GParsPool.withPool withPool {
Closure plus = {Integer a, Integer b ->
sleep 3000
println 'Adding numbers'
a + b
}.asyncFun() Closure multiply = {Integer a, Integer b ->
sleep 2000
a * b
}.asyncFun() Closure measureTime = {->
sleep 3000
4
}.asyncFun() Closure distance = {Integer initialDistance, Integer velocity, Integer time ->
plus(initialDistance, multiply(velocity, time))
}.asyncFun() Closure chattyDistance = {Integer initialDistance, Integer velocity, Integer time ->
println 'All parameters are now ready - starting'
println 'About to call another asynchronous function'
def innerResultPromise = plus(initialDistance, multiply(velocity, time))
println 'Returning the promise for the inner calculation as my own result'
return innerResultPromise
}.asyncFun() println "Distance = " + distance(100, 20, measureTime()).get() + ' m'
println "ChattyDistance = " + chattyDistance(100, 20, measureTime()).get() + ' m'
}
If an asynchronous function (e.f. the
distance function in the example) in its body calls another asynchronous function
(e.g.
plus ) and returns the the promise of the invoked function, the inner function's (
plus ) result promise will compose with the outer function's (
distance )
result promise. The inner function (
plus ) will now bind its result to the outer function's (
distance ) promise, once the inner function (plus) finishes its calculation.
This ability of promises to compose allows functions to cease their calculation without blocking a thread not only when waiting for parameters,
but also whenever they call another asynchronous function anywhere in their body.
Methods as asynchronous functions
Methods can be referred to as closures using the
.& operator. These closures can then be transformed using
asyncFun into composable asynchronous functions just like ordinary closures.
class DownloadHelper {
String download(String url) {
url.toURL().text
} int scanFor(String word, String text) {
text.findAll(word).size()
} String lower(s) {
s.toLowerCase()
}
}
//now we'll make the methods asynchronous
withPool {
final DownloadHelper d = new DownloadHelper()
Closure download = d.&download.asyncFun()
Closure scanFor = d.&scanFor.asyncFun()
Closure lower = d.&lower.asyncFun() //asynchronous processing
def result = scanFor('groovy', lower(download('http://www.infoq.com')))
println 'Allowed to do something else now'
println result.get()
}
Using annotation to create asynchronous functions
Instead of calling the
asyncFun() function, the
@AsyncFun annotation can be used to annotate Closure-typed fields.
The fields have to be initialized in-place and the containing class needs to be instantiated withing a
withPool block.
import static groovyx.gpars.GParsPool.withPool
import groovyx.gpars.AsyncFunclass DownloadingSearch {
@AsyncFun Closure download = {String url ->
url.toURL().text
} @AsyncFun Closure scanFor = {String word, String text ->
text.findAll(word).size()
} @AsyncFun Closure lower = {s -> s.toLowerCase()} void scan() {
def result = scanFor('groovy', lower(download('http://www.infoq.com'))) //synchronous processing
println 'Allowed to do something else now'
println result.get()
}
}withPool {
new DownloadingSearch().scan()
}
Alternative pools
The
AsyncFun annotation by default uses an instance of
GParsPool from the wrapping withPool block. You may, however, specify the type of pool explicitly:
@AsyncFun(GParsExecutorsPoolUtil) def sum6 = {a, b -> a + b }
Blocking functions through annotations
The
AsyncFun also allows the user to specify, whether the resulting function should have blocking (true) or non-blocking (false - default) semantics.
@AsyncFun(blocking = true)
def sum = {a, b -> a + b }
On our side this is a very interesting domain to explore, so any comments, questions or suggestions on combining asynchronous functions or hints about its limits are welcome.
With processor cores having become plentiful, some algorithms might benefit from brutal-force parallel duplication.
Instead of deciding up-front about how to solve a problem, what algorithm to use or which location to connect to, you run all potential
solutions in parallel.
Parallel speculations
Imagine you need to perform a task like e.g. calculate an expensive function or read data from a file, database or internet. Luckily, you know of several good ways (e.g. functions or urls)
to achieve your goal. However, they are not all equal. Although they return back the same (as far as your needs are concerned) result, they may all take different amount of time to complete
and some of them may even fail (e.g. network issues). What's worse, no-one is going to tell you which path gives you the solution first nor which paths lead to no solution at all. Shall I
run
quick sort or
merge sort on my list? Which url will work best? Is this service available at its primary location or should I use the backup one?
GPars speculations give you the option to try all the available alternatives in parallel and so get the result from the fastest functional path, silently ignoring the slow or broken ones.
This is what the
speculate() methods on
GParsPool and
GParsExecutorsPool() can do.
def numbers = …
def quickSort = …
def mergeSort = …
def sortedNumbers = speculate(quickSort, mergeSort)
Here we're performing both
quick sort and
merge sort concurrently, while getting the result of the faster one. Given the parallel resources available these days on mainstream hardware,
running the two functions in parallel will not have dramatic impact on speed of calculation of either one, and so we get the result in about the same time as if we ran solely the faster of the two
calculations. And we get the result sooner than when running the slower one. Yet we didn't have to know up-front, which of the two sorting algorithms would perform better on our data. Thus
we speculated.
Similarly, downloading a document from multiple sources of different speed and reliability would look like this:
import static groovyx.gpars.GParsPool.speculate
import static groovyx.gpars.GParsPool.withPooldef alternative1 = {
'http://www.dzone.com/links/index.html'.toURL().text
}def alternative2 = {
'http://www.dzone.com/'.toURL().text
}def alternative3 = {
'http://www.dzzzzzone.com/'.toURL().text //wrong url
}def alternative4 = {
'http://dzone.com/'.toURL().text
}withPool(4) {
println speculate([alternative1, alternative2, alternative3, alternative4]).contains('groovy')
}
Make sure the surrounding thread pool has enough threads to process all alternatives in parallel. The size of the pool should match
the number of closures supplied.
Alternatives using dataflow variables and streams
In cases, when stopping unsuccessful alternatives is not needed, dataflow variables or streams may be used to obtain the result value
from the winning speculation.
Please refer to the Dataflow Concurrency section of the User Guide for details on Dataflow variables and streams.
import groovyx.gpars.dataflow.DataflowQueue
import static groovyx.gpars.dataflow.Dataflow.taskdef alternative1 = {
'http://www.dzone.com/links/index.html'.toURL().text
}def alternative2 = {
'http://www.dzone.com/'.toURL().text
}def alternative3 = {
'http://www.dzzzzzone.com/'.toURL().text //will fail due to wrong url
}def alternative4 = {
'http://dzone.com/'.toURL().text
}//Pick either one of the following, both will work:
final def result = new DataflowQueue()
// final def result = new DataflowVariable()[alternative1, alternative2, alternative3, alternative4].each {code ->
task {
try {
result << code()
} catch (ignore) { } //We deliberately ignore unsuccessful urls
}
}println result.val.contains('groovy')
Fork/Join or Divide and Conquer is a very powerful abstraction to solve hierarchical problems.
The abstraction
When talking about hierarchical problems, think about quick sort, merge sort, file system or general tree navigation and such.
- Fork / Join algorithms essentially split a problem at hands into several smaller sub-problems and recursively apply the same algorithm to each of the sub-problems.
- Once the sub-problem is small enough, it is solved directly.
- The solutions of all sub-problems are combined to solve their parent problem, which in turn helps solve its own parent problem.
Check out the fancy interactive Fork/Join visualization demo ,
which will show you how threads cooperate to solve a common divide-and-conquer algorithm.
The mighty
JSR-166y library solves Fork / Join orchestration pretty nicely for us, but leaves a couple of rough edges, which can hurt you, if you don't pay attention enough. You still deal
with threads, pools or synchronization barriers.
The GPars abstraction convenience layer
GPars can hide the complexities of dealing with threads, pools and recursive tasks from you, yet let you leverage the powerful Fork/Join implementation in jsr166y.
import static groovyx.gpars.GParsPool.runForkJoin
import static groovyx.gpars.GParsPool.withPoolwithPool() {
println """Number of files: ${
runForkJoin(new File("./src")) {file ->
long count = 0
file.eachFile {
if (it.isDirectory()) {
println "Forking a child task for $it"
forkOffChild(it) //fork a child task
} else {
count++
}
}
return count + (childrenResults.sum(0))
//use results of children tasks to calculate and store own result
}
}"""
}
The
runForkJoin() factory method will use the supplied recursive code together with the provided values and build a hierarchical Fork/Join
calculation. The number of values passed to the
runForkJoin() method must match the number of expected parameters of the closure as well as
the number of arguments passed into the
forkOffChild() or
runChildDirectly() methods.
def quicksort(numbers) {
withPool {
runForkJoin(0, numbers) {index, list ->
def groups = list.groupBy {it <=> list[list.size().intdiv(2)]}
if ((list.size() < 2) || (groups.size() == 1)) {
return [index: index, list: list.clone()]
}
(-1..1).each {forkOffChild(it, groups[it] ?: [])}
return [index: index, list: childrenResults.sort {it.index}.sum {it.list}]
}.list
}
}
Alternative approach
Alternatively, the underlying mechanism of nested Fork/Join worker tasks can be used directly. Custom-tailored workers can
eliminate the performance overhead associated with parameter spreading imposed when using the generic workers. Also, custom
workers can be implemented in Java and so further increase the performance of the algorithm.
public final class FileCounter extends AbstractForkJoinWorker<Long> {
private final File file; def FileCounter(final File file) {
this.file = file
} @Override
protected Long computeTask() {
long count = 0;
file.eachFile {
if (it.isDirectory()) {
println "Forking a thread for $it"
forkOffChild(new FileCounter(it)) //fork a child task
} else {
count++
}
}
return count + ((childrenResults)?.sum() ?: 0) //use results of children tasks to calculate and store own result
}
}withPool(1) {pool -> //feel free to experiment with the number of fork/join threads in the pool
println "Number of files: ${runForkJoin(new FileCounter(new File("..")))}"
}
The AbstractForkJoinWorker subclasses may be written both in Java or Groovy, giving you the option to easily optimize
for execution speed, if row performance of the worker becomes a bottleneck.
Fork / Join saves your resources
Fork/Join operations can be safely run with small number of threads thanks to internally using the TaskBarrier class to synchronize the threads. While a thread is blocked inside an algorithm waiting for its sub-problems to be calculated, the thread is silently returned to the pool to take on any of the available sub-problems from the task queue and process them.
Although the algorithm creates as many tasks as there are sub-directories and tasks wait for the sub-directory tasks to complete, as few as one thread is enough to keep the computation going and eventually calculate a valid result.
Mergesort example
import static groovyx.gpars.GParsPool.runForkJoin
import static groovyx.gpars.GParsPool.withPool/**
* Splits a list of numbers in half
*/
def split(List<Integer> list) {
int listSize = list.size()
int middleIndex = listSize / 2
def list1 = list[0..<middleIndex]
def list2 = list[middleIndex..listSize - 1]
return [list1, list2]
}/**
* Merges two sorted lists into one
*/
List<Integer> merge(List<Integer> a, List<Integer> b) {
int i = 0, j = 0
final int newSize = a.size() + b.size()
List<Integer> result = new ArrayList<Integer>(newSize) while ((i < a.size()) && (j < b.size())) {
if (a[i] <= b[j]) result << a[i++]
else result << b[j++]
} if (i < a.size()) result.addAll(a[i..-1])
else result.addAll(b[j..-1])
return result
}final def numbers = [1, 5, 2, 4, 3, 8, 6, 7, 3, 4, 5, 2, 2, 9, 8, 7, 6, 7, 8, 1, 4, 1, 7, 5, 8, 2, 3, 9, 5, 7, 4, 3]withPool(3) { //feel free to experiment with the number of fork/join threads in the pool
println """Sorted numbers: ${
runForkJoin(numbers) {nums ->
println "Thread ${Thread.currentThread().name[-1]}: Sorting $nums"
switch (nums.size()) {
case 0..1:
return nums //store own result
case 2:
if (nums[0] <= nums[1]) return nums //store own result
else return nums[-1..0] //store own result
default:
def splitList = split(nums)
[splitList[0], splitList[1]].each {forkOffChild it} //fork a child task
return merge(* childrenResults) //use results of children tasks to calculate and store own result
}
}
}"""
}
Mergesort example using a custom-tailored worker class
public final class SortWorker extends AbstractForkJoinWorker<List<Integer>> {
private final List numbers def SortWorker(final List<Integer> numbers) {
this.numbers = numbers.asImmutable()
} /**
* Splits a list of numbers in half
*/
def split(List<Integer> list) {
int listSize = list.size()
int middleIndex = listSize / 2
def list1 = list[0..<middleIndex]
def list2 = list[middleIndex..listSize - 1]
return [list1, list2]
} /**
* Merges two sorted lists into one
*/
List<Integer> merge(List<Integer> a, List<Integer> b) {
int i = 0, j = 0
final int newSize = a.size() + b.size()
List<Integer> result = new ArrayList<Integer>(newSize) while ((i < a.size()) && (j < b.size())) {
if (a[i] <= b[j]) result << a[i++]
else result << b[j++]
} if (i < a.size()) result.addAll(a[i..-1])
else result.addAll(b[j..-1])
return result
} /**
* Sorts a small list or delegates to two children, if the list contains more than two elements.
*/
@Override
protected List<Integer> computeTask() {
println "Thread ${Thread.currentThread().name[-1]}: Sorting $numbers"
switch (numbers.size()) {
case 0..1:
return numbers //store own result
case 2:
if (numbers[0] <= numbers[1]) return numbers //store own result
else return numbers[-1..0] //store own result
default:
def splitList = split(numbers)
[new SortWorker(splitList[0]), new SortWorker(splitList[1])].each{forkOffChild it} //fork a child task
return merge(* childrenResults) //use results of children tasks to calculate and store own result
}
}
}final def numbers = [1, 5, 2, 4, 3, 8, 6, 7, 3, 4, 5, 2, 2, 9, 8, 7, 6, 7, 8, 1, 4, 1, 7, 5, 8, 2, 3, 9, 5, 7, 4, 3]withPool(1) { //feel free to experiment with the number of fork/join threads in the pool
println "Sorted numbers: ${runForkJoin(new SortWorker(numbers))}"
}
Running child tasks directly
The
forkOffChild() method has a sibling - the
runChildDirectly() method, which will run the child task directly and immediately
within the current thread instead of scheduling the child task for asynchronous processing on the thread pool. Typically you'll
call _forkOffChild() on all sub-tasks but the last, which you invoke directly without the scheduling overhead.
Closure fib = {number ->
if (number <= 2) {
return 1
}
forkOffChild(number - 1) // This task will run asynchronously, probably in a different thread
final def result = runChildDirectly(number - 2) // This task is run directly within the current thread
return (Integer) getChildrenResults().sum() + result
} withPool {
assert 55 == runForkJoin(10, fib)
}
Availability
This feature is only available when using in the Fork/Join-based
GParsPool , not in
GParsExecutorsPool .
The CSP (Communicating Sequential Processes) abstraction builds on independent composable processes, which exchange messages in a synchronous manner.
GPars leverages
the JCSP library developed at the University of Kent, UK.
Jon Kerridge, the author of the CSP implementation in GPars, provides exhaustive examples on of GroovyCSP use at
his website:
The GroovyCSP implementation leverages JCSP, a Java-based CSP library, which is licensed under GPL. Unlike the liberal
Apache 2 license, which GPars uses, GPL is more restrictive on use in commercial software. Please make sure your application conforms to the GPL
rules before enabling use of JCSP in your code.
If the GPL license is too restrictive for your use, you might consider checking out the Dataflow Concurrency chapter of this User Guide
to learn about
tasks ,
selectors and
operators , which may help you resolve concurrency issues in ways similar to the CSP approach.
In fact the dataflow and CSP concepts stand very close to each other.
By default, without actively adding an explicit dependency on JCSP in your build file or downloading and including the JCSP jar file in your project,
the standard commercial-software-friendly Apache 2 License terms apply to your project. GPars directly only depends on software licensed under licenses
compatible with the Apache 2 License.
The actor support in GPars was originally inspired by the Actors library in Scala, but has since gone well
beyond what Scala offers as standard.
Actors allow for a message passing-based concurrency model: programs are collections of independent active
objects that exchange messages and have no mutable shared state. Actors can help developers avoid issues
such as deadlock, live-lock and starvation, which are common problems for shared memory based approaches.
Actors are a way of leveraging the multi-core nature of today's hardware without all the problems
traditionally associated with shared-memory multi-threading, which is why programming languages such as
Erlang and Scala have taken up this model.
A nice article summarizing the key
concepts behind
actors was written recently by Ruben
Vermeersch. Actors always guarantee that
at most one thread processes the actor's body at any one time
and also, under the covers, that the memory gets synchronized
each time a thread gets assigned to an actor so the actor's state
can be safely modified by code in the body
without any other extra (synchronization or locking) effort .
Ideally actor's code should
never be invoked directly from outside so all the code of the actor class can only be executed by the thread
handling the last received message and so all the actor's code is
implicitly thread-safe .
If any of the actor's methods is allowed to be called by other objects directly, the thread-safety guarantee for the actor's code and state are
no longer valid .
Types of actors
In general, you can find two types of actors in the wild - ones that hold
implicit state and those, who don't. GPars gives you both options.
Stateless actors, represented in
GPars by the
DynamicDispatchActor and the
ReactiveActor classes, keep no track of what messages have arrived previously.
You may thing of these as flat message handlers, which process messages as they come. Any state-based behavior has to be implemented by the user.
The
stateful actors, represented in GPars by the
DefaultActor class (and previously also by the
AbstractPooledActor class), allow the user to handle implicit state directly.
After receiving a message the actor moves into a new state with different ways to handle future messages.
To give you an example, a freshly started actor may only accept some types of messages, e.g. encrypted messages for decryption,
only after it has received the encryption keys. The stateful actors allow to encode such dependencies directly in the structure
of the message-handling code.
Implicit state management, however, comes at a slight performance cost, mainly due to the lack of continuations support on JVM.
Actor threading model
Since actors are detached from the system threads, a great number of actors can share a relatively small thread pool.
This can go as far as having many concurrent actors that share a single pooled thread. This architecture allows to avoid
some of the threading limitations of the JVM. In general, while the JVM can only give you a limited number of threads (typically around a couple of thousands),
the number of actors is only limited by the available memory. If an actor has no work to do, it doesn't consume threads.
Actor code is processed in chunks separated by quiet periods of waiting for new events (messages).
This can be naturally modeled through
continuations . As JVM doesn't support continuations directly, they have to be simulated in the actors frameworks,
which has slight impact on organization of the actors' code. However, the benefits in most cases outweigh the difficulties.
import groovyx.gpars.actor.Actor
import groovyx.gpars.actor.DefaultActorclass GameMaster extends DefaultActor {
int secretNum void afterStart() {
secretNum = new Random().nextInt(10)
} void act() {
loop {
react { int num ->
if (num > secretNum)
reply 'too large'
else if (num < secretNum)
reply 'too small'
else {
reply 'you win'
terminate()
}
}
}
}
}class Player extends DefaultActor {
String name
Actor server
int myNum void act() {
loop {
myNum = new Random().nextInt(10)
server.send myNum
react {
switch (it) {
case 'too large': println "$name: $myNum was too large"; break
case 'too small': println "$name: $myNum was too small"; break
case 'you win': println "$name: I won $myNum"; terminate(); break
}
}
}
}
}def master = new GameMaster().start()
def player = new Player(name: 'Player', server: master).start()//this forces main thread to live until both actors stop
[master, player]*.join()
example by
Jordi Campos i Miralles, Departament de MatemĂ tica Aplicada i AnĂ lisi, MAiA Facultat de MatemĂ tiques, Universitat de Barcelona Usage of Actors
Gpars provides consistent Actor APIs and DSLs. Actors in principal perform three specific operations - send messages, receive messages and create new actors. Although not specifically enforced by
GPars
messages should be immutable or at least follow the
hands-off policy when the sender never touches the messages after the message has been sent off.
Sending messages
Messages can be sent to actors using the
send() method.
def passiveActor = Actors.actor{
loop {
react { msg -> println "Received: $msg"; }
}
}
passiveActor.send 'Message 1'
passiveActor << 'Message 2' //using the << operator
passiveActor 'Message 3' //using the implicit call() method
Alternatively, the
<< operator or the implicit
call() method can be used. A family of
sendAndWait() methods is available to block the caller until a reply from the actor is available.
The
reply is returned from the
sendAndWait() method as a return value.
The
sendAndWait() methods may also return after a timeout expires or in case of termination of the called actor.
def replyingActor = Actors.actor{
loop {
react { msg ->
println "Received: $msg";
reply "I've got $msg"
}
}
}
def reply1 = replyingActor.sendAndWait('Message 4')
def reply2 = replyingActor.sendAndWait('Message 5', 10, TimeUnit.SECONDS)
use (TimeCategory) {
def reply3 = replyingActor.sendAndWait('Message 6', 10.seconds)
}
The
sendAndContinue() method allows the caller to continue its processing while the supplied closure is waiting for a reply from the actor.
friend.sendAndContinue 'I need money!', {money -> pocket money}
println 'I can continue while my friend is collecting money for me'
All
send() ,
sendAndWait() or
sendAndContinue() methods will throw an exception if invoked on a non-active actor.
Receiving messages
Non-blocking message retrieval
Calling the
react() method, optionally with a timeout parameter, from within the actor's code will consume the next message from the actor's inbox,
potentially waiting, if there is no message to be processed immediately.
println 'Waiting for a gift'
react {gift ->
if (myWife.likes gift) reply 'Thank you!'
}
Under the covers the supplied closure is not invoked directly, but scheduled for processing by any thread in the thread pool once
a message is available. After scheduling the current thread will then be detached from the actor and freed to process any other actor,
which has received a message already.
To allow detaching actors from the threads the
react() method demands the code to be written in a special
Continuation-style.
Actors.actor {
loop {
println 'Waiting for a gift'
react {gift ->
if (myWife.likes gift) reply 'Thank you!'
else {
reply 'Try again, please'
react {anotherGift ->
if (myChildren.like gift) reply 'Thank you!'
}
println 'Never reached'
}
}
println 'Never reached'
}
println 'Never reached'
}
The
react() method has a special semantics to allow actors to be detached from threads when no messages are available in their mailbox.
Essentially,
react() schedules the supplied code (closure) to be executed upon next message arrival and returns.
The closure supplied to the
react() methods is the code where the computation should
continue . Thus
continuation style .
Since actor has to preserve the guarantee of at most one thread active within the actor's body, the next message cannot be handled
before the current message processing finishes. Typically, there shouldn't be a need to put code after calls to
react() .
Some actor implementations even enforce this, however, GPars does not for performance reasons.
The
loop() method allows iteration within the actor body. Unlike typical looping constructs, like
for or
while loops,
loop() cooperates with nested
react() blocks and will ensure looping across subsequent message retrievals.
Sending replies
The
reply/replyIfExists methods are not only defined on the actors themselves, but for
AbstractPooledActor (not available in
DefaultActor ,
DynamicDispatchActor nor
ReactiveActor classes) also on the processed messages themselves
upon their reception, which is particularly handy when handling multiple messages in a single call. In such cases
reply() invoked on the actor sends a reply to authors of all the currently processed message (the last one), whereas
reply() called on messages sends a reply to the author of the particular message only.
See demo hereThe sender property
Messages upon retrieval offer the sender property to identify the originator of the message. The property is available inside the Actor's closure:
react {tweet ->
if (isSpam(tweet)) ignoreTweetsFrom sender
sender.send 'Never write me again!'
}
Forwarding
When sending a message, a different actor can be specified as the sender so that potential replies to the message will be forwarded to the specified actor and not to the actual originator.
def decryptor = Actors.actor {
react {message ->
reply message.reverse()
// sender.send message.reverse() //An alternative way to send replies
}
}def console = Actors.actor { //This actor will print out decrypted messages, since the replies are forwarded to it
react {
println 'Decrypted message: ' + it
}
}decryptor.send 'lellarap si yvoorG', console //Specify an actor to send replies to
console.join()
Creating Actors
Actors share a
pool of threads, which are dynamically assigned to actors when the actors need to
react to messages sent to them. The threads are returned to back the pool once a message has been processed and the actor is idle waiting for some more messages to arrive.
For example, this is how you create an actor that prints out all messages that it receives.
def console = Actors.actor {
loop {
react {
println it
}
}
}
Notice the
loop() method call, which ensures that the actor doesn't stop after having processed the first message.
Here's an example with a decryptor service, which can decrypt submitted messages and send the decrypted messages back to the originators.
final def decryptor = Actors.actor {
loop {
react {String message ->
if ('stopService' == message) {
println 'Stopping decryptor'
stop()
}
else reply message.reverse()
}
}
}Actors.actor {
decryptor.send 'lellarap si yvoorG'
react {
println 'Decrypted message: ' + it
decryptor.send 'stopService'
}
}.join()
Here's an example of an actor that waits for up to 30 seconds to receive a reply to its message.
def friend = Actors.actor {
react {
//this doesn't reply -> caller won't receive any answer in time
println it
//reply 'Hello' //uncomment this to answer conversation
react {
println it
}
}
}def me = Actors.actor {
friend.send('Hi')
//wait for answer 1sec
react(1000) {msg ->
if (msg == Actor.TIMEOUT) {
friend.send('I see, busy as usual. Never mind.')
stop()
} else {
//continue conversation
println "Thank you for $msg"
}
}
}me.join()
Undelivered messages
Sometimes messages cannot be delivered to the target actor. When special action needs to be taken for undelivered messages, at actor termination all unprocessed messages from its queue have their
onDeliveryError() method called. The
onDeliveryError() method or closure defined on the message can, for example, send a notification back to the original sender of the message.
final DefaultActor me
me = Actors.actor {
def message = 1 message.metaClass.onDeliveryError = {->
//send message back to the caller
me << "Could not deliver $delegate"
} def actor = Actors.actor {
react {
//wait 2sec in order next call in demo can be emitted
Thread.sleep(2000)
//stop actor after first message
stop()
}
} actor << message
actor << message react {
//print whatever comes back
println it
}}me.join()
Alternatively the
onDeliveryError() method can be specified on the sender itself. The method can be added both dynamically
final DefaultActor me
me = Actors.actor {
def message1 = 1
def message2 = 2 def actor = Actors.actor {
react {
//wait 2sec in order next call in demo can be emitted
Thread.sleep(2000)
//stop actor after first message
stop()
}
} me.metaClass.onDeliveryError = {msg ->
//callback on actor inaccessibility
println "Could not deliver message $msg"
} actor << message1
actor << message2 actor.join()}me.join()
and statically in actor definition:
class MyActor extends DefaultActor {
public void onDeliveryError(msg) {
println "Could not deliver message $msg"
}
…
}
Joining actors
Actors provide a
join() method to allow callers to wait for the actor to terminate. A variant accepting a timeout is also available. The Groovy
spread-dot operator comes in handy when joining multiple actors at a time.
def master = new GameMaster().start()
def player = new Player(name: 'Player', server: master).start()[master, player]*.join()
Conditional and counting loops
The
loop() method allows for either a condition or a number of iterations to be specified, optionally accompanied with a closure
to invoke once the loop finishes -
After Loop Termination Code Handler .
The following actor will loop three times to receive 3 messages and then prints out the maximum of the received messages.
final Actor actor = Actors.actor {
def candidates = []
def printResult = {-> println "The best offer is ${candidates.max()}"} loop(3, printResult) {
react {
candidates << it
}
}
}actor 10
actor 30
actor 20
actor.join()
The following actor will receive messages until a value greater then 30 arrives.
final Actor actor = Actors.actor {
def candidates = []
final Closure printResult = {-> println "Reached best offer - ${candidates.max()}"} loop({-> candidates.max() < 30}, printResult) {
react {
candidates << it
}
}
}actor 10
actor 20
actor 25
actor 31
actor 20
actor.join()
The After Loop Termination Code Handler can use actor's react{} but not loop() .
DefaultActor can be set to behave in a fair on non-fair (default) manner. Depending on the strategy chosen, the actor
either makes the thread available to other actors sharing the same parallel group (fair), or keeps the thread fot itself
until the message queue gets empty (non-fair). Generally, non-fair actors perform 2 - 3 times better than fair ones.Use either the fairActor() factory method or the actor's makeFair() method.
Custom schedulers
Actors leverage the standard JDK concurrency library by default. To provide a custom thread scheduler use the appropriate constructor parameter when creating a parallel group (PGroup class). The supplied scheduler will orchestrate threads in the group's thread pool.
Please also see the numerous
Actor Demos .
Actors share a
pool of threads, which are dynamically assigned to actors when the actors need to
react to messages sent to them.
The threads are returned back to the pool once a message has been processed and the actor is idle waiting for some more messages to arrive.
Actors become detached from the underlying threads and so a relatively small thread pool can serve potentially unlimited number of actors.
Virtually unlimited scalability in number of actors is the main advantage of
event-based actors , which are detached from the underlying physical threads.
Here are some examples of how to use actors. This is how you create an actor that prints out all messages that it receives.
import static groovyx.gpars.actor.Actors.*def console = actor {
loop {
react {
println it
}
}
Notice the
loop() method call, which ensures that the actor doesn't stop after having processed the first message.
As an alternative you can extend the
DefaultActor class and override the
act() method. Once you instantiate the actor, you need to start it so that it attaches itself to the thread pool and can start accepting messages.
The
actor() factory method will take care of starting the actor.
class CustomActor extends DefaultActor {
@Override
protected void act() {
loop {
react {
println it
}
}
}
}def console=new CustomActor()
console.start()
Messages can be sent to the actor using multiple methods
console.send('Message')
console 'Message'
console.sendAndWait 'Message' //Wait for a reply
console.sendAndContinue 'Message', {reply -> println "I received reply: $reply"} //Forward the reply to a function
Creating an asynchronous service
import static groovyx.gpars.actor.Actors.*final def decryptor = actor {
loop {
react {String message->
reply message.reverse()
}
}
}def console = actor {
decryptor.send 'lellarap si yvoorG'
react {
println 'Decrypted message: ' + it
}
}console.join()
As you can see, you create new actors with the
actor() method passing in the actor's body as a closure parameter. Inside
the actor's body you can use
loop() to iterate,
react() to receive messages and
reply() to send a message to the actor,
which has sent the currently processed message. The sender of the current message is also available through the actor's
sender property.
When the decryptor actor doesn't find a message in its message queue at the time when
react() is called,
the
react() method gives up the thread and returns it back to the thread pool for other actors to pick it up.
Only after a new message arrives to the actor's message queue, the closure of the
react() method gets scheduled for processing with the pool.
Event-based actors internally simulate continuations - actor's work is split into sequentially run chunks, which get invoked
once a message is available in the inbox. Each chunk for a single actor can be performed by a different thread from the thread pool.
Groovy flexible syntax with closures allows our library to offer multiple ways to define actors.
For instance, here's an example of an actor that waits for up to 30 seconds to receive a reply to its message.
Actors allow time DSL defined by org.codehaus.groovy.runtime.TimeCategory class to be used for timeout specification to the
react() method,
provided the user wraps the call within a
TimeCategory use block.
def friend = Actors.actor {
react {
//this doesn't reply -> caller won't receive any answer in time
println it
//reply 'Hello' //uncomment this to answer conversation
react {
println it
}
}
}def me = Actors.actor {
friend.send('Hi')
//wait for answer 1sec
react(1000) {msg ->
if (msg == Actor.TIMEOUT) {
friend.send('I see, busy as usual. Never mind.')
stop()
} else {
//continue conversation
println "Thank you for $msg"
}
}
}me.join()
When a timeout expires when waiting for a message, the Actor.TIMEOUT message arrives instead. Also the
onTimeout() handler
is invoked, if present on the actor:
def friend = Actors.actor {
react {
//this doesn't reply -> caller won't receive any answer in time
println it
//reply 'Hello' //uncomment this to answer conversation
react {
println it
}
}
}def me = Actors.actor {
friend.send('Hi') delegate.metaClass.onTimeout = {->
friend.send('I see, busy as usual. Never mind.')
stop()
} //wait for answer 1sec
react(1000) {msg ->
if (msg != Actor.TIMEOUT) {
//continue conversation
println "Thank you for $msg"
}
}
}me.join()
Notice the possibility to use Groovy meta-programming to define actor's lifecycle notification methods (e.g.
onTimeout() ) dynamically.
Obviously, the lifecycle methods can be defined the usual way when you decide to define a new class for your actor.
class MyActor extends DefaultActor {
public void onTimeout() {
…
} protected void act() {
…
}
}
Actors guarantee thread-safety for non-thread-safe code
Actors guarantee that always at most one thread processes the actor's body at a time and also under the covers the memory gets synchronized
each time a thread gets assigned to an actor so the actor's state
can be safely modified by code in the body
without any other extra (synchronization or locking) effort .
class MyCounterActor extends DefaultActor {
private Integer counter = 0 protected void act() {
loop {
react {
counter++
}
}
}
}
Ideally actor's code should
never be invoked directly from outside so all the code of the actor class can only be executed by the thread
handling the last received message and so all the actor's code is
implicitly thread-safe .
If any of the actor's methods is allowed to be called by other objects directly, the thread-safety guarantee for the actor's code and state are
no longer valid .
Simple calculator
A little bit more realistic example of an event-driven actor that receives two numeric messages, sums them up and sends the result to the console actor.
import groovyx.gpars.group.DefaultPGroup//not necessary, just showing that a single-threaded pool can still handle multiple actors
def group = new DefaultPGroup(1);final def console = group.actor {
loop {
react {
println 'Result: ' + it
}
}
}final def calculator = group.actor {
react {a ->
react {b ->
console.send(a + b)
}
}
}calculator.send 2
calculator.send 3calculator.join()
group.shutdown()
Notice that event-driven actors require special care regarding the
react() method. Since
event_driven actors need to split the code into independent chunks assignable to different threads sequentially
and
continuations are not natively supported on JVM, the chunks are created artificially. The
react() method creates the next message handler.
As soon as the current message handler finishes, the next message handler (continuation) gets scheduled.
Concurrent Merge Sort Example
For comparison I'm also including a more involved example performing a concurrent merge sort of a list of integers using actors. You can see that thanks to flexibility of Groovy we came pretty close to the Scala model, although I still miss Scala pattern matching for message handling.
import groovyx.gpars.group.DefaultPGroup
import static groovyx.gpars.actor.Actors.actorClosure createMessageHandler(def parentActor) {
return {
react {List<Integer> message ->
assert message != null
switch (message.size()) {
case 0..1:
parentActor.send(message)
break
case 2:
if (message[0] <= message[1]) parentActor.send(message)
else parentActor.send(message[-1..0])
break
default:
def splitList = split(message) def child1 = actor(createMessageHandler(delegate))
def child2 = actor(createMessageHandler(delegate))
child1.send(splitList[0])
child2.send(splitList[1]) react {message1 ->
react {message2 ->
parentActor.send merge(message1, message2)
}
}
}
}
}
}def console = new DefaultPGroup(1).actor {
react {
println "Sorted array:t${it}"
System.exit 0
}
}def sorter = actor(createMessageHandler(console))
sorter.send([1, 5, 2, 4, 3, 8, 6, 7, 3, 9, 5, 3])
console.join()def split(List<Integer> list) {
int listSize = list.size()
int middleIndex = listSize / 2
def list1 = list[0..<middleIndex]
def list2 = list[middleIndex..listSize - 1]
return [list1, list2]
}List<Integer> merge(List<Integer> a, List<Integer> b) {
int i = 0, j = 0
final int newSize = a.size() + b.size()
List<Integer> result = new ArrayList<Integer>(newSize) while ((i < a.size()) && (j < b.size())) {
if (a[i] <= b[j]) result << a[i++]
else result << b[j++]
} if (i < a.size()) result.addAll(a[i..-1])
else result.addAll(b[j..-1])
return result
}
Since
actors reuse threads from a pool, the script will work with virtually
any size of a thread pool, no matter how many actors are created along the way.
Actor lifecycle methods
Each Actor can define lifecycle observing methods, which will be called whenever a certain lifecycle event occurs.
- afterStart() - called right after the actor has been started.
- afterStop(List undeliveredMessages) - called right after the actor is stopped, passing in all the unprocessed messages from the queue.
- onInterrupt(InterruptedException e) - called when the actor's thread gets interrupted. Thread interruption will result in the stopping the actor in any case.
- onTimeout() - called when no messages are sent to the actor within the timeout specified for the currently blocking react method.
- onException(Throwable e) - called when an exception occurs in the actor's event handler. Actor will stop after return from this method.
You can either define the methods statically in your Actor class or add them dynamically to the actor's metaclass:
class MyActor extends DefaultActor {
public void afterStart() {
…
}
public void onTimeout() {
…
} protected void act() {
…
}
}
def myActor = actor {
delegate.metaClass.onException = {
log.error('Exception occurred', it)
}…
}
To help performance, you may consider using the silentStart() method instead of start() when starting a DynamicDispatchActor or a ReactiveActor .
Calling silentStart() will by-pass some of the start-up machinery and as a result will also avoid calling the afterStart() method.
Due to its stateful nature, DefaultActor cannot be started silently.
Pool management
Actors can be organized into groups and as a default there's always an application-wide pooled actor group available. And just like the
Actors abstract factory can be used to create actors in the default group, custom groups can be used as abstract factories to create new actors instances belonging to these groups.
def myGroup = new DefaultPGroup()def actor1 = myGroup.actor {
…
}def actor2 = myGroup.actor {
…
}
The actors belonging to the same group share the
underlying thread pool of that group. The pool by default contains
n + 1 threads, where
n stands for the number of
CPUs detected by the JVM. The
pool size can be set
explicitly either by setting the
gpars.poolsize system property or individually for each actor group by specifying the appropriate constructor parameter.
def myGroup = new DefaultPGroup(10) //the pool will contain 10 threads
The thread pool can be manipulated through the appropriate
DefaultPGroup class, which
delegates to the
Pool interface of the thread pool. For example, the
resize() method allows you to change the pool size any time and the
resetDefaultSize() sets it back to the default value. The
shutdown() method can be called when you need to safely finish all tasks, destroy the pool and stop all the threads in order to exit JVM in an organized manner.
… (n+1 threads in the default pool after startup)Actors.defaultActorPGroup.resize 1 //use one-thread pool… (1 thread in the pool)Actors.defaultActorPGroup.resetDefaultSize()… (n+1 threads in the pool)Actors.defaultActorPGroup.shutdown()
As an alternative to the
DefaultPGroup , which creates a pool of daemon threads, the
NonDaemonPGroup class can be used when non-daemon threads are required.
def daemonGroup = new DefaultPGroup()def actor1 = daemonGroup.actor {
…
}def nonDaemonGroup = new NonDaemonPGroup()def actor2 = nonDaemonGroup.actor {
…
}class MyActor {
def MyActor() {
this.parallelGroup = nonDaemonGroup
} void act() {...}
}
Actors belonging to the same group share the
underlying thread pool. With pooled actor groups you can split your actors to leverage multiple thread pools of different sizes and so assign resources to different components of your system and tune their performance.
def coreActors = new NonDaemonPGroup(5) //5 non-daemon threads pool
def helperActors = new DefaultPGroup(1) //1 daemon thread pooldef priceCalculator = coreActors.actor {
…
}def paymentProcessor = coreActors.actor {
…
}def emailNotifier = helperActors.actor {
…
}def cleanupActor = helperActors.actor {
…
}//increase size of the core actor group
coreActors.resize 6//shutdown the group's pool once you no longer need the group to release resources
helperActors.shutdown()
Do not forget to shutdown custom pooled actor groups, once you no longer need them and their actors, to preserve system resources.
Common trap: App terminates while actors do not receive messages
Most likely you're using daemon threads and pools, which is the default setting, and your main thread finishes. Calling
actor.join() on any, some or all of your actors would block the main thread until the actor terminates and thus keep all your actors running.
Alternatively use instances of
NonDaemonPGroup and assign some of your actors to these groups.
def nonDaemonGroup = new NonDaemonPGroup()
def myActor = nonDaemonGroup.actor {...}
alternatively
def nonDaemonGroup = new NonDaemonPGroup()class MyActor extends DefaultActor {
def MyActor() {
this.parallelGroup = nonDaemonGroup
} void act() {...}
}def myActor = new MyActor()
Blocking Actors
Instead of event-driven continuation-styled actors, you may in some scenarios prefer using blocking actors.
Blocking actors hold a single pooled thread for their whole life-time including the time when waiting for messages.
They avoid some of the thread management overhead, since they never fight for threads after start,
and also they let you write straight code without the necessity of continuation style, since they only do blocking message reads via the
receive method.
Obviously the number of blocking actors running concurrently is limited by the number of threads available in the shared pool.
On the other hand, blocking actors typically provide better performance compared to continuation-style actors,
especially when the actor's message queue rarely gets empty.
def decryptor = blockingActor {
while (true) {
receive {message ->
if (message instanceof String) reply message.reverse()
else stop()
}
}
}def console = blockingActor {
decryptor.send 'lellarap si yvoorG'
println 'Decrypted message: ' + receive()
decryptor.send false
}[decryptor, console]*.join()
Blocking actors increase the number of options to tune performance of your applications. They may in particular be good candidates
for high-traffic positions in your actor network.
Dynamic Dispatch Actor
The
DynamicDispatchActor class is an actor allowing for an alternative structure of the message handling code. In general
DynamicDispatchActor repeatedly scans for messages and dispatches arrived messages to one
of the
onMessage(message) methods defined on the actor. The
DynamicDispatchActor leverages the Groovy dynamic method dispatch mechanism under the covers.
Since, unlike
DefaultActor descendants, a
DynamicDispatchActor not
ReactiveActor (discussed below) do not need to implicitly remember actor's state between subsequent
message receptions, they provide much better performance characteristics, generally comparable to other actor frameworks, like e.g. Scala Actors.
import groovyx.gpars.actor.Actors
import groovyx.gpars.actor.DynamicDispatchActorfinal class MyActor extends DynamicDispatchActor { void onMessage(String message) {
println 'Received string'
} void onMessage(Integer message) {
println 'Received integer'
reply 'Thanks!'
} void onMessage(Object message) {
println 'Received object'
sender.send 'Thanks!'
} void onMessage(List message) {
println 'Received list'
stop()
}
}final def myActor = new MyActor().start()Actors.actor {
myActor 1
myActor ''
myActor 1.0
myActor(new ArrayList())
myActor.join()
}.join()
In some scenarios, typically when no implicit conversation-history-dependent state needs to be preserved for the actor, the dynamic dispatch code structure may be more intuitive than the traditional one using nested
loop and
react statements.
The
DynamicDispatchActor class also provides a handy facility to add message handlers dynamically at actor construction time or any time later
using the
when handlers, optionally wrapped inside a
become method:
final Actor myActor = new DynamicDispatchActor().become {
when {String msg -> println 'A String'; reply 'Thanks'}
when {Double msg -> println 'A Double'; reply 'Thanks'}
when {msg -> println 'A something ...'; reply 'What was that?';stop()}
}
myActor.start()
Actors.actor {
myActor 'Hello'
myActor 1.0d
myActor 10 as BigDecimal
myActor.join()
}.join()
Obviously the two approaches can be combined:
final class MyDDA extends DynamicDispatchActor { void onMessage(String message) {
println 'Received string'
} void onMessage(Integer message) {
println 'Received integer'
} void onMessage(Object message) {
println 'Received object'
} void onMessage(List message) {
println 'Received list'
stop()
}
}final def myActor = new MyDDA().become {
when {BigDecimal num -> println 'Received BigDecimal'}
when {Float num -> println 'Got a float'}
}.start()
Actors.actor {
myActor 'Hello'
myActor 1.0f
myActor 10 as BigDecimal
myActor.send([])
myActor.join()
}.join()
The dynamic message handlers registered via
when take precedence over the static
onMessage handlers.
DynamicDispatchActor can be set to behave in a fair on non-fair (default) manner. Depending on the strategy chosen, the actor
either makes the thread available to other actors sharing the same parallel group (fair), or keeps the thread fot itself
until the message queue gets empty (non-fair). Generally, non-fair actors perform 2 - 3 times better than fair ones.Use either the fairMessageHandler() factory method or the actor's makeFair() method.
def fairActor = Actors.fairMessageHandler {...}
Reactive Actor
The
ReactiveActor class, constructed typically by calling
Actors.reactor() or
DefaultPGroup.reactor() , allow for more event-driven like approach. When a reactive actor receives a message, the supplied block of code, which makes up the reactive actor's body, is run with the message as a parameter. The result returned from the code is sent in reply.
final def group = new DefaultPGroup()final def doubler = group.reactor {
2 * it
}group.actor {
println 'Double of 10 = ' + doubler.sendAndWait(10)
}group.actor {
println 'Double of 20 = ' + doubler.sendAndWait(20)
}group.actor {
println 'Double of 30 = ' + doubler.sendAndWait(30)
}for(i in (1..10)) {
println "Double of $i = ${doubler.sendAndWait(i)}"
}doubler.stop()
doubler.join()
Here's an example of an actor, which submits a batch of numbers to a
ReactiveActor for processing and then prints the results gradually as they arrive.
import groovyx.gpars.actor.Actor
import groovyx.gpars.actor.Actorsfinal def doubler = Actors.reactor {
2 * it
}Actor actor = Actors.actor {
(1..10).each {doubler << it}
int i = 0
loop {
i += 1
if (i > 10) stop()
else {
react {message ->
println "Double of $i = $message"
}
}
}
}actor.join()
doubler.stop()
doubler.join()
Essentially reactive actors provide a convenience shortcut for an actor that would wait for messages in a loop, process them and send back the result. This is schematically how the reactive actor looks inside:
public class ReactiveActor extends DefaultActor {
Closure body void act() {
loop {
react {message ->
reply body(message)
}
}
}
}
ReactiveActor can be set to behave in a fair on non-fair (default) manner. Depending on the strategy chosen, the actor
either makes the thread available to other actors sharing the same parallel group (fair), or keeps the thread fot itself
until the message queue gets empty (non-fair). Generally, non-fair actors perform 2 - 3 times better than fair ones.Use either the fairReactor() factory method or the actor's makeFair() method.
def fairActor = Actors.fairReactor {...}
Structuring actor's code
When extending the
DefaultActor class, you can call any actor's methods from within the
act() method and use the
react() or
loop() methods in them.
class MyDemoActor extends DefaultActor { protected void act() {
handleA()
} private void handleA() {
react {a ->
handleB(a)
}
} private void handleB(int a) {
react {b ->
println a + b
reply a + b
}
}
}final def demoActor = new MyDemoActor()
demoActor.start()Actors.actor {
demoActor 10
demoActor 20
react {
println "Result: $it"
}
}.join()
Bear in mind that the methods
handleA() and
handleB() in all our examples will only schedule the supplied message handlers to run as continuations of the current calculation in reaction to the next message arriving.
Alternatively, when using the
actor() factory method, you can add event-handling code through the meta class as closures.
Actor demoActor = Actors.actor {
delegate.metaClass {
handleA = {->
react {a ->
handleB(a)
}
} handleB = {a ->
react {b ->
println a + b
reply a + b
}
}
} handleA()
}Actors.actor {
demoActor 10
demoActor 20
react {
println "Result: $it"
}
}.join()
Closures, which have the actor set as their delegate can also be used to structure event-handling code.
Closure handleB = {a ->
react {b ->
println a + b
reply a + b
}
}Closure handleA = {->
react {a ->
handleB(a)
}
}Actor demoActor = Actors.actor {
handleA.delegate = delegate
handleB.delegate = delegate handleA()
}Actors.actor {
demoActor 10
demoActor 20
react {
println "Result: $it"
}
}.join()
Event-driven loops
When coding event-driven actors you have to have in mind that calls to
react() and
loop() methods have slightly different semantics. This becomes a bit of a challenge once you try to implement any types of loops in your actors.
On the other hand, if you leverage the fact that
react() only schedules a continuation and returns, you may call methods recursively without fear to fill up the stack. Look at the examples below, which respectively use the three described techniques for structuring actor's code.
A subclass of
DefaultActor
class MyLoopActor extends DefaultActor { protected void act() {
outerLoop()
} private void outerLoop() {
react {a ->
println 'Outer: ' + a
if (a != 0) innerLoop()
else println 'Done'
}
} private void innerLoop() {
react {b ->
println 'Inner ' + b
if (b == 0) outerLoop()
else innerLoop()
}
}
}final def actor = new MyLoopActor().start()
actor 10
actor 20
actor 0
actor 0
actor.join()
Enhancing the actor's metaClass
Actor actor = Actors.actor { delegate.metaClass {
outerLoop = {->
react {a ->
println 'Outer: ' + a
if (a!=0) innerLoop()
else println 'Done'
}
} innerLoop = {->
react {b ->
println 'Inner ' + b
if (b==0) outerLoop()
else innerLoop()
}
}
} outerLoop()
}actor 10
actor 20
actor 0
actor 0
actor.join()
Using Groovy closures
Closure innerLoopClosure outerLoop = {->
react {a ->
println 'Outer: ' + a
if (a!=0) innerLoop()
else println 'Done'
}
}innerLoop = {->
react {b ->
println 'Inner ' + b
if (b==0) outerLoop()
else innerLoop()
}
}Actor actor = Actors.actor {
outerLoop.delegate = delegate
innerLoop.delegate = delegate outerLoop()
}actor 10
actor 20
actor 0
actor 0
actor.join()
Plus don't forget about the possibility to use the actor's
loop() method to create a loop that runs until the actor terminates.
class MyLoopingActor extends DefaultActor { protected void act() {
loop {
outerLoop()
}
} private void outerLoop() {
react {a ->
println 'Outer: ' + a
if (a!=0) innerLoop()
else println 'Done for now, but will loop again'
}
} private void innerLoop() {
react {b ->
println 'Inner ' + b
if (b == 0) outerLoop()
else innerLoop()
}
}
}final def actor = new MyLoopingActor().start()
actor 10
actor 20
actor 0
actor 0
actor 10
actor.stop()
actor.join()
Active objects provide an OO facade on top of actors, allowing you to avoid dealing directly with the actor machinery,
having to match messages, wait for results and send replies.
Actors with a friendly facade
import groovyx.gpars.activeobject.ActiveObject
import groovyx.gpars.activeobject.ActiveMethod@ActiveObject
class Decryptor {
@ActiveMethod
def decrypt(String encryptedText) {
return encryptedText.reverse()
} @ActiveMethod
def decrypt(Integer encryptedNumber) {
return -1*encryptedNumber + 142
}
}final Decryptor decryptor = new Decryptor()
def part1 = decryptor.decrypt(' noitcA ni yvoorG')
def part2 = decryptor.decrypt(140)
def part3 = decryptor.decrypt('noittide dn')print part1.get()
print part2.get()
println part3.get()
You mark active objects with the
@ActiveObject annotation. This will ensure a hidden actor instance is created for each instance of your class.
Now you can mark methods with the
@ActiveMethod annotation indicating that you want the method to be invoked asynchronously
by the target object's internal actor.
An optional boolean
blocking parameter to the
@ActiveMethod annotation specifies, whether the caller should block until a result is available
or whether instead the caller should only receive a
promise for a future result in a form of a
DataflowVariable and so the caller is not blocked waiting.
By default, all active methods are set to be non-blocking . However, methods, which declare their return type explicitly, must be configured
as blocking, otherwise the compiler will report an error. Only def , void and DataflowVariable are allowed return types for non-blocking methods.
Under the covers, GPars will translate your method call to
a message being sent to the internal actor . The actor will eventually handle that message by invoking the desired method
on behalf of the caller and once finished a reply will be sent back to the caller.
Non-blocking methods return promises for results, aka
DataflowVariables .
But blocking means we're not really asynchronous, are we?
Indeed, if you mark your active methods as
blocking , the caller will be blocked waiting for the result, just like when doing normal plain method invocation.
All we've achieved is being thread-safe inside the Active object from concurrent access. Something the
synchronized keyword could give you as well.
So it is the
non-blocking methods that should drive your decision towards using active objects. Blocking methods will then provide the usual synchronous semantics yet give the consistency guarantees
across concurrent method invocations. The blocking methods are then still very useful when used in combination with non-blocking ones.
import groovyx.gpars.activeobject.ActiveMethod
import groovyx.gpars.activeobject.ActiveObject
import groovyx.gpars.dataflow.DataflowVariable@ActiveObject
class Decryptor {
@ActiveMethod(blocking=true)
String decrypt(String encryptedText) {
encryptedText.reverse()
} @ActiveMethod(blocking=true)
Integer decrypt(Integer encryptedNumber) {
-1*encryptedNumber + 142
}
}final Decryptor decryptor = new Decryptor()
print decryptor.decrypt(' noitcA ni yvoorG')
print decryptor.decrypt(140)
println decryptor.decrypt('noittide dn')
Non-blocking semantics
Now calling the non-blocking active method will return as soon as the actor has been sent a message.
The caller is now allowed to do whatever he likes, while the actor is taking care of the calculation.
The state of the calculation can be polled using the
bound property on the promise.
Calling the
get() method on the returned promise will block the caller until a value is available.
The call to
get() will eventually return a value or throw an exception, depending on the outcome of the actual calculation.
The get() method has also a variant with a timeout parameter, if you want to avoid the risk of waiting indefinitely.
Annotation rules
There are a few rules to follow when annotating your objects:
- The ActiveMethod annotations are only accepted in classes annotated as ActiveObject
- Only instance (non-static) methods can be annotated as ActiveMethod
- You can override active methods with non-active ones and vice versa
- Subclasses of active objects can declare additional active methods, provided they are themselves annotated as ActiveObject
- Combining concurrent use of active and non-active methods may result in race conditions. Ideally design your active objects as completely encapsulated classes with all non-private methods marked as active
Inheritance
The
@ActiveObject annotation can appear on any class in an inheritance hierarchy. The actor field will only be created in top-most annotated class in the hierarchy, the subclasses will reuse the field.
import groovyx.gpars.activeobject.ActiveObject
import groovyx.gpars.activeobject.ActiveMethod
import groovyx.gpars.dataflow.DataflowVariable@ActiveObject
class A {
@ActiveMethod
def fooA(value) {
…
}
}class B extends A {
}@ActiveObject
class C extends B {
@ActiveMethod
def fooC(value1, value2) {
…
}
}
In our example the actor field will be generated into class
A . Class
C has to be annotated with
@ActiveObject since it holds
the
@ActiveMethod annotation on method
fooC() , while class
B does not need the annotation, since none of its methods is active.
Groups
Just like actors can be grouped around thread pools, active objects can be configured to use threads from particular parallel groups.
@ActiveObject("group1")
class MyActiveObject {
…
}
The
value parameter to the
@ActiveObject annotation specifies a name of parallel group to bind the internal actor to.
Only threads from the specified group will be used to run internal actors of instances of the class.
The groups, however, need to be created and registered prior to creation of any of the active object instances belonging to that group.
If not specified explicitly, an active object will use the default actor group -
Actors.defaultActorPGroup .
final DefaultPGroup group = new DefaultPGroup(10)
ActiveObjectRegistry.instance.register("group1", group)
Alternative names for the internal actor
You will probably only rarely run into name collisions with the default name for the active object's internal actor field.
May you need to change the default name
internalActiveObjectActor , use the
actorName parameter to the
@ActiveObject annotation.
@ActiveObject(actorName = "alternativeActorName")
class MyActiveObject {
…
}
Alternative names for internal actors as well as their desired groups cannot be overriden in subclasses.
Make sure you only specify these values in the top-most active objects in your inheritance hierarchy. Obviously, the top most active object
is still allowed to subclass other classes, just none of the predecessors must be an active object.
A few examples on Actors use
Examples
- The Sieve of Eratosthenes
- Sleeping Barber
- Dining Philosophers
- Word Sort
- Load Balancer
The Sieve of Eratosthenes
Problem descriptionimport groovyx.gpars.actor.DynamicDispatchActor/**
* Demonstrates concurrent implementation of the Sieve of Eratosthenes using actors
*
* In principle, the algorithm consists of concurrently run chained filters,
* each of which detects whether the current number can be divided by a single prime number.
* (generate nums 1, 2, 3, 4, 5, ...) -> (filter by mod 2) -> (filter by mod 3) -> (filter by mod 5) -> (filter by mod 7) -> (filter by mod 11) -> (caution! Primes falling out here)
* The chain is built (grows) on the fly, whenever a new prime is found.
*/int requestedPrimeNumberBoundary = 1000final def firstFilter = new FilterActor(2).start()/**
* Generating candidate numbers and sending them to the actor chain
*/
(2..requestedPrimeNumberBoundary).each {
firstFilter it
}
firstFilter.sendAndWait 'Poison'/**
* Filter out numbers that can be divided by a single prime number
*/
final class FilterActor extends DynamicDispatchActor {
private final int myPrime
private def follower def FilterActor(final myPrime) { this.myPrime = myPrime; } /**
* Try to divide the received number with the prime. If the number cannot be divided, send it along the chain.
* If there's no-one to send it to, I'm the last in the chain, the number is a prime and so I will create and chain
* a new actor responsible for filtering by this newly found prime number.
*/
def onMessage(int value) {
if (value % myPrime != 0) {
if (follower) follower value
else {
println "Found $value"
follower = new FilterActor(value).start()
}
}
} /**
* Stop the actor on poisson reception
*/
def onMessage(def poisson) {
if (follower) {
def sender = sender
follower.sendAndContinue(poisson, {this.stop(); sender?.send('Done')}) //Pass the poisson along and stop after a reply
} else { //I am the last in the chain
stop()
reply 'Done'
}
}
}
Sleeping Barber
Problem descriptionimport groovyx.gpars.group.DefaultPGroup
import groovyx.gpars.actor.DefaultActor
import groovyx.gpars.group.DefaultPGroup
import groovyx.gpars.actor.Actorfinal def group = new DefaultPGroup()final def barber = group.actor {
final def random = new Random()
loop {
react {message ->
switch (message) {
case Enter:
message.customer.send new Start()
println "Barber: Processing customer ${message.customer.name}"
doTheWork(random)
message.customer.send new Done()
reply new Next()
break
case Wait:
println "Barber: No customers. Going to have a sleep"
break
}
}
}
}private def doTheWork(Random random) {
Thread.sleep(random.nextInt(10) * 1000)
}final Actor waitingRoomwaitingRoom = group.actor {
final int capacity = 5
final List<Customer> waitingCustomers = []
boolean barberAsleep = true loop {
react {message ->
switch (message) {
case Enter:
if (waitingCustomers.size() == capacity) {
reply new Full()
} else {
waitingCustomers << message.customer
if (barberAsleep) {
assert waitingCustomers.size() == 1
barberAsleep = false
waitingRoom.send new Next()
}
else reply new Wait()
}
break
case Next:
if (waitingCustomers.size()>0) {
def customer = waitingCustomers.remove(0)
barber.send new Enter(customer:customer)
} else {
barber.send new Wait()
barberAsleep = true
}
}
}
}}class Customer extends DefaultActor {
String name
Actor localBarbers void act() {
localBarbers << new Enter(customer:this)
loop {
react {message ->
switch (message) {
case Full:
println "Customer: $name: The waiting room is full. I am leaving."
stop()
break
case Wait:
println "Customer: $name: I will wait."
break
case Start:
println "Customer: $name: I am now being served."
break
case Done:
println "Customer: $name: I have been served."
stop();
break }
}
}
}
}class Enter { Customer customer }
class Full {}
class Wait {}
class Next {}
class Start {}
class Done {}def customers = []
customers << new Customer(name:'Joe', localBarbers:waitingRoom).start()
customers << new Customer(name:'Dave', localBarbers:waitingRoom).start()
customers << new Customer(name:'Alice', localBarbers:waitingRoom).start()sleep 15000
customers << new Customer(name: 'James', localBarbers: waitingRoom).start()
sleep 5000
customers*.join()
barber.stop()
waitingRoom.stop()
Dining Philosophers
Problem descriptionimport groovyx.gpars.actor.DefaultActor
import groovyx.gpars.actor.ActorsActors.defaultActorPGroup.resize 5final class Philosopher extends DefaultActor {
private Random random = new Random() String name
def forks = [] void act() {
assert 2 == forks.size()
loop {
think()
forks*.send new Take()
def messages = []
react {a ->
messages << [a, sender]
react {b ->
messages << [b, sender]
if ([a, b].any {Rejected.isCase it}) {
println "$name: tOops, can't get my forks! Giving up."
final def accepted = messages.find {Accepted.isCase it[0]}
if (accepted!=null) accepted[1].send new Finished()
} else {
eat()
reply new Finished()
}
}
}
}
} void think() {
println "$name: tI'm thinking"
Thread.sleep random.nextInt(5000)
println "$name: tI'm done thinking"
} void eat() {
println "$name: tI'm EATING"
Thread.sleep random.nextInt(2000)
println "$name: tI'm done EATING"
}
}final class Fork extends DefaultActor { String name
boolean available = true void act() {
loop {
react {message ->
switch (message) {
case Take:
if (available) {
available = false
reply new Accepted()
} else reply new Rejected()
break
case Finished:
assert !available
available = true
break
default: throw new IllegalStateException("Cannot process the message: $message")
}
}
}
}
}final class Take {}
final class Accepted {}
final class Rejected {}
final class Finished {}def forks = [
new Fork(name:'Fork 1'),
new Fork(name:'Fork 2'),
new Fork(name:'Fork 3'),
new Fork(name:'Fork 4'),
new Fork(name:'Fork 5')
]def philosophers = [
new Philosopher(name:'Joe', forks:[forks[0], forks[1]]),
new Philosopher(name:'Dave', forks:[forks[1], forks[2]]),
new Philosopher(name:'Alice', forks:[forks[2], forks[3]]),
new Philosopher(name:'James', forks:[forks[3], forks[4]]),
new Philosopher(name:'Phil', forks:[forks[4], forks[0]]),
]forks*.start()
philosophers*.start()sleep 10000
forks*.stop()
philosophers*.stop()
Word sort
Given a folder name, the script will sort words in all files in the folder. The
SortMaster actor creates a given number of
WordSortActors , splits among them the files to sort words in and collects the results.
Inspired by Scala Concurrency blog post by Michael Galpin//Messages
private final class FileToSort { String fileName }
private final class SortResult { String fileName; List<String> words }//Worker actor
final class WordSortActor extends DefaultActor { private List<String> sortedWords(String fileName) {
parseFile(fileName).sort {it.toLowerCase()}
} private List<String> parseFile(String fileName) {
List<String> words = []
new File(fileName).splitEachLine(' ') {words.addAll(it)}
return words
} void act() {
loop {
react {message ->
switch (message) {
case FileToSort:
println "Sorting file=${message.fileName} on thread ${Thread.currentThread().name}"
reply new SortResult(fileName: message.fileName, words: sortedWords(message.fileName))
}
}
}
}
}//Master actor
final class SortMaster extends DefaultActor { String docRoot = '/'
int numActors = 1 List<List<String>> sorted = []
private CountDownLatch startupLatch = new CountDownLatch(1)
private CountDownLatch doneLatch private void beginSorting() {
int cnt = sendTasksToWorkers()
doneLatch = new CountDownLatch(cnt)
} private List createWorkers() {
return (1..numActors).collect {new WordSortActor().start()}
} private int sendTasksToWorkers() {
List<Actor> workers = createWorkers()
int cnt = 0
new File(docRoot).eachFile {
workers[cnt % numActors] << new FileToSort(fileName: it)
cnt += 1
}
return cnt
} public void waitUntilDone() {
startupLatch.await()
doneLatch.await()
} void act() {
beginSorting()
startupLatch.countDown()
loop {
react {
switch (it) {
case SortResult:
sorted << it.words
doneLatch.countDown()
println "Received results for file=${it.fileName}"
}
}
}
}
}//start the actors to sort words
def master = new SortMaster(docRoot: 'c:/tmp/Logs/', numActors: 5).start()
master.waitUntilDone()
println 'Done'File file = new File("c:/tmp/Logs/sorted_words.txt")
file.withPrintWriter { printer ->
master.sorted.each { printer.println it }
}
Load Balancer
Demonstrates work balancing among adaptable set of workers. The load balancer receives tasks and queues them in a temporary task queue. When a worker finishes his assignment, it asks the load balancer for a new task.
If the load balancer doesn't have any tasks available in the task queue, the worker is stopped.
If the number of tasks in the task queue exceeds certain limit, a new worker is created to increase size of the worker pool.
import groovyx.gpars.actor.Actor
import groovyx.gpars.actor.DefaultActor/**
* Demonstrates work balancing among adaptable set of workers.
* The load balancer receives tasks and queues them in a temporary task queue.
* When a worker finishes his assignment, it asks the load balancer for a new task.
* If the load balancer doesn't have any tasks available in the task queue, the worker is stopped.
* If the number of tasks in the task queue exceeds certain limit, a new worker is created
* to increase size of the worker pool.
*/final class LoadBalancer extends DefaultActor {
int workers = 0
List taskQueue = []
private static final QUEUE_SIZE_TRIGGER = 10 void act() {
loop {
react { message ->
switch (message) {
case NeedMoreWork:
if (taskQueue.size() == 0) {
println 'No more tasks in the task queue. Terminating the worker.'
reply DemoWorker.EXIT
workers -= 1
} else reply taskQueue.remove(0)
break
case WorkToDo:
taskQueue << message
if ((workers == 0) || (taskQueue.size() >= QUEUE_SIZE_TRIGGER)) {
println 'Need more workers. Starting one.'
workers += 1
new DemoWorker(this).start()
}
}
println "Active workers=${workers}tTasks in queue=${taskQueue.size()}"
}
}
}
}final class DemoWorker extends DefaultActor {
final static Object EXIT = new Object()
private static final Random random = new Random() Actor balancer def DemoWorker(balancer) {
this.balancer = balancer
} void act() {
loop {
this.balancer << new NeedMoreWork()
react {
switch (it) {
case WorkToDo:
processMessage(it)
break
case EXIT: terminate()
}
}
} } private void processMessage(message) {
synchronized (random) {
Thread.sleep random.nextInt(5000)
}
}
}
final class WorkToDo {}
final class NeedMoreWork {}final Actor balancer = new LoadBalancer().start()//produce tasks
for (i in 1..20) {
Thread.sleep 100
balancer << new WorkToDo()
}//produce tasks in a parallel thread
Thread.start {
for (i in 1..10) {
Thread.sleep 1000
balancer << new WorkToDo()
}
}Thread.sleep 35000 //let the queues get empty
balancer << new WorkToDo()
balancer << new WorkToDo()
Thread.sleep 10000balancer.stop()
balancer.join()
The Agent class, which is a thread-safe non-blocking shared mutable state wrapper implementation inspired by Agents in Clojure.
A lot of the concurrency problems disappear when you eliminate the need for Shared Mutable State with your architecture.
Indeed, concepts like actors, CSP or dataflow concurrency avoid or isolate mutable state completely.
In some cases, however, sharing mutable data is either inevitable or makes the design more natural and understandable. Think, for example,
of a shopping cart in a typical e-commerce application, when multiple AJAX requests may hit the cart with read or write requests concurrently.
Introduction
In the Clojure programing language you can find a concept of Agents, the purpose of which is to protect mutable data that need to be shared across threads.
Agents hide the data and protect it from direct access. Clients can only send commands (functions) to the agent. The commands will be serialized and processed against the data one-by-one in turn.
With the commands being executed serially the commands do not need to care about concurrency and can assume the data is all theirs when run.
Although implemented differently, GPars Agents, called
Agent , fundamentally behave like actors. They accept messages and process them asynchronously.
The messages, however, must be commands (functions or Groovy closures) and will be executed inside the agent.
After reception the received function is run against the internal state of the Agent and the return value of the function is considered to be the new internal state of the Agent.
Essentially, agents safe-guard mutable values by allowing only a single
agent-managed thread to make modifications to them. The mutable values are
not directly accessible from outside, but instead
requests have to be sent to the agent and the agent guarantees to process the requests sequentially on behalf of the callers.
Agents guarantee sequential execution of all requests and so consistency of the values.
Schematically:
agent = new Agent(0) //created a new Agent wrapping an integer with initial value 0
agent.send {increment()} //asynchronous send operation, sending the increment() function
…
//after some delay to process the message the internal Agent's state has been updated
…
assert agent.val== 1
To wrap integers, we can certainly use AtomicXXX types on the Java platform, but when the state is a more complex object we need more support.
Concepts
GPars provides an Agent class, which is a special-purpose thread-safe non-blocking implementation inspired by Agents in Clojure.
An Agent wraps a reference to mutable state, held inside a single field, and accepts code (closures / commands) as messages, which can be sent to the Agent just like to any other actor using the '<<' operator, the send() methods or the implicit
call() method.
At some point after reception of a closure / command, the closure is invoked against the internal mutable field and can make changes to it. The closure is guaranteed to be run without intervention from other threads and so may freely alter the internal state of the Agent held in the internal <i>data</i> field.
The whole update process is of the fire-and-forget type, since once the message (closure) is sent to the Agent, the caller thread can go off to do other things and come back later to check the current value with Agent.val or Agent.valAsync(closure).
Basic rules
- When executed, the submitted commands obtain the agent's state ar a parameter.
- The submitted commands /closures can call any methods on the agent's state.
- Replacing the state object with a new one is also possible and is done using the updateValue() method.
- The return value of the submitted closure doesn't have a special meaning and is ignored.
- If the message sent to an Agent is not a closure, it is considered to be a new value for the internal reference field.
- The val property of an Agent will wait until all preceding commands in the agent's queue are consumed and then safely return the value of the Agent.
- The instantVal property will return an immediate snapshot of the internal agent's state.
- The valAsync() method will do the same without blocking the caller.
- All Agent instances share a default daemon thread pool. Setting the threadPool property of an Agent instance will allow it to use a different thread pool.
- Exceptions thrown by the commands can be collected using the errors property.
Examples
Shared list of members
The Agent wraps a list of members, who have been added to the jug. To add a new member a message (command to add a member) has to be sent to the
jugMembers Agent.
import groovyx.gpars.agent.Agent
import java.util.concurrent.ExecutorService
import java.util.concurrent.Executors/**
* Create a new Agent wrapping a list of strings
*/
def jugMembers = new Agent<List<String>>(['Me']) //add MejugMembers.send {it.add 'James'} //add Jamesfinal Thread t1 = Thread.start {
jugMembers.send {it.add 'Joe'} //add Joe
}final Thread t2 = Thread.start {
jugMembers << {it.add 'Dave'} //add Dave
jugMembers {it.add 'Alice'} //add Alice (using the implicit call() method)
}[t1, t2]*.join()
println jugMembers.val
jugMembers.valAsync {println "Current members: $it"}jugMembers.await()
Shared conference counting number of registrations
The Conference class allows registration and un-registration, however these methods can only be called from the commands sent to the
conference Agent.
import groovyx.gpars.agent.Agent/**
* Conference stores number of registrations and allows parties to register and unregister.
* It inherits from the Agent class and adds the register() and unregister() private methods,
* which callers may use it the commands they submit to the Conference.
*/
class Conference extends Agent<Long> {
def Conference() { super(0) }
private def register(long num) { data += num }
private def unregister(long num) { data -= num }
}final Agent conference = new Conference() //new Conference created/**
* Three external parties will try to register/unregister concurrently
*/final Thread t1 = Thread.start {
conference << {register(10L)} //send a command to register 10 attendees
}final Thread t2 = Thread.start {
conference << {register(5L)} //send a command to register 5 attendees
}final Thread t3 = Thread.start {
conference << {unregister(3L)} //send a command to unregister 3 attendees
}[t1, t2, t3]*.join()assert 12L == conference.val
Factory methods
Agent instances can also be created using the
Agent.agent() factory method.
def jugMembers = Agent.agent ['Me'] //add Me
Listeners and validators
Agents allow the user to add listeners and validators. While listeners will get notified each time the internal state changes,
validators get a chance to reject a coming change by throwing an exception.
final Agent counter = new Agent()counter.addListener {oldValue, newValue -> println "Changing value from $oldValue to $newValue"}
counter.addListener {agent, oldValue, newValue -> println "Agent $agent changing value from $oldValue to $newValue"}counter.addValidator {oldValue, newValue -> if (oldValue > newValue) throw new IllegalArgumentException('Things can only go up in Groovy')}
counter.addValidator {agent, oldValue, newValue -> if (oldValue == newValue) throw new IllegalArgumentException('Things never stay the same for $agent')}counter 10
counter 11
counter {updateValue 12}
counter 10 //Will be rejected
counter {updateValue it - 1} //Will be rejected
counter {updateValue it} //Will be rejected
counter {updateValue 11} //Will be rejected
counter 12 //Will be rejected
counter 20
counter.await()
Both listeners and validators are essentially closures taking two or three arguments. Exceptions thrown from the validators
will be logged inside the agent and can be tested using the
hasErrors() method or retrieved through the
errors property.
assert counter.hasErrors()
assert counter.errors.size() == 5
Validator gotchas
With Groovy being not very strict on data types and immutability, agent users should be aware of potential bumps on the road.
If the submitted code modifies the state directly, validators will not be able to un-do the change in case of a validation rule violation.
There are two possible solutions available:
- Make sure you never change the supplied object representing current agent state
- Use custom copy strategy on the agent to allow the agent to create copies of the internal state
In both cases you need to call
updateValue() to set and validate the new state properly.
The problem as well as both of the solutions are shown below:
//Create an agent storing names, rejecting 'Joe'
final Closure rejectJoeValidator = {oldValue, newValue -> if ('Joe' in newValue) throw new IllegalArgumentException('Joe is not allowed to enter our list.')}Agent agent = new Agent([])
agent.addValidator rejectJoeValidatoragent {it << 'Dave'} //Accepted
agent {it << 'Joe'} //Erroneously accepted, since by-passes the validation mechanism
println agent.val//Solution 1 - never alter the supplied state object
agent = new Agent([])
agent.addValidator rejectJoeValidatoragent {updateValue(['Dave', * it])} //Accepted
agent {updateValue(['Joe', * it])} //Rejected
println agent.val//Solution 2 - use custom copy strategy on the agent
agent = new Agent([], {it.clone()})
agent.addValidator rejectJoeValidatoragent {updateValue it << 'Dave'} //Accepted
agent {updateValue it << 'Joe'} //Rejected, since 'it' is now just a copy of the internal agent's state
println agent.val
Grouping
By default all Agent instances belong to the same group sharing its daemon thread pool.
Custom groups can also create instances of Agent. These instances will belong to the group, which created them, and will share a thread pool.
To create an Agent instance belonging to a group, call the
agent() factory method on the group. This way you can organize
and tune performance of agents.
final def group = new NonDaemonPGroup(5) //create a group around a thread pool
def jugMembers = group.agent(['Me']) //add Me
The default thread pool for agents contains daemon threads. Make sure that your custom thread pools either use daemon threads, too, which can be achieved
either by using DefaultPGroup or by providing your own thread factory to a thread pool constructor,
or in case your thread pools use non-daemon threads, such as when using the NonDaemonPGroup group class, make sure you shutdown the group or the thread pool explicitly by calling its shutdown() method,
otherwise your applications will not exit.
Direct pool replacement
Alternatively, by calling the
attachToThreadPool() method on an Agent instance a custom thread pool can be specified for it.
def jugMembers = new Agent<List<String>>(['Me']) //add Mefinal ExecutorService pool = Executors.newFixedThreadPool(10)
jugMembers.attachToThreadPool(new DefaultPool(pool))
Remember, like actors, a single Agent instance (aka agent) can never use more than one thread at a time
The shopping cart example
import groovyx.gpars.agent.Agentclass ShoppingCart {
private def cartState = new Agent([:])
//----------------- public methods below here ----------------------------------
public void addItem(String product, int quantity) {
cartState << {it[product] = quantity} //the << operator sends
//a message to the Agent
} public void removeItem(String product) {
cartState << {it.remove(product)}
} public Object listContent() {
return cartState.val
} public void clearItems() {
cartState << performClear
} public void increaseQuantity(String product, int quantityChange) {
cartState << this.&changeQuantity.curry(product, quantityChange)
}
//----------------- private methods below here ---------------------------------
private void changeQuantity(String product, int quantityChange, Map items) {
items[product] = (items[product] ?: 0) + quantityChange
} private Closure performClear = { it.clear() }
}
//----------------- script code below here -------------------------------------
final ShoppingCart cart = new ShoppingCart()
cart.addItem 'Pilsner', 10
cart.addItem 'Budweisser', 5
cart.addItem 'Staropramen', 20cart.removeItem 'Budweisser'
cart.addItem 'Budweisser', 15println "Contents ${cart.listContent()}"cart.increaseQuantity 'Budweisser', 3
println "Contents ${cart.listContent()}"cart.clearItems()
println "Contents ${cart.listContent()}"
You might have noticed two implementation strategies in the code.
- Public methods may internally just send the required code off to the Agent, instead of executing the same functionality directly
And so sequential code like
public void addItem(String product, int quantity) {
cartState[product]=quantity}
becomes
public void addItem(String product, int quantity) {
cartState << {it[product] = quantity}
}
2. Public methods may send references to internal private methods or closures, which hold the desired functionality to perform
public void clearItems() {
cartState << performClear
}private Closure performClear = { it.clear() }
Currying might be necessary, if the closure takes other arguments besides the current internal state instance. See the
increaseQuantity method.
The printer service example
Another example - a not thread-safe printer service shared by multiple threads. The printer needs to have the document and quality properties set before printing, so obviously a potential for race conditions if not guarded properly. Callers don't want to block until the printer is available, which the fire-and-forget nature of actors solves very elegantly.
import groovyx.gpars.agent.Agent/**
* A non-thread-safe service that slowly prints documents on at a time
*/
class PrinterService {
String document
String quality public void printDocument() {
println "Printing $document in $quality quality"
Thread.sleep 5000
println "Done printing $document"
}
}def printer = new Agent<PrinterService>(new PrinterService())final Thread thread1 = Thread.start {
for (num in (1..3)) {
final String text = "document $num"
printer << {printerService ->
printerService.document = text
printerService.quality = 'High'
printerService.printDocument()
}
Thread.sleep 200
}
println 'Thread 1 is ready to do something else. All print tasks have been submitted'
}final Thread thread2 = Thread.start {
for (num in (1..4)) {
final String text = "picture $num"
printer << {printerService ->
printerService.document = text
printerService.quality = 'Medium'
printerService.printDocument()
}
Thread.sleep 500
}
println 'Thread 2 is ready to do something else. All print tasks have been submitted'
}[thread1, thread2]*.join()
printer.await()
For latest update, see the respective Demos.
Reading the value
To follow the clojure philosophy closely the Agent class gives reads higher priority than to writes.
By using the
instantVal property your read request will bypass the incoming message queue of the Agent
and return the current snapshot of the internal state.
The
val property will wait in the message queue for processing, just like the non-blocking variant
valAsync(Clojure cl) , which will invoke the provided closure with the internal state as a parameter.
You have to bear in mind that the
instantVal property might return although correct, but randomly looking results, since the internal state
of the Agent at the time of
instantVal execution is non-deterministic and depends on the messages that have been processed
before the thread scheduler executes the body of
instantVal .
The
await() method allows you to wait for processing all the messages submitted to the Agent before and so blocks
the calling thread.
State copy strategy
To avoid leaking the internal state the Agent class allows to specify a copy strategy as the second constructor argument.
With the copy strategy specified, the internal state is processed by the copy strategy closure and the output value
of the copy strategy value is returned to the caller instead of the actual internal state. This applies to
instantVal ,
val as well as to
valAsync() .
Error handling
Exceptions thrown from within the submitted commands are stored inside the agent and can be obtained from the
errors property.
The property gets cleared once read.
def jugMembers = new Agent<List>()
assert jugMembers.errors.empty jugMembers.send {throw new IllegalStateException('test1')}
jugMembers.send {throw new IllegalArgumentException('test2')}
jugMembers.await() List errors = jugMembers.errors
assertEquals(2, errors.size())
assert errors[0] instanceof IllegalStateException
assertEquals 'test1', errors[0].message
assert errors[1] instanceof IllegalArgumentException
assertEquals 'test2', errors[1].message assert jugMembers.errors.empty
Fair and Non-fair agents
Agents can be either fair or non-fair. Fair agents give up the thread after processing each message, non-fair agents keep a thread until their message queue is empty.
As a result, non-fair agents tend to perform better than fair ones.
The default setting for all Agent instances is to be
non-fair, however by calling its
makeFair() method the instance can be made fair.
def jugMembers = new Agent<List>(['Me']) //add Me
jugMembers.makeFair()
Dataflow concurrency offers an alternative concurrency model, which is inherently safe and robust.
Introduction
Check out the small example written in Groovy using GPars, which sums results of calculations performed by three concurrently run tasks:
import static groovyx.gpars.dataflow.Dataflow.taskfinal def x = new DataflowVariable()
final def y = new DataflowVariable()
final def z = new DataflowVariable()task {
z << x.val + y.val
}task {
x << 10
}task {
y << 5
}println "Result: ${z.val}"
Or the same algorithm rewritten using the
Dataflows class.
import static groovyx.gpars.dataflow.Dataflow.taskfinal def df = new Dataflows()task {
df.z = df.x + df.y
}task {
df.x = 10
}task {
df.y = 5
}println "Result: ${df.z}"
We start three logical tasks, which can run in parallel and perform their particular activities. The tasks need to exchange data and they do so using
Dataflow Variables.
Think of Dataflow Variables as one-shot channels safely and reliably transferring data from producers to their consumers.
The Dataflow Variables have a pretty straightforward semantics. When a task needs to read a value from
DataflowVariable (through the val property), it will block until the value has been set by another task or thread (using the '<<' operator). Each
DataflowVariable can be set
only once in its lifetime. Notice that you don't have to bother with ordering and synchronizing the tasks or threads and their access to shared variables. The values are magically transferred among tasks at the right time without your intervention.
The data flow seamlessly among tasks / threads without your intervention or care.
Implementation detail: The three tasks in the example
do not necessarily need to be mapped to three physical threads. Tasks represent so-called "green" or "logical" threads and can be mapped under the covers to any number of physical threads. The actual mapping depends on the scheduler, but the outcome of dataflow algorithms doesn't depend on the actual scheduling.
The bind operation of dataflow variables silently accepts re-binding to a value, which is equal to an already bound value. Call bindUnique to reject equal values on already-bound variables.
Benefits
Here's what you gain by using Dataflow Concurrency (by
Jonas Bonér ):
- No race-conditions
- No live-locks
- Deterministic deadlocks
- Completely deterministic programs
- BEAUTIFUL code.
This doesn't sound bad, does it?
Concepts
Dataflow programming
Quoting Wikipedia
Operations (in Dataflow programs) consist of "black boxes" with inputs and outputs, all of which are always explicitly defined. They run as soon as all of their inputs become valid, as opposed to when the program encounters them. Whereas a traditional program essentially consists of a series of statements saying "do this, now do this", a dataflow program is more like a series of workers on an assembly line, who will do their assigned task as soon as the materials arrive. This is why dataflow languages are inherently parallel; the operations have no hidden state to keep track of, and the operations are all "ready" at the same time.
Principles
With Dataflow Concurrency you can safely share variables across tasks. These variables (in Groovy instances of the
DataflowVariable class) can only be assigned (using the '<<' operator) a value once in their lifetime. The values of the variables, on the other hand, can be read multiple times (in Groovy through the val property), even before the value has been assigned. In such cases the reading task is suspended until the value is set by another task.
So you can simply write your code for each task sequentially using Dataflow Variables and the underlying mechanics will make sure you get all the values you need in a thread-safe manner.
In brief, you generally perform three operations with Dataflow variables:
- Create a dataflow variable
- Wait for the variable to be bound (read it)
- Bind the variable (write to it)
And these are the three essential rules your programs have to follow:
- When the program encounters an unbound variable it waits for a value.
- It is not possible to change the value of a dataflow variable once it is bound.
- Dataflow variables makes it easy to create concurrent stream agents.
Dataflow Queues and Broadcasts
Before you go to check the samples of using
Dataflow Variables,
Tasks and
Operators, you should know a bit about streams and queues to have a full picture of Dataflow Concurrency.
Except for dataflow variables there are also the concepts of
DataflowQueues and
DataflowBroadcast that you can leverage in your code.
You may think of them as thread-safe buffers or queues for message transfer among concurrent tasks or threads. Check out a typical producer-consumer demo:
import static groovyx.gpars.dataflow.Dataflow.taskdef words = ['Groovy', 'fantastic', 'concurrency', 'fun', 'enjoy', 'safe', 'GPars', 'data', 'flow']
final def buffer = new DataflowQueue()task {
for (word in words) {
buffer << word.toUpperCase() //add to the buffer
}
}task {
while(true) println buffer.val //read from the buffer in a loop
}
Both
DataflowBroadcasts and
DataflowQueues , just like
DataflowVariables , implement the
DataflowChannel interface with common methods allowing users
to write to them and read values from them. The ability to treat both types identically through the
DataflowChannel interface comes in handy
once you start using them to wire
tasks ,
operators or
selectors together.
The DataflowChannel interface combines two interfaces, each serving its purpose:
- DataflowReadChannel holding all the methods necessary for reading values from a channel
- DataflowWriteChannel holding all the methods necessary for writing values into a channel
You may prefer using these dedicated interfaces instead of the general DataflowChannel interface, to better express the intended usage.
Point-to-point communication
The
DataflowQueue class can be viewed as a point-to-point (1 to 1, many to 1) communication channel. It allows one or more producers send messages to one reader.
If multiple readers read from the same
DataflowQueue , they will each consume different messages. Or to put it a different way, each message is consumed by exactly one reader.
You can easily imagine a simple load-balancing scheme built around a shared
DataflowQueue with readers being added dynamically when the consumer part of your algorithm needs to scale up.
This is also a useful default choice when connecting tasks or operators.
Publish-subscribe communication
The
DataflowBroadcast class offers a publish-subscribe (1 to many, many to many) communication model. One or more producers write messages,
while all registered readers will receive all the messages. Each message is thus consumed by all readers with a valid subscription at the moment when the message is being written to the channel.
The readers subscribe by calling the
createReadChannel() method.
DataflowWriteChannel broadcastStream = new DataflowBroadcast()
DataflowReadChannel stream1 = broadcastStream.createReadChannel()
DataflowReadChannel stream2 = broadcastStream.createReadChannel()
broadcastStream << 'Message1'
broadcastStream << 'Message2'
broadcastStream << 'Message3'
assert stream1.val == stream2.val
assert stream1.val == stream2.val
assert stream1.val == stream2.val
Under the hood
DataflowBroadcast uses the
DataflowStream class to implement the message delivery.
DataflowStream
The
DataflowStream class represents a deterministic dataflow channel. It is build around the concept of a functional queue and so provides a lock-free thread-safe implementation for message passing.
Essentially, you may think of
DataflowStream as a 1 to many communication channel, since when a reader consumes a messages,
other readers will still be able to read the message. Also, all messages arrive to all readers in the same order.
Since
DataflowStream is implemented as a functional queue, its API requires that users traverse the values in the stream themselves.
On the other hand
DataflowStream offers handy methods for value filtering or transformation together with interesting performance characteristics.
The DataflowStream class, unlike the other communication elements, does not implement the DataflowChannel interface, since the semantics of its use is different.
Use DataflowStreamReadAdapter and DataflowStreamWriteAdapter classes to wrap instances of the DataflowChannel class
in DataflowReadChannel or DataflowWriteChannel implementations.
import groovyx.gpars.dataflow.stream.DataflowStream
import groovyx.gpars.group.DefaultPGroup
import groovyx.gpars.scheduler.ResizeablePool/**
* Demonstrates concurrent implementation of the Sieve of Eratosthenes using dataflow tasks
*
* In principle, the algorithm consists of a concurrently run chained filters,
* each of which detects whether the current number can be divided by a single prime number.
* (generate nums 1, 2, 3, 4, 5, ...) -> (filter by mod 2) -> (filter by mod 3) -> (filter by mod 5) -> (filter by mod 7) -> (filter by mod 11) -> (caution! Primes falling out here)
* The chain is built (grows) on the fly, whenever a new prime is found
*//**
* We need a resizeable thread pool, since tasks consume threads while waiting blocked for values at DataflowQueue.val
*/
group = new DefaultPGroup(new ResizeablePool(true))final int requestedPrimeNumberCount = 100/**
* Generating candidate numbers
*/
final DataflowStream candidates = new DataflowStream()
group.task {
candidates.generate(2, {it + 1}, {it < 1000})
}/**
* Chain a new filter for a particular prime number to the end of the Sieve
* @param inChannel The current end channel to consume
* @param prime The prime number to divide future prime candidates with
* @return A new channel ending the whole chain
*/
def filter(DataflowStream inChannel, int prime) {
inChannel.filter { number ->
group.task {
number % prime != 0
}
}
}/**
* Consume Sieve output and add additional filters for all found primes
*/
def currentOutput = candidates
requestedPrimeNumberCount.times {
int prime = currentOutput.first
println "Found: $prime"
currentOutput = filter(currentOutput, prime)
}
For convenience and for the ability to use
DataflowStream with other dataflow constructs, like e.g. operators,
you can wrap it with
DataflowReadAdapter for read access or
DataflowWriteAdapter for write access.
The
DataflowStream class is designed for single-threaded producers and consumers. If multiple threads are supposed to read or write values
to the stream, their access to the stream must be serialized externally or the adapters should be used.
DataflowStream Adapters
Since the
DataflowStream API as well as the semantics of its use are very different from the one defined by
Dataflow(Read/Write)Channel , adapters have to be used in order to allow
DataflowStreams
to be used with other dataflow elements.
The
DataflowStreamReadAdapter class will wrap a
DataflowStream with necessary methods to read values, while the
DataflowStreamWriteAdapter class
will provide write methods around the wrapped
DataflowStream .
It is important to mention that the DataflowStreamWriteAdapter is thread safe allowing multiple threads to add values to the wrapped DataflowStream through the adapter.
On the other hand, DataflowStreamReadAdapter is designed to be used by a single thread.To minimize the overhead and stay in-line with the DataflowStream semantics, the DataflowStreamReadAdapter class is not thread-safe
and should only be used from within a single thread.
If multiple threads need to read from a DataflowStream, they should each create their own wrapping DataflowStreamReadAdapter .
Thanks to the adapters
DataflowStream can be used for communication between operators or selectors, which expect
Dataflow(Read/Write)Channels .
import groovyx.gpars.dataflow.DataflowQueue
import groovyx.gpars.dataflow.stream.DataflowStream
import groovyx.gpars.dataflow.stream.DataflowStreamReadAdapter
import groovyx.gpars.dataflow.stream.DataflowStreamWriteAdapter
import static groovyx.gpars.dataflow.Dataflow.selector
import static groovyx.gpars.dataflow.Dataflow.operator/**
* Demonstrates the use of DataflowStreamAdapters to allow dataflow operators to use DataflowStreams
*/final DataflowStream a = new DataflowStream()
final DataflowStream b = new DataflowStream()
def aw = new DataflowStreamWriteAdapter(a)
def bw = new DataflowStreamWriteAdapter(b)
def ar = new DataflowStreamReadAdapter(a)
def br = new DataflowStreamReadAdapter(b)def result = new DataflowQueue()def op1 = operator(ar, bw) {
bindOutput it
}
def op2 = selector([br], [result]) {
result << it
}aw << 1
aw << 2
aw << 3
assert([1, 2, 3] == [result.val, result.val, result.val])
op1.stop()
op2.stop()
op1.join()
op2.join()
Also the ability to select a value from multiple
DataflowChannels can only be used through an adapter around a
DataflowStream :
import groovyx.gpars.dataflow.Select
import groovyx.gpars.dataflow.stream.DataflowStream
import groovyx.gpars.dataflow.stream.DataflowStreamReadAdapter
import groovyx.gpars.dataflow.stream.DataflowStreamWriteAdapter
import static groovyx.gpars.dataflow.Dataflow.select
import static groovyx.gpars.dataflow.Dataflow.task/**
* Demonstrates the use of DataflowStreamAdapters to allow dataflow select to select on DataflowStreams
*/final DataflowStream a = new DataflowStream()
final DataflowStream b = new DataflowStream()
def aw = new DataflowStreamWriteAdapter(a)
def bw = new DataflowStreamWriteAdapter(b)
def ar = new DataflowStreamReadAdapter(a)
def br = new DataflowStreamReadAdapter(b)final Select<?> select = select(ar, br)
task {
aw << 1
aw << 2
aw << 3
}
assert 1 == select().value
assert 2 == select().value
assert 3 == select().value
task {
bw << 4
aw << 5
bw << 6
}
def result = (1..3).collect{select()}.sort{it.value}
assert result*.value == [4, 5, 6]
assert result*.index == [1, 0, 1]
If you don't need any of the functional queue DataflowStream-special functionality, like generation, filtering or mapping,
you may consider using the DataflowBroadcast class instead, which offers the publish-subscribe communication model through the DataflowChannel interface.
Bind handlers
def a = new DataflowVariable()
a >> {println "The variable has just been bound to $it"}
a.whenBound {println "Just to confirm that the variable has been really set to $it"}
...
A bound handlers can be registered on all dataflow channels (variables, queues or broadcasts) either using the >> operator or the
whenBound() method. They will be run once a value is bound to the variable.
Dataflow queues and broadcasts also support a
wheneverBound method to register a closure or a message handler to run each time a value is bound to them.
def queue = new DataflowQueue()
queue.wheneverBound {println "A value $it arrived to the queue"}
Dataflow variables and broadcasts are one of several possible ways to implement Parallel Speculations . For details, please check out Parallel Speculations in the Parallel Collections section
of the User Guide.
Further reading
Scala Dataflow library by Jonas Bonér
JVM concurrency presentation slides by Jonas Bonér
Dataflow Concurrency library for Ruby
The
Dataflow tasks give you an easy-to-grasp abstraction of mutually-independent logical tasks or threads, which can run
concurrently and exchange data solely through Dataflow Variables, Queues, Broadcasts and Streams.
Dataflow tasks with their easy-to-express mutual dependencies and inherently sequential body could also be used as a practical implementation of UML
Activity Diagrams .
Check out the examples.
A simple mashup example
In the example we're downloading the front pages of three popular web sites, each in their own task, while in a separate task we're filtering out sites talking about Groovy today and forming the output. The output task synchronizes automatically with the three download tasks on the three Dataflow variables through which the content of each website is passed to the output task.
import static groovyx.gpars.GParsPool.*
import groovyx.gpars.dataflow.DataflowVariable
import static groovyx.gpars.dataflow.Dataflow.task
/**
* A simple mashup sample, downloads content of three websites
* and checks how many of them refer to Groovy.
*/def dzone = new DataflowVariable()
def jroller = new DataflowVariable()
def theserverside = new DataflowVariable()task {
println 'Started downloading from DZone'
dzone << 'http://www.dzone.com'.toURL().text
println 'Done downloading from DZone'
}task {
println 'Started downloading from JRoller'
jroller << 'http://www.jroller.com'.toURL().text
println 'Done downloading from JRoller'
}task {
println 'Started downloading from TheServerSide'
theserverside << 'http://www.theserverside.com'.toURL().text
println 'Done downloading from TheServerSide'
}task {
withPool {
println "Number of Groovy sites today: " +
([dzone, jroller, theserverside].findAllParallel {
it.val.toUpperCase().contains 'GROOVY'
}).size()
}
}.join()
Grouping tasks
Dataflow tasks can be organized into groups to allow for performance fine-tuning. Groups provide a handy
task() factory method
to create tasks attached to the groups.
Using groups allows you to organize tasks or operators around different thread pools (wrapped inside the group).
While the Dataflow.task() command schedules the task on a default thread pool (java.util.concurrent.Executor, fixed size=#cpu+1, daemon threads),
you may prefer being able to define your own thread pool(s) to run your tasks.
import groovyx.gpars.group.DefaultPGroupdef group = new DefaultPGroup()group.with {
task {
…
} task {
…
}
}
The default thread pool for dataflow tasks contains daemon threads, which means your application will exit as soon as the main thread finishes and won't wait for all tasks to complete.
When grouping tasks, make sure that your custom thread pools either use daemon threads, too, which can be achieved by
using DefaultPGroup or by providing your own thread factory to a thread pool constructor,
or in case your thread pools use non-daemon threads, such as when using the NonDaemonPGroup group class, make sure you shutdown the group or the thread pool explicitly by calling its shutdown() method,
otherwise your applications will not exit.
A mashup variant with methods
To avoid giving you wrong impression about structuring the Dataflow code, here's a rewrite of the mashup example, with a
downloadPage() method performing the actual download in a separate task and returning a DataflowVariable instance, so that the main application thread could eventually get hold of the downloaded content.
Dataflow variables can obviously be passed around as parameters or return values.
package groovyx.gpars.samples.dataflowimport static groovyx.gpars.GParsExecutorsPool.*
import groovyx.gpars.dataflow.DataflowVariable
import static groovyx.gpars.dataflow.Dataflow.task
/**
* A simple mashup sample, downloads content of three websites and checks how many of them refer to Groovy.
*/
final List urls = ['http://www.dzone.com', 'http://www.jroller.com', 'http://www.theserverside.com']task {
def pages = urls.collect { downloadPage(it) }
withPool {
println "Number of Groovy sites today: " +
(pages.findAllParallel {
it.val.toUpperCase().contains 'GROOVY'
}).size()
}
}.join()def downloadPage(def url) {
def page = new DataflowVariable()
task {
println "Started downloading from $url"
page << url.toURL().text
println "Done downloading from $url"
}
return page
}
A physical calculation example
Dataflow programs naturally scale with the number of processors. Up to a certain level, the more processors you have the faster the program runs.
Check out, for example, the following script, which calculates parameters of a simple physical experiment and prints out the results. Each task performs its part of the calculation and may depend on values calculated by some other tasks as well as its result might be needed by some of the other tasks. With Dataflow Concurrency you can split the work between tasks or reorder the tasks themselves as you like and the dataflow mechanics will ensure the calculation will be accomplished correctly.
import groovyx.gpars.dataflow.DataflowVariable
import static groovyx.gpars.dataflow.Dataflow.taskfinal def mass = new DataflowVariable()
final def radius = new DataflowVariable()
final def volume = new DataflowVariable()
final def density = new DataflowVariable()
final def acceleration = new DataflowVariable()
final def time = new DataflowVariable()
final def velocity = new DataflowVariable()
final def decelerationForce = new DataflowVariable()
final def deceleration = new DataflowVariable()
final def distance = new DataflowVariable()def t = task {
println """
Calculating distance required to stop a moving ball.
====================================================
The ball has a radius of ${radius.val} meters and is made of a material with ${density.val} kg/m3 density,
which means that the ball has a volume of ${volume.val} m3 and a mass of ${mass.val} kg.
The ball has been accelerating with ${acceleration.val} m/s2 from 0 for ${time.val} seconds and so reached a velocity of ${velocity.val} m/s.Given our ability to push the ball backwards with a force of ${decelerationForce.val} N (Newton), we can cause a deceleration
of ${deceleration.val} m/s2 and so stop the ball at a distance of ${distance.val} m.=======================================================================================================================
This example has been calculated asynchronously in multiple tasks using GPars Dataflow concurrency in Groovy.
Author: ${author.val}
""" System.exit 0
}task {
mass << volume.val * density.val
}task {
volume << Math.PI * (radius.val ** 3)
}task {
radius << 2.5
density << 998.2071 //water
acceleration << 9.80665 //free fall
decelerationForce << 900
}task {
println 'Enter your name:'
def name = new InputStreamReader(System.in).readLine()
author << (name?.trim()?.size()>0 ? name : 'anonymous')
}task {
time << 10
velocity << acceleration.val * time.val
}task {
deceleration << decelerationForce.val / mass.val
}task {
distance << deceleration.val * ((velocity.val/deceleration.val) ** 2) * 0.5
}t.join()
Note: I did my best to make all the physical calculations right. Feel free to change the values and see how long distance you need to stop the rolling ball.
Deterministic deadlocks
If you happen to introduce a deadlock in your dependencies, the deadlock will occur each time you run the code. No randomness allowed. That's one of the benefits of Dataflow concurrency. Irrespective of the actual thread scheduling scheme, if you don't get a deadlock in tests, you won't get them in production.
task {
println a.val
b << 'Hi there'
}task {
println b.val
a << 'Hello man'
}
Dataflows map
As a handy shortcut the
Dataflows class can help you reduce the amount of code you have to write to leverage Dataflow variables.
def df = new Dataflows()
df.x = 'value1'
assert df.x == 'value1'Dataflow.task {df.y = 'value2}assert df.y == 'value2'
Think of Dataflows as a map with Dataflow Variables as keys storing their bound values as appropriate map values. The semantics of reading a value (e.g. df.x) and binding a value (e.g. df.x = 'value') remain identical to the semantics of plain Dataflow Variables (x.val and x << 'value' respectively).
Mixing Dataflows and Groovy with blocks
When inside a
with block of a Dataflows instance, the dataflow variables stored inside the Dataflows instance can be accessed directly
without the need to prefix them with the Dataflows instance identifier.
new Dataflows().with {
x = 'value1'
assert x == 'value1' Dataflow.task {y = 'value2} assert y == 'value2'
}
Returning a value from a task
Typically dataflow tasks communicate through dataflow variables. On top of that, tasks can also return values, again through a dataflow variable.
When you invoke the
task() factory method, you get back an instance of DataflowVariable, on which you can listen for the task's return value,
just like when using any other DataflowVariable.
final DataflowVariable t1 = task {
return 10
}
final DataflowVariable t2 = task {
return 20
}
def results = [t1, t2]*.val
println 'Both sub-tasks finished and returned values: ' + results
Obviously the value can also be obtained without blocking the caller using the
whenBound() method.
def task = task {
println 'The task is running and calculating the return value'
30
}
task >> {value -> println "The task finished and returned $value"}
h2. Joining tasks
Using the
join() operation on the result dataflow variable of a task you can block until the task finishes.
task {
final DataflowVariable t1 = task {
println 'First sub-task running.'
}
final DataflowVariable t2 = task {
println 'Second sub-task running'
}
[t1, t2]*.join()
println 'Both sub-tasks finished'
}.join()
Frequently a value needs to be obtained from one of several dataflow channels (variables, queues, broadcasts or streams). The
Select class
is suitable for such scenarios.
Select can scan multiple dataflow channels and pick one channel from all the input channels, which currently have a value available for read.
The value from that channels is read and returned to the caller together with the index of the originating channel.
Picking the channel is either random, or based on channel priority, in which case channels with lower position index in the
Select constructor
have higher priority.
Selecting a value from multiple channels
import groovyx.gpars.dataflow.DataflowQueue
import groovyx.gpars.dataflow.DataflowVariable
import static groovyx.gpars.dataflow.Dataflow.select
import static groovyx.gpars.dataflow.Dataflow.task/**
* Shows a basic use of Select, which monitors a set of input channels for values and makes these values
* available on its output irrespective of their original input channel.
* Note that dataflow variables and queues can be combined for Select.
*
* You might also consider checking out the prioritySelect method, which prioritizes values by the index of their input channel
*/
def a = new DataflowVariable()
def b = new DataflowVariable()
def c = new DataflowQueue()task {
sleep 3000
a << 10
}task {
sleep 1000
b << 20
}task {
sleep 5000
c << 30
}def select = select([a, b, c])
println "The fastest result is ${select().value}"
Note that the return type from select() is SelectResult , holding the value as well as the originating channel index.
There are multiple ways to read values from a Select:
def sel = select(a, b, c, d)
def result = sel.select() //Random selection
def result = sel() //Random selection (a short-hand variant)
def result = sel.select([true, true, false, true]) //Random selection with guards specified
def result = sel([true, true, false, true]) //Random selection with guards specified (a short-hand variant)
def result = sel.prioritySelect() //Priority selection
def result = sel.prioritySelect([true, true, false, true]) //Priority selection with guards specifies
By default the
Select blocks the caller until a value to read is available. Alternatively,
Select allows to have the value sent
to a provided
MessageStream (e.g. an actor) without blocking the caller.
def handler = actor {...}
def sel = select(a, b, c, d)sel.select(handler) //Random selection
sel(handler) //Random selection (a short-hand variant)
sel.select(handler, [true, true, false, true]) //Random selection with guards specified
sel(handler, [true, true, false, true]) //Random selection with guards specified (a short-hand variant)
sel.prioritySelect(handler) //Priority selection
sel.prioritySelect(handler, [true, true, false, true]) //Priority selection with guards specifies
Guards
Guards allow the caller to omit some input channels from the selection. Guards are specified as a List of boolean flags
passed to the
select() or
prioritySelect() methods.
def sel = select(leaders, seniors, experts, juniors)
def teamLead = sel([true, true, false, false]).value //Only 'leaders' and 'seniors' qualify for becoming a teamLead here
A typical use for guards is to make Selects flexible to adopt to the changes in the user state.
import groovyx.gpars.dataflow.DataflowQueue
import static groovyx.gpars.dataflow.Dataflow.select
import static groovyx.gpars.dataflow.Dataflow.task/**
* Demonstrates the ability to enable/disable channels during a value selection on a select by providing boolean guards.
*/
final DataflowQueue operations = new DataflowQueue()
final DataflowQueue numbers = new DataflowQueue()def t = task {
final def select = select(operations, numbers)
3.times {
def instruction = select([true, false]).value
def num1 = select([false, true]).value
def num2 = select([false, true]).value
final def formula = "$num1 $instruction $num2"
println "$formula = ${new GroovyShell().evaluate(formula)}"
}
}task {
operations << '+'
operations << '+'
operations << '*'
}task {
numbers << 10
numbers << 20
numbers << 30
numbers << 40
numbers << 50
numbers << 60
}t.join()
Priority Select
When certain channels should have precedence over others when selecting, the prioritySelect methods should be used instead.
/**
* Shows a basic use of Priority Select, which monitors a set of input channels for values and makes these values
* available on its output irrespective of their original input channel.
* Note that dataflow variables, queues and broadcasts can be combined for Select.
* Unlike plain select method call, the prioritySelect call gives precedence to input channels with lower index.
* Available messages from high priority channels will be served before messages from lower-priority channels.
* Messages received through a single input channel will have their mutual order preserved.
*
*/
def critical = new DataflowVariable()
def ordinary = new DataflowQueue()
def whoCares = new DataflowQueue()task {
ordinary << 'All working fine'
whoCares << 'I feel a bit tired'
ordinary << 'We are on target'
}task {
ordinary << 'I have just started my work. Busy. Will come back later...'
sleep 5000
ordinary << 'I am done for now'
}task {
whoCares << 'Huh, what is that noise'
ordinary << 'Here I am to do some clean-up work'
whoCares << 'I wonder whether unplugging this cable will eliminate that nasty sound.'
critical << 'The server room goes on UPS!'
whoCares << 'The sound has disappeared'
}def select = select([critical, ordinary, whoCares])
println 'Starting to monitor our IT department'
sleep 3000
10.times {println "Received: ${select.prioritySelect().value}"}
Dataflow Operators and Selectors provide a full Dataflow implementation with all the usual ceremony.
Concepts
Full dataflow concurrency builds on the concept of channels connecting operators and selectors, which consume
values coming through input channels, transform them into new values and output the new values into their output channels.
While
Operators wait for
all input channels to have a value available for read before they start process them,
Selectors are triggered by a value available on
any of the input channels.
operator(inputs: [a, b, c], outputs: [d]) {x, y, z ->
…
bindOutput 0, x + y + z
}
/**
* CACHE
*
* Caches sites' contents. Accepts requests for url content, outputs the content. Outputs requests for download
* if the site is not in cache yet.
*/
operator(inputs: [urlRequests], outputs: [downloadRequests, sites]) {request -> if (!request.content) {
println "[Cache] Retrieving ${request.site}"
def content = cache[request.site]
if (content) {
println "[Cache] Found in cache"
bindOutput 1, [site: request.site, word:request.word, content: content]
} else {
def downloads = pendingDownloads[request.site]
if (downloads != null) {
println "[Cache] Awaiting download"
downloads << request
} else {
pendingDownloads[request.site] = []
println "[Cache] Asking for download"
bindOutput 0, request
}
}
} else {
println "[Cache] Caching ${request.site}"
cache[request.site] = request.content
bindOutput 1, request
def downloads = pendingDownloads[request.site]
if (downloads != null) {
for (downloadRequest in downloads) {
println "[Cache] Waking up"
bindOutput 1, [site: downloadRequest.site, word:downloadRequest.word, content: request.content]
}
pendingDownloads.remove(request.site)
}
}
}
The standard error handling will print out an error message to standard error output and stop the operator in case an uncaught
exception is thrown from withing the operator's body. To alter the behavior, you can redefine the
reportError() method
on the operator:
op.metaClass.reportError = {Throwable e ->
//handle the exception
stop() //You can also stop the operator
}
Types of operators
There are specialized versions of operators serving specific purposes:
- operator - the basic general-purpose operator
- selector - operator that is triggered by a value being available in any of its input channels
- prioritySelector - a selector that prefers delivering messages from lower-indexed input channels over higher-indexed ones
- splitter - a single-input operator copying its input values to all of its output channels
Chaining operators
Operators are typically combined into networks, when some operators consume output by other operators.
operator(inputs:[a, b], outputs:[c, d]) {...}
splitter(c, [e, f])
selector(inputs:[e, d]: outputs:[]) {...}
You may alternatively refer to output channels through operators themselves:
def op1 = operator(inputs:[a, b], outputs:[c, d]) {...}
def sp1 = splitter(op1.outputs[0], [e, f]) //takes the first output of op1
selector(inputs:[sp1.outputs[0], op1.outputs[1]]: outputs:[]) {...} //takes the first output of sp1 and the second output of op1
Parallelize operators
By default an operator's body is processed by a single thread at a time. While this is a safe setting allowing the operator's
body to be written in a non-thread-safe manner, once an operator becomes "hot" and data start to accumulate in the operator's
input queues, you might consider allowing multiple threads to run the operator's body concurrently. Bear in mind that in such a case
you need to avoid or protect shared resources from multi-threaded access.
To enable multiple threads to run the operator's body concurrently, pass an extra
maxForks parameter when creating an operator:
def op = operator(inputs: [a, b, c], outputs: [d, e], maxForks: 2) {x, y, z ->
bindOutput 0, x + y + z
bindOutput 1, x * y * z
}
The value of the
maxForks parameter indicates the maximum of threads running the operator concurrently. Only positive
numbers are allowed with value 1 being the default.
Please always make sure the group serving the operator holds enough threads to support all requested forks.
Using groups allows you to organize tasks or operators around different thread pools (wrapped inside the group).
While the Dataflow.task() command schedules the task on a default thread pool (java.util.concurrent.Executor, fixed size=#cpu+1, daemon threads),
you may prefer being able to define your own thread pool(s) to run your tasks.def group = new DefaultPGroup(10)
group.operator((inputs: [a, b, c], outputs: [d, e], maxForks: 5) {x, y, z -> ...}
The default group uses a resizeable thread pool as so will never run out of threads.
Synchronizing the output
When enabling internal parallelization of an operator by setting the value for
maxForks to a value greater than 1
it is important to remember that without explicit or implicit synchronization in the operators' body race-conditions may occur.
Especially bear in mind that values written to multiple output channels are not guarantied to be written atomically in the same order to all the channels
operator(inputs:[inputChannel], outputs:[a, b], maxForks:5) {msg ->
bindOutput 0, msg
bindOutput 1, msg
}
inputChannel << 1
inputChannel << 2
inputChannel << 3
inputChannel << 4
inputChannel << 5
May result in output channels having the values mixed-up something like:
a -> 1, 3, 2, 4, 5
b -> 2, 1, 3, 5, 4
Explicit synchronization is one way to get correctly bound all output channels and protect operator not-thread local state:
def lock = new Object()
operator(inputs:[inputChannel], outputs:[a, b], maxForks:5) {msg ->
doStuffThatIsThreadSafe() synchronized(lock) {
doSomethingThatMustNotBeAccessedByMultipleThreadsAtTheSameTime()
bindOutput 0, msg
bindOutput 1, 2*msg
}
}
Obviously you need to weight the pros and cons here, since synchronization may defeat the purpose of setting
maxForks to a value greater than 1.
To set values of all the operator's output channels in one atomic step, you may also consider calling either the
bindAllOutputsAtomically method, passing in
a single value to write to all output channels or the
bindAllOutputsAtomically method, which takes a multiple values, each of which will be written
to the output channel with the same position index.
operator(inputs:[inputChannel], outputs:[a, b], maxForks:5) {msg ->
doStuffThatIsThreadSafe()
bindAllOutputValuesAtomically msg, 2*msg
}
}
Using the bindAllOutputs or the bindAllOutputValues methods will not guarantee atomicity of writes across al the output channels when using internal parallelism.
If preserving the order of messages in multiple output channels is not an issue, bindAllOutputs as well as bindAllOutputValues will provide better performance over the atomic variants.
Stopping operators
Dataflow operators and selectors can be stopped in two ways:
- by calling the stop() method on all operators that need to be stopped
- by sending a poisson message.
Using the stop() method:
def op1 = operator(inputs: [a, b, c], outputs: [d, e]) {x, y, z -> }def op2 = selector(inputs: [d], outputs: [f, out]) { }def op3 = prioritySelector(inputs: [e, f], outputs: [b]) {value, index -> }[op1, op2, op3]*.stop() //Stop all operators by calling the stop() method on them
op1.join()
op2.join()
op3.join()
Using the poisson message:
def op1 = operator(inputs: [a, b, c], outputs: [d, e]) {x, y, z -> }def op2 = selector(inputs: [d], outputs: [f, out]) { }def op3 = prioritySelector(inputs: [e, f], outputs: [b]) {value, index -> }a << PoisonPill.instance //Send the poissonop1.join()
op2.join()
op3.join()
After receiving a poisson an operator stops. It only makes sure the poisson is first sent to all its output channels, so that the poisson can spread to the connected operators.
Grouping operators
Dataflow operators can be organized into groups to allow for performance fine-tuning. Groups provide a handy
operator() factory method
to create tasks attached to the groups.
import groovyx.gpars.group.DefaultPGroupdef group = new DefaultPGroup()group.with {
operator(inputs: [a, b, c], outputs: [d]) {x, y, z ->
…
bindOutput 0, x + y + z
}
}
The default thread pool for dataflow operators contains daemon threads, which means your application will exit as soon as the main thread finishes and won't wait for all tasks to complete.
When grouping operators, make sure that your custom thread pools either use daemon threads, too, which can be achieved by
using DefaultPGroup or by providing your own thread factory to a thread pool constructor,
or in case your thread pools use non-daemon threads, such as when using the NonDaemonPGroup group class, make sure you shutdown the group or the thread pool explicitly by calling its shutdown() method,
otherwise your applications will not exit.
Selectors
Selector's body should be a closure consuming either one or two arguments.
selector (inputs : [a, b, c], outputs : [d, e]) {value ->
....
}
The two-argument closure will get a value plus an index of the input channel, the value of which is currently being processed.
This allows the selector to distinguish between values coming through different input channels.
selector (inputs : [a, b, c], outputs : [d, e]) {value, index ->
....
}
Priority Selector
When priorities need to be preserved among input channels, a
DataflowPrioritySelector should be used.
prioritySelector(inputs : [a, b, c], outputs : [d, e]) {value, index ->
…
}
The priority selector will always prefer values from channels with lower position index over values coming through the channels with higher position index.
Join selector
A selector without a body closure specified will copy all incoming values to all of its output channels.
def join = selector (inputs : [programmers, analysis, managers], outputs : [employees, colleagues])
Internal parallelism
The
maxForks attribute allowing for internal selectors parallelism is also available.
selector (inputs : [a, b, c], outputs : [d, e], maxForks : 5) {value ->
....
}
Guards
Just like
Selects ,
Selectors also allow the users to temporarily include/exclude individual input channels from selection.
The
guards input property can be used to set the initial mask on all input channels and the
setGuards and
setGuard methods
are then available in the selector's body.
import groovyx.gpars.dataflow.DataflowQueue
import static groovyx.gpars.dataflow.Dataflow.selector
import static groovyx.gpars.dataflow.Dataflow.task/**
* Demonstrates the ability to enable/disable channels during a value selection on a select by providing boolean guards.
*/
final DataflowQueue operations = new DataflowQueue()
final DataflowQueue numbers = new DataflowQueue()def instruction
def nums = []selector(inputs: [operations, numbers], outputs: [], guards: [true, false]) {value, index -> //initial guards is set here
if (index == 0) {
instruction = value
setGuard(0, false) //setGuard() used here
setGuard(1, true)
}
else nums << value
if (nums.size() == 2) {
setGuards([true, false]) //setGuards() used here
final def formula = "${nums[0]} $instruction ${nums[1]}"
println "$formula = ${new GroovyShell().evaluate(formula)}"
nums.clear()
}
}task {
operations << '+'
operations << '+'
operations << '*'
}task {
numbers << 10
numbers << 20
numbers << 30
numbers << 40
numbers << 50
numbers << 60
}
Avoid combining guards and maxForks greater than 1. Although the Selector is thread-safe and won't be damaged in any way, the guards are likely not to be set
the way you expect. The multiple threads running selector's body concurrently will tend to over-write each-other's settings to the guards property.
The Dataflow Concurrency in GPars builds on top of its actor support. All of the dataflow tasks share a thread pool and so the number threads created through
Dataflow.task() factory method don't need to correspond to the number of physical threads required from the system.
The
PGroup.task() factory method can be used to attach the created task to a group. Since each group defines its own thread pool, you can easily organize tasks around different thread pools just like you do with actors.
Combining actors and Dataflow Concurrency
The good news is that you can combine actors and Dataflow Concurrency in any way you feel fit for your particular problem at hands. You can freely you use Dataflow Variables from actors.
final DataflowVariable a = new DataflowVariable()final Actor doubler = Actors.actor {
react {message->
a << 2 * message
}
}final Actor fakingDoubler = actor {
react {
doubler.send it //send a number to the doubler
println "Result ${a.val}" //wait for the result to be bound to 'a'
}
}fakingDoubler << 10
In the example you see the "fakingDoubler" using both messages and a
DataflowVariable to communicate with the
doubler actor.
Using plain java threads
The
DataflowVariable as well as the
DataflowQueue classes can obviously be used from any thread of your application, not only from the tasks created by
Dataflow.task() . Consider the following example:
import groovyx.gpars.dataflow.DataflowVariablefinal DataflowVariable a = new DataflowVariable<String>()
final DataflowVariable b = new DataflowVariable<String>()Thread.start {
println "Received: $a.val"
Thread.sleep 2000
b << 'Thank you'
}Thread.start {
Thread.sleep 2000
a << 'An important message from the second thread'
println "Reply: $b.val"
}
We're creating two plain
java.lang.Thread instances, which exchange data using the two data flow variables. Obviously, neither the actor lifecycle methods, nor the send/react functionality or thread pooling take effect in such scenarios.
The Sieve of Eratosthenes implementation using dataflow tasks
import groovyx.gpars.dataflow.DataflowQueue
import static groovyx.gpars.dataflow.Dataflow.task/**
* Demonstrates concurrent implementation of the Sieve of Eratosthenes using dataflow tasks
*/final int requestedPrimeNumberCount = 1000final DataflowQueue initialChannel = new DataflowQueue()/**
* Generating candidate numbers
*/
task {
(2..10000).each {
initialChannel << it
}
}/**
* Chain a new filter for a particular prime number to the end of the Sieve
* @param inChannel The current end channel to consume
* @param prime The prime number to divide future prime candidates with
* @return A new channel ending the whole chain
*/
def filter(inChannel, int prime) {
def outChannel = new DataflowQueue() task {
while (true) {
def number = inChannel.val
if (number % prime != 0) {
outChannel << number
}
}
}
return outChannel
}/**
* Consume Sieve output and add additional filters for all found primes
*/
def currentOutput = initialChannel
requestedPrimeNumberCount.times {
int prime = currentOutput.val
println "Found: $prime"
currentOutput = filter(currentOutput, prime)
}
The Sieve of Eratosthenes implementation using a combination of dataflow tasks and operators
import groovyx.gpars.dataflow.DataflowQueue
import static groovyx.gpars.dataflow.Dataflow.operator
import static groovyx.gpars.dataflow.Dataflow.task /**
* Demonstrates concurrent implementation of the Sieve of Eratosthenes using dataflow tasks and operators
*/ final int requestedPrimeNumberCount = 100 final DataflowQueue initialChannel = new DataflowQueue() /**
* Generating candidate numbers
*/
task {
(2..1000).each {
initialChannel << it
}
} /**
* Chain a new filter for a particular prime number to the end of the Sieve
* @param inChannel The current end channel to consume
* @param prime The prime number to divide future prime candidates with
* @return A new channel ending the whole chain
*/
def filter(inChannel, int prime) {
def outChannel = new DataflowQueue() operator([inputs: [inChannel], outputs: [outChannel]]) {
if (it % prime != 0) {
bindOutput it
}
}
return outChannel
} /**
* Consume Sieve output and add additional filters for all found primes
*/
def currentOutput = initialChannel
requestedPrimeNumberCount.times {
int prime = currentOutput.val
println "Found: $prime"
currentOutput = filter(currentOutput, prime)
}
Software Transactional Memory (STM) gives developers transactional semantics for accessing in-memory data. When multiple threads
share data in memory, by marking blocks of code as transactional (atomic) the developer delegates the responsibility
for data consistency to the Stm engine.
GPars leverages the Multiverse Stm engine. Check out more details on the transactional engine at the
Multiverse siteRunning a piece of code atomically
When using Stm, developers organize their code into transactions. A transaction is a piece of code, which is executed
atomically - either all the code is run or none at all.
The data used by the transactional code remains
consistent irrespective of whether the transaction finishes normally or abruptly.
While running inside a transaction the code is given an illusion of being
isolated from the other concurrently run transactions so that changes to data in one transaction
are not visible in the other ones until the transactions commit. This gives us the
ACI part of the
ACID characteristics of database transactions. The
durability transactional aspect
so typical for databases, is not typically mandated for Stm.
GPars allows developers to specify transaction boundaries by using the
atomic closures.
import groovyx.gpars.stm.GParsStm
import org.multiverse.api.references.IntRef
import static org.multiverse.api.StmUtils.newIntRefpublic class Account {
private final IntRef amount = newIntRef(0); public void transfer(final int a) {
GParsStm.atomic {
amount.increment(a);
}
} public int getCurrentAmount() {
GParsStm.atomicWithInt {
amount.get();
}
}
}
There are several types of
atomic closures, each for different type of return value:
- atomic - returning Object
- atomicWithInt - returning int
- atomicWithLong - returning long
- atomicWithBoolean - returning boolean
- atomicWithDouble - returning double
- atomicWithVoid - no return value
Multiverse by default uses optimistic locking strategy and automatically rolls back and retries colliding transactions.
Developers should thus restrain from irreversible actions (e.g. writing to the console, sending and e-mail, launching a missile, etc.) in their transactional code.
To increase flexibility, the default Multiverse settings can be customized through custom
atomic blocks .
Customizing the transactional properties
Frequently it may be desired to specify different values for some of the transaction properties (e.g. read-only transactions, locking strategy, isolation level, etc.).
The
createAtomicBlock method will create a new
AtomicBlock configured with the supplied values:
import groovyx.gpars.stm.GParsStm
import org.multiverse.api.AtomicBlock
import org.multiverse.api.PropagationLevelfinal AtomicBlock block = GParsStm.createAtomicBlock(maxRetries: 3000, familyName: 'Custom', PropagationLevel: PropagationLevel.Requires, interruptible: false)
assert GParsStm.atomicWithBoolean(block) {
true
}
The customized
AtomicBlock can then be used to create transactions following the specified settings.
AtomicBlock instances are thread-safe and can be freely reused among threads and transactions.
Using the Transaction object
The atomic closures are provided the current
Transaction as a parameter. The
Transaction objects can then be used
to manually control the transaction.
This is illustrated in the example below, where we use the
retry() method to block the current transaction until the counter reaches the desired value:
import groovyx.gpars.stm.GParsStm
import org.multiverse.api.AtomicBlock
import org.multiverse.api.PropagationLevel
import static org.multiverse.api.StmUtils.newIntReffinal AtomicBlock block = GParsStm.createAtomicBlock(maxRetries: 3000, familyName: 'Custom', PropagationLevel: PropagationLevel.Requires, interruptible: false)def counter = newIntRef(0)
final int max = 100
Thread.start {
while (counter.atomicGet() < max) {
counter.atomicIncrementAndGet(1)
sleep 10
}
}
assert max + 1 == GParsStm.atomicWithInt(block) {tx ->
if (counter.get() == max) return counter.get() + 1
tx.retry()
}
Data structures
You might have noticed in the code examples above that we use dedicated data structures to hold values. The fact is that normal Java classes
do not support transactions and thus cannot be used directly, since Multiverse would not be able to share them safely among concurrent transactions, commit them nor roll them back.
We need to use data that know about transactions:
- IntRef
- LongRef
- BooleanRef
- DoubleRef
- Ref
You typically create these through the factory methods of the
org.multiverse.api.StmUtils class.
More information
We decided not to duplicate the information that is already available on the Multiverse website. Please visit
the
Multiverse site and use it as a reference for your further Stm adventures with GPars.
General GPars Tips
Grouping
High-level concurrency concepts, like Agents, Actors or Dataflow tasks and operators can be grouped around shared thread pools.
The
PGroup class and its sub-classes represent convenient GPars wrappers around thread pools.
Objects created using the group's factory methods will share the group's thread pool.
def group1 = new DefaultPGroup()
def group2 = new NonDaemonPGroup()group1.with {
task {...}
task {...}
def op = operator(...) {...}
def actor = actor{...}
def anotherActor = group2.actor{...} //will belong to group2
def agent = safe(0)
}
When customizing the thread pools for groups, consider using the existing GPars implementations - the DefaultPool or ResizeablePool classes.
Or you may create your own implementation of the groovyx.gpars.scheduler.Pool interface to pass to the DefaultPGroup or NonDaemonPGroup constructors.
Java API
Most of GPars functionality can be used from Java just as well as from Groovy. Checkout the
2.6 Java API - Using GPars from Java section of the User Guide
and experiment with the maven-based stand-alone Java
demo application .
Take GPars with you wherever you go!
Your code in Groovy can be just as fast as code written in Java, Scala or any other programing language.
This should not be surprising, since GPars is technically a solid tasty Java-made cake with a Groovy DSL cream on it.
Unlike in Java, however, with GPars, as well as with other DSL-friendly languages, you are very likely to experience a useful kind of code speed-up for free,
a speed-up coming from a better and cleaner design of your application. Coding with a concurrency DSL will give you smaller code-base with code
using the concurrency primitives as language constructs. So it is much easier to build robust concurrent applications, identify potential
bottle-necks or errors and eliminate them.
While this whole User Guide is describing how to use Groovy and GPars to create beautiful and robust concurrent code, let's use this chapter
to highlight a few places, where some code tuning or minor design compromises could give you interesting performance gains.
Parallel Collections
Methods for parallel collection processing, like
eachParallel() ,
collectParallel() and such use
Parallel Array , an efficient tree-like data structure behind the scenes.
This data structure has to be built from the original collection each time you call any of the parallel collection methods.
Thus when chaining parallel method calls you might consider using the
map/reduce API instead or resort to using the
ParallelArray API directly, to avoid the
Parallel Array creation overhead.
GParsPool.withPool {
people.findAllParallel{it.isMale()}.collectParallel{it.name}.any{it == 'Joe'}
people.parallel.filter{it.isMale()}.map{it.name}.filter{it == 'Joe'}.size() > 0
people.parallelArray.withFilter({it.isMale()} as Predicate).withMapping({it.name} as Mapper).any{it == 'Joe'} != null
}
In many scenarios changing the pool size from the default value may give you performance benefits. Especially if your tasks
perform IO operations, like file or database access, networking and such, increasing the number of threads in the pool is likely to help performance.
GParsPool.withPool(50) {
…
}
Since the closures you provide to the parallel collection processing methods will get executed frequently and concurrently,
you may further slightly benefit from turning them into Java.
Actors
GPars actors are fast.
DynamicDispatchActors and
ReactiveActors are about twice as fast as the
DefaultActors , since they don't have to maintain
an implicit state between subsequent message arrivals. The
DefaultActors are in fact on par in performance with actors in
Scala , which you can hardly hear of as being slow.
If top performance is what you're looking for, a good start is to identify the following patterns in your actor code:
actor {
loop {
react {msg ->
switch(msg) {
case String:…
case Integer:…
}
}
}
}
and replace them with
DynamicDispatchActor :
messageHandler {
when{String msg -> ...}
when{Integer msg -> ...}
}
The
loop and
react methods are rather costly to call.
Defining a
DynamicDispatchActor or
ReactiveActor as classes instead of using the
messageHandler and
reactor factory methods will also give you some more speed:
class MyHandler extends DynamicDispatchActor {
public void handleMessage(String msg) {
…
} public void handleMessage(Integer msg) {
…
}
}
Now, moving the
MyHandler class into Java will squeeze the last bit of performance from GPars.
Pool adjustment
GPars allows you to group actors around thread pools, giving you the freedom to organize actors any way you like.
It is always worthwhile to experiment with the actor pool size and type.
FJPool usually gives better characteristics that
DefaultPool , but seems to be more sensitive to the number of threads in the pool.
Sometimes using a
ResizeablePool or
ResizeableFJPool could help performance by automatic eliminating unneeded threads.
def attackerGroup = new DefaultPGroup(new ResizeableFJPool(10))
def defenderGroup = new DefaultPGroup(new DefaultPool(5))def attacker = attackerGroup.actor {...}
def defender = defenderGroup.messageHandler {...}
...
Agents
GPars
Agents are even a bit faster in processing messages than actors. The advice to group agents wisely around thread pools
and tune the pool sizes and types applies to agents as well as to actors.
With agents, you may also benefit from submitting Java-written closures as messages.
Share your experience
The more we hear about GPars uses in the wild the better we can adapt it for the future. Let us know how you use GPars and how it performs.
Send us your benchmarks, performance comparisons or profiling reports to help us tune GPars for you.
This was quite a wild ride, wasn't it? Now, after going through the User Guide, you're certainly ready to build fast, robust and reliable concurrent applications.
You've seen that there are many concepts you can choose from and each has its own areas of applicability. The ability
to pick the right concept to apply to a given problem and combine it with the rest of the system is key to being a successful developer.
If you feel you can do this with GPars, the mission of the User Guide has been accomplished.
Now, go ahead, use GPars and have fun!