我們在使用ExecutorService的時候知道，在ExecutorService中有個一個Queue來保存提交的任務，通過不同的構造函數，我們可以創建無界的隊列（ExecutorService.newCachedThreadPool）和有界的隊列(ExecutorService newFixedThreadPool(int nThreads))。

無界隊列很好理解，我們可以無限制的向ExecutorService提交任務。那么對于有界隊列來說，如果隊列滿了該怎么處理呢？

今天我們要介紹一下java中ExecutorService的飽和策略(reject policy)。

以ExecutorService的具體實現ThreadPoolExecutor來說，它定義了4種飽和策略。分別是AbortPolicy，DiscardPolicy，DiscardOldestPolicy和CallerRunsPolicy。

如果要在ThreadPoolExecutor中設定飽和策略可以調用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() );

上面的例子中我們定義了一個初始5個，最大10個工作線程的Thread Pool，并且定義其中的Queue的容量是20。如果提交的任務超出了容量，則會使用AbortPolicy策略。

AbortPolicy

AbortPolicy意思是如果隊列滿了，最新的提交任務將會被拒絕，并拋出RejectedExecutionException異常：

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()); } }

上面的代碼中，rejectedExecution方法中我們直接拋出了RejectedExecutionException異常。

DiscardPolicy

DiscardPolicy將會悄悄的丟棄提交的任務，而不報任何異常。

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將會丟棄最老的任務，保存最新插入的任務。

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); } } }

我們看到在rejectedExecution方法中，poll了最老的一個任務，然后使用ThreadPoolExecutor提交了一個最新的任務。

CallerRunsPolicy

CallerRunsPolicy和其他的幾個策略不同，它既不會拋棄任務，也不會拋出異常，而是將任務回退給調用者，使用調用者的線程來執行任務，從而降低調用者的調用速度。我們看下是怎么實現的：

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，直接調用了 r.run()方法，這會導致該方法直接在調用者的主線程中執行，而不是在線程池中執行。從而導致主線程在該任務執行結束之前不能提交任何任務。從而有效的阻止了任務的提交。

使用Semaphore

如果我們并沒有定義飽和策略，那么有沒有什么方法來控制任務的提交速度呢？考慮下之前我們講到的Semaphore，我們可以指定一定的資源信號量來控制任務的提交，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(); } }}

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

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支持好吧啦網。