【并发编程】-- AbstractExecutorService实现原理（2）

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AbstractExecutorService实现原理（2）

抽象执行器服务部分源码如下：

public abstract class AbstractExecutorService implements ExecutorService {
   .....
   for (;;) {
                Future<T> f = ecs.poll();
                if (f == null) {
                    if (ntasks > 0) {
                        --ntasks;
                        futures.add(ecs.submit(it.next()));
                        ++active;
                    }
                    else if (active == 0)
                        break;
                    else if (timed) {
                        /**
                        	如果执行了超时时间，等待nacos纳秒后如果future还是为空，则抛出超时异常
                        **/
                        f = ecs.poll(nanos, TimeUnit.NANOSECONDS);
                        if (f == null)
                            throw new TimeoutException();
                        nanos = deadline - System.nanoTime();
                    }
                    else
                        f = ecs.take();
                }
                if (f != null) {
                    --active;
                    try {
                        return f.get();
                    } catch (ExecutionException eex) {
                        ee = eex;
                    } catch (RuntimeException rex) {
                        ee = new ExecutionException(rex);
                    }
                }
            }

            if (ee == null)
                ee = new ExecutionException();
            throw ee;

        } finally {
            for (int i = 0, size = futures.size(); i < size; i++)
                futures.get(i).cancel(true);
        }
    }

    public <T> T invokeAny(Collection<? extends Callable<T>> tasks)
        throws InterruptedException, ExecutionException {
        try {
            return doInvokeAny(tasks, false, 0);
        } catch (TimeoutException cannotHappen) {
            assert false;
            return null;
        }
    }

    public <T> T invokeAny(Collection<? extends Callable<T>> tasks,
                           long timeout, TimeUnit unit)
        throws InterruptedException, ExecutionException, TimeoutException {
        return doInvokeAny(tasks, true, unit.toNanos(timeout));
    }

    public <T> List<Future<T>> invokeAll(Collection<? extends Callable<T>> tasks)
        throws InterruptedException {
        if (tasks == null)
            throw new NullPointerException();
        ArrayList<Future<T>> futures = new ArrayList<Future<T>>(tasks.size());
        boolean done = false;
        try {
            for (Callable<T> t : tasks) {
                RunnableFuture<T> f = newTaskFor(t);
                futures.add(f);
                execute(f);
            }
            for (int i = 0, size = futures.size(); i < size; i++) {
                Future<T> f = futures.get(i);
                if (!f.isDone()) {
                    try {
                        f.get();
                    } catch (CancellationException ignore) {
                    } catch (ExecutionException ignore) {
                    }
                }
            }
            done = true;
            return futures;
        } finally {
            if (!done)
                for (int i = 0, size = futures.size(); i < size; i++)
                    futures.get(i).cancel(true);
        }
    }

    public <T> List<Future<T>> invokeAll(Collection<? extends Callable<T>> tasks,
                                         long timeout, TimeUnit unit)
        throws InterruptedException {
        if (tasks == null)
            throw new NullPointerException();
        long nanos = unit.toNanos(timeout);
        ArrayList<Future<T>> futures = new ArrayList<Future<T>>(tasks.size());
        boolean done = false;
        try {
            for (Callable<T> t : tasks)
                futures.add(newTaskFor(t));

            final long deadline = System.nanoTime() + nanos;
            final int size = futures.size();

            for (int i = 0; i < size; i++) {
                execute((Runnable)futures.get(i));
                nanos = deadline - System.nanoTime();
                if (nanos <= 0L)
                    return futures;
            }

            for (int i = 0; i < size; i++) {
                Future<T> f = futures.get(i);
                if (!f.isDone()) {
                    if (nanos <= 0L)
                        return futures;
                    try {
                        f.get(nanos, TimeUnit.NANOSECONDS);
                    } catch (CancellationException ignore) {
                    } catch (ExecutionException ignore) {
                    } catch (TimeoutException toe) {
                        return futures;
                    }
                    nanos = deadline - System.nanoTime();
                }
            }
            done = true;
            return futures;
        } finally {
            if (!done)
                for (int i = 0, size = futures.size(); i < size; i++)
                    futures.get(i).cancel(true);
        }
    }
}

解析方法如下： new ExecutorCompletionService(this)方法：创建一个ExecutorCompletionService，从ExecutorCompletionService的构造器看出ExecutorCompletionService(Executor executor,BlockingQueue<Future> completionQueue),传入参数是一个执行器，一个阻塞队列，而ExecutorCompletionService的功能在执行器executor中执行任务，任务完成后将结果放入completionQueue队列中，然后生产端从这个队列中获取结果，这就是生产者消费者模型。是从ExecutorCompletionService中获取一个执行任务的future，如果future为空，判断任务数量是否大于0，如果还有下一个任务，继续递交执行，如果future不为空，直接调用future.get()返回执行结果值，然后在finally中取消所有任务。

ecs.take()、 ecs.poll()方法：所有任务都放在线程池里执行了，那直接调用take()方法阻塞当前线程，等待任务执行完成后返回。poll()方法非阻塞或者等待超时时间的阻塞，take方法阻塞直到队列放入future返回的结果。