Java并发（三）线程池

目录

　　一、线程池实现原理

　　二、线程池的使用

　　　　1. 创建线程池

　　　　2. 向线程池提交任务

　　　　3. 关闭线程池

　　　　4. 合理配置线程池

　　　　5. 线程池的监控

Java中的线程池是运用场景最多的并发框架，几乎所有需要异步或并发执行任务的程序都可以使用线程池。在开发过程中，合理地使用线程池能够带来3个好处。

1. 降低资源消耗。通过重复利用已创建的线程降低线程创建和销毁造成的消耗；

2. 提高响应速度。当任务到达时，任务可以不需要等到线程创建就能立即执行；

3. 提高线程的可管理性。线程是稀缺资源，如果无限制地创建，不仅会消耗系统资源，还会降低系统的稳定性，使用线程池可以进行统一分配、调优和监控。但是，要做到合理利用线程池，必须对其实现原理了如指掌。

一、线程池实现原理

当向线程池提交一个任务之后，线程池是如何处理这个任务的呢？本节来看一下线程池的主要处理流程，处理流程图如下图所示：

从图中可以看出，当提交一个新任务到线程池时，线程池的处理流程如下。

1. 线程池判断核心线程池里的线程是否都在执行任务。如果不是，则创建一个新的工作线程来执行任务。如果核心线程池里的线程都在执行任务，则进入下个流程。

2. 线程池判断工作队列是否已经满。如果工作队列没有满，则将新提交的任务存储在这个工作队列里。如果工作队列满了，则进入下个流程。

3. 线程池判断线程池的线程是否都处于工作状态。如果没有，则创建一个新的工作线程来执行任务。如果已经满了，则交给饱和策略来处理这个任务。

ThreadPoolExecutor执行execute()方法的示意图，如下图所示

ThreadPoolExecutor执行execute方法分下面4种情况。

1）如果当前运行的线程少于corePoolSize，则创建新线程来执行任务（注意，执行这一步骤需要获取全局锁）。

2）如果运行的线程等于或多于corePoolSize，则将任务加入BlockingQueue。

3）如果无法将任务加入BlockingQueue（队列已满），则创建新的线程来处理任务（注意，执行这一步骤需要获取全局锁）。

4）如果创建新线程将使当前运行的线程超出maximumPoolSize，任务将被拒绝，并调用RejectedExecutionHandler.rejectedExecution()方法。

ThreadPoolExecutor采取上述步骤的总体设计思路，是为了在执行execute()方法时，尽可能地避免获取全局锁（那将会是一个严重的可伸缩瓶颈）。在ThreadPoolExecutor完成预热之后（当前运行的线程数大于等于corePoolSize），几乎所有的execute()方法调用都是执行步骤2，而步骤2不需要获取全局锁。

源码分析：上面的流程分析让我们很直观地了解了线程池的工作原理，让我们再通过源代码来看看是如何实现的，线程池执行任务的方法如下。

变量

public class ThreadPoolExecutor extends AbstractExecutorService {
    /**
     * The main pool control state, ctl, is an atomic integer packing
     * two conceptual fields
     *   workerCount, indicating the effective number of threads
     *   runState,    indicating whether running, shutting down etc
     *
     * In order to pack them into one int, we limit workerCount to
     * (2^29)-1 (about 500 million) threads rather than (2^31)-1 (2
     * billion) otherwise representable. If this is ever an issue in
     * the future, the variable can be changed to be an AtomicLong,
     * and the shift/mask constants below adjusted. But until the need
     * arises, this code is a bit faster and simpler using an int.
     *
     * The workerCount is the number of workers that have been
     * permitted to start and not permitted to stop.  The value may be
     * transiently different from the actual number of live threads,
     * for example when a ThreadFactory fails to create a thread when
     * asked, and when exiting threads are still performing
     * bookkeeping before terminating. The user-visible pool size is
     * reported as the current size of the workers set.
     *
     * The runState provides the main lifecycle control, taking on values:
     *
     *   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
     *
     * The numerical order among these values matters, to allow
     * ordered comparisons. The runState monotonically increases over
     * time, but need not hit each state. The transitions are:
     *
     * 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
     *
     * Threads waiting in awaitTermination() will return when the
     * state reaches TERMINATED.
     *
     * Detecting the transition from SHUTDOWN to TIDYING is less
     * straightforward than you'd like because the queue may become
     * empty after non-empty and vice versa during SHUTDOWN state, but
     * we can only terminate if, after seeing that it is empty, we see
     * that workerCount is 0 (which sometimes entails a recheck -- see
     * below).
     */
    /**
     * ctl 为原子类型的变量, 有两个概念
     * workerCount, 表示有效的线程数
     * runState, 表示线程状态, 是否正在运行, 关闭等
     */
    private final AtomicInteger ctl = new AtomicInteger(ctlOf(RUNNING, 0));
    // 29
    private static final int COUNT_BITS = Integer.SIZE - 3;
    // 容量 2²⁹-1
    private static final int CAPACITY   = (1 << COUNT_BITS) - 1;

    // runState is stored in the high-order bits
    // 线程池的五种状态
    // 即高3位为111, 接受新任务并处理排队任务
    private static final int RUNNING    = -1 << COUNT_BITS;
    // 即高3位为000, 不接受新任务, 但处理排队任务
    private static final int SHUTDOWN   =  0 << COUNT_BITS;
    // 即高3位为001, 不接受新任务, 不处理排队任务, 并中断正在进行的任务
    private static final int STOP       =  1 << COUNT_BITS;
    // 即高3位为010, 所有任务都已终止, 工作线程为0, 线程转换到状态TIDYING, 将运行terminate()钩子方法
    private static final int TIDYING    =  2 << COUNT_BITS;
    // 即高3位为011, 标识terminate（）已经完成
    private static final int TERMINATED =  3 << COUNT_BITS;

    // Packing and unpacking ctl 用来计算线程的方法
    private static int runStateOf(int c)     { return c & ~CAPACITY; }
    private static int workerCountOf(int c)  { return c & CAPACITY; }
    private static int ctlOf(int rs, int wc) { return rs | wc; }

    ... ...
}

execute方法

/**
 * 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) {
    // 空则抛出异常
    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.
     */
    // 获取当前线程池的状态
    int c = ctl.get();
    // 计算工作线程数 并判断是否小于核心线程数
    if (workerCountOf(c) < corePoolSize) {
        // addWorker提交任务, 提交成功则结束
        if (addWorker(command, true))
            return;
        // 提交失败再次获取当前状态
        c = ctl.get();
    }
    // 判断线程状态, 并插入队列, 失败则移除
    if (isRunning(c) && workQueue.offer(command)) {
        // 再次获取状态
        int recheck = ctl.get();
        // 如果状态不是RUNNING, 并移除失败
        if (! isRunning(recheck) && remove(command))
            // 调用拒绝策略
            reject(command);
            // 如果工作线程为0 则调用 addWorker
        else if (workerCountOf(recheck) == 0)
            addWorker(null, false);
    }
    // 提交任务失败 走拒绝策略
    else if (!addWorker(command, false))
        reject(command);
}

addWorker方法

/**
 * 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
 */
/**
 * 检查任务是否可以提交
 */
private boolean addWorker(Runnable firstTask, boolean core) {
    retry:
    // 外层循环
    for (;;) {
        // 获取当前状态
        int c = ctl.get();
        int rs = runStateOf(c);

        // Check if queue empty only if necessary. 检查线程池是否关闭
        if (rs >= SHUTDOWN &&
            ! (rs == SHUTDOWN &&
               firstTask == null &&
               ! workQueue.isEmpty()))
            return false;
        // 内层循环
        for (;;) {
            int wc = workerCountOf(c);
            // 工作线程大于容量 或者大于 核心或最大线程数
            if (wc >= CAPACITY ||
                wc >= (core ? corePoolSize : maximumPoolSize))
                return false;
            // CAS 线程数增加, 成功则调到外层循环
            if (compareAndIncrementWorkerCount(c))
                break retry;
            // 失败则再次获取线程状态
            c = ctl.get();  // Re-read ctl
            // 不相等则重新走外层循环
            if (runStateOf(c) != rs)
                continue retry;
            // else CAS failed due to workerCount change; retry inner loop
        }
    }

    /**
     * 创建新worker 开始新线程
     */
    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;
            // 加锁
            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)) {
                    // 判断线程是否存活, 已存活抛出非法异常
                    if (t.isAlive()) // precheck that t is startable
                        throw new IllegalThreadStateException();
                    //  设置包含池中的所有工作线程。仅在持有mainLock时访问 workers是 HashSet 集合
                    workers.add(w);
                    int s = workers.size();
                    // 设置池最大大小, 并将 workerAdded设置为 true
                    if (s > largestPoolSize)
                        largestPoolSize = s;
                    workerAdded = true;
                }
            } finally {
                // 解锁
                mainLock.unlock();
            }
            // 添加成功 开始启动线程 并将 workerStarted 设置为 true
            if (workerAdded) {
                t.start();
                workerStarted = true;
            }
        }
    } finally {
        // 启动线程失败
        if (! workerStarted)
            addWorkerFailed(w);
    }
    return workerStarted;
}

工作线程：线程池创建线程时，会将线程封装成工作线程Worker，Worker在执行完任务后，还会循环获取工作队列里的任务来执行。我们可以从Worker类的run()方法里看到这点。

/**
 * 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
{
    ... ...

    /** Delegates main run loop to outer runWorker  */
    public void run() {
        runWorker(this);
    }
    
    ... ...
)

runWorker方法

/**
 * 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 then we
 * 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 {
        // getTask()方法循环获取工作队列的任务
        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);
    }
}

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, and if the queue is
 *    non-empty, this worker is not the last thread in the pool.
 *
 * @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?

    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?
        // 允许核心线程超时 或者当前线程数大于核心线程数
        boolean timed = allowCoreThreadTimeOut || wc > corePoolSize;

        if ((wc > maximumPoolSize || (timed && timedOut))
            && (wc > 1 || workQueue.isEmpty())) {
            if (compareAndDecrementWorkerCount(c))
                return null;
            continue;
        }

        try {
            Runnable r = timed ?
                    // 从工作队列poll任务，不阻塞
                workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :
                    // 阻塞等待任务
                workQueue.take();
            if (r != null)
                return r;
            timedOut = true;
        } catch (InterruptedException retry) {
            timedOut = false;
        }
    }
}

ThreadPoolExecutor中线程执行任务的示意图如下图所示。

线程池中的线程执行任务分两种情况，如下。

1）在execute()方法中创建一个线程时，会让这个线程执行当前任务。

2）这个线程执行完上图中1的任务后，会反复从BlockingQueue获取任务来执行。

二、线程池的使用

1. 创建线程池

我们可以通过ThreadPoolExecutor来创建一个线程池。

new ThreadPoolExecutor(corePoolSize, maximumPoolSize, keepAliveTime,
milliseconds,runnableTaskQueue, handler);

创建一个线程池时需要输入几个参数，如下。

1）corePoolSize（线程池的基本大小）：当提交一个任务到线程池时，线程池会创建一个线程来执行任务，即使其他空闲的基本线程能够执行新任务也会创建线程，等到需要执行的任务数大于线程池基本大小时就不再创建。如果调用了线程池的prestartAllCoreThreads()方法，线程池会提前创建并启动所有基本线程。

2）runnableTaskQueue（任务队列）：用于保存等待执行的任务的阻塞队列。可以选择以下几个阻塞队列。

• ArrayBlockingQueue：是一个基于数组结构的有界阻塞队列，此队列按FIFO（先进先出）原则对元素进行排序。
• LinkedBlockingQueue：一个基于链表结构的阻塞队列，此队列按FIFO排序元素，吞吐量通常要高于ArrayBlockingQueue。静态工厂方法Executors.newFixedThreadPool()使用了这个队列。
• SynchronousQueue：一个不存储元素的阻塞队列。每个插入操作必须等到另一个线程调用移除操作，否则插入操作一直处于阻塞状态，吞吐量通常要高于Linked-BlockingQueue，静态工厂方法Executors.newCachedThreadPool使用了这个队列。
• PriorityBlockingQueue：一个具有优先级的无限阻塞队列。

3）maximumPoolSize（线程池最大数量）：线程池允许创建的最大线程数。如果队列满了，并且已创建的线程数小于最大线程数，则线程池会再创建新的线程执行任务。值得注意的是，如果使用了无界的任务队列这个参数就没什么效果。

4）ThreadFactory：用于设置创建线程的工厂，可以通过线程工厂给每个创建出来的线程设置更有意义的名字。使用开源框架guava提供的ThreadFactoryBuilder可以快速给线程池里的线程设置有意义的名字，代码如下。

new ThreadFactoryBuilder().setNameFormat("XX-task-%d").build();

5）RejectedExecutionHandler（饱和策略）：当队列和线程池都满了，说明线程池处于饱和状态，那么必须采取一种策略处理提交的新任务。这个策略默认情况下是AbortPolicy，表示无法处理新任务时抛出异常。在JDK 1.5中Java线程池框架提供了以下4种策略。

• AbortPolicy：直接抛出异常。
• CallerRunsPolicy：只用调用者所在线程来运行任务。
• DiscardOldestPolicy：丢弃队列里最近的一个任务，并执行当前任务。
• DiscardPolicy：不处理，丢弃掉。

当然，也可以根据应用场景需要来实现RejectedExecutionHandler接口自定义策略。如记录日志或持久化存储不能处理的任务。

• keepAliveTime（线程活动保持时间）：线程池的工作线程空闲后，保持存活的时间。所以，如果任务很多，并且每个任务执行的时间比较短，可以调大时间，提高线程的利用率。
• TimeUnit（线程活动保持时间的单位）：可选的单位有天（DAYS）、小时（HOURS）、分钟（MINUTES）、毫秒（MILLISECONDS）、微秒（MICROSECONDS，千分之一毫秒）和纳秒（NANOSECONDS，千分之一微秒）。

2. 向线程池提交任务

可以使用两个方法向线程池提交任务，分别为execute()和submit()方法。

execute()方法用于提交不需要返回值的任务，所以无法判断任务是否被线程池执行成功。通过以下代码可知execute()方法输入的任务是一个Runnable类的实例。

threadsPool.execute(new Runnable() {
                    @Override
                    public void run() {
                        // TODO Auto-generated method stub
                    }
});

submit()方法用于提交需要返回值的任务。线程池会返回一个future类型的对象，通过这个future对象可以判断任务是否执行成功，并且可以通过future的get()方法来获取返回值，get()方法会阻塞当前线程直到任务完成，而使用get（long timeout，TimeUnit unit）方法则会阻塞当前线程一段时间后立即返回，这时候有可能任务没有执行完。

Future<Object> future = executor.submit(harReturnValuetask);
        try {
            Object s = future.get();
        } catch (InterruptedException e) {
            // 处理中断异常
        } catch (ExecutionException e) {
            // 处理无法执行任务异常
        } finally {
            // 关闭线程池
            executor.shutdown();
        }

3. 关闭线程池

可以通过调用线程池的shutdown或shutdownNow方法来关闭线程池。它们的原理是遍历线程池中的工作线程，然后逐个调用线程的interrupt方法来中断线程，所以无法响应中断的任务可能永远无法终止。但是它们存在一定的区别，shutdownNow首先将线程池的状态设置成STOP，然后尝试停止所有的正在执行或暂停任务的线程，并返回等待执行任务的列表，而shutdown只是将线程池的状态设置成SHUTDOWN状态，然后中断所有没有正在执行任务的线程。

只要调用了这两个关闭方法中的任意一个，isShutdown方法就会返回true。当所有的任务都已关闭后，才表示线程池关闭成功，这时调用isTerminaed方法会返回true。至于应该调用哪一种方法来关闭线程池，应该由提交到线程池的任务特性决定，通常调用shutdown方法来关闭线程池，如果任务不一定要执行完，则可以调用shutdownNow方法。

4. 合理配置线程池

要想合理地配置线程池，就必须首先分析任务特性，可以从以下几个角度来分析。

• 任务的性质：CPU密集型任务、IO密集型任务和混合型任务。
• 任务的优先级：高、中和低。
• 任务的执行时间：长、中和短。
• 任务的依赖性：是否依赖其他系统资源，如数据库连接。

性质不同的任务可以用不同规模的线程池分开处理。CPU密集型任务应配置尽可能小的线程，如配置Ncpu+1个线程的线程池。由于IO密集型任务线程并不是一直在执行任务，则应配置尽可能多的线程，如2*Ncpu。混合型的任务，如果可以拆分，将其拆分成一个CPU密集型任务和一个IO密集型任务，只要这两个任务执行的时间相差不是太大，那么分解后执行的吞吐量将高于串行执行的吞吐量。如果这两个任务执行时间相差太大，则没必要进行分解。可以通过Runtime.getRuntime().availableProcessors()方法获得当前设备的CPU个数。

优先级不同的任务可以使用优先级队列PriorityBlockingQueue来处理。它可以让优先级高的任务先执行。

注意　如果一直有优先级高的任务提交到队列里，那么优先级低的任务可能永远不能执行。

执行时间不同的任务可以交给不同规模的线程池来处理，或者可以使用优先级队列，让执行时间短的任务先执行。

依赖数据库连接池的任务，因为线程提交SQL后需要等待数据库返回结果，等待的时间越长，则CPU空闲时间就越长，那么线程数应该设置得越大，这样才能更好地利用CPU。

建议使用有界队列。有界队列能增加系统的稳定性和预警能力，可以根据需要设大一点儿，比如几千。有一次，我们系统里后台任务线程池的队列和线程池全满了，不断抛出抛弃任务的异常，通过排查发现是数据库出现了问题，导致执行SQL变得非常缓慢，因为后台任务线程池里的任务全是需要向数据库查询和插入数据的，所以导致线程池里的工作线程全部阻塞，任务积压在线程池里。如果当时我们设置成无界队列，那么线程池的队列就会越来越多，有可能会撑满内存，导致整个系统不可用，而不只是后台任务出现问题。当然，我们的系统所有的任务是用单独的服务器部署的，我们使用不同规模的线程池完成不同类型的任务，但是出现这样问题时也会影响到其他任务。

5. 线程池的监控

如果在系统中大量使用线程池，则有必要对线程池进行监控，方便在出现问题时，可以根据线程池的使用状况快速定位问题。可以通过线程池提供的参数进行监控，在监控线程池的时候可以使用以下属性。

• taskCount：线程池需要执行的任务数量。
• completedTaskCount：线程池在运行过程中已完成的任务数量，小于或等于taskCount。
• largestPoolSize：线程池里曾经创建过的最大线程数量。通过这个数据可以知道线程池是否曾经满过。如该数值等于线程池的最大大小，则表示线程池曾经满过。
• getPoolSize：线程池的线程数量。如果线程池不销毁的话，线程池里的线程不会自动销毁，所以这个大小只增不减。
• getActiveCount：获取活动的线程数。

通过扩展线程池进行监控。可以通过继承线程池来自定义线程池，重写线程池的beforeExecute、afterExecute和terminated方法，也可以在任务执行前、执行后和线程池关闭前执行一些代码来进行监控。例如，监控任务的平均执行时间、最大执行时间和最小执行时间等。这几个方法在线程池里是空方法。

protected void beforeExecute(Thread t, Runnable r) { }