ThreadPoolExecutor线程池是如何实现的?
本文基于 JDK1.8 编写。
1. 背景
当只有一个任务时,使用单个线程执行可以很好地满足需求;但由于单个线程是串行执行任务的,随着任务的增多,处理速度会很慢,如下代码:
List<Task> tasks = IntStream.range(1, 100)
.mapToObj(Task::new)
.collect(Collectors.toList());
tasks.forEach(Task::run);
为了提高多个任务的处理速度,可以使用多个线程执行任务,如下代码:
tasks.stream().map(Thread::new).forEach(Thread::start);
上面的代码为每个要执行的任务都创建了一个线程,但实际却不能这样做,有以下几个方面的原因:
- 线程的创建和销毁需要消耗资源,并不是零成本的。
- CPU的处理速度是有限的,创建过多的线程并不能提高速度。
- 受限于操作系统和JVM栈的限制,并不能创建无限多的线程。
可以使用线程池解决以上问题,线程池采用池化的思想,将线程视为可以共享的资源以实现线程的复用,如下代码:
ExecutorService executorService = Executors.newFixedThreadPool(10);
tasks.forEach(executorService::execute);
在JDK中线程池的实现是ThreadPoolExecutor
,下面让我们看下ThreadPoolExecutor
是如何设计的?
2. ThreadPoolExecutor是如何设计的?
2.1. 线程池有哪些核心参数?
ThreadPoolExecutor
线程池有以下几个核心参数:
corePoolSize
:核心线程数。maximumPoolSize
:最大线程数。keepAliveTime
和unit
:非核心线程的空闲存活时间及单位。workQueue
:阻塞队列。threadFactory
:线程工厂。handler
:拒绝策略。
以上参数决定了线程池的行为,在线程池处理任务时会使用到。
2.2. 线程池是如何处理任务的?
当一个任务被提交到ThreadPoolExecutor
线程池时,会首先判断线程池中的线程数是否达到核心线程数corePoolSize
:
如果没有达到,则使用线程工厂
threadFactory
创建新的线程执行该任务并返回。如果达到,则将该任务加入到阻塞队列
workQueue
中,等待执行。
如果阻塞队列已经满了,则判断线程池中的线程数是否达到最大线程数maximumPoolSize
:
- 如果没有达到,则使用线程工厂
threadFactory
创建新的线程执行该任务并返回。 - 如果达到,则执行拒绝策略
handler
。
另外,当线程的空闲时间超过keepAliveTime
且线程池中的线程数大于核心线程数时,会销毁线程。
类比发廊中的理发师和顾客:
起初每来一个顾客时,都会分配一个理发师;当没有多余的理发师时,顾客只能在排队等待理发师空闲;当排队等待的顾客太多时,会招来了几个理发师,来缓解发廊的压力。
当所有的理发师都在忙碌且排队等待的顾客还是太多时,只能拒绝新的顾客。当顾客没有这么多且某个理发师很久都没有工作时,理发师就可以被解雇了(理论上)。现实中为什么没有这样的发廊?原因是招聘解雇理发师的成本要大于顾客带来的消费。
2.3. 线程池有哪些拒绝策略?
ThreadPoolExecutor
线程池提供了四种拒绝策略RejectedExecutionHandler
:
CallerRunsPolicy
:调用运行策略,会在调用execute
方法的线程中执行被拒绝的任务。AbortPolicy
:终止策略,会抛出拒绝执行异常RejectedExecutionException
,也是默认的拒绝策略。DiscardPolicy
:放弃策略,空操作。DiscardOldestPolicy
:放弃最旧策略,会使阻塞队列中最旧的任务出队,并重新提交当前任务。
除了上面的拒绝策略,还可以自定义拒绝策略,如下代码是日志拒绝策略装饰器:
public class LoggerDecoratorPolicy implements RejectedExecutionHandler {
private final RejectedExecutionHandler rejectionHandler;
private LoggerDecoratorPolicy(RejectedExecutionHandler rejectionHandler) {
this.rejectionHandler = rejectionHandler;
}
public static RejectedExecutionHandler decorate(RejectedExecutionHandler rejectionHandler) {
return new LoggerDecoratorPolicy(rejectionHandler);
}
@Override
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
System.out.println("[" + executor + "] Rejected task " + r);
rejectionHandler.rejectedExecution(r, executor);
}
}
2.4. JDK中有哪些阻塞队列?
JDK的并发包中提供了以下几种阻塞队列:
ArrayBlockingQueue
:基于数组的有界阻塞队列。SynchronousQueue
:可以看作大小为0的阻塞队列。LinkedBlockingQueue
:基于链表的有界阻塞队列。PriorityBlockingQueue
:基于优先级的无界阻塞队列。
3. 如何使用线程池?
3.1. 常用的线程池有哪些?
在JDK的线程池工厂Executors
中提供了以下四种常用的线程池:
newSingleThreadExecutor
:单线程的线程池。newCachedThreadPool
:缓存的线程池。newFixedThreadPool
:固定大小的线程池。newScheduledThreadPool
:定时和周期性执行的线程池。
在线程池的核心参数设置上,这些线程池有以下不同:
newSingleThreadExecutor | newCachedThreadPool | newFixedThreadPool | newScheduledThreadPool | |
---|---|---|---|---|
corePoolSize | 1 | 0 | nThreads | corePoolSize |
maximumPoolSize | 1 | Integer.MAX_VALUE | nThreads | Integer.MAX_VALUE |
keepAliveTime | 0毫秒 | 60秒 | 0毫秒 | 0纳秒 |
workQueue | LinkedBlockingQueue | SynchronousQueue | LinkedBlockingQueue | DelayedWorkQueue |
3.2. 如何自定义线程池?
如果线程池工厂Executors
提供的线程池不能满足需求,可以考虑通过自定义线程池的方式,即根据需求设置线程池的核心参数。如下示例:
ThreadPoolExecutor threadPoolExecutor = new ThreadPoolExecutor(
8,
8,
0L, TimeUnit.SECONDS,
new LinkedBlockingQueue<>(1000),
Executors.defaultThreadFactory(),
LoggerDecoratorPolicy.decorate(new ThreadPoolExecutor.AbortPolicy()));
3.3. 如何设置合适的线程数?
- 对于CPU密集型任务,线程数建议设置为
CPU核心数 + 1
[1]。 - 对于IO密集型任务,线程数建议设置为
CPU核心数 * (1 + IO耗时 / CPU耗时)
。
3.4. 如何提交和终止任务?
先线程池中提交任务有以下两种方式:
Executor#execute
:提交Runnable
类型的任务,并且没有返回值。AbstractExecutorService#submit(Callable<T>)
:提交Callable
类型的任务,并且返回Future
。
终止线程池中的任务有以下两种方式:
ThreadPoolExecutor#shutdown
:关闭线程池,不接收新的任务但会将已提交的任务处理完。ThreadPoolExecutor#shutdownNow
:立即关闭线程池,中断执行中的任务,返回等待的任务列表。
4. ThreadPoolExecutor源码浅析
介于笔者水平有限,这里仅展示关键节点,不逐行解析。
4.1. 构造器
public ThreadPoolExecutor(int corePoolSize, // 核心线程数
int maximumPoolSize, // 最大线程数
long keepAliveTime, // 空闲存活时间
TimeUnit unit, // 空闲存活时间的单位
BlockingQueue<Runnable> workQueue, // 阻塞队列
ThreadFactory threadFactory, // 线程工厂
RejectedExecutionHandler handler) // 拒绝策略
4.2. 提交任务
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();
// 判断线程数是否小于核心线程数
if (workerCountOf(c) < corePoolSize) {
// 如果小于则创建新的线程
if (addWorker(command, true))
return;
c = ctl.get();
}
// 如果线程数大于等于核心线程数,则将任务加入阻塞队列
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);
}
// 加入阻塞队列失败,则尝试创建新的线程
else if (!addWorker(command, false))
// 创建新的线程失败,则执行拒绝策略
reject(command);
}
4.3. 创建worker
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);
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 {
// 创建新的 Worker
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;
}
4.4. Worker运行逻辑
final void runWorker(Worker w) {
Thread wt = Thread.currentThread();
// 找到 Worker 关联的任务
Runnable task = w.firstTask;
w.firstTask = null;
w.unlock(); // allow interrupts
boolean completedAbruptly = true;
try {
// 如果有任务则执行,没有任务则等待从阻塞队列中获取
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 {
// 执行前置操作
beforeExecute(wt, task);
Throwable thrown = null;
try {
// 运行任务
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 {
// 执行后置操作
afterExecute(task, thrown);
}
} finally {
task = null;
w.completedTasks++;
w.unlock();
}
}
completedAbruptly = false;
} finally {
processWorkerExit(w, completedAbruptly);
}
}
4.5. 任务的获取
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;
}
}
}