Parallel Streams with Custom Thread Pool

To control the number of threads used by parallel streams in Java, you need to use a custom ForkJoinPool. This allows you to specify the exact number of threads you want the parallel stream operations to be executed on. Here is a simple example:

import java.util.List;
import java.util.concurrent.ForkJoinPool;
import java.util.stream.Collectors;

public class ParallelStreamExample {

    public static void main(String[] args) {
        List<Integer> data = List.of(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
        int numberOfThreads = 4; // Specify the number of threads

        ForkJoinPool customThreadPool = new ForkJoinPool(numberOfThreads);
        try {
            // Submit the parallel stream execution to the custom thread pool
            List<Integer> result = customThreadPool.submit(
                () -> data.parallelStream()
                          .map(ParallelStreamExample::process)
                          .collect(Collectors.toList())
            ).join(); // This will wait for all tasks to complete

            System.out.println("Processed data: " + result);
        } finally {
            customThreadPool.shutdown(); // Always shutdown the thread pool
        }
    }

    private static int process(int input) {
        // Example processing (this could be any operation)
        return input * input;
    }
}

In this example:

• A list of integers is processed in parallel using a parallel stream.
• A ForkJoinPool is explicitly created with a specified number of threads (numberOfThreads).
• The submit() method of the custom ForkJoinPool is used to execute the parallel stream operation, ensuring it runs within the context of the specified thread pool.
• The join() method is called on the Future returned by submit() to wait for the processing to complete.
• Finally, the custom thread pool is shut down to release resources.

This approach allows you to control the number of threads used by the parallel stream, which is otherwise not possible with the standard parallel streams API in Java. Remember, using too many threads can lead to increased overhead due to context switching, and too few might not fully utilize the available resources, so it's important to choose the number of threads wisely based on your system's capabilities and the nature of the task.