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; //天 TimeUnit.HOURS; //小时 TimeUnit.MINUTES; //分钟 TimeUnit.SECONDS; //秒 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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