我们在使用ExecutorService的时候知道，在ExecutorService中有个一个Queue来保存提交的任务，通过不同的构造函数，我们可以创建无界的队列（ExecutorService.newCachedThreadPool）和有界的队列(ExecutorService newFixedThreadPool(int nThreads))。

无界队列很好理解，我们可以无限制的向ExecutorService提交任务。那么对于有界队列来说，如果队列满了该怎么处理呢？

今天我们要介绍一下java中ExecutorService的饱和策略(reject policy)。

以ExecutorService的具体实现ThreadPoolExecutor来说，它定义了4种饱和策略。分别是AbortPolicy，DiscardPolicy，DiscardOldestPolicy和CallerRunsPolicy。

如果要在ThreadPoolExecutor中设定饱和策略可以调用setRejectedExecutionHandler方法，如下所示：

ThreadPoolExecutor threadPoolExecutor= new ThreadPoolExecutor(5, 10, 10, TimeUnit.SECONDS, new LinkedBlockingDeque<Runnable>(20)); threadPoolExecutor.setRejectedExecutionHandler( new ThreadPoolExecutor.AbortPolicy() );

上面的例子中我们定义了一个初始5个，最大10个工作线程的Thread Pool，并且定义其中的Queue的容量是20。如果提交的任务超出了容量，则会使用AbortPolicy策略。

AbortPolicy

AbortPolicy意思是如果队列满了，最新的提交任务将会被拒绝，并抛出RejectedExecutionException异常：

public static class AbortPolicy implements RejectedExecutionHandler { /** * Creates an {@code AbortPolicy}. */ public AbortPolicy() { } /** * Always throws RejectedExecutionException. * * @param r the runnable task requested to be executed * @param e the executor attempting to execute this task * @throws RejectedExecutionException always */ public void rejectedExecution(Runnable r, ThreadPoolExecutor e) { throw new RejectedExecutionException("Task " + r.toString() + " rejected from " + e.toString()); } }

上面的代码中，rejectedExecution方法中我们直接抛出了RejectedExecutionException异常。

DiscardPolicy

DiscardPolicy将会悄悄的丢弃提交的任务，而不报任何异常。

public static class DiscardPolicy implements RejectedExecutionHandler { /** * Creates a {@code DiscardPolicy}. */ public DiscardPolicy() { } /** * Does nothing, which has the effect of discarding task r. * * @param r the runnable task requested to be executed * @param e the executor attempting to execute this task */ public void rejectedExecution(Runnable r, ThreadPoolExecutor e) { } }

DiscardOldestPolicy

DiscardOldestPolicy将会丢弃最老的任务，保存最新插入的任务。

public static class DiscardOldestPolicy implements RejectedExecutionHandler { /** * Creates a {@code DiscardOldestPolicy} for the given executor. */ public DiscardOldestPolicy() { } /** * Obtains and ignores the next task that the executor * would otherwise execute, if one is immediately available, * and then retries execution of task r, unless the executor * is shut down, in which case task r is instead discarded. * * @param r the runnable task requested to be executed * @param e the executor attempting to execute this task */ public void rejectedExecution(Runnable r, ThreadPoolExecutor e) { if (!e.isShutdown()) { e.getQueue().poll(); e.execute(r); } } }

我们看到在rejectedExecution方法中，poll了最老的一个任务，然后使用ThreadPoolExecutor提交了一个最新的任务。

CallerRunsPolicy

CallerRunsPolicy和其他的几个策略不同，它既不会抛弃任务，也不会抛出异常，而是将任务回退给调用者，使用调用者的线程来执行任务，从而降低调用者的调用速度。我们看下是怎么实现的：

public static class CallerRunsPolicy implements RejectedExecutionHandler { /** * Creates a {@code CallerRunsPolicy}. */ public CallerRunsPolicy() { } /** * Executes task r in the caller's thread, unless the executor * has been shut down, in which case the task is discarded. * * @param r the runnable task requested to be executed * @param e the executor attempting to execute this task */ public void rejectedExecution(Runnable r, ThreadPoolExecutor e) { if (!e.isShutdown()) { r.run(); } } }

在rejectedExecution方法中，直接调用了 r.run()方法，这会导致该方法直接在调用者的主线程中执行，而不是在线程池中执行。从而导致主线程在该任务执行结束之前不能提交任何任务。从而有效的阻止了任务的提交。

使用Semaphore

如果我们并没有定义饱和策略，那么有没有什么方法来控制任务的提交速度呢？考虑下之前我们讲到的Semaphore，我们可以指定一定的资源信号量来控制任务的提交，如下所示：

public class SemaphoreUsage { private final Executor executor; private final Semaphore semaphore; public SemaphoreUsage(Executor executor, int count) { this.executor = executor; this.semaphore = new Semaphore(count); } public void submitTask(final Runnable command) throws InterruptedException { semaphore.acquire(); try { executor.execute(() -> { try { command.run(); } finally { semaphore.release(); } } ); } catch (RejectedExecutionException e) { semaphore.release(); } }}

本文的例子可参考https://github.com/ddean2009/learn-java-concurrency/tree/master/rejectPolicy

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。