使用Guava的RateLimiter完成简单的大流量限流详解编程语言

1、随机丢弃一定规则的用户（迅速过滤掉90%的用户）；

2、MQ削峰（比如设一个MQ可以容纳的最大消息量，达到这个量后MQ给予reject）；

3、业务逻辑层使用RateLimiter进行限流；

4、最终可以承受的流量到达DB层。

package ratelimiter; 

import com.google.common.util.concurrent.RateLimiter; 

import org.springframework.stereotype.Component; 

import java.util.concurrent.ExecutorService; 

import java.util.concurrent.Executors; 

import java.util.concurrent.TimeUnit; 

 * @author xfyou 

 * @date 2018/10/31 

@Component 

public class AccessLimitService { 

 private static volatile int acquireCount = 0; 

 private static final Object OBJECT = new Object(); 

 /** 

 * 每秒可以获得50个许可证 

 private RateLimiter rateLimiter = RateLimiter.create(50); 

 private boolean tryAcquire() { 

 // 等待1秒钟如果未能获取到许可证就返回false，否则返回true 

 return rateLimiter.tryAcquire(1, 1000, TimeUnit.MILLISECONDS); 

 public static void main(String[] args) { 

 AccessLimitService accessLimitService = new AccessLimitService(); 

 ExecutorService executorService = Executors.newCachedThreadPool(); 

 for (int i = 0; i 1000; i++) { 

 executorService.execute(new Runnable() { 

 @Override 

 public void run() { 

 if (accessLimitService.tryAcquire()) { 

 System.out.println("获取许可证，执行业务逻辑。"); 

 synchronized (OBJECT) { 

 System.out.println(++acquireCount); 

 try { 

 Thread.sleep(20); 

 } catch (InterruptedException ex) { 

 } else { 

 System.err.println("未获取到许可证，请求可以丢弃。"); 

 }); 

 try { 

 Thread.sleep(1); 

 } catch (InterruptedException e) { 

 e.printStackTrace(); 

 executorService.shutdown(); 

}

原创文章，作者：Maggie-Hunter，如若转载，请注明出处：https://blog.ytso.com/15893.html