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Spring Boot线程池使用的一些实用心得

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

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@Configuration

@EnableAsync

public 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

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# 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

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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

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@Service

public 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

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@Autowired

private 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 executeAsync
2018-07-16 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 executeAsync
2018-07-16 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 executeAsync
2018-07-16 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 executeAsync
2018-07-16 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

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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()

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@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 executeAsync
2018-07-16 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 executeAsync
2018-07-16 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 executeAsync
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]
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;

总结

到此这篇关于Spring Boot线程池使用心得的文章就介绍到这了,更多相关Spring Boot线程池使用内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持!

原文链接:https://blog.csdn.net/m0_37701381/article/details/81072774

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