How many threads are spawned in parallelStream in

问题:

In JDK8, how many threads are spawned when i'm using parallelStream? For instance, in the code:

list.parallelStream().forEach(/** Do Something */);

If this list has 100000 items, how many threads will be spawned?

Also, do each of the threads get the same number of items to work on or is it randomly allotted?

回答1:

The Oracle's implementation[1] of parallel stream uses the current thread and in addition to that, if needed, also the threads that compose the default fork join pool ForkJoinPool.commonPool(), which has a default size equal to one less than the number of cores of your CPU.

That default size of the common pool can be changed with this property:

-Djava.util.concurrent.ForkJoinPool.common.parallelism=8

Alternatively, you can use your own pool:

ForkJoinPool myPool = new ForkJoinPool(8);
myPool.submit(() ->
    list.parallelStream().forEach(/* Do Something */);
).get();

Regarding the order, jobs will be executed as soon as a thread is available, in no specific order.

As correctly pointed out by @Holger this is an implementation specific detail (with just one vague reference at the bottom of a document), both approaches will work on Oracle's JVM but are definitely not guaranteed to work on JVMs from other vendors, the property could not exist in a non-Oracle implementation and Streams could not even use a ForkJoinPool internally rendering the alternative based on the behavior of ForkJoinTask.fork completely useless (see here for details on this).

回答2:

While @uraimo is correct, the answer depends on exactly what "Do Something" does. The parallel.streams API uses the CountedCompleter Class which has some interesting problems. Since the F/J framework does not use a separate object to hold results, long chains may result in an OOME. Also those long chains can sometimes cause a Stack Overflow. The answer to those problems is the use of the Paraquential technique as I pointed out in this article.

The other problem is excessive thread creation when using nested parallel forEach.