告警性能优化过程中,遇到如下问题:1、 在数据库计算几十万个实体的KPI值的方差;2、 计算结果进行表格化处理。
这里KPI包含多个Counter的数据库函数运算(比如Decode,AVG等函数),方差也是数据库函数运算,性能比较差。
步骤1中每个实体独立计算方差,步骤2需要方差结果协同处理,所以很自然的联想到步骤1分实体多线程处理,步骤2等待步骤1所有线程完成后才开始处理。这里我们使用CountDownLatch进行线程等待,示例代码如下:
package com.coshaho.threadpool;
import java.util.concurrent.CountDownLatch;
/**
* CountDownLatch学习
* @author coshaho
*/
public class MyCountDownLatch
{
public static void main(String[] args) throws InterruptedException
{
// 定义线程等待变量CountDownLatch,此处定义等待3个线程执行完成
CountDownLatch latch = new CountDownLatch(3);
// 定义3个线程,并传入线程等待变量
new Thread(new MyCountDownLatch().new MyWork("Thread1",latch)).start();
new Thread(new MyCountDownLatch().new MyWork("Thread2",latch)).start();
new Thread(new MyCountDownLatch().new MyWork("Thread3",latch)).start();
// 等待3个线程执行完成
latch.await();
System.out.println("All works are done.");
}
/**
* 线程任务
* @author coshaho
*/
private class MyWork implements Runnable
{
private String workName;
private CountDownLatch latch;
public MyWork(String workName, CountDownLatch latch)
{
this.workName = workName;
this.latch = latch;
}
@Override
public void run()
{
try
{
System.out.println("Thread " + workName + " is running.");
Thread.sleep(1000);
System.out.println("Thread " + workName + " is stop.");
}
catch (InterruptedException e)
{
e.printStackTrace();
}
finally
{
// 线程执行完成,线程等待变量减少
latch.countDown();
}
}
}
}
运行结果如下:
Thread Thread1 is running.
Thread Thread3 is running.
Thread Thread2 is running.
Thread Thread1 is stop.
Thread Thread3 is stop.
Thread Thread2 is stop.
All works are done.