优化这个方法,使其提高加载速度, public LinkedHashMap<String, Object> getDistinctData(senseDataDto senseDataDto) { LinkedHashMap<String, Object> distinctData = new LinkedHashMap<>(); distinctData.put("pin35", senseDataMapper.getDistinctPin35(senseDataDto)); distinctData.put("pin36", senseDataMapper.getDistinctPin36(senseDataDto)); distinctData.put("pin29", senseDataMapper.getDistinctPin29(senseDataDto)); distinctData.put("pin30", senseDataMapper.getDistinctPin30(senseDataDto)); distinctData.put("pin31", senseDataMapper.getDistinctPin31(senseDataDto)); return distinctData; }
时间: 2024-04-15 18:28:15 浏览: 18
要优化这个方法以提高加载速度,你可以考虑使用多线程并行查询数据库。通过同时发起多个数据库查询请求,可以减少查询时间并提高效率。下面是一个示例:
```java
import java.util.LinkedHashMap;
import java.util.concurrent.*;
@Service
public class YourService {
private final YourMapper yourMapper;
public YourService(YourMapper yourMapper) {
this.yourMapper = yourMapper;
}
public LinkedHashMap<String, Object> getDistinctData(senseDataDto senseDataDto) throws ExecutionException, InterruptedException {
LinkedHashMap<String, Object> distinctData = new LinkedHashMap<>();
ExecutorService executorService = Executors.newFixedThreadPool(5); // 创建线程池,设置并发查询数量
CompletionService<Object> completionService = new ExecutorCompletionService<>(executorService);
// 提交并发查询任务
completionService.submit(() -> yourMapper.getDistinctPin35(senseDataDto));
completionService.submit(() -> yourMapper.getDistinctPin36(senseDataDto));
completionService.submit(() -> yourMapper.getDistinctPin29(senseDataDto));
completionService.submit(() -> yourMapper.getDistinctPin30(senseDataDto));
completionService.submit(() -> yourMapper.getDistinctPin31(senseDataDto));
// 获取并发查询结果
for (int i = 0; i < 5; i++) {
Future<Object> future = completionService.take();
String pinName = "pin" + (35 + i);
distinctData.put(pinName, future.get());
}
executorService.shutdown(); // 关闭线程池
return distinctData;
}
}
```
在这个示例中,我们使用了`ExecutorService`和`CompletionService`来实现并发查询。通过`ExecutorService`创建一个线程池,并通过`CompletionService`提交并发查询任务。然后,使用`take()`方法从`CompletionService`获取已完成的任务,并获取查询结果。最后,将查询结果存入`distinctData`中并返回。
请注意,在这个示例中,我们使用了固定大小的线程池(5个线程),你可以根据实际情况进行调整。
通过使用多线程并行查询,可以提高加载速度并减少查询时间。希望对你有帮助!