Java 8 Stream並行流

流能夠並行執行，以增長大量輸入元素的運行時性能。並行流ForkJoinPool經過靜態ForkJoinPool.commonPool()方法使用公共可用的流。底層線程池的大小最多使用五個線程 - 具體取決於可用物理CPU核心的數量：

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

在個人機器上，公共池初始化爲默認值爲3的並行度。經過設置如下JVM參數能夠減少或增長此值：

-Djava.util.concurrent.ForkJoinPool.common.parallelism=5

集合支持建立並行元素流的方法parallelStream()。或者，您能夠在給定流上調用中間方法parallel()，以將順序流轉換爲並行流。

爲了評估並行流的並行執行行爲，下一個示例將有關當前線程的信息打印出來：

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()));

經過調查調試輸出，咱們應該更好地理解哪些線程實際用於執行流操做：

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]

如您所見，並行流利用公共中的全部可用線程ForkJoinPool來執行流操做。輸出在連續運行中可能不一樣，由於實際使用的特定線程的行爲是非肯定性的。

讓咱們經過一個額外的流操做來擴展該示例：

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()));

結果可能最初看起來很奇怪：

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]

彷佛sort只在主線程上順序執行。實際上，sort在並行流上使用新的Java 8方法Arrays.parallelSort()。如Javadoc中所述，若是排序將按順序或並行執行，則此方法決定數組的長度：

若是指定數組的長度小於最小粒度，則使用適當的Arrays.sort方法對其進行排序。

回到reduce一節的例子。咱們已經發現組合器函數只是並行調用，而不是順序流調用。讓咱們看看實際涉及哪些線程：

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;
        });

控制檯輸出顯示累加器和組合器函數在全部可用線程上並行執行：

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]

總之，並行流能夠爲具備大量輸入元素的流帶來良好的性能提高。但請記住，某些並行流操做reduce,collect須要額外的計算（組合操做），這在順序執行時是不須要的。

此外，咱們瞭解到全部並行流操做共享相同的JVM範圍ForkJoinPool。所以，您可能但願避免實施慢速阻塞流操做，由於這可能會減慢嚴重依賴並行流的應用程序的其餘部分。