Why is CompletableFuture join/get faster in separate streams than using one stream

Question

For the following program I am trying to figure out why using 2 different streams parallelizes the task and using the same stream and calling join/get on the Completable future makes them take longer time equivalent to as if they were sequentially processed).

public class HelloConcurrency {

    private static Integer sleepTask(int number) {
        System.out.println(String.format("Task with sleep time %d", number));
        try {
            TimeUnit.SECONDS.sleep(number);
        } catch (InterruptedException e) {
            e.printStackTrace();
            return -1;
        }
        return number;
    }

    public static void main(String[] args) {
        List<Integer> sleepTimes = Arrays.asList(1,2,3,4,5,6);
        System.out.println("WITH SEPARATE STREAMS FOR FUTURE AND JOIN");
        ExecutorService executorService = Executors.newFixedThreadPool(6);
        long start = System.currentTimeMillis();
        List<CompletableFuture<Integer>> futures = sleepTimes.stream()
                .map(sleepTime -> CompletableFuture.supplyAsync(() -> sleepTask(sleepTime), executorService)
                        .exceptionally(ex -> { ex.printStackTrace(); return -1; }))
                .collect(Collectors.toList());
        executorService.shutdown();
        List<Integer> result = futures.stream()
                .map(CompletableFuture::join)
                .collect(Collectors.toList());
        long finish = System.currentTimeMillis();
        long timeElapsed = (finish - start)/1000;
        System.out.println(String.format("done in %d seconds.", timeElapsed));
        System.out.println(result);

        System.out.println("WITH SAME STREAM FOR FUTURE AND JOIN");
        ExecutorService executorService2 = Executors.newFixedThreadPool(6);
        start = System.currentTimeMillis();
        List<Integer> results = sleepTimes.stream()
                .map(sleepTime -> CompletableFuture.supplyAsync(() -> sleepTask(sleepTime), executorService2)
                        .exceptionally(ex -> { ex.printStackTrace(); return -1; }))
                .map(CompletableFuture::join)
                .collect(Collectors.toList());
        executorService2.shutdown();
        finish = System.currentTimeMillis();
        timeElapsed = (finish - start)/1000;
        System.out.println(String.format("done in %d seconds.", timeElapsed));
        System.out.println(results);
    }
}

Output

WITH SEPARATE STREAMS FOR FUTURE AND JOIN
Task with sleep time 6
Task with sleep time 5
Task with sleep time 1
Task with sleep time 3
Task with sleep time 2
Task with sleep time 4
done in 6 seconds.
[1, 2, 3, 4, 5, 6]
WITH SAME STREAM FOR FUTURE AND JOIN
Task with sleep time 1
Task with sleep time 2
Task with sleep time 3
Task with sleep time 4
Task with sleep time 5
Task with sleep time 6
done in 21 seconds.
[1, 2, 3, 4, 5, 6]

Answer 1

The two approaches are quite different, let me try to explain it clearly

1st approach : In the first approach you are spinning up all Async requests for all 6 tasks and then calling join function on each one of them to get the result

2st approach : But in the second approach you are calling the join immediately after spinning the Async request for each task. For example after spinning Async thread for task 1 calling join, make sure that thread to complete task and then only spin up the second task with Async thread

Note : Another side if you observe the output clearly, In the 1st approach output appears in random order since the all six tasks were executed asynchronously. But during second approach all tasks were executed sequentially one after the another.

I believe you have an idea how stream map operation is performed, or you can get more information from here or here

To perform a computation, stream operations are composed into a stream pipeline. A stream pipeline consists of a source (which might be an array, a collection, a generator function, an I/O channel, etc), zero or more intermediate operations (which transform a stream into another stream, such as filter(Predicate)), and a terminal operation (which produces a result or side-effect, such as count() or forEach(Consumer)). Streams are lazy; computation on the source data is only performed when the terminal operation is initiated, and source elements are consumed only as needed.