Batch Processing with Multithreading in Java: Splitting Large Collections and Using Thread Pools

When developers need to perform batch operations on massive data sets, efficiency becomes a critical concern; using multithreading to process the data in parallel is a straightforward yet powerful solution.

The overall workflow involves three main steps: (1) split the large collection into N smaller sub‑collections, (2) configure and start a thread pool to process each sub‑collection, and (3) control the execution order of the threads.

Below is a reusable utility class SplitListUtils that leverages Guava and Apache Commons to divide a list into sub‑lists of a specified size.

import com.google.common.collect.Lists;
import org.apache.commons.collections.CollectionUtils;
import java.util.List;
/**
* 拆分结合工具类
*
* @author shiwen
* @date 2020/12/27
*/
public class SplitListUtils {
/**
* 拆分集合
*
* @param
泛型对象
* @param resList 需要拆分的集合
* @param subListLength 每个子集合的元素个数
* @return 返回拆分后的各个集合组成的列表
* @throws 代码里面用到了guava和common的结合工具类
*/
public static
List
> split(List
resList, int subListLength) {
if (CollectionUtils.isEmpty(resList) || subListLength <= 0) {
return Lists.newArrayList();
}
List
> ret = Lists.newArrayList();
int size = resList.size();
if (size <= subListLength) {
// 数据量不足 subListLength 指定的大小
ret.add(resList);
} else {
int pre = size / subListLength;
int last = size % subListLength;
// 前面 pre 个集合，每个大小都是 subListLength 个元素
for (int i = 0; i < pre; i++) {
List
itemList = Lists.newArrayList();
for (int j = 0; j < subListLength; j++) {
itemList.add(resList.get(i * subListLength + j));
}
ret.add(itemList);
}
// last 的进行处理
if (last > 0) {
List
itemList = Lists.newArrayList();
for (int i = 0; i < last; i++) {
itemList.add(resList.get(pre * subListLength + i));
}
ret.add(itemList);
}
}
return ret;
}
// 运行代码示例
public static void main(String[] args) {
List
list = Lists.newArrayList();
int size = 1099;
for (int i = 0; i < size; i++) {
list.add("hello-" + i);
}
List
> temps = split(list, 100);
int j = 0;
for (List
obj : temps) {
System.out.println(String.format("row:%s -> size:%s,data:%s", ++j, obj.size(), obj));
}
}
}

To execute the batch updates asynchronously, a ThreadPoolExecutor is created with tuned parameters, the large collection is split, and each sub‑list is submitted to the pool; a CountDownLatch ensures the main thread waits for all tasks to finish before performing the final batch insert.

public void threadMethod() {
List
updateList = new ArrayList();
// 初始化线程池, 参数一定要调好！！！！
ThreadPoolExecutor threadPool = new ThreadPoolExecutor(20, 50,
4, TimeUnit.SECONDS, new ArrayBlockingQueue(10), new ThreadPoolExecutor.AbortPolicy());
// 大集合拆分成 N 个小集合, 这里集合的 size 可以稍微小一些（这里我用 100 刚刚好）
List
splitNList = SplitListUtils.split(totalList, 100);
// 记录单个任务的执行次数
CountDownLatch countDownLatch = new CountDownLatch(splitNList.size());
// 对拆分的集合进行批量处理, 先拆分的集合, 再多线程执行
for (List
singleList : splitNList) {
// 线程池执行
threadPool.execute(new Thread(new Runnable(){
@Override
public void run() {
for (Entity yangshiwen : singleList) {
// 将每一个对象进行数据封装, 并添加到一个用于存储更新数据的 list
// ......
}
}
}));
// 任务个数 - 1, 直至为 0 时唤醒 await()
countDownLatch.countDown();
}
try {
// 让当前线程处于阻塞状态，直到锁存器计数为零
countDownLatch.await();
} catch (InterruptedException e) {
throw new BusinessLogException(ResponseEnum.FAIL);
}
// 通过 MyBatis 的批量插入方式来进行数据的插入, 这一步还是要做判空
if (GeneralUtil.listNotNull(updateList)) {
batchUpdateEntity(updateList);
LogUtil.info("xxxxxxxxxxxxxxx");
}
}

Finally, the article reminds readers that multithreading in Java is challenging but enjoyable, encouraging them to practice and share the knowledge.