Java 多线程批量处理数据

1 需求
在项目开发中需要处理100万多的数据，这些数据需要从mysql数据库中读取出来，再通过调用其他平台的接口推送数据。由于时间紧迫，数据需要在短时间内完成推送，采用单线程推送很慢，所以采用多线程推送来提高效率。

2 配置多线程
2.1 application.yml

thread-pool:
  core-pool-size: 4
  max-pool-size: 16
  queue-capacity: 80
  keep-alive-seconds: 120
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2.2 创建ThreadPoolProperties

import lombok.Data;
import org.springframework.stereotype.Component;
import org.springframework.boot.context.properties.ConfigurationProperties;

@Data
@Component
@ConfigurationProperties(prefix = "thread-pool")
public class ThreadPoolProperties {
    /**
     * 线程池创建时候初始化的线程数
     */
    private int corePoolSize;

    /**
     * 线程池最大的线程数，只有在缓冲队列满了之后才会申请超过核心线程数的线程
     */
    private int maxPoolSize;

    /**
     * 用来缓冲执行任务的队列
     */
    private int queueCapacity;

    /**
     * 允许线程的空闲时间：当超过了核心线程出之外的线程在空闲时间到达之后会被销毁
     */
    private int keepAliveSeconds;
}
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2.3 创建ThreadPoolConfig

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.scheduling.annotation.EnableAsync;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;

@EnableAsync
@Configuration
public class ThreadPoolConfig {
    private final ThreadPoolProperties threadPoolProperties;

    @Autowired
    public ThreadPoolConfig(ThreadPoolProperties threadPoolProperties) {
        this.threadPoolProperties = threadPoolProperties;
    }

    @Bean(name = "threadPoolTaskExecutor")
    public ThreadPoolTaskExecutor threadPoolTaskExecutor() {
        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
        executor.setCorePoolSize(threadPoolProperties.getCorePoolSize());
        executor.setMaxPoolSize(threadPoolProperties.getMaxPoolSize());
        executor.setQueueCapacity(threadPoolProperties.getQueueCapacity());
        executor.setKeepAliveSeconds(threadPoolProperties.getKeepAliveSeconds());
        executor.setThreadNamePrefix("thread-pool-");
        return executor;
    }
}
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3 多线程批量数据处理

public RequestResult multiThreadPush() {
    List<HistoryStudent> historyStudentList = historyStudentMapper.getList(0, 65867);
    // 分割集合
    List<List<HistoryStudent>> partitionData = partitionData(historyStudentList, 4);

    ThreadPoolTaskExecutor executor = SpringUtil.getBean("threadPoolTaskExecutor", ThreadPoolTaskExecutor.class);
  	// 计数器
    CountDownLatch latch = new CountDownLatch(partitionData.size());

    for (List<HistoryStudent> historyStudents : partitionData) {
        executor.execute(() -> {
            try {
                for (HistoryStudent historyStudent : historyStudents) {
                	// 单个数据处理
                    //processSingleData(historyStudent);
                }
            } catch (Exception e) {
                e.printStackTrace();
            } finally {
                latch.countDown();
            }
        });
    }

    try {
        latch.await();
    } catch (InterruptedException e) {
        e.printStackTrace();
    }

    return RequestResult.success();
}
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private List<List<HistoryStudent>> partitionData(List<HistoryStudent> dataList, int partitionSize) {
    List<List<HistoryStudent>> partitions = new ArrayList<>();

    int size = dataList.size();
    int batchSize = size / partitionSize;

    for (int i = 0; i < partitionSize; i++) {
        int fromIndex = i * batchSize;
        int toIndex = (i == partitionSize - 1) ? size : fromIndex + batchSize;

        partitions.add(dataList.subList(fromIndex, toIndex));
    }

    return partitions;
}
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4 参考博客
Java多线程批量处理、线程池的使用
Java多线程处理大批量数据
java多线程批量处理数据