Distributed Computing

From Wikipedia: Distributed computing refers to the use of distributed systems to solve computational problems. In distributed computing, a problem is divided into many tasks, each of which is solved by one or more computers.

Executor Service

One of the coolest features of Java 1.5 is the Executor framework, which allows you to asynchronously execute your tasks (logical units of work), such as database query, complex calculation, and image rendering.

Executor Overview

The default implementation of this framework (ThreadPoolExecutor) is designed to run within a single JVM. In distributed systems, this implementation is not desired since you may want a task submitted in one JVM and processed in another one. Hazelcast offers IExecutorService for you to use in distributed environments: it implements java.util.concurrent.ExecutorService to serve the applications requiring computational and data processing power.

With IExecutorService, you can execute tasks asynchronously and perform other useful tasks. If your task execution takes longer than expected, you can cancel the task execution. Tasks should be Serializable since they will be distributed.

In the Java Executor framework, you implement tasks two ways: Callable or Runnable.

  • Callable: If you need to return a value and submit to Executor, implement the task as java.util.concurrent.Callable.
  • Runnable: If you do not need to return a value, implement the task as java.util.concurrent.Runnable.


In Hazelcast, when you implement a task as java.util.concurrent.Callable (a task that returns a value), you implement Callable and Serializable.

Below is an example of a Callable.

import com.hazelcast.core.HazelcastInstance;
import com.hazelcast.core.HazelcastInstanceAware;
import com.hazelcast.core.IMap;

import java.io.Serializable;
import java.util.concurrent.Callable;

public class SumTask
    implements Callable<Integer>, Serializable, HazelcastInstanceAware {

  private transient HazelcastInstance hazelcastInstance;

  public void setHazelcastInstance( HazelcastInstance hazelcastInstance ) {
    this.hazelcastInstance = hazelcastInstance;

  public Integer call() throws Exception {
    IMap<String, Integer> map = hazelcastInstance.getMap( "map" );
    int result = 0;
    for ( String key : map.localKeySet() ) {
      System.out.println( "Calculating for key: " + key );
      result += map.get( key );
    System.out.println( "Local Result: " + result );
    return result;

Another example is the Echo callable below. In its call() method, it returns the local member and the input passed in. Remember that instance.getCluster().getLocalMember() returns the local member and toString() returns the member's address (IP + port) in String form, just to see which member actually executed the code for our example. Of course, the call() method can do and return anything you like.

import java.util.concurrent.Callable;
import java.io.Serializable;

public class Echo implements Callable<String>, Serializable {
    String input = null;

    public Echo() {

    public Echo(String input) {
        this.input = input;

    public String call() {
        Config cfg = new Config();
        HazelcastInstance instance = Hazelcast.newHazelcastInstance(cfg);
        return instance.getCluster().getLocalMember().toString() + ":" + input;

To execute a task with the executor framework:

* Obtain an `ExecutorService` instance (generally via `Executors`).
* Submit a task which returns a `Future`. 
* After executing the task, you do not have to wait for the execution to complete, you can process other things. 
* When ready, use the `Future` object to retrieve the result as shown in the code example below.

Below, the Echo task is executed.

ExecutorService executorService = Executors.newSingleThreadExecutor();
Future<String> future = executorService.submit( new Echo( "myinput") );
//while it is executing, do some useful stuff
//when ready, get the result of your execution
String result = future.get();

Please note that the Echo callable in the above code sample also implements a Serializable interface, since it may be sent to another JVM to be processed.

image NOTE: When a task is deserialized, HazelcastInstance needs to be accessed. To do this, the task should implement HazelcastInstanceAware interface. Please see the HazelcastInstanceAware Interface section for more information.


In Hazelcast, when you implement a task as java.util.concurrent.runnable (a task that does not return a value), you implement Runnable and Serializable.

Below is Runnable example code. It is a task that waits for some time and echoes a message.

public class EchoTask implements Runnable, Serializable {
  private final String msg;

  public EchoTask( String msg ) {
    this.msg = msg;

  public void run() {
    try {
      Thread.sleep( 5000 );
    } catch ( InterruptedException e ) {
    System.out.println( "echo:" + msg );

To execute the task:

  • Retrieve the Executor from HazelcastInstance.
  • Submit the tasks to the Executor.

Now let's write a class that submits and executes these echo messages. Executor is retrieved from HazelcastInstance and 1000 echo tasks are submitted.

public class MasterMember {
  public static void main( String[] args ) throws Exception {
    HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance();
    IExecutorService executor = hazelcastInstance.getExecutorService( "exec" );
    for ( int k = 1; k <= 1000; k++ ) {
      Thread.sleep( 1000 );
      System.out.println( "Producing echo task: " + k );
      executor.execute( new EchoTask( String.valueOf( k ) ) );
    System.out.println( "EchoTaskMain finished!" );

Executor Thread Configuration

By default, Executor is configured to have 8 threads in the pool. You can change that with the pool-size property in the declarative configuration (hazelcast.xml). An example is shown below (using the above Executor).

<executor-service name="exec">


Please refer to the Executor Service Configuration section for a full description of Hazelcast Distributed Executor Service configuration.


You can scale the Executor service both vertically (scale up) and horizontally (scale out).

To scale up, you should improve the processing capacity of the JVM. You can do this by increasing the pool-size property mentioned in the Executor Thread Configuration section (i.e., increasing the thread count). However, please be aware of your JVM's capacity. If you think it cannot handle such an additional load caused by increasing the thread count, you may want to consider improving the JVM's resources (CPU, memory, etc.). As an example, set the pool-size to 5 and run the above MasterMember. You will see that EchoTask is run as soon as it is produced.

To scale out, more JVMs should be added instead of increasing only one JVM's capacity. In reality, you may want to expand your cluster by adding more physical or virtual machines. For example, in the EchoTask example in the Runnable section, you can create another Hazelcast instance. That instance will automatically get involved in the executions started in MasterMember and start processing.