相信大多数人都了解过Java中线程池相关的架构，了解了这些内容其实我们就可以使用java的线程池为我们工作了，使用其提供的线程池我们可以很方便的写出高质量的多线程代码，本文将分析ThreadPoolExecutor的实现，来探索线程池的运行原理。下面是几个比较关键的类成员：
// 任务队列，我们的任务会添加到该队列里面，线程将从该队列获取任务来执行
private final BlockingQueue<Runnable> workQueue;
//任务的执行值集合，来消费workQueue里面的任务
private final HashSet<Worker> workers = new HashSet<Worker>();
//线程工厂
private volatile ThreadFactory threadFactory;
//拒绝策略，默认会抛出异异常，还要其他几种拒绝策略如下：
private volatile RejectedExecutionHandler handler;
1、CallerRunsPolicy：在调用者线程里面运行该任务
2、DiscardPolicy：丢弃任务
3、DiscardOldestPolicy：丢弃workQueue的头部任务
//最下保活work数量
private volatile int corePoolSize;
//work上限
private volatile int maximumPoolSize;
AI 代码解读
我们尝试执行submit方法，下面是执行的关键路径，总结起来就是：如果Worker数量还没达到上限则继续创建，否则提交任务到workQueue，然后让worker来调度运行任务。
step 1: <ExecutorService>
Future<?> submit(Runnable task);
step 2:<AbstractExecutorService>
public Future<?> submit(Runnable task) {
if (task == null) throw new NullPointerException();
RunnableFuture<Void> ftask = newTaskFor(task, null);
execute(ftask);
return ftask;
}
step 3:<Executor>
void execute(Runnable command);
step 4:<ThreadPoolExecutor>
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();
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
if (isRunning(c) && workQueue.offer(command)) {
//提交我们的额任务到workQueue
int recheck = ctl.get();
if (! isRunning(recheck) && remove(command))
reject(command);
else if (workerCountOf(recheck) == 0)
addWorker(null, false);
}
else if (!addWorker(command, false)) //使用maximumPoolSize作为边界
reject(command); //还不行？拒绝提交的任务
}
step 5:<ThreadPoolExecutor>
private boolean addWorker(Runnable firstTask, boolean core)
step 6:<ThreadPoolExecutor>
w = new Worker(firstTask); //包装任务
final Thread t = w.thread; //获取线程（包含任务）
workers.add(w); // 任务被放到works中
t.start(); //执行任务
AI 代码解读
上面的流程是高度概括的，实际情况远比这复杂得多，但是我们关心的是怎么打通整个流程，所以这样分析问题是没有太大的问题的。观察上面的流程，我们发现其实关键的地方在于Worker，如果弄明白它是如何工作的，那么我们也就大概明白了线程池是怎么工作的了。下面分析一下Worker类。
上面的图片展示了Worker的类关系图，关键在于他实现了Runnable接口，所以问题的关键就在于run方法上。在这之前，我们来看一下Worker类里面的关键成员：
final Thread thread;
Runnable firstTask; //我们提交的任务，可能被立刻执行，也可能被放到队列里面
AI 代码解读
thread是Worker的工作线程，上面的分析我们也发现了在addWorker中会获取worker里面的thread然后start，也就是这个线程的执行，而Worker实现了Runnable接口，所以在构造thread的时候Worker将自己传递给了构造函数，thread.start执行的其实就是Worker的run方法。下面是run方法的内容：
public void run() {
runWorker(this);
}
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);
}
}
AI 代码解读
我们来分析一下runWorker这个方法，这就是整个线程池的核心。首先获取到了我们刚提交的任务firstTask，然后会循环从workQueue里面获取任务来执行，获取任务的方法如下：
private Runnable getTask() {
boolean timedOut = false; // Did the last poll() time out?
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;
}
int wc = workerCountOf(c);
// Are workers subject to culling?
boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;
if ((wc > maximumPoolSize || (timed && timedOut))
&& (wc > 1 || workQueue.isEmpty())) {
if (compareAndDecrementWorkerCount(c))
return null;
continue;
}
try {
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
if (r != null)
return r;
timedOut = true;
} catch (InterruptedException retry) {
timedOut = false;
}
}
}
AI 代码解读
其实核心也就一句：
Runnable r = timed ?
workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
workQueue.take();
AI 代码解读
我们再回头看一下execute，其实我们上面只走了一条逻辑，在execute的时候，我们的worker的数量还没有到达我们设定的corePoolSize的时候，会走上面我们分析的逻辑，而如果达到了我们设定的阈值之后，execute中会尝试去提交任务，如果提交成功了就结束，否则会拒绝任务的提交。我们上面还提到一个成员：maximumPoolSize，其实线程池的最大的Worker数量应该是maximumPoolSize，但是我们上面的分析是corePoolSize，这是因为我们的private boolean addWorker(Runnable firstTask, boolean core)的参数core的值来控制的，core为true则使用corePoolSize来设定边界，否则使用maximumPoolSize来设定边界。
直观的解释一下，当线程池里面的Worker数量还没有到corePoolSize，那么新添加的任务会伴随着产生一个新的worker，如果Worker的数量达到了corePoolSize，那么就将任务存放在阻塞队列中等待Worker来获取执行，如果没有办法再向阻塞队列放任务了，那么这个时候maximumPoolSize就变得有用了，新的任务将会伴随着产生一个新的Worker，如果线程池里面的Worker已经达到了maximumPoolSize，那么接下来提交的任务只能被拒绝策略拒绝了。可以参考下面的描述来理解：
|---corePoolSize[创建]---||---workQueue[等待keepAliveTime]---||---maximumPoolSize[创建]---||---拒绝策略---|
* When a new task is submitted in method {
@link #execute(Runnable)},
* and fewer than corePoolSize threads are running, a new thread is
* created to handle the request, even if other worker threads are
* idle. If there are more than corePoolSize but less than
* maximumPoolSize threads running, a new thread will be created only
* if the queue is full. By setting corePoolSize and maximumPoolSize
* the same, you create a fixed-size thread pool. By setting
* maximumPoolSize to an essentially unbounded value such as {
@code
* Integer.MAX_VALUE}, you allow the pool to accommodate an arbitrary
* number of concurrent tasks. Most typically, core and maximum pool
* sizes are set only upon construction, but they may also be changed
* dynamically using {
@link #setCorePoolSize} and {
@link
* #setMaximumPoolSize}.
在方法{
@link #execute(Runnable)}中提交新任务时，
如果运行的线程小于corePoolSize，则创建新线程处理请求，即使其他工作线程闲置。
如果运行的线程大于corePoolSize，但是小于maximumPoolSize,当线程运行时，如果队列已满则会创建一个新线程
同样通过设置corePoolSize和maximumPoolSize，创建一个固定大小的线程池。通过设置maximumPoolSize到一个
本质上无界的值，比如{
@code Integer.MAX_VALUE}，您允许池容纳任意的并发任务的数量。
最典型的是核心池和最大池尺寸只在构造时设置，但也可以更改动态使用{
@link #setCorePoolSize}和{
@link #setMaximumPoolSize}。
AI 代码解读
在此需要说明一点，有一个重要的成员：keepAliveTime，当线程池里面的线程数量超过corePoolSize了，那么超出的线程将会在空闲keepAliveTime之后被terminated。
* If the pool currently has more than corePoolSize threads,
* excess threads will be terminated if they have been idle for more
* than the keepAliveTime (see {
@link #getKeepAliveTime(TimeUnit)}).
AI 代码解读
