java中有界队列的饱和策略(reject policy)
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。
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