Spring Boot线程池的使用心得

前言

前两天做项目的时候，想提高一下插入表的性能优化，因为是两张表，先插旧的表，紧接着插新的表，一万多条数据就有点慢了

后面就想到了线程池ThreadPoolExecutor，而用的是Spring Boot项目，可以用Spring提供的对ThreadPoolExecutor封装的线程池ThreadPoolTaskExecutor，直接使用注解启用

使用步骤

先创建一个线程池的配置，让Spring Boot加载，用来定义如何创建一个ThreadPoolTaskExecutor，要使用@Configuration和@EnableAsync这两个注解，表示这是个配置类，并且是线程池的配置类

@Configuration@EnableAsyncpublic class ExecutorConfig {    private static final Logger logger = LoggerFactory.getLogger(ExecutorConfig.class);    @Value("${async.executor.thread.core_pool_size}")    private int corePoolSize;    @Value("${async.executor.thread.max_pool_size}")    private int maxPoolSize;    @Value("${async.executor.thread.queue_capacity}")    private int queueCapacity;    @Value("${async.executor.thread.name.prefix}")    private String namePrefix;    @Bean(name = "asyncServiceExecutor")    public Executor asyncServiceExecutor() {        logger.info("start asyncServiceExecutor");        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();        //配置核心线程数        executor.setCorePoolSize(corePoolSize);        //配置最大线程数        executor.setMaxPoolSize(maxPoolSize);        //配置队列大小        executor.setQueueCapacity(queueCapacity);        //配置线程池中的线程的名称前缀        executor.setThreadNamePrefix(namePrefix);        // rejection-policy：当pool已经达到max size的时候，如何处理新任务        // CALLER_RUNS：不在新线程中执行任务，而是有调用者所在的线程来执行        executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());        //执行初始化        executor.initialize();        return executor;    }}

@Value是我配置在application.properties，可以参考配置，自由定义

# 异步线程配置# 配置核心线程数async.executor.thread.core_pool_size = 5# 配置最大线程数async.executor.thread.max_pool_size = 5# 配置队列大小async.executor.thread.queue_capacity = 99999# 配置线程池中的线程的名称前缀async.executor.thread.name.prefix = async-service-

创建一个Service接口，是异步线程的接口

public interface AsyncService {    /**     * 执行异步任务     * 可以根据需求，自己加参数拟定，我这里就做个测试演示     */    void executeAsync();}

实现类

@Servicepublic class AsyncServiceImpl implements AsyncService {    private static final Logger logger = LoggerFactory.getLogger(AsyncServiceImpl.class);    @Override    @Async("asyncServiceExecutor")    public void executeAsync() {        logger.info("start executeAsync");        System.out.println("异步线程要做的事情");        System.out.println("可以在这里执行批量插入等耗时的事情");        logger.info("end executeAsync");    }}

将Service层的服务异步化，在executeAsync()方法上增加注解@Async("asyncServiceExecutor")，asyncServiceExecutor方法是前面ExecutorConfig.java中的方法名，表明executeAsync方法进入的线程池是asyncServiceExecutor方法创建的

接下来就是在Controller里或者是哪里通过注解@Autowired注入这个Service

@Autowiredprivate AsyncService asyncService;@GetMapping("/async")public void async(){    asyncService.executeAsync();}

用postmain或者其他工具来多次测试请求一下

 2018-07-16 22:15:47.655  INFO 10516 --- [async-service-5] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:15:47.655  INFO 10516 --- [async-service-5] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync2018-07-16 22:15:47.770  INFO 10516 --- [async-service-1] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:15:47.770  INFO 10516 --- [async-service-1] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync2018-07-16 22:15:47.816  INFO 10516 --- [async-service-2] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:15:47.816  INFO 10516 --- [async-service-2] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync2018-07-16 22:15:48.833  INFO 10516 --- [async-service-3] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:15:48.834  INFO 10516 --- [async-service-3] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync2018-07-16 22:15:48.986  INFO 10516 --- [async-service-4] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:15:48.987  INFO 10516 --- [async-service-4] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync

通过以上日志可以发现，[async-service-]是有多个线程的，显然已经在我们配置的线程池中执行了，并且每次请求中，controller的起始和结束日志都是连续打印的，表明每次请求都快速响应了，而耗时的操作都留给线程池中的线程去异步执行；

虽然我们已经用上了线程池，但是还不清楚线程池当时的情况，有多少线程在执行，多少在队列中等待呢？这里我创建了一个ThreadPoolTaskExecutor的子类，在每次提交线程的时候都会将当前线程池的运行状况打印出来

import org.slf4j.Logger;import org.slf4j.LoggerFactory;import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;import org.springframework.util.concurrent.ListenableFuture;import java.util.concurrent.Callable;import java.util.concurrent.Future;import java.util.concurrent.ThreadPoolExecutor;/** * @Author: ChenBin * @Date: 2018/7/16/0016 22:19 */public class VisiableThreadPoolTaskExecutor extends ThreadPoolTaskExecutor {    private static final Logger logger = LoggerFactory.getLogger(VisiableThreadPoolTaskExecutor.class);    private void showThreadPoolInfo(String prefix) {        ThreadPoolExecutor threadPoolExecutor = getThreadPoolExecutor();        if (null == threadPoolExecutor) {            return;        }        logger.info("{}, {},taskCount [{}], completedTaskCount [{}], activeCount [{}], queueSize [{}]",                this.getThreadNamePrefix(),                prefix,                threadPoolExecutor.getTaskCount(),                threadPoolExecutor.getCompletedTaskCount(),                threadPoolExecutor.getActiveCount(),                threadPoolExecutor.getQueue().size());    }    @Override    public void execute(Runnable task) {        showThreadPoolInfo("1. do execute");        super.execute(task);    }    @Override    public void execute(Runnable task, long startTimeout) {        showThreadPoolInfo("2. do execute");        super.execute(task, startTimeout);    }    @Override    public Future<?> submit(Runnable task) {        showThreadPoolInfo("1. do submit");        return super.submit(task);    }    @Override    public <T> Future<T> submit(Callable<T> task) {        showThreadPoolInfo("2. do submit");        return super.submit(task);    }    @Override    public ListenableFuture<?> submitListenable(Runnable task) {        showThreadPoolInfo("1. do submitListenable");        return super.submitListenable(task);    }    @Override    public <T> ListenableFuture<T> submitListenable(Callable<T> task) {        showThreadPoolInfo("2. do submitListenable");        return super.submitListenable(task);    }}

如上所示，showThreadPoolInfo方法中将任务总数、已完成数、活跃线程数，队列大小都打印出来了，然后Override了父类的execute、submit等方法，在里面调用showThreadPoolInfo方法，这样每次有任务被提交到线程池的时候，都会将当前线程池的基本情况打印到日志中；

修改ExecutorConfig.java的asyncServiceExecutor方法，将ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor()改为ThreadPoolTaskExecutor executor = new VisiableThreadPoolTaskExecutor()

@Bean(name = "asyncServiceExecutor")    public Executor asyncServiceExecutor() {        logger.info("start asyncServiceExecutor");        //在这里修改        ThreadPoolTaskExecutor executor = new VisiableThreadPoolTaskExecutor();        //配置核心线程数        executor.setCorePoolSize(corePoolSize);        //配置最大线程数        executor.setMaxPoolSize(maxPoolSize);        //配置队列大小        executor.setQueueCapacity(queueCapacity);        //配置线程池中的线程的名称前缀        executor.setThreadNamePrefix(namePrefix);        // rejection-policy：当pool已经达到max size的时候，如何处理新任务        // CALLER_RUNS：不在新线程中执行任务，而是有调用者所在的线程来执行        executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());        //执行初始化        executor.initialize();        return executor;    }

再次启动该工程测试

2018-07-16 22:23:30.951  INFO 14088 --- [nio-8087-exec-2] u.d.e.e.i.VisiableThreadPoolTaskExecutor : async-service-, 2. do submit,taskCount [0], completedTaskCount [0], activeCount [0], queueSize [0]2018-07-16 22:23:30.952  INFO 14088 --- [async-service-1] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:23:30.953  INFO 14088 --- [async-service-1] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync2018-07-16 22:23:31.351  INFO 14088 --- [nio-8087-exec-3] u.d.e.e.i.VisiableThreadPoolTaskExecutor : async-service-, 2. do submit,taskCount [1], completedTaskCount [1], activeCount [0], queueSize [0]2018-07-16 22:23:31.353  INFO 14088 --- [async-service-2] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:23:31.353  INFO 14088 --- [async-service-2] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync2018-07-16 22:23:31.927  INFO 14088 --- [nio-8087-exec-5] u.d.e.e.i.VisiableThreadPoolTaskExecutor : async-service-, 2. do submit,taskCount [2], completedTaskCount [2], activeCount [0], queueSize [0]2018-07-16 22:23:31.929  INFO 14088 --- [async-service-3] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:23:31.930  INFO 14088 --- [async-service-3] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync2018-07-16 22:23:32.496  INFO 14088 --- [nio-8087-exec-7] u.d.e.e.i.VisiableThreadPoolTaskExecutor : async-service-, 2. do submit,taskCount [3], completedTaskCount [3], activeCount [0], queueSize [0]2018-07-16 22:23:32.498  INFO 14088 --- [async-service-4] c.u.d.e.executor.impl.AsyncServiceImpl   : start executeAsync异步线程要做的事情可以在这里执行批量插入等耗时的事情2018-07-16 22:23:32.499  INFO 14088 --- [async-service-4] c.u.d.e.executor.impl.AsyncServiceImpl   : end executeAsync

注意这一行日志：

2018-07-16 22:23:32.496  INFO 14088 --- [nio-8087-exec-7] u.d.e.e.i.VisiableThreadPoolTaskExecutor : async-service-, 2. do submit,taskCount [3], completedTaskCount [3], activeCount [0], queueSize [0]

这说明提交任务到线程池的时候，调用的是submit(Callable task)这个方法，当前已经提交了3个任务，完成了3个，当前有0个线程在处理任务，还剩0个任务在队列中等待，线程池的基本情况一路了然；