ThreadPoolExecutor机制探索－我们到底能走多远系列(41)
我们到底能走多远系列(41)
扯淡：
这一年过的不匆忙，也颇多感受，成长的路上难免弯路，这个世界上没人关心你有没有变强，只有自己时刻提醒自己，不要忘记最初出发的原因。
其实这个世界上比我们聪明的人无数，很多人都比我们努力，当我门奇怪为什么他们可以如此轻松的时候，是不会问他们付出过什么。怨天尤人是无用的，使自己变好，哪怕是变好一点点，我觉得生活着就是有意义的。
未来，太远。唯有不停的积累，不要着急，抓得住的才能叫机会。
羊年，一定要不做被动的人。大家加油！
目录留白：
主题：
直接进ThreadPoolExecutor源码看一看：（版本是1.7.0）
首先，这个线程池的状态是怎么样的呢？
我们看下面的字段定义，ctl作为ThreadPoolExecutor的核心状态控制字段，包含来两个信息：
1，工作线程总数 workerCount
2，线程池状态 RUNNING SHUTDOWN STOP TIDYING TERMINATED
下面代码解释一下：
COUNT_BITS 是32减去3 就是29，下面的线程池状态就是－1 到 3 分别向左移动29位。
如此，int的右侧29位，代表着线程数量，总数可以达到2的29次，29位后的3位代表线程池的状态
这样，线程池增加一个线程，只需吧ctl加1即可，而我们也发现实际这个线程池的最高线程数量是2的29次减1。并不是先前我们现象的2的32次减1。这个作者在注释中也提到了，说如果后续需要增大这个值， 可以吧ctl定义成AtomicLong。
这个关键的控制字段的理解，对阅读源码很有帮助。
private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0)); private static final int COUNT_BITS = Integer.SIZE - 3; private static final int CAPACITY = (1 << COUNT_BITS) - 1; // runState is stored in the high-order bits private static final int RUNNING = -1 << COUNT_BITS;// 111 00000000000000000000000000000 private static final int SHUTDOWN = 0 << COUNT_BITS;// 000 00000000000000000000000000000 private static final int STOP = 1 << COUNT_BITS;// 001 00000000000000000000000000000 private static final int TIDYING = 2 << COUNT_BITS;// 010 00000000000000000000000000000 private static final int TERMINATED = 3 << COUNT_BITS;// 100 00000000000000000000000000000 // Packing and unpacking ctl private static int runStateOf(int c) { return c & ~CAPACITY; }//最高3位 private static int workerCountOf(int c) { return c & CAPACITY; }//后29位 private static int ctlOf(int rs, int wc) { return rs | wc; }
代码里我们可能会这样使用ThreadPoolExecutor的方法：
Future<?> future = this.threadPoolExecutor.submit(runnable);
那么就从submit方法入手，这个submit的代码在 AbstractExecutorService，因为 ThreadPoolExecutor继承了它。
public Future<?> submit(Runnable task) { if (task == null) throw new NullPointerException(); RunnableFuture<Void> ftask = newTaskFor(task, null); execute(ftask); return ftask; }
把task包装成RunnableFuture，然后执行execute，下面是ThreadPoolExecutor的execute方法：
这个方法就是我们把任务提交给线程池去完成，至于线程池按照怎样的一个管理机制来完成这个task我们不关心，task关系的是run方法中的逻辑。
如此，对于开发来说是极其方便的，配置一个线程池，只需一句代码，然后专心完成task的逻辑。
那么，了解这个线程池的机制，我感觉只需要看下这个execute方法大概也明白了。特别是方法中的注释。
1，当一个task被安排进来的时候，再确定不是空值后，直接判断在池中已经有工作的线程是否小于corePoolSize，小于则增加一个线程来负责这个task。
2，如果池中已经工作的线程大于等于corePoolSize，就向队列里存task，而不是继续增加线程。
3，当workQueue.offer失败时，也就是说task不能再向队列里放的时候，而此时工作线程大于等于corePoolSize，那么新进的task，就要新开一个线程来接待了。
根据代码分析诸多判断和逻辑，而对于使用这个线程池的外部来说，机制是这样：
a、如果正在运行的线程数 < corePoolSize，那就马上创建线程并运行这个任务，而不会进行排队。
b、如果正在运行的线程数 >= corePoolSize，那就把这个任务放入队列。
c、如果队列满了，并且正在运行的线程数 < maximumPoolSize，那么还是要创建线程并运行这个任务。
d、如果队列满了，并且正在运行的线程数 >= maximumPoolSize，那么线程池就会调用handler里方法。(采用LinkedBlockingDeque就不会出现队列满情况)
/** * 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(); if (workerCountOf(c) < corePoolSize) { if (addWorker(command, true)) return; c = ctl.get(); } 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); } else if (!addWorker(command, false)) reject(command); }
单从execute方法，大概能了解整个线程池的工作机制。
那么，全局的观看以下，我们一定明白这个ThreadPoolExecutor维护着一个池：
View Code
/** * Set containing all worker threads in pool. Accessed only when * holding mainLock. */ private final HashSet<Worker> workers = new HashSet<Worker>();
猜测execute方法中的addWorker应该是向这个set中add一个worker，而这里面的worker里有一个线程，这个线程执行完成时，就会从这个set中remove掉。
看一下开进程开始工作的addWorker方法：
/* * Methods for creating, running and cleaning up after workers */ /** * Checks if a new worker can be added with respect to current * pool state and the given bound (either core or maximum). If so, * the worker count is adjusted accordingly, and, if possible, a * new worker is created and started, running firstTask as its * first task. This method returns false if the pool is stopped or * eligible to shut down. It also returns false if the thread * factory fails to create a thread when asked. If the thread * creation fails, either due to the thread factory returning * null, or due to an exception (typically OutOfMemoryError in * Thread#start), we roll back cleanly. * * @param firstTask the task the new thread should run first (or * null if none). Workers are created with an initial first task * (in method execute()) to bypass queuing when there are fewer * than corePoolSize threads (in which case we always start one), * or when the queue is full (in which case we must bypass queue). * Initially idle threads are usually created via * prestartCoreThread or to replace other dying workers. * * @param core if true use corePoolSize as bound, else * maximumPoolSize. (A boolean indicator is used here rather than a * value to ensure reads of fresh values after checking other pool * state). * @return true if successful */ 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 { 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(); } if (workerAdded) { t.start(); workerStarted = true; } } } finally { if (! workerStarted) addWorkerFailed(w); } return workerStarted; }
方法前面的retry循环，最终break的时候，执行compareAndIncrementWorkerCount(c)，是的，最前面提到的ctl加1啦！这里利用CAS原则，可以参考先前的文章：摸我
/** * Attempt to CAS-increment the workerCount field of ctl. */ private boolean compareAndIncrementWorkerCount(int expect) { return ctl.compareAndSet(expect, expect + 1); }
retry循环break之后，就是做核心的事，new一个worker出来然后add进set，然后启动worker里的thread。
我们注意到做把worker放入set这个操作前，先获取了锁，这个mainLock是类静态成员变量，是一个公用的可重入锁：
/** * 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();
其实调用这个 addWorker方法有4种传参的方式：
1， addWorker(command, true);
2， addWorker(command, false);
3， addWorker(null, false);
4， addWorker(null, true);
在execute方法中就使用了前3种，结合这个核心方法我们先进行一下分析。
第一个：线程数小于corePoolSize时，放一个需要处理的task进worker set。如果worker set长度超过corePoolSize，就返回false。
第二个：当队列被放满时，就尝试将这个新来的task直接放入worker set，而此时worker set 的长度限制是maximumPoolSize。如果线程池也满了的话就返回false。
第三个：放入一个空的task进set，比较的的长度限制是maximumPoolSize。这样一个task为空的worker在线程执行的时候会判断出后去任务队列里拿任务，这样就相当于世创建了一个新的线程，只是没有马上分配任务。
第四个：这个方法就是放一个null的task进set，而且是在小于corePoolSize时。实际使用中是在 prestartCoreThread() 方法。这个方法用来为线程池先启动一个worker等待在那边，如果此时set中的数量已经达到corePoolSize那就返回false，什么也不干。还有是 prestartAllCoreThreads() 方法，准备corePoolSize个worker：
/** * Starts all core threads, causing them to idly wait for work. This * overrides the default policy of starting core threads only when * new tasks are executed. * * @return the number of threads started */ public int prestartAllCoreThreads() { int n = 0; while (addWorker(null, true)) ++n; return n; }
在addWorker中 t.start() 使线程就绪，thread是怎么来的，就看下Worker的代码
Worker类的源码：
/** * Class Worker mainly maintains interrupt control state for * threads running tasks, along with other minor bookkeeping. * This class opportunistically extends AbstractQueuedSynchronizer * to simplify acquiring and releasing a lock surrounding each * task execution. This protects against interrupts that are * intended to wake up a worker thread waiting for a task from * instead interrupting a task being run. We implement a simple * non-reentrant mutual exclusion lock rather than use * ReentrantLock because we do not want worker tasks to be able to * reacquire the lock when they invoke pool control methods like * setCorePoolSize. Additionally, to suppress interrupts until * the thread actually starts running tasks, we initialize lock * state to a negative value, and clear it upon start (in * runWorker). */ private final class Worker extends AbstractQueuedSynchronizer implements Runnable { /** * This class will never be serialized, but we provide a * serialVersionUID to suppress a javac warning. */ private static final long serialVersionUID = 6138294804551838833L; /** Thread this worker is running in. Null if factory fails. */ final Thread thread; /** Initial task to run. Possibly null. */ Runnable firstTask; /** Per-thread task counter */ volatile long completedTasks; /** * 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 this.firstTask = firstTask; this.thread = getThreadFactory().newThread(this); } /** Delegates main run loop to outer runWorker */ public void run() { runWorker(this); } // Lock methods // // The value 0 represents the unlocked state. // The value 1 represents the locked state. protected boolean isHeldExclusively() { return getState() != 0; } protected boolean tryAcquire(int unused) { if (compareAndSetState(0, 1)) { setExclusiveOwnerThread(Thread.currentThread()); return true; } return false; } protected boolean tryRelease(int unused) { setExclusiveOwnerThread(null); setState(0); return true; } public void lock() { acquire(1); } public boolean tryLock() { return tryAcquire(1); } public void unlock() { release(1); } public boolean isLocked() { return isHeldExclusively(); } void interruptIfStarted() { Thread t; if (getState() >= 0 && (t = thread) != null && !t.isInterrupted()) { try { t.interrupt(); } catch (SecurityException ignore) { } } } }
线程启动后就会调用run方法，也就是调用runWorker(Worker w)，核心代码了，英文注释十分详细。
在执行task之前会先执行beforeExecute，task结束后执行afterExecute，pool的扩展性利用：摸我
/** * 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); } }
while循环条件：先取worker自己的task，如果没有，就是上面提到addWorker时task放null的那种，就调用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;//如果线程池允许线程 timeout或者当前线程数大于核心线程数，则会进行timeout的处理 if (wc <= maximumPoolSize && ! (timedOut && timed))//如果线程小于最大值，也不需要timeout判断的，就直接退出 break; if (compareAndDecrementWorkerCount(c))//削减线程 return null; c = ctl.get(); // Re-read ctl if (runStateOf(c) != rs)//状态再判断是否变化，发生变化需要重新再来 continue retry; // else CAS failed due to workerCount change; retry inner loop } try { //keepAliveTime来控制获取queue中元素时的等待时间 Runnable r = timed ? workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) : workQueue.take(); if (r != null) return r; timedOut = true; } catch (InterruptedException retry) { timedOut = false; } } }
至此基本了解了ThreadPoolExecutor源码。在使用是也会更明了一些。
让我们继续前行
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努力不一定成功，但不努力肯定不会成功。
