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前言

当我们需要批量执行一些比较耗时任务时，使用并发的方式减少业务处理的整体时间，防止客户端响应时间过长。

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一、创建线程池

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;import lombok.extern.slf4j.Slf4j;import java.util.concurrent.ThreadPoolExecutor;/*** @ClassName : ThreadPoolConfig* @Description : ThreadPoolConfig* @Author : zhuguangkui* @Date: 2022-08-03*/
@Configuration
@Slf4j
public class ThreadPoolConfig {@AutowiredThreadPoolProperties threadPoolProperties;/*** 获得Java虚拟机可用的处理器个数 + 1*/private static final int THREADS = Runtime.getRuntime().availableProcessors() + 1;/***   默认情况下，在创建了线程池后，线程池中的线程数为0，当有任务来之后，就会创建一个线程去执行任务，*  当线程池中的线程数目达到corePoolSize后，就会把到达的任务放到缓存队列当中；*  当队列满了，就继续创建线程，当线程数量大于等于maxPoolSize后，开始使用拒绝策略拒绝*/@Bean(name = "varHandleThreadPool")public ThreadPoolTaskExecutor varHandleThreadPool(){int corePoolSizeConfig = threadPoolProperties.getCorePoolSizeConfig();//核心线程数int corePoolSize = corePoolSizeConfig ==0 ? THREADS : corePoolSizeConfig;ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();executor.setMaxPoolSize(2 * corePoolSize);executor.setCorePoolSize(corePoolSize);executor.setQueueCapacity(threadPoolProperties.getQueueCapacity());executor.setKeepAliveSeconds(threadPoolProperties.getKeepAliveSeconds());executor.setThreadNamePrefix(threadPoolProperties.getThreadNamePrefix());// 线程池对拒绝任务(无线程可用)的处理策略// CallerRunsPolicy：由调用线程（提交任务的线程）处理该任务executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());// 初始化executor.initialize();log.info("doc 线程池初始化配置：{}，THREADS：{}", threadPoolProperties, THREADS);return executor;}
}

二、Future 类并发实现

/*** 批量并发处理业务*/
@Override
public void generateBatchFile(List<String> fileNameList) {List<Future<IdocDoc>> futureList = new ArrayList<>(); // 并发处理结果集// 批量处理业务for (String fileName : fileNameList) {Future<IdocDoc> future = generateFile(fileName);futureList.add(future);}// 依次获取异步结果while (true) {for (Future<IdocDoc> future : futureList) {if (future.isDone() && !future.isCancelled()) { // 判断任务执行是否完成IdocDoc idocDoc = future.get(); // 获取异步结果idocDocList.add(idocDoc);futureList.remove(future);}}if (CollectionUtil.isEmpty()) {break;}Thread.sleep(1); // 每次轮询休息1毫秒，避免CPU占用}
}/*** 子业务*/
@Async("varHandleThreadPool")
public Future<IdocDoc> generateFile(String fileName) {IdocDoc idoDoc = new IdoDoc();idocDoc.setName(fileName);... // 业务操作// 返回异步结果return new AsyncResult<>(idocDoc);
}

三、CompletableFuture 类并发实现

/*** 批量并发处理业务*/
@Override
public void generateBatchFile(List<String> fileNameList) {List<CompletableFuture<IdocDoc>> futureList = new ArrayList<>(); // 并发处理结果集// 批量处理业务for (String fileName : fileNameList) {CompletableFuture<IdocDoc> future = CompletableFuture.supplyAsync(() -> {return generateFile(fileName);}, threadPoolTaskExecutor);futureList.add(future);}// 依次获取异步结果while (true) {for (CompletableFuture<IdocDoc> future : futureList) {if (future.isDone() && !future.isCancelled()) { // 判断任务执行是否完成IdocDoc idocDoc = future.get(); // 获取异步结果idocDocList.add(idocDoc);futureList.remove(future);}}if (CollectionUtil.isEmpty()) {break;}Thread.sleep(1); // 每次轮询休息1毫秒，避免CPU占用}
}/*** 子业务*/
public IdocDoc generateFile(String fileName) {IdocDoc idoDoc = new IdoDoc();idocDoc.setName(fileName);... // 业务操作// 返回异步结果return idocDoc;
}