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Java并发编程--线程池

2017-08-22 15:03  deadMan_wyy  阅读(365)  评论(0编辑  收藏  举报

1.ThreadPoolExecutor类

java.uitl.concurrent.ThreadPoolExecutor类是线程池中最核心的一个类,下面我们来看一下ThreadPoolExecutor类的具体实现源码(内容基于JDK1.7)。

在ThreadPoolExecutor类中提供了四个构造方法:   

public class ThreadPoolExecutor extends AbstractExecutorService {
   .....
   public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue) {
        this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
             Executors.defaultThreadFactory(), defaultHandler);
    }
 
    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue,
                              ThreadFactory threadFactory) {
        this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
             threadFactory, defaultHandler);
    }
 
    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue,
                              RejectedExecutionHandler handler) {
        this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
             Executors.defaultThreadFactory(), handler);
    }
 
    public ThreadPoolExecutor(int corePoolSize,
                              int maximumPoolSize,
                              long keepAliveTime,
                              TimeUnit unit,
                              BlockingQueue<Runnable> workQueue,
                              ThreadFactory threadFactory,
                              RejectedExecutionHandler handler) {
        if (corePoolSize < 0 ||
            maximumPoolSize <= 0 ||
            maximumPoolSize < corePoolSize ||
            keepAliveTime < 0)
            throw new IllegalArgumentException();
        if (workQueue == null || threadFactory == null || handler == null)
            throw new NullPointerException();
        this.corePoolSize = corePoolSize;
        this.maximumPoolSize = maximumPoolSize;
        this.workQueue = workQueue;
        this.keepAliveTime = unit.toNanos(keepAliveTime);
        this.threadFactory = threadFactory;
        this.handler = handler;
    }
    ...
}

 从上面的代码可以得知,ThreadPoolExecutor继承了AbstractExecutorService类,并提供了四个构造器,事实上,发现前面三个构造器都是调用的第四个构造器进行的初始化工作。

下面解释下一下构造器中各个参数的含义:

  • corePoolSize:线程池核心线程大小。在创建了线程池后,默认情况下,线程池中并没有任何线程,而是等待有任务到来才创建线程去执行任务,除非调用了prestartAllCoreThreads()或者prestartCoreThread()方法,从这2个方法的名字就可以看出,是预创建线程的意思,即在没有任务到来之前就创建corePoolSize个线程或者一个线程。默认情况下,在创建了线程池后,线程池中的线程数为0,当有任务来之后,就会创建一个线程去执行任务,当线程池中的线程数目达到corePoolSize后,就会把到达的任务放到缓存队列当中;
  • maximumPoolSize:线程池最大线程数,表示在线程池中最多能创建多少个线程;
  • keepAliveTime:表示线程没有任务执行时最多保持多久时间会终止。默认情况下,只有当线程池中的线程数大于corePoolSize时,keepAliveTime才会起作用,直到线程池中的线程数不大于corePoolSize,即当线程池中的线程数大于corePoolSize时,如果一个线程空闲的时间达到keepAliveTime,则会终止,直到线程池中的线程数不超过corePoolSize。但是如果调用了allowCoreThreadTimeOut(boolean)方法,在线程池中的线程数不大于corePoolSize时,keepAliveTime参数也会起作用,直到线程池中的线程数为0;
  • unit:参数的时间单位,有7种取值,在TimeUnit类中有7种静态属性:
    TimeUnit.DAYS;               //
    TimeUnit.HOURS;             //小时
    TimeUnit.MINUTES;           //分钟
    TimeUnit.SECONDS;           //
    TimeUnit.MILLISECONDS;      //毫秒
    TimeUnit.MICROSECONDS;      //微秒
    TimeUnit.NANOSECONDS;       //纳秒
  • workQueue:一个阻塞队列,用来存储等待执行的任务;
  • threadFactory:线程工厂,主要用来创建线程;
  • handler:表示当拒绝处理任务时的策略,有以下四种取值:
    ThreadPoolExecutor.AbortPolicy:丢弃任务并抛出RejectedExecutionException异常。 
    ThreadPoolExecutor.DiscardPolicy:也是丢弃任务,但是不抛出异常。 
    ThreadPoolExecutor.DiscardOldestPolicy:丢弃队列最前面的任务,然后重新尝试执行任务(重复此过程)
    ThreadPoolExecutor.CallerRunsPolicy:由调用线程处理该任务 

从ThreadPoolExecutor类可以知道,ThreadPoolExecutor继承了AbstractExecutorService,我们来看一下AbstractExecutorService的实现:

public abstract class AbstractExecutorService implements ExecutorService {

    protected <T> RunnableFuture<T> newTaskFor(Runnable runnable, T value) {
        return new FutureTask<T>(runnable, value);
    }

    protected <T> RunnableFuture<T> newTaskFor(Callable<T> callable) {
        return new FutureTask<T>(callable);
    }

    public Future<?> submit(Runnable task) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<Void> ftask = newTaskFor(task, null);
        execute(ftask);
        return ftask;
    }

    public <T> Future<T> submit(Runnable task, T result) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<T> ftask = newTaskFor(task, result);
        execute(ftask);
        return ftask;
    }

    public <T> Future<T> submit(Callable<T> task) {
        if (task == null) throw new NullPointerException();
        RunnableFuture<T> ftask = newTaskFor(task);
        execute(ftask);
        return ftask;
    }

    private <T> T doInvokeAny(Collection<? extends Callable<T>> tasks,
                            boolean timed, long nanos)
        throws InterruptedException, ExecutionException, TimeoutException {
        if (tasks == null)
            throw new NullPointerException();
        int ntasks = tasks.size();
        if (ntasks == 0)
            throw new IllegalArgumentException();
        List<Future<T>> futures= new ArrayList<Future<T>>(ntasks);
        ExecutorCompletionService<T> ecs =
            new ExecutorCompletionService<T>(this);

        // For efficiency, especially in executors with limited
        // parallelism, check to see if previously submitted tasks are
        // done before submitting more of them. This interleaving
        // plus the exception mechanics account for messiness of main
        // loop.

        try {
            // Record exceptions so that if we fail to obtain any
            // result, we can throw the last exception we got.
            ExecutionException ee = null;
            long lastTime = timed ? System.nanoTime() : 0;
            Iterator<? extends Callable<T>> it = tasks.iterator();

            // Start one task for sure; the rest incrementally
            futures.add(ecs.submit(it.next()));
            --ntasks;
            int active = 1;

            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) {
                        f = ecs.poll(nanos, TimeUnit.NANOSECONDS);
                        if (f == null)
                            throw new TimeoutException();
                        long now = System.nanoTime();
                        nanos -= now - lastTime;
                        lastTime = now;
                    }
                    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 (Future<T> f : futures)
                f.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();
        List<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 (Future<T> f : futures) {
                if (!f.isDone()) {
                    try {
                        f.get();
                    } catch (CancellationException ignore) {
                    } catch (ExecutionException ignore) {
                    }
                }
            }
            done = true;
            return futures;
        } finally {
            if (!done)
                for (Future<T> f : futures)
                    f.cancel(true);
        }
    }

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

            long lastTime = System.nanoTime();

            // Interleave time checks and calls to execute in case
            // executor doesn't have any/much parallelism.
            Iterator<Future<T>> it = futures.iterator();
            while (it.hasNext()) {
                execute((Runnable)(it.next()));
                long now = System.nanoTime();
                nanos -= now - lastTime;
                lastTime = now;
                if (nanos <= 0)
                    return futures;
            }

            for (Future<T> f : futures) {
                if (!f.isDone()) {
                    if (nanos <= 0)
                        return futures;
                    try {
                        f.get(nanos, TimeUnit.NANOSECONDS);
                    } catch (CancellationException ignore) {
                    } catch (ExecutionException ignore) {
                    } catch (TimeoutException toe) {
                        return futures;
                    }
                    long now = System.nanoTime();
                    nanos -= now - lastTime;
                    lastTime = now;
                }
            }
            done = true;
            return futures;
        } finally {
            if (!done)
                for (Future<T> f : futures)
                    f.cancel(true);
        }
    }

}

AbstractExecutorService是一个抽象类,它实现了ExecutorService接口,而ExecutorService又是继承了Executor接口,它们的基本关系如下:

  • Executor是一个顶层接口,在它里面只声明了一个方法execute(Runnable),返回值为void,参数为Runnable类型,用来执行传进去的任务的;
  • ExecutorService接口继承了Executor接口,并声明了一些方法:submit、invokeAll、invokeAny以及shutDown等;
  • 抽象类AbstractExecutorService实现了ExecutorService接口,基本实现了ExecutorService中声明的所有方法;
  • ThreadPoolExecutor继承了类AbstractExecutorService,

在ThreadPoolExecutor类中有几个非常重要的方法:

execute()
submit()
shutdown()
shutdownNow()
  • execute()方法实际上是Executor中声明的方法,在ThreadPoolExecutor进行了具体的实现,这个方法是ThreadPoolExecutor的核心方法,通过这个方法可以向线程池提交一个任务,交由线程池去执行。
  • submit()方法是在ExecutorService中声明的方法,在AbstractExecutorService就已经有了具体的实现,在ThreadPoolExecutor中并没有对其进行重写,这个方法也是用来向线程池提交任务的,但是它和execute()方法不同,它能够返回任务执行的结果,去看submit()方法的实现,会发现它实际上还是调用的execute()方法,只不过它利用了Future来获取任务执行结果。
  • shutdown()和shutdownNow()是用来关闭线程池的。

2.分析线程池实现原理

线程池状态

成员变量ctl是个Integer的原子变量用来记录线程池状态和线程池线程个数,其中Integer类型是32位二进制标示,其中高3位用来表示线程池状态,后面 29位用来记录线程池线程个数。

//用来标记线程池状态(高3位),线程个数(低29位)
//默认是RUNNING状态,线程个数为0
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));

//线程个数掩码位数
private static final int COUNT_BITS = Integer.SIZE - 3;

//线程最大个数(低29位)00011111111111111111111111111111
private static final int CAPACITY   = (1 << COUNT_BITS) - 1;

//(高3位):11100000000000000000000000000000
private static final int RUNNING    = -1 << COUNT_BITS;

//(高3位):00000000000000000000000000000000
private static final int SHUTDOWN   =  0 << COUNT_BITS;

//(高3位):00100000000000000000000000000000
private static final int STOP       =  1 << COUNT_BITS;

//(高3位):01000000000000000000000000000000
private static final int TIDYING    =  2 << COUNT_BITS;

//(高3位):01100000000000000000000000000000
private static final int TERMINATED =  3 << COUNT_BITS;

// 获取高三位 运行状态
private static int runStateOf(int c)     { return c & ~CAPACITY; }

//获取低29位 线程个数
private static int workerCountOf(int c)  { return c & CAPACITY; }

//计算ctl新值,线程状态 与 线程个数
private static int ctlOf(int rs, int wc) { return rs | wc; }

线程池状态含义:

  • RUNNING:接受新任务并且处理阻塞队列里的任务
  • SHUTDOWN:拒绝新任务但是处理阻塞队列里的任务
  • STOP:拒绝新任务并且抛弃阻塞队列里的任务同时会中断正在处理的任务
  • TIDYING:所有任务都执行完(包含阻塞队列里面任务)当前线程池活动线程为0,将要调用terminated方法
  • TERMINATED:终止状态。terminated方法调用完成以后的状态

     *   RUNNING:  Accept new tasks and process queued tasks
     *   SHUTDOWN: Don't accept new tasks, but process queued tasks
     *   STOP:  Don't accept new tasks, don't process queued tasks,and interrupt in-progress tasks
     *   TIDYING:  All tasks have terminated, workerCount is zero, the thread transitioning to state TIDYING, will run the terminated() hook method
     *   TERMINATED: terminated() has completed

线程池状态转换:

RUNNING -> SHUTDOWN
显式调用shutdown()方法,或者隐式调用了finalize(),它里面调用了shutdown()方法。

RUNNING or SHUTDOWN)-> STOP
显式 shutdownNow()方法

SHUTDOWN -> TIDYING
当线程池和任务队列都为空的时候

STOP -> TIDYING
当线程池为空的时候

TIDYING -> TERMINATED
当 terminated() hook 方法执行完成时候

任务运行

ThreadPoolExecutor主要变量,对应定义可查看英文:

     
     /**
       * The queue used for holding tasks and handing off to worker
       * threads.  We do not require that workQueue.poll() returning
       * null necessarily means that workQueue.isEmpty(), so rely
       * solely on isEmpty to see if the queue is empty (which we must
       * do for example when deciding whether to transition from
       * SHUTDOWN to TIDYING).  This accommodates special-purpose
       * queues such as DelayQueues for which poll() is allowed to
       * return null even if it may later return non-null when delays
       * expire.
       */
     private
final BlockingQueue<Runnable> workQueue; /** * Lock held on access to workers set and related bookkeeping. * While we could use a concurrent set of some sort, it turns out * to be generally preferable to use a lock. Among the reasons is * that this serializes interruptIdleWorkers, which avoids * unnecessary interrupt storms, especially during shutdown. * Otherwise exiting threads would concurrently interrupt those * that have not yet interrupted. It also simplifies some of the * associated statistics bookkeeping of largestPoolSize etc. We * also hold mainLock on shutdown and shutdownNow, for the sake of * ensuring workers set is stable while separately checking * permission to interrupt and actually interrupting. */ private final ReentrantLock mainLock = new ReentrantLock(); /** * Set containing all worker threads in pool. Accessed only when * holding mainLock. */ private final HashSet<Worker> workers = new HashSet<Worker>(); /** * Wait condition to support awaitTermination */ private final Condition termination = mainLock.newCondition(); /** * Tracks largest attained pool size. Accessed only under * mainLock. */ private int largestPoolSize; /** * Counter for completed tasks. Updated only on termination of * worker threads. Accessed only under mainLock. */ private long completedTaskCount; /** * Factory for new threads. All threads are created using this * factory (via method addWorker). All callers must be prepared * for addWorker to fail, which may reflect a system or user's * policy limiting the number of threads. Even though it is not * treated as an error, failure to create threads may result in * new tasks being rejected or existing ones remaining stuck in * the queue. * * We go further and preserve pool invariants even in the face of * errors such as OutOfMemoryError, that might be thrown while * trying to create threads. Such errors are rather common due to * the need to allocate a native stack in Thread#start, and users * will want to perform clean pool shutdown to clean up. There * will likely be enough memory available for the cleanup code to * complete without encountering yet another OutOfMemoryError. */ private volatile ThreadFactory threadFactory; /** * Handler called when saturated or shutdown in execute. */ private volatile RejectedExecutionHandler handler; /** * Timeout in nanoseconds for idle threads waiting for work. * Threads use this timeout when there are more than corePoolSize * present or if allowCoreThreadTimeOut. Otherwise they wait * forever for new work. */ private volatile long keepAliveTime; /** * If false (default), core threads stay alive even when idle. * If true, core threads use keepAliveTime to time out waiting * for work. */ private volatile boolean allowCoreThreadTimeOut; /** * Core pool size is the minimum number of workers to keep alive * (and not allow to time out etc) unless allowCoreThreadTimeOut * is set, in which case the minimum is zero. */ private volatile int corePoolSize; /** * Maximum pool size. Note that the actual maximum is internally * bounded by CAPACITY. */ private volatile int maximumPoolSize;

在ThreadPoolExecutor类中,最核心的任务提交方法是execute()方法,我们看下execute()方法源码:

 /**
     * Executes the given task sometime in the future.  The task
     * may execute in a new thread or in an existing pooled thread.
     *
     * If the task cannot be submitted for execution, either because this
     * executor has been shutdown or because its capacity has been reached,
     * the task is handled by the current {@code RejectedExecutionHandler}.
     *
     * @param command the task to execute
     * @throws RejectedExecutionException at discretion of
     *         {@code RejectedExecutionHandler}, if the task
     *         cannot be accepted for execution
     * @throws NullPointerException if {@code command} is null
     */
    public void execute(Runnable command) {
        if (command == null)
            throw new NullPointerException();
        /*
         * Proceed in 3 steps:
         *
         * 1. If fewer than corePoolSize threads are running, try to
         * start a new thread with the given command as its first
         * task.  The call to addWorker atomically checks runState and
         * workerCount, and so prevents false alarms that would add
         * threads when it shouldn't, by returning false.
         *
         * 2. If a task can be successfully queued, then we still need
         * to double-check whether we should have added a thread
         * (because existing ones died since last checking) or that
         * the pool shut down since entry into this method. So we
         * recheck state and if necessary roll back the enqueuing if
         * stopped, or start a new thread if there are none.
         *
         * 3. If we cannot queue task, then we try to add a new
         * thread.  If it fails, we know we are shut down or saturated
         * and so reject the task.
         */
        int c = ctl.get(); //获取当前线程池的状态+线程个数变量

     //当前线程池线程个数是否小于corePoolSize,小于则开启新线程运行
        if (workerCountOf(c) < corePoolSize) {
            if (addWorker(command, true))
                return;
            c = ctl.get();
        }

     //如果线程池处于RUNNING状态,则添加任务到阻塞队列
        if (isRunning(c) && workQueue.offer(command)) {
            int recheck = ctl.get();

       //如果当前线程池状态不是RUNNING则从队列删除任务,并执行拒绝策略
            if (! isRunning(recheck) && remove(command))
                reject(command);
       //否者如果当前线程池线程空,则添加一个线程
            else if (workerCountOf(recheck) == 0)
                addWorker(null, false);
        }
     //如果队列满了,则新增线程,如果线程个数>maximumPoolSize则执行拒绝策略
        else if (!addWorker(command, false))
            reject(command);
    }

由代码可看出,该方法主要调用addWorker方法,其源码如下:

private boolean addWorker(Runnable firstTask, boolean core) {
        retry:
        for (;;) {
            int c = ctl.get();
            int rs = runStateOf(c);

            // Check if queue empty only if necessary.
            // 等价于s >= SHUTDOWN &&(rs != SHUTDOWN ||firstTask != null || workQueue.isEmpty())
            if (rs >= SHUTDOWN &&
                ! (rs == SHUTDOWN &&
                   firstTask == null &&
                   ! workQueue.isEmpty()))
                return false;
            //循环增加线程个数
            for (;;) {
                int wc = workerCountOf(c);
                if (wc >= CAPACITY ||
                    wc >= (core ? corePoolSize : maximumPoolSize))
                    return false;
                
                //cas增加线程个数
                if (compareAndIncrementWorkerCount(c))
                    break retry;
                
              //cas失败,则查看线程池状态是否变化,变化则跳到外层循环重试重新获取线程池状态,否者内层循环。
                c = ctl.get();  // Re-read ctl
                if (runStateOf(c) != rs)
                    continue retry;
                // else CAS failed due to workerCount change; retry inner loop
            }
        }

        //ctl更新成功,新增worker
        boolean workerStarted = false;
        boolean workerAdded = false;
        Worker w = null;
        try {
            final ReentrantLock mainLock = this.mainLock;
            w = new Worker(firstTask);
            final Thread t = w.thread;
            if (t != null) {
                mainLock.lock();
                try {
                    // Recheck while holding lock.
                    // Back out on ThreadFactory failure or if
                    // shut down before lock acquired.
                    int c = ctl.get();
                    int rs = runStateOf(c);

                    if (rs < SHUTDOWN ||
                        (rs == SHUTDOWN && firstTask == null)) {
                        if (t.isAlive()) // precheck that t is startable
                            throw new IllegalThreadStateException();
                        workers.add(w);
                        int s = workers.size();
                        if (s > largestPoolSize)
                            largestPoolSize = s;
                        workerAdded = true;
                    }
                } finally {
                    mainLock.unlock();
                }
                
                //添加成功,启动线程
                if (workerAdded) {
                    t.start();
                    workerStarted = true;
                }
            }
        } finally {
            if (! workerStarted)
                addWorkerFailed(w);
        }
        return workerStarted;
    }

创建worker成功,Worker类实现啦Runnable接口,我们接下来看下其run方法:

    /**
     * Creates with given first task and thread from ThreadFactory.
     * @param firstTask the first task (null if none)
     */
     Worker(Runnable firstTask) {
        setState(-1); // inhibit interrupts until runWorker
        his.firstTask = firstTask;
        this.thread = getThreadFactory().newThread(this);
     }

   /** Delegates main run loop to outer runWorker  */
     public void run() {
       runWorker(this);
     }   

   /**
     * Main worker run loop.  Repeatedly gets tasks from queue and
     * executes them, while coping with a number of issues:
     *
     * 1. We may start out with an initial task, in which case we
     * don't need to get the first one. Otherwise, as long as pool is
     * running, we get tasks from getTask. If it returns null then the
     * worker exits due to changed pool state or configuration
     * parameters.  Other exits result from exception throws in
     * external code, in which case completedAbruptly holds, which
     * usually leads processWorkerExit to replace this thread.
     *
     * 2. Before running any task, the lock is acquired to prevent
     * other pool interrupts while the task is executing, and
     * clearInterruptsForTaskRun called to ensure that unless pool is
     * stopping, this thread does not have its interrupt set.
     *
     * 3. Each task run is preceded by a call to beforeExecute, which
     * might throw an exception, in which case we cause thread to die
     * (breaking loop with completedAbruptly true) without processing
     * the task.
     *
     * 4. Assuming beforeExecute completes normally, we run the task,
     * gathering any of its thrown exceptions to send to
     * afterExecute. We separately handle RuntimeException, Error
     * (both of which the specs guarantee that we trap) and arbitrary
     * Throwables.  Because we cannot rethrow Throwables within
     * Runnable.run, we wrap them within Errors on the way out (to the
     * thread's UncaughtExceptionHandler).  Any thrown exception also
     * conservatively causes thread to die.
     *
     * 5. After task.run completes, we call afterExecute, which may
     * also throw an exception, which will also cause thread to
     * die. According to JLS Sec 14.20, this exception is the one that
     * will be in effect even if task.run throws.
     *
     * The net effect of the exception mechanics is that afterExecute
     * and the thread's UncaughtExceptionHandler have as accurate
     * information as we can provide about any problems encountered by
     * user code.
     *
     * @param w the worker
     */
    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 {
            processWorkerExit(w, completedAbruptly);
        }
    }       

创建worker成功,循环从阻塞队列获取task,若获取task==null,循环结束,移除该工作线程,下面我们看下getTask方法:

 /**
     * Performs blocking or timed wait for a task, depending on
     * current configuration settings, or returns null if this worker
     * must exit because of any of:
     * 1. There are more than maximumPoolSize workers (due to
     *    a call to setMaximumPoolSize).
     * 2. The pool is stopped.
     * 3. The pool is shutdown and the queue is empty.
     * 4. This worker timed out waiting for a task, and timed-out
     *    workers are subject to termination (that is,
     *    {@code allowCoreThreadTimeOut || workerCount > corePoolSize})
     *    both before and after the timed wait.
     *
     * @return task, or null if the worker must exit, in which case
     *         workerCount is decremented
     */
    private Runnable getTask() {
        boolean timedOut = false; // Did the last poll() time out?

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

            // Check if queue empty only if necessary.
            if (rs >= SHUTDOWN && (rs >= STOP || workQueue.isEmpty())) {
                decrementWorkerCount();
                return null;
            }

            boolean timed;      // Are workers subject to culling?

            for (;;) {
                int wc = workerCountOf(c);
                timed = allowCoreThreadTimeOut || wc > corePoolSize;

                if (wc <= maximumPoolSize && ! (timedOut && timed))
                    break;
                //工作线程数量减1;runTask循环结束,执行processWorkerExit(w, completedAbruptly),移除工作线程
                if (compareAndDecrementWorkerCount(c))
                    return null;
                c = ctl.get();  // Re-read ctl
                if (runStateOf(c) != rs)
                    continue retry;
                // else CAS failed due to workerCount change; retry inner loop
            }

            try {
                //timed=true,阻塞等待一段时间,若取到task==null,则移除该worker;timed=false:一直阻塞等待
                Runnable r = timed ?
                    workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
                    workQueue.take();
                if (r != null)
                    return r;
                timedOut = true;
            } catch (InterruptedException retry) {
                timedOut = false;
            }
        }
    }

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