ThreadPoolExecutor构造方法
工作中很多时候都会用到线程池，但是线程池内部是怎么实现的呢
先看一下ThreadPoolExecutor类的构造方法
public ThreadPoolExecutor(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
BlockingQueue<Runnable> workQueue,
ThreadFactory threadFactory,
RejectedExecutionHandler handler);
复制代码corePoolSize: 核心线程池大小，当线程池中的线程数小于corePoolSize时，每提交一个任务，都会新起一个线程来处理任务。线程会不断的从workQueue中取出任务执行。线程一般情况下即使空闲，也不会回收，除非设置了allowCoreThreadTimeOut参数
workQueue：工作队列，当线程数达到了corePoolSize后，后续提交的任务就会插入到workQueue中
maximumPoolSize：线程池最大线程数， 当workQueue满了之后，线程池就会启动新的线程来处理任务，但是整个线程池的线程数最大不会超过maximumPoolSize
keepAliveTime和unit：非core线程的最大空闲时间和时间单位
threadFactory： 线程工厂，线程池会使用线程工厂来创建线程
handler：饱和策略，当线程池的线程达到maximumPoolSize且workQueue满了后，会使用handler处理新提交的任务
注意：很多人会搞错corePoolSize，maximumPoolSize，workQueue之间的关系，认为是core线程满了之后，会直接创建新的线程处理任务而不用插入到workQueue中。实际上是workQueue满了之后才会创建新的线程，总的线程数量不超过maximumPoolSize
这里是一个线程池使用demo
static void demo()throws Exception{
ExecutorService executorService = new ThreadPoolExecutor(1, 1,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<>());
Future<String> future = executorService.submit(() -> {
try {
Thread.sleep(2000);
} catch (InterruptedException e) {
e.printStackTrace();
}
return "hello world";
});
System.out.println(future.get());;
}
复制代码线程池是如何创建线程的？
AbstractExecutorService类
public <T> Future<T> submit(Callable<T> task) {
if (task == null) throw new NullPointerException();
RunnableFuture<T> ftask = newTaskFor(task);
execute(ftask); //执行任务
return ftask;
}
protected <T> RunnableFuture<T> newTaskFor(Runnable runnable, T value) {
return new FutureTask<T>(runnable, value);
}
复制代码由于submit方法返回的是提供Future，所以提交任务的时候实际上提交的是一个RunnableFuture接口的实现类FutureTask。而execure(ftask)则是任务执行的核心
public void execute(Runnable command) {
if (command == null)
throw new NullPointerException();
int c = ctl.get();
// 当worker数小于corePoolSize时则创建worker
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
// 当worker大于等于corePoolSize且线程池是运行中时，则尝试插入任务到workerQueue中
if (isRunning(c) && workQueue.offer(command)) {
int recheck = ctl.get();
if (! isRunning(recheck) && remove(command))
reject(command);
else if (workerCountOf(recheck) == 0)
addWorker(null, false);
}
// 当线程数大于等于coreSize且workerQueue满了时，则再次尝试增加worker
else if (!addWorker(command, false))
reject(command);
}
复制代码代码中的worker可以理解为线程池中执行任务的线程，可以看到corePoolSize，workQueue间的关系是：
- 当worker数小于corePoolSize时则创建worker
- 当worker大于等于corePoolSize且线程池是运行中时，则尝试插入任务到workerQueue中
- 当线程数大于等于coreSize且workerQueue满了时，则再次尝试增加worker
这里有个比较有意思的设计就是private final AtomicInteger ctl;这个变量。它是一个32位的整数类型,高3位代表了线程池的状态，低29位代表线程池中活跃的线程数。
为什么要把两个变量合并到一个变量中呢？我的理解就是这样设计就可以在同一个cas操作中保证在设置数量的时候，状态是不变的。如果分开成两个变量，除非加更重的锁，否则在增加数量的过程中，状态是有可能改变的。
那么问题来了：maximumPoolSize的作用是怎么体现的呢？ 先看看private boolean addWorker(Runnable firstTask, boolean core)方法
private boolean addWorker(Runnable firstTask, boolean core) {
retry:
for (;;) {
int c = ctl.get();
int rs = runStateOf(c);
// Check if queue empty only if necessary.
if (rs >= SHUTDOWN &&
! (rs == SHUTDOWN &&
firstTask == null &&
! workQueue.isEmpty()))
return false;
for (;;) {
int wc = workerCountOf(c);
//核心worker大于corePoolSize，非核心线程大于maximumPoolSize则增加失败
if (wc >= CAPACITY ||
wc >= (core ? corePoolSize : maximumPoolSize))
return false;
if (compareAndIncrementWorkerCount(c))
break retry;
c = ctl.get(); // Re-read ctl
if (runStateOf(c) != rs)
continue retry;
// else CAS failed due to workerCount change; retry inner loop
}
}
boolean workerStarted = false;
boolean workerAdded = false;
Worker w = null;
try {
w = new Worker(firstTask);
final Thread t = w.thread;
if (t != null) {
final ReentrantLock mainLock = this.mainLock;
mainLock.lock();
try {
// Recheck while holding lock.
// Back out on ThreadFactory failure or if
// shut down before lock acquired.
int rs = runStateOf(ctl.get());
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();
}
if (workerAdded) {
t.start();
workerStarted = true;
}
}
} finally {
if (! workerStarted)
addWorkerFailed(w);
}
return workerStarted;
}
复制代码第17行可以看到在增加worker时，是会校验当前的worker数量的 在方法的第一个嵌套自旋中可以看到，里面有很多的状态判断和worker数量判断，当所有判断成功时会通过compareAndIncrementWorkerCount方法去修改ctl变量的worker数量
在JUC包中，作者大量的使用了自旋和CAS操作来代替锁操作，这种操作属于乐观锁
上面提到了线程池状态，而线程池存在五个状态，且各个状态间能够转化
五个状态：
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
状态间的转化
RUNNING -> SHUTDOWN
On invocation of shutdown(), perhaps implicitly in finalize()
(RUNNING or SHUTDOWN) -> STOP
On invocation of shutdownNow()
SHUTDOWN -> TIDYING
When both queue and pool are empty
STOP -> TIDYING
When pool is empty
TIDYING -> TERMINATED
When the terminated() hook method has completed
复制代码Worker是怎么从Queue中消费任务的？
先看看Worker类的
private final class Worker
extends AbstractQueuedSynchronizer
implements Runnable{
private static final long serialVersionUID = 6138294804551838833L;
final Thread thread;
Runnable firstTask;
volatile long completedTasks;
Worker(Runnable firstTask) {
setState(-1); // inhibit interrupts until runWorker
this.firstTask = firstTask;
this.thread = getThreadFactory().newThread(this);
}
public void run() {
runWorker(this);
}
//
}
复制代码Worker本身就是一个Runnable，它包含了一个Thread字段用于执行认为。线程池中线程的数量其实就是Worker的数量。而Worker中的线程最终执行的就是里面的runWorker方法
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 ((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);
}
}
复制代码可以看到有一个while循环会不断的获取任务执行，当获取到task后，接下来就会执行task.run方法。 那么假如队列为空时，core线程不是会继续保存在线程池中，非core线程会等待一段时间后再销毁吗？这个逻辑是怎么实现的？答案就在getTask()方法中
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;
}
}
}
复制代码可以看到getTask方法会根据线程数是否大于corePoolSize来或者allowCoreThreadTimeOut是否为true来决定从workQueue中获取任务时能否超时返回。 当允许超时返回，则超时后getTask会返回null，且在runWorker中当getTask返回null时则会调用processWorkerExit方法终止当前worker的线程。
当不允许超时返回时，则会一直阻塞在workQueue.take()中
到这里为止就搞懂这3个问题了
- 线程池的构造参数是如何起作用的？
- 线程池是如何创建线程的？
- Worker是怎么从Queue中消费任务的？