1、遇到的场景

• 提高一下插入表的性能优化，两张表，先插旧的表，紧接着插新的表，若是一万多条数据就有点慢了

2、使用步骤

• 用Spring提供的对 ThreadPoolExecutor 封装的线程池 ThreadPoolTaskExecutor ，直接使用注解启用

配置

@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 取值配置是在 application.properties 中的

# 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-

Demo测试

• Service接口

public interface AsyncService {

    /**
     * 执行异步任务
     * 可以根据需求，自己加参数拟定
     */
    void executeAsync();
}

• Service实现类

@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");
    }
}

• 在Controller层注入刚刚的Service即可

@Autowired
private AsyncService asyncService;

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

• 使用测试工具测试即可看到相应的打印结果

3、摸索一下

-** 弄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？**

• 创建一个 ThreadPoolTaskExecutor 的子类，在每次提交线程的时候都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;

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);
    }
}

• 进过测试发现： 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

• 现在修改 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");
        // 在这里进行修改
        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;
    }

• 经最后测试得到的结果：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