Can Java 8 Parallel Streams Use Custom Thread Pools for Task Isolation?

Custom Thread Pools for Java 8 Parallel Streams

In the realm of Java 8 parallel streams, the question arises: is it possible to employ custom thread pools for specific tasks? Despite extensive searching, this capability has eluded many developers.

Consider a multi-threaded server application where parallel streams are desired. However, to maintain compartmentalization and prevent tasks from one module blocking others, different thread pools are necessary for each module.

To illustrate the issue, consider the following example:

ExecutorService es = Executors.newCachedThreadPool();

es.execute(() -> runTask(1000)); // incorrect task
es.execute(() -> runTask(0));
es.execute(() -> runTask(0));
es.execute(() -> runTask(0));
es.execute(() -> runTask(0));
es.execute(() -> runTask(0));

In this contrived example, CPU-intensive tasks are executed in parallel using Executors.newCachedThreadPool(). The first task is deliberately slowed down by simulated thread sleep. As a result, other tasks become stuck, awaiting its completion. This demonstrates how a slow task in one module can hinder tasks in other modules.

However, there is a clever solution to this dilemma: executing parallel operations as tasks within a specific fork-join pool. By doing so, they remain isolated from the common fork-join pool used by other parallel stream operations.

final int parallelism = 4;
ForkJoinPool forkJoinPool = null;
try {
    forkJoinPool = new ForkJoinPool(parallelism);
    final List<Integer> primes = forkJoinPool.submit(() ->
        // Parallel task here, for example
        IntStream.range(1, 1_000_000).parallel()
                .filter(PrimesPrint::isPrime)
                .boxed().collect(Collectors.toList())
    ).get();
    System.out.println(primes);
} catch (InterruptedException | ExecutionException e) {
    throw new RuntimeException(e);
} finally {
    if (forkJoinPool != null) {
        forkJoinPool.shutdown();
    }
}

This technique leverages ForkJoinTask.fork(), which specifies that if the current task is running in a fork-join pool, the asynchronous execution should occur in that pool. Otherwise, ForkJoinPool.commonPool() will be used.

By utilizing this approach, you can safely use parallel streams in various modules within a multi-threaded application without compromising performance or task compartmentalization.

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