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1. Overview

The fork/join framework was presented in Java 7. It provides tools to help speed up parallel processing by attempting to use all available processor cores – which is accomplished through a divide and conquer approach.

In practice, this means that the framework first “forks”, recursively breaking the task into smaller independent subtasks until they are simple enough to be executed asynchronously.

After that, the “join” part begins, in which results of all subtasks are recursively joined into a single result, or in the case of a task which returns void, the program simply waits until every subtask is executed.

To provide effective parallel execution, the fork/join framework uses a pool of threads called the ForkJoinPool, which manages worker threads of type ForkJoinWorkerThread. 

2. ForkJoinPool

The ForkJoinPool is the heart of the framework. It is an implementation of the ExecutorService that manages worker threads and provides us with tools to get information about the thread pool state and performance.

Worker threads can execute only one task at the time, but the ForkJoinPool doesn’t create a separate thread for every single subtask. Instead, each thread in the pool has its own double-ended queue (or deque, pronounced deck) which stores tasks.

This architecture is vital for balancing the thread’s workload with the help of the work-stealing algorithm.

2.1. Work Stealing Algorithm

Simply put – free threads try to “steal” work from deques of busy threads.

By default, a worker thread gets tasks from the head of its own deque. When it is empty, the thread takes a task from the tail of the deque of another busy thread or from the global entry queue, since this is where the biggest pieces of work are likely to be located.

This approach minimizes the possibility that threads will compete for tasks. It also reduces the number of times the thread will have to go looking for work, as it works on the biggest available chunks of work first.

2.2. ForkJoinPool Instantiation

In Java 8, the most convenient way to get access to the instance of the ForkJoinPool is to use its static method commonPool(). As its name suggests, this will provide a reference to the common pool, which is a default thread pool for every ForkJoinTask.

According to Oracle’s documentation, using the predefined common pool reduces resource consumption, since this discourages the creation of a separate thread pool per task.

ForkJoinPool commonPool = ForkJoinPool.commonPool();

The same behavior can be achieved in Java 7 by creating a ForkJoinPool and assigning it to a public static field of a utility class:

public static ForkJoinPool forkJoinPool = new ForkJoinPool(2);

Now it can be easily accessed:

ForkJoinPool forkJoinPool = PoolUtil.forkJoinPool;

With ForkJoinPool’s constructors, it is possible to create a custom thread pool with a specific level of parallelism, thread factory, and exception handler. In the example above, the pool has a parallelism level of 2. This means that pool will use 2 processor cores.

3. ForkJoinTask<V>

ForkJoinTask is the base type for tasks executed inside ForkJoinPool. In practice, one of its two subclasses should be extended: the RecursiveAction for void tasks and the RecursiveTask<V> for tasks that return a value. They both have an abstract method compute() in which the task’s logic is defined.

3.1. RecursiveAction – An Example

In the example below, the unit of work to be processed is represented by a String called workload. For demonstration purposes, the task is a nonsensical one: it simply uppercases its input and logs it.

To demonstrate the forking behavior of the framework, the example splits the task if workload.length() is larger than a specified threshold using the createSubtask() method.

The String is recursively divided into substrings, creating CustomRecursiveTask instances which are based on these substrings.

As a result, the method returns a List<CustomRecursiveAction>.  

The list is submitted to the ForkJoinPool using the invokeAll() method:

public class CustomRecursiveAction extends RecursiveAction {

    private String workload = "";
    private static final int THRESHOLD = 4;

    private static Logger logger = 

    public CustomRecursiveAction(String workload) {
        this.workload = workload;

    protected void compute() {
        if (workload.length() > THRESHOLD) {
        } else {

    private List<CustomRecursiveAction> createSubtasks() {
        List<CustomRecursiveAction> subtasks = new ArrayList<>();

        String partOne = workload.substring(0, workload.length() / 2);
        String partTwo = workload.substring(workload.length() / 2, workload.length());

        subtasks.add(new CustomRecursiveAction(partOne));
        subtasks.add(new CustomRecursiveAction(partTwo));

        return subtasks;

    private void processing(String work) {
        String result = work.toUpperCase();
        logger.info("This result - (" + result + ") - was processed by " 
          + Thread.currentThread().getName());

This pattern can be used to develop your own RecursiveAction classes. To do this, create an object which represents the total amount of work, chose a suitable threshold, define a method to divide the work, and define a method to do the work.

3.2. RecursiveTask<V>

For tasks that return a value, the logic here is similar, except that resulting for each subtask is united in a single result:

public class CustomRecursiveTask extends RecursiveTask<Integer> {
    private int[] arr;

    private static final int THRESHOLD = 20;

    public CustomRecursiveTask(int[] arr) {
        this.arr = arr;

    protected Integer compute() {
        if (arr.length > THRESHOLD) {
            return ForkJoinTask.invokeAll(createSubtasks())
        } else {
            return processing(arr);

    private Collection<CustomRecursiveTask> createSubtasks() {
        List<CustomRecursiveTask> dividedTasks = new ArrayList<>();
        dividedTasks.add(new CustomRecursiveTask(
          Arrays.copyOfRange(arr, 0, arr.length / 2)));
        dividedTasks.add(new CustomRecursiveTask(
          Arrays.copyOfRange(arr, arr.length / 2, arr.length)));
        return dividedTasks;

    private Integer processing(int[] arr) {
        return Arrays.stream(arr)
          .filter(a -> a > 10 && a < 27)
          .map(a -> a * 10)

In this example, the work is represented by an array stored in the arr field of the CustomRecursiveTask class. The createSubtask() method recursively divides the task into smaller pieces of work until each piece is smaller than the threshold. Then, the invokeAll() method submits subtasks to the common pull and returns a list of Future.

To trigger execution, the join() method called for each subtask.

In this example, this is accomplished using Java 8’s Stream API; the sum() method is used as a representation of combining sub results into the final result.

4. Submitting Tasks to the ForkJoinPool

To submit tasks to the thread pool, few approaches can be used.

The submit() or execute() method (their use cases are the same):

int result = customRecursiveTask.join();

The invoke() method forks the task and waits for the result, and doesn’t need any manual joining:

int result = forkJoinPool.invoke(customRecursiveTask);

The invokeAll() method is the most convenient way to submit a sequence of ForkJoinTasks to the ForkJoinPool. It takes tasks as parameters (two tasks, var args, or a collection), forks them returns a collection of Future objects in the order in which they were produced.

Alternatively, you can use separate fork() and join() methods. The fork() method submits a task to a pool, but it doesn’t trigger its execution. The join() method is be used for this purpose. In the case of RecursiveAction, the join() returns nothing but null; for RecursiveTask<V>, it returns the result of the task’s execution:

result = customRecursiveTaskLast.join();

In our RecursiveTask<V> example we used the invokeAll() method to submit a sequence of subtasks to the pool. The same job can be done with fork() and join(), though this has consequences for the ordering of the results.

To avoid confusion, it is generally a good idea to use invokeAll() method to submit more than one task to the ForkJoinPool.

5. Conclusions

Using the fork/join framework can speed up processing of large tasks, but to achieve this outcome, some guidelines should be followed:

  • Use as few thread pools as possible – in most cases, the best decision is to use one thread pool per application or system
  • Use the default common thread pool, if no specific tuning is needed
  • Use a reasonable threshold for splitting ForkJoingTask into subtasks
  • Avoid any blocking in your ForkJoingTasks

The examples used in this article are available in the linked GitHub repository.

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