如果我在并行流中使用 lambda 会发生死锁，但如果我改用匿名 class 则不会发生死锁？

Question

以下代码导致死锁（在我的电脑上）：

public class Test {
    static {
        final int SUM = IntStream.range(0, 100)
                .parallel()
                .reduce((n, m) -> n + m)
                .getAsInt();
    }

    public static void main(String[] args) {
        System.out.println("Finished");
    }
}

但是如果我用匿名 class 替换 reduce lambda 参数，它不会导致死锁：

public class Test {
    static {
        final int SUM = IntStream.range(0, 100)
                .parallel()
                .reduce(new IntBinaryOperator() {
                    @Override
                    public int applyAsInt(int n, int m) {
                        return n + m;
                    }
                })
                .getAsInt();
    }

    public static void main(String[] args) {
        System.out.println("Finished");
    }
}

你能解释一下这种情况吗？

P.S.

我找到了那个代码（与之前的有点不同）：

public class Test {
    static {
        final int SUM = IntStream.range(0, 100)
                .parallel()
                .reduce(new IntBinaryOperator() {
                    @Override
                    public int applyAsInt(int n, int m) {
                        return sum(n, m);
                    }
                })
                .getAsInt();
    }

    private static int sum(int n, int m) {
        return n + m;
    }

    public static void main(String[] args) {
        System.out.println("Finished");
    }
}

工作不稳定。在大多数情况下，它会挂起，但有时会成功完成：

我真的无法理解为什么这种行为不稳定。实际上我重新测试了第一个代码片段并且行为相同。所以最新的代码等于第一个。

为了了解使用了哪些线程，我添加了以下 "logging":

public class Test {
    static {
        final int SUM = IntStream.range(0, 100)
                .parallel()
                .reduce((n, m) -> {
                    System.out.println(Thread.currentThread().getName());
                    return (n + m);
                })
                .getAsInt();
    }

    public static void main(String[] args) {
        System.out.println("Finished");
    }
}

如果应用程序成功完成，我会看到以下日志：

main
main
main
main
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
main
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
main
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
ForkJoinPool.commonPool-worker-1
Finished

P.S。 2

我不明白 reduce 是足够复杂的操作。我找到了一个更简单的例子来说明这个问题：

public class Test {
    static {
        System.out.println("static initializer: " + Thread.currentThread().getName());

        final long SUM = IntStream.range(0, 2)
                .parallel()
                .mapToObj(i -> {
                    System.out.println("map: " + Thread.currentThread().getName() + " " + i);
                    return i;
                })
                .count();
    }

    public static void main(String[] args) {
        System.out.println("Finished");
    }
}

对于满意的情况（罕见情况）我看到以下输出：

static initializer: main
map: main 1
map: main 0
Finished

扩展流范围的满意案例示例：

static initializer: main
map: main 2
map: main 3
map: ForkJoinPool.commonPool-worker-2 4
map: ForkJoinPool.commonPool-worker-1 1
map: ForkJoinPool.commonPool-worker-3 0
Finished

导致死锁的例子：

static initializer: main
map: main 1

它也会导致死锁，但不是每次启动都会导致死锁。

Answer 1

不同的是，lambda body的写法是一样的Testclass，即合成法

private static int lambda$static[=10=](int n, int m) {
    return n + m;
}

在第二种情况下，接口的实现驻留在 不同的 Test class 中。因此并行流的线程不调用 Test 的静态方法，因此不依赖于 Test 初始化。

如果我在并行流中使用 lambda 会发生死锁，但如果我改用匿名 class 则不会发生死锁？

Deadlock happens if I use lambda in parallel stream but it doesn't happen if I use anonymous class instead?

java

deadlock

initialization

forkjoinpool

java-stream

P.S.

P.S。 2