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;
}
}
}
部分内容来源网络，仅供参考