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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.
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原文地址：http://www.cnblogs.com/coshaho/p/5427289.html
