Java 8 Stream并行流

迈不过友情╰ 2023-09-30 17:14 8阅读 0赞

流可以并行执行，以增加大量输入元素的运行时性能。并行流ForkJoinPool通过静态ForkJoinPool.commonPool()方法使用公共可用的流。底层线程池的大小最多使用五个线程 - 具体取决于可用物理CPU核心的数量：

ForkJoinPool commonPool = ForkJoinPool.commonPool();
    System.out.println(commonPool.getParallelism()); // 3
    复制代码

在我的机器上，公共池初始化为默认值为3的并行度。通过设置以下JVM参数可以减小或增加此值：

-Djava.util.concurrent.ForkJoinPool.common.parallelism=5
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集合支持创建并行元素流的方法parallelStream()。或者，您可以在给定流上调用中间方法parallel()，以将顺序流转换为并行流。

为了评估并行流的并行执行行为，下一个示例将有关当前线程的信息打印出来：

Arrays.asList("a1", "a2", "b1", "c2", "c1")
        .parallelStream()
        .filter(s -> {
            System.out.format("filter: %s [%s]\n",
                s, Thread.currentThread().getName());
            return true;
        })
        .map(s -> {
            System.out.format("map: %s [%s]\n",
                s, Thread.currentThread().getName());
            return s.toUpperCase();
        })
        .forEach(s -> System.out.format("forEach: %s [%s]\n",
            s, Thread.currentThread().getName()));
    复制代码

通过调查调试输出，我们应该更好地理解哪些线程实际用于执行流操作：

filter:  b1 [main]
    filter:  a2 [ForkJoinPool.commonPool-worker-1]
    map:     a2 [ForkJoinPool.commonPool-worker-1]
    filter:  c2 [ForkJoinPool.commonPool-worker-3]
    map:     c2 [ForkJoinPool.commonPool-worker-3]
    filter:  c1 [ForkJoinPool.commonPool-worker-2]
    map:     c1 [ForkJoinPool.commonPool-worker-2]
    forEach: C2 [ForkJoinPool.commonPool-worker-3]
    forEach: A2 [ForkJoinPool.commonPool-worker-1]
    map:     b1 [main]
    forEach: B1 [main]
    filter:  a1 [ForkJoinPool.commonPool-worker-3]
    map:     a1 [ForkJoinPool.commonPool-worker-3]
    forEach: A1 [ForkJoinPool.commonPool-worker-3]
    forEach: C1 [ForkJoinPool.commonPool-worker-2]
    复制代码

如您所见，并行流利用公共中的所有可用线程ForkJoinPool来执行流操作。输出在连续运行中可能不同，因为实际使用的特定线程的行为是非确定性的。

让我们通过一个额外的流操作来扩展该示例：

Arrays.asList("a1", "a2", "b1", "c2", "c1")
        .parallelStream()
        .filter(s -> {
            System.out.format("filter: %s [%s]\n",
                s, Thread.currentThread().getName());
            return true;
        })
        .map(s -> {
            System.out.format("map: %s [%s]\n",
                s, Thread.currentThread().getName());
            return s.toUpperCase();
        })
        .sorted((s1, s2) -> {
            System.out.format("sort: %s <> %s [%s]\n",
                s1, s2, Thread.currentThread().getName());
            return s1.compareTo(s2);
        })
        .forEach(s -> System.out.format("forEach: %s [%s]\n",
            s, Thread.currentThread().getName()));
    复制代码

结果可能最初看起来很奇怪：

filter:  c2 [ForkJoinPool.commonPool-worker-3]
    filter:  c1 [ForkJoinPool.commonPool-worker-2]
    map:     c1 [ForkJoinPool.commonPool-worker-2]
    filter:  a2 [ForkJoinPool.commonPool-worker-1]
    map:     a2 [ForkJoinPool.commonPool-worker-1]
    filter:  b1 [main]
    map:     b1 [main]
    filter:  a1 [ForkJoinPool.commonPool-worker-2]
    map:     a1 [ForkJoinPool.commonPool-worker-2]
    map:     c2 [ForkJoinPool.commonPool-worker-3]
    sort:    A2 <> A1 [main]
    sort:    B1 <> A2 [main]
    sort:    C2 <> B1 [main]
    sort:    C1 <> C2 [main]
    sort:    C1 <> B1 [main]
    sort:    C1 <> C2 [main]
    forEach: A1 [ForkJoinPool.commonPool-worker-1]
    forEach: C2 [ForkJoinPool.commonPool-worker-3]
    forEach: B1 [main]
    forEach: A2 [ForkJoinPool.commonPool-worker-2]
    forEach: C1 [ForkJoinPool.commonPool-worker-1]
    复制代码

似乎sort只在主线程上顺序执行。实际上，sort在并行流上使用新的Java 8方法Arrays.parallelSort()。如[Javadoc][]中所述，如果排序将按顺序或并行执行，则此方法决定数组的长度：

> 如果指定数组的长度小于最小粒度，则使用适当的Arrays.sort方法对其进行排序。

回到[reduce][]一节的例子。我们已经发现组合器函数只是并行调用，而不是顺序流调用。让我们看看实际涉及哪些线程：

List<Person> persons = Arrays.asList(
        new Person("Max", 18),
        new Person("Peter", 23),
        new Person("Pamela", 23),
        new Person("David", 12));
    
    persons
        .parallelStream()
        .reduce(0,
            (sum, p) -> {
                System.out.format("accumulator: sum=%s; person=%s [%s]\n",
                    sum, p, Thread.currentThread().getName());
                return sum += p.age;
            },
            (sum1, sum2) -> {
                System.out.format("combiner: sum1=%s; sum2=%s [%s]\n",
                    sum1, sum2, Thread.currentThread().getName());
                return sum1 + sum2;
            });
    复制代码

控制台输出显示累加器和组合器函数在所有可用线程上并行执行：

accumulator: sum=0; person=Pamela; [main]
    accumulator: sum=0; person=Max;    [ForkJoinPool.commonPool-worker-3]
    accumulator: sum=0; person=David;  [ForkJoinPool.commonPool-worker-2]
    accumulator: sum=0; person=Peter;  [ForkJoinPool.commonPool-worker-1]
    combiner:    sum1=18; sum2=23;     [ForkJoinPool.commonPool-worker-1]
    combiner:    sum1=23; sum2=12;     [ForkJoinPool.commonPool-worker-2]
    combiner:    sum1=41; sum2=35;     [ForkJoinPool.commonPool-worker-2]
    复制代码

总之，并行流可以为具有大量输入元素的流带来良好的性能提升。但请记住，某些并行流操作`reduce`,`collect`需要额外的计算（组合操作），这在顺序执行时是不需要的。

此外，我们了解到所有并行流操作共享相同的JVM范围ForkJoinPool。因此，您可能希望避免实施慢速阻塞流操作，因为这可能会减慢严重依赖并行流的应用程序的其他部分。

[Javadoc]: https://link.juejin.cn?target=https%3A%2F%2Fdocs.oracle.com%2Fjavase%2F8%2Fdocs%2Fapi%2Fjava%2Futil%2FArrays.html%23parallelSort-T%3AA-
[reduce]: https://link.juejin.cn?target=%24stream_advanced_action