Android ScheduleExecutorService源码解析

最近全投了一个难度很大的项目，使用的还是不太熟悉的C语言，目前终于基本开发完了，近期没那么忙了，这期间之前的一个项目在线程池部分出现了问题，根本原因还是对线程池的理解不够深引起的，因此今天分析下线程池的使用和源码。

上一个项目创建线程池使用的是：

Executors.newScheduledThreadPool()

即：ScheduledThreadPoolExecutor，这个类中schedule的实现如下：

    /**
     * @throws RejectedExecutionException {@inheritDoc}
     * @throws NullPointerException       {@inheritDoc}
     */
    public ScheduledFuture<?> schedule(Runnable command,
                                       long delay,
                                       TimeUnit unit) {
        if (command == null || unit == null)
            throw new NullPointerException();
        RunnableScheduledFuture<?> t = decorateTask(command,
            new ScheduledFutureTask<Void>(command, null,
                                          triggerTime(delay, unit)));
        delayedExecute(t);
        return t;
    }

将提交的task包装成ScheduleFutureTask，再通过delayedExecute执行：

        ScheduledFutureTask(Runnable r, V result, long ns) {
            super(r, result);
            this.time = ns;
            this.period = 0;
            this.sequenceNumber = sequencer.getAndIncrement();
        }

    private void delayedExecute(RunnableScheduledFuture<?> task) {
        if (isShutdown())
            reject(task);
        else {
            super.getQueue().add(task);
            if (isShutdown() &&
                !canRunInCurrentRunState(task.isPeriodic()) &&
                remove(task))
                task.cancel(false);
            else
                ensurePrestart();
        }
    }

首先将任务放入等待队列（workQueue），之后再次检查线程池的状态来判断是否需要取消任务。如果线程池被关闭了，canRunInCurrentRunState返回false，那么从任务队列中取出任务。
canRunInCurrentRunState会根据任务是否是周期性来返回不同的状态值：

    boolean canRunInCurrentRunState(boolean periodic) {
        return isRunningOrShutdown(periodic ?
                                   continueExistingPeriodicTasksAfterShutdown :
                                   executeExistingDelayedTasksAfterShutdown);
    }

ScheduledThreadPoolExecutor的任务队列是DelayedWorkQueue实现的，这个类的代码有450行，这里不全部贴出，只给出部分的代码。
任务的存储形式是一个数组，实际上是一个以task的time为索引的二叉树：

        private RunnableScheduledFuture<?>[] queue =
            new RunnableScheduledFuture<?>[INITIAL_CAPACITY];

当添加任务时，会调用offer方法：

        public boolean add(Runnable e) {
            return offer(e);
        }

        public boolean offer(Runnable x) {
            if (x == null)
                throw new NullPointerException();
            RunnableScheduledFuture<?> e = (RunnableScheduledFuture<?>)x;
            final ReentrantLock lock = this.lock;
            lock.lock();
            try {
                int i = size;
                // 当数组达到最大容量的时候，进行动态的扩容
                if (i >= queue.length)
                    grow();
                size = i + 1;
                // 如果数组是空的，直接将task放入索引为0的位置即可
                if (i == 0) {
                    queue[0] = e;
                    setIndex(e, 0);
                } else {
                // 如果数组中原本有值，需要调整这个二叉树
                    siftUp(i, e);
                }
                if (queue[0] == e) {
                    leader = null;
                    available.signal();
                }
            } finally {
                lock.unlock();
            }
            return true;
        }

简单逻辑在注释中已做说明，siftUp函数的实现如下，本质上是一个堆排序：

        /**
         * Sifts element added at bottom up to its heap-ordered spot.
         * Call only when holding lock.
         */
        private void siftUp(int k, RunnableScheduledFuture<?> key) {
            while (k > 0) {
                int parent = (k - 1) >>> 1;
                RunnableScheduledFuture<?> e = queue[parent];
                if (key.compareTo(e) >= 0)
                    break;
                queue[k] = e;
                setIndex(e, k);
                k = parent;
            }
            queue[k] = key;
            setIndex(key, k);
        }

首先通过减一后向右移一位的方法来获取当前索引的parent结点。
接下来通过while循环寻找合入的task位置，比较的值就是task的time值：

        public int compareTo(Delayed other) {
            if (other == this) // compare zero if same object
                return 0;
            if (other instanceof ScheduledFutureTask) {
                ScheduledFutureTask<?> x = (ScheduledFutureTask<?>)other;
                long diff = time - x.time;
                if (diff < 0)
                    return -1;
                else if (diff > 0)
                    return 1;
                else if (sequenceNumber < x.sequenceNumber)
                    return -1;
                else
                    return 1;
            }
            long diff = getDelay(NANOSECONDS) - other.getDelay(NANOSECONDS);
            return (diff < 0) ? -1 : (diff > 0) ? 1 : 0;
        }

同理当线程池取出任务时，也会进行堆的调整：

        public RunnableScheduledFuture<?> poll() {
            final ReentrantLock lock = this.lock;
            lock.lock();
            try {
                RunnableScheduledFuture<?> first = queue[0];
                if (first == null || first.getDelay(NANOSECONDS) > 0)
                    return null;
                else
                    return finishPoll(first);
            } finally {
                lock.unlock();
            }
        }

        /**
         * Performs common bookkeeping for poll and take: Replaces
         * first element with last and sifts it down.  Call only when
         * holding lock.
         * @param f the task to remove and return
         */
        private RunnableScheduledFuture<?> finishPoll(RunnableScheduledFuture<?> f) {
            int s = --size;
            RunnableScheduledFuture<?> x = queue[s];
            queue[s] = null;
            if (s != 0)
                siftDown(0, x);
            setIndex(f, -1);
            return f;
        }

        /**
         * Sifts element added at top down to its heap-ordered spot.
         * Call only when holding lock.
         */
        private void siftDown(int k, RunnableScheduledFuture<?> key) {
            int half = size >>> 1;
            while (k < half) {
                // 通过同样的移位操作选择出left child结点
                int child = (k << 1) + 1;
                RunnableScheduledFuture<?> c = queue[child];
                int right = child + 1;
                if (right < size && c.compareTo(queue[right]) > 0)
                    // 如果left结点的time值大于right结点， 将标记变量指向right结点的RunnableScheduledFuture
                    c = queue[child = right];
                // 如果当前标记结点的time相等或小于最末位的结点，break
                if (key.compareTo(c) <= 0)
                    break;
                // 否则将当前的选取出的结点上移到parent结点的位置    
                queue[k] = c;
                setIndex(c, k);
                k = child;
            }
            queue[k] = key;
            setIndex(key, k);
        }

接下来围绕这个任务队列分析一个线程池的运行原理：
首先介绍下线程池中很重要的一个变量：ctl
这个变量的高3位用了表示线程池的状态，分别是：RUNNING、SHUTDOWN、、STOP、TIDYING和TERMINATED。

 *   RUNNING:  接受新任务并处理排队的任务
 *   SHUTDOWN: 不接受新任务，但处理排队的任务
 *   STOP:     不接受新任务，不处理排队的任务，并中断正在进行的任务
 *   TIDYING:  所有任务已终止，workerCount为零，线程过渡到TIDYING状态将运行终止的（）挂钩方法
 *   TERMINATED: terminate()已完成

而剩下的29位用来表示线程的数量。
在线程池完成初始化操作之后，会执行runWorker从任务队列中不停的取出任务来执行：

final void runWorker(Worker w) {
        Thread wt = Thread.currentThread();
        Runnable task = w.firstTask;
        w.firstTask = null;
        w.unlock(); // allow interrupts
        boolean completedAbruptly = true;
        try {
            while (task != null || (task = getTask()) != null) {
                w.lock();
                // If pool is stopping, ensure thread is interrupted;
                // if not, ensure thread is not interrupted.  This
                // requires a recheck in second case to deal with
                // shutdownNow race while clearing interrupt
                if ((runStateAtLeast(ctl.get(), STOP) ||
                     (Thread.interrupted() &&
                      runStateAtLeast(ctl.get(), STOP))) &&
                    !wt.isInterrupted())
                    wt.interrupt();
                try {
                    beforeExecute(wt, task);
                    Throwable thrown = null;
                    try {
                        task.run();
                    } catch (RuntimeException x) {
                        thrown = x; throw x;
                    } catch (Error x) {
                        thrown = x; throw x;
                    } catch (Throwable x) {
                        thrown = x; throw new Error(x);
                    } finally {
                        afterExecute(task, thrown);
                    }
                } finally {
                    task = null;
                    w.completedTasks++;
                    w.unlock();
                }
            }
            completedAbruptly = false;
        } finally {
            // 如果一直没有获取到任务，说明该线程是需要被回收的
            processWorkerExit(w, completedAbruptly);
        }
    }

private Runnable getTask() {
        boolean timedOut = false; // Did the last poll() time out?

        for (;;) {
            int c = ctl.get();
            int rs = runStateOf(c);

            // 首先判断线程池的状态，如果处于被关闭的状态且任务队列为空，或者状态为STOP、TIDYING、TERMINATED时，返回空任务，并将线程数量减1
            // Check if queue empty only if necessary.
            if (rs >= SHUTDOWN && (rs >= STOP || workQueue.isEmpty())) {
                decrementWorkerCount();
                return null;
            }

            int wc = workerCountOf(c);


            // Are workers subject to culling?
            // 当前线程数大于corePoolSize或者allowCoreThreadTimeOut为true时，timed为true
            boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;

           /**
           * 如果工作线程大于1 ，任务队列为空时出现如下场景，线程池的数量会减1
            * 1.当前线程数大于最大线程数
            * 2.等待task超时
            */
            if ((wc > maximumPoolSize || (timed && timedOut))
                && (wc > 1 || workQueue.isEmpty())) {
                if (compareAndDecrementWorkerCount(c))
                    return null;
                continue;
            }

            // 如果timed为true，需要通过poll(keepAliveTime, timeUnit)来获取，因为这时需要对闲置线程进行超时回收
            // 如果timed为false，直接从任务队列中取出数据即可
            try {
                Runnable r = timed ?
                    workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
                    workQueue.take();
                if (r != null)
                    return r;
                // 标记等待超时，该结果会直接影响下一次循环的逻辑
                timedOut = true;
            } catch (InterruptedException retry) {
                timedOut = false;
            }
        }
    }

compareAndDecrementWorkerCount的实现如下：

    /**
     * Attempts to CAS-decrement the workerCount field of ctl.
     */
    private boolean compareAndDecrementWorkerCount(int expect) {
        return ctl.compareAndSet(expect, expect - 1);
    }

其中poll是等待固定时间的取任务函数：

public RunnableScheduledFuture<?> poll(long timeout, TimeUnit unit)
            throws InterruptedException {
            long nanos = unit.toNanos(timeout);
            final ReentrantLock lock = this.lock;
            lock.lockInterruptibly();
            try {
                for (;;) {
                    RunnableScheduledFuture<?> first = queue[0];
                    if (first == null) {
                        if (nanos <= 0)
                            return null;
                        else
                            nanos = available.awaitNanos(nanos);
                    } else {
                        long delay = first.getDelay(NANOSECONDS);
                        if (delay <= 0)
                            return finishPoll(first);
                        if (nanos <= 0)
                            return null;
                        first = null; // don't retain ref while waiting
                        if (nanos < delay || leader != null)
                            nanos = available.awaitNanos(nanos);
                        else {
                            Thread thisThread = Thread.currentThread();
                            leader = thisThread;
                            try {
                                long timeLeft = available.awaitNanos(delay);
                                nanos -= delay - timeLeft;
                            } finally {
                                if (leader == thisThread)
                                    leader = null;
                            }
                        }
                    }
                }
            } finally {
                if (leader == null && queue[0] != null)
                    available.signal();
                lock.unlock();
            }
        }

可以看到如果任务队列为空的话，会等待指定的时间后再次尝试获取，如果依旧为空的话，则不会再等待，直接返回null，之后就会进入线程的回收逻辑。
take函数的实现如下：

public RunnableScheduledFuture<?> take() throws InterruptedException {
    final ReentrantLock lock = this.lock;
    lock.lockInterruptibly();
    try {
        for (;;) {
			/*
			 * 获取任务队列中的首任务,
			 * 由于任务队列是根据等待时间排序的,
			 * 首任务一定是所需要的等待时间最少的任务
			 */
            RunnableScheduledFuture<?> first = queue[0];
            if (first == null)
                available.await();
            else {
				// getDelay会返回所剩的等待时间
                long delay = first.getDelay(NANOSECONDS);
				// 如果任务已到达等待时间直接返回该任务即可
                if (delay <= 0)
                    return finishPoll(first);
                first = null; // don't retain ref while waiting
				
                if (leader != null)
                    available.await();
                else {
                    Thread thisThread = Thread.currentThread();
                    leader = thisThread;
                    /*
                     * 如果还需要继续等待再执行该任务,
                     * available实例锁会阻塞delay指定的时间,
                     * delay时间后重新执行for循环来获取任务
					 * 这期间如果有新的任务进入队列
					 * available.signal会被调用，
					 * 此时会优先执行不需要等待、或者是等待时间短的任务
                     */
                    try {
                        available.awaitNanos(delay);
                    } finally {
                        if (leader == thisThread)
                            leader = null;
                    }
                }
            }
        }
    } finally {
        if (leader == null && queue[0] != null)
            available.signal();
        lock.unlock();
    }
}

最后

以上就是拼搏宝马为你收集整理的Android ScheduleExecutorService源码解析的全部内容，希望文章能够帮你解决Android ScheduleExecutorService源码解析所遇到的程序开发问题。

如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。