Custom Thread Pools In Java 8 Parallel Streams

1. Overview

Java 8 introduced the concept of Streams as an efficient way of carrying out bulk operations on data. And parallel Streams can be obtained in environments that support concurrency.

These streams can come with improved performance – at the cost of multi-threading overhead.

In this quick tutorial, we’ll look at one of the biggest limitations of Stream API and see how to make a parallel stream work with a custom ThreadPool instance.

2. Parallel Stream

Let’s start with a simple example – calling the parallelStream method on any of the Collection types – which will return a possibly parallel Stream:

@Test
public void givenList_whenCallingParallelStream_shouldBeParallelStream(){
    List<Long> aList = new ArrayList<>();
    Stream<Long> parallelStream = aList.parallelStream();
        
    assertTrue(parallelStream.isParallel());
}

The default processing that occurs in such a Stream uses the ForkJoinPool.commonPool(), a Thread Pool shared by the entire application.

3. Custom Thread Pool

We can actually pass a custom ThreadPool when processing the stream.

The following example lets have a parallel Stream use a custom Thread Pool to calculate the sum of long values from 1 to 1,000,000, inclusive:

@Test
public void giveRangeOfLongs_whenSummedInParallel_shouldBeEqualToExpectedTotal() 
  throws InterruptedException, ExecutionException {
    
    long firstNum = 1;
    long lastNum = 1_000_000;

    List<Long> aList = LongStream.rangeClosed(firstNum, lastNum).boxed()
      .collect(Collectors.toList());

    ForkJoinPool customThreadPool = new ForkJoinPool(4);
    long actualTotal = customThreadPool.submit(
      () -> aList.parallelStream().reduce(0L, Long::sum)).get();
 
    assertEquals((lastNum + firstNum) * lastNum / 2, actualTotal);
}

We used the ForkJoinPool constructor with a parallelism level of 4. Some experimentation is required to determine the optimal value for different environments, but a good rule of thumb is simply choosing the number based on how many cores your CPU has.

Next, we processed the content of the parallel Stream, summing them up in the reduce call.

This simple example may not demonstrate the full usefulness of using a custom Thread Pool, but the benefits become obvious in situations where we do not want to tie-up the common Thread Pool with long-running tasks (e.g. processing data from a network source), or the common Thread Pool is being used by other components within the application.

4. Conclusion

We have briefly looked at how to run a parallel Stream using a custom Thread Pool. In the right environment and with the proper use of the parallelism level, performance gains can be had in certain situations.

The complete code samples referenced in this article can be found over on Github.