Java中有界队列的饱和策略(reject policy)原理解析
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*的队列(executorservice.newcachedthreadpool)和有界的队列(executorservice newfixedthreadpool(int nthreads))。
*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。