【高并发】亿级流量场景下如何实现分布式限流？看完我彻底懂了！！

local key = KEYS[1]  --限流KEY(一秒一个)
local limit = tonumber(ARGV[1]) --限流大小
local current = tonumber(redis.call('get', key) or "0")
if current + 1 > limit then --如果超出限流大小
    return 0
else --请求数+1，并设置2秒过期
    redis.call("INCRBY", key, "1")
    redis.call("expire", key "2")
    return 1
end

//List设置Lua的KEYS[1]
String key = "ip:" + System.currentTimeMillis() / 1000;
List<String> keyList = Lists.newArrayList(key);
//List设置Lua的ARGV[1]
List<String> argvList = Lists.newArrayList(String.valueOf(value));
//调用Lua脚本并执行
List result = stringRedisTemplate.execute(redisScript, keyList, argvList)

package io.mykit.limiter.annotation;
import org.springframework.core.annotation.AliasFor;
import java.lang.annotation.*;
/**
 * @author binghe
 * @version 1.0.0
 * @description 自定义注解实现分布式限流
 */
@Target(value = ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
@Documented
public @interface MyRedisLimiter {
    @AliasFor("limit")
    double value() default Double.MAX_VALUE;
    double limit() default Double.MAX_VALUE;
}

package io.mykit.limiter.aspect;
import com.google.common.collect.Lists;
import io.mykit.limiter.annotation.MyRedisLimiter;
import org.aspectj.lang.ProceedingJoinPoint;
import org.aspectj.lang.annotation.Around;
import org.aspectj.lang.annotation.Aspect;
import org.aspectj.lang.annotation.Pointcut;
import org.aspectj.lang.reflect.MethodSignature;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.core.io.ClassPathResource;
import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.data.redis.core.script.DefaultRedisScript;
import org.springframework.scripting.support.ResourceScriptSource;
import org.springframework.stereotype.Component;
import javax.annotation.PostConstruct;
import javax.servlet.http.HttpServletResponse;
import java.io.PrintWriter;
import java.util.List;
/**
 * @author binghe
 * @version 1.0.0
 * @description MyRedisLimiter注解的切面类
 */
@Aspect
@Component
public class MyRedisLimiterAspect {
    private final Logger logger = LoggerFactory.getLogger(MyRedisLimiter.class);
    @Autowired
    private HttpServletResponse response;
    @Autowired
    private StringRedisTemplate stringRedisTemplate;
    private DefaultRedisScript<List> redisScript;
    @PostConstruct
    public void init(){
        redisScript = new DefaultRedisScript<List>();
        redisScript.setResultType(List.class);
        redisScript.setScriptSource(new ResourceScriptSource(new ClassPathResource(("limit.lua"))));
    }
    @Pointcut("execution(public * io.mykit.limiter.controller.*.*(..))")
    public void pointcut(){
    }
    @Around("pointcut()")
    public Object process(ProceedingJoinPoint proceedingJoinPoint) throws Throwable{
        MethodSignature signature = (MethodSignature) proceedingJoinPoint.getSignature();
        //使用反射获取MyRedisLimiter注解
        MyRedisLimiter myRedisLimiter = signature.getMethod().getDeclaredAnnotation(MyRedisLimiter.class);
        if(myRedisLimiter == null){
            //正常执行方法
            return proceedingJoinPoint.proceed();
        }
        //获取注解上的参数，获取配置的速率
        double value = myRedisLimiter.value();
        //List设置Lua的KEYS[1]
        String key = "ip:" + System.currentTimeMillis() / 1000;
        List<String> keyList = Lists.newArrayList(key);
        //List设置Lua的ARGV[1]
        List<String> argvList = Lists.newArrayList(String.valueOf(value));
        //调用Lua脚本并执行
        List result = stringRedisTemplate.execute(redisScript, keyList, String.valueOf(value));
        logger.info("Lua脚本的执行结果：" + result);
        //Lua脚本返回0，表示超出流量大小，返回1表示没有超出流量大小。
        if("0".equals(result.get(0).toString())){
            fullBack();
            return null;
        }
        //获取到令牌，继续向下执行
        return proceedingJoinPoint.proceed();
    }
    private void fullBack() {
        response.setHeader("Content-Type" ,"text/html;charset=UTF8");
        PrintWriter writer = null;
        try{
            writer = response.getWriter();
            writer.println("回退失败，请稍后阅读。。。");
            writer.flush();
        }catch (Exception e){
            e.printStackTrace();
        }finally {
            if(writer != null){
                writer.close();
            }
        }
    }
}

local key = KEYS[1]  --限流KEY(一秒一个)
local limit = tonumber(ARGV[1]) --限流大小
local current = tonumber(redis.call('get', key) or "0")
if current + 1 > limit then --如果超出限流大小
    return 0
else --请求数+1，并设置2秒过期
    redis.call("INCRBY", key, "1")
    redis.call("expire", key "2")
    return 1
end

@MyRedisLimiter(limit = 10)
@RequestMapping("/boot/send/message2")
public String sendMessage2(){
    //记录返回接口
    String result = "";
    boolean flag = messageService.sendMessage("恭喜您成长值+1");
    if (flag){
        result = "短信发送成功！";
        return result;
    }
    result = "哎呀，服务器开小差了，请再试一下吧";
    return result;
}

local locks = require "resty.lock"
local function acquire()
    local lock =locks:new("locks")
    local elapsed, err =lock:lock("limit_key") --互斥锁
    local limit_counter =ngx.shared.limit_counter --计数器
    local key = "ip:" ..os.time()
    local limit = 5 --限流大小
    local current =limit_counter:get(key)
    if current ~= nil and current + 1> limit then --如果超出限流大小
       lock:unlock()
       return 0
    end
    if current == nil then
       limit_counter:set(key, 1, 1) --第一次需要设置过期时间，设置key的值为1，过期时间为1秒
    else
        limit_counter:incr(key, 1) --第二次开始加1即可
    end
    lock:unlock()
    return 1
end
ngx.print(acquire())

http {
    ……
    lua_shared_dict locks 10m;
    lua_shared_dict limit_counter 10m;
}