JAVA中提供的线程池
Executors工厂类
Executors工具类提供了5种线程池的创建方法
// 线程数动态创建，每个空闲线程会在默认60秒后被回收
ExecutorService newCachedThreadPool = Executors.newCachedThreadPool();
// 固定线程数的线程池
ExecutorService newFixedThreadPool = Executors.newFixedThreadPool(10);
// 定时器线程
ScheduledExecutorService newScheduledThreadPool = Executors.newScheduledThreadPool(10);
// 单线程，只有一个核心线程的线程池
ExecutorService newSingleThreadExecutor = Executors.newSingleThreadExecutor();
// fork/join线程池
ExecutorService newWorkStealingPool = Executors.newWorkStealingPool();
每种线程池都有不同的作用，这里不一一展开，只从他们的原理说明。
我们看下这五种线程池的构造方法
public static ExecutorService newCachedThreadPool() {
return new ThreadPoolExecutor(0, Integer.MAX_VALUE,
60L, TimeUnit.SECONDS,
new SynchronousQueue<Runnable>());
}
public static ExecutorService newFixedThreadPool(int nThreads) {
return new ThreadPoolExecutor(nThreads, nThreads,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>());
}
public static ScheduledExecutorService newScheduledThreadPool(int corePoolSize) {
return new ScheduledThreadPoolExecutor(corePoolSize);
}
public static ExecutorService newSingleThreadExecutor() {
return new FinalizableDelegatedExecutorService
(new ThreadPoolExecutor(1, 1,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>()));
}
public static ExecutorService newWorkStealingPool() {
return new ForkJoinPool
(Runtime.getRuntime().availableProcessors(),
ForkJoinPool.defaultForkJoinWorkerThreadFactory,
null, true);
}
除了newScheduledThreadPool和newWorkStealingPool,其他都是通过创建
ThreadPoolExecutor实现线程池.下面对ThreadPoolExecutor构造函数的参数进行了解。
public ThreadPoolExecutor(int corePoolSize, //核心线程数
int maximumPoolSize, // 最大线程数
long keepAliveTime, //空闲超时时间，worker线程无任务执行，等待时间
TimeUnit unit, //超时时间单位
BlockingQueue<Runnable> workQueue, //等待队列
ThreadFactory threadFactory, //线程工厂
RejectedExecutionHandler handler //拒绝策略
)
通过对ThreadPoolExecutor参数的不同设置，Executors创建了不同功能的线程池。
ThreadPoolExecutor的实现原理
ThreadPoolExecutor中包含属性Ctl使用高三位保存线程池的运行状态，低29位保存工作线程数，使用位运算进行操作。
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
再看一下execute方法
public void execute(Runnable command) {
if (command == null)
throw new NullPointerException();
int c = ctl.get();
// 当前worker数量小于核心线程数 则添加worker
if (workerCountOf(c) < corePoolSize) {
if (addWorker(command, true))
return;
c = ctl.get();
}
// 如果超过核心线程数则尝试加入等待队列
if (isRunning(c) && workQueue.offer(command)) {
int recheck = ctl.get();
// 如果这时有线程池shutdown了，把刚刚添加的移除
if (! isRunning(recheck) && remove(command))
//执行拒绝策略
reject(command);
// 添加空worker
else if (workerCountOf(recheck) == 0)
//注意此时core参数为false，用于判断最大线程数
addWorker(null, false);
}
// worker数量达到最大线程数，添加失败，拒绝
else if (!addWorker(command, false))
reject(command);
}
再来看addWorker方法
private boolean addWorker(Runnable firstTask, boolean core) {
//第一部分开始 主要作用判断添加的worker是否超出数量
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);
//工作线程数大于线程池容量，或则大于需要判断的线程数大小，则添加失败，进入等待队列
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
}
}
// 第一部分结束
// 第二部分 添加worker信息，将线程信息封装worker对象，添加到workers（HashSet集合）
boolean workerStarted = false;
boolean workerAdded = false;
Worker w = null;
try {
// 初始化了一个线程，firstTask为execute方法传入的线程实现。
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) {
// 启动worker初始化是创建的线程
t.start();
workerStarted = true;
}
}
} finally {
if (! workerStarted)
addWorkerFailed(w);
}
// 第二部分结束
return workerStarted;
}
再来看下worker结构
private final class Worker extends AbstractQueuedSynchronizer implements Runnable {
final Thread thread;
Runnable firstTask;
volatile long completedTasks;
}
Worker(Runnable firstTask) {
setState(-1); // inhibit interrupts until runWorker
this.firstTask = firstTask;
//通过传入当前worker对象创建线程
this.thread = getThreadFactory().newThread(this);
}
public void run() {
runWorker(this);
}
worker继承了AQS,通过实现lock和unlock方法实现任务的执行，防止执行过程中被中断。并且包含一个Thread.
当新建一个worker的时候创建了一个线程，而addWorker完成后启动了这个线程,而这个线程传入了worker，worker实现了runnable接口的run方法。真正启动的是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 {
// 当前worker是不断循环，线程复用的地方是getTask，getTask从等待队列中获取任务，如果拿到任务了就继续运行。如果为空则销毁非核心线程worker
while (task != null || (task = getTask()) != null) {
// 这里的作用是当线程池shutdown时，不中断已经运行的线程
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 {
//通过worker线程执行线程的run方法到达，线程池线程复用的作用
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 {
// 执行完成移除worker，核心线程会被复用。核心线程如果需要回收需要设置allowCoreThreadTimeOut
processWorkerExit(w, completedAbruptly);
}
}
线程池的核心是复用线程，更好的管理线程的创建。阿里P3C规范中不建议使用Executors创建线程池，原因是为了让使用者更好的控制ThreadPoolExecutor参数，实现自己想要的结果。
另外ThreadPoolExecutor提供submit方法支持传入Callable接口，并且带返回值。其实现原理是FutureTask维护任务的执行状态，通过Future.get()方法中判断任务未完成，则调用LockSupport.park挂起线程，当任务执行完成再unpark返回结果。
线程池大小的设置
- 硬件和软件上的限制。
- CPU核心数的考虑
- 程序运行任务的类别，当时IO密集型的任务，则可以设置更多的线程数，例如设置CPU核心数*2。而如果是CPU密集型的任务，则设置更多线程数反而影响性能，例如可以设置CPU核心数+1。
