redis+lua实现限流

1、需要引入Redis的maven坐标

<!--redis和 springboot集成的包 -->
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-redis</artifactId>
    <version>2.3.0.RELEASE</version>
</dependency>

2、redis配置

spring:
  # Redis数据库索引
  redis:
    database: 0
  # Redis服务器地址
    host: 127.0.0.1
  # Redis服务器连接端口
    port: 6379
  # Redis服务器连接密码（默认为空）
    password:
  # 连接池最大连接数（使用负值表示没有限制）
    jedis:
      pool:
        max-active: 8
  # 连接池最大阻塞等待时间（使用负值表示没有限制）
        max-wait: -1
  # 连接池中的最大空闲连接
        max-idle: 8
  # 连接池中的最小空闲连接
        min-idle: 0
  # 连接超时时间（毫秒）
    timeout: 10000

3、新建脚本放在该项目的 resources 目录下，新建 limit.lua

local key = KEYS[1] --限流KEY 
local limit = tonumber(ARGV[1]) --限流大小 
local current = tonumber(redis.call('get', key) or "0") if current + 1 > limit then 
return 0 else redis.call("INCRBY", key,"1") redis.call("expire", key,"2") return current + 1 end

4、自定义限流注解

import java.lang.annotation.*;

@Target(value = ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
@Documented
public @interface RedisRateLimiter {

   //往令牌桶放入令牌的速率
    double value() default  Double.MAX_VALUE;
    //获取令牌的超时时间
    double limit() default  Double.MAX_VALUE;
}

5、自定义切面类 RedisLimiterAspect 类 ，修改扫描自己controller类

import com.imooc.annotation.RedisRateLimiter;
import org.apache.commons.lang3.StringUtils;
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.assertj.core.util.Lists;
import org.json.JSONObject;
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;

@Aspect
@Component
public class RedisLimiterAspect {
    @Autowired
    private HttpServletResponse response;

    /**
     * 注入redis操作类
     */
    @Autowired
    private StringRedisTemplate stringRedisTemplate;

     private DefaultRedisScript<List> redisScript;

    /**
     * 初始化 redisScript 类
     * 返回值为 List
     */
    @PostConstruct
    public void init(){
        redisScript = new DefaultRedisScript<List>();
        redisScript.setResultType(List.class);
        redisScript.setScriptSource(new ResourceScriptSource(new ClassPathResource("limit.lua")));
    }

    public final static Logger log = LoggerFactory.getLogger(RedisLimiterAspect.class);

    @Pointcut("execution( public * com.zz.controller.*.*(..))")
    public void pointcut(){

    }
    @Around("pointcut()")
    public Object process(ProceedingJoinPoint proceedingJoinPoint) throws  Throwable {
        MethodSignature  signature = (MethodSignature)proceedingJoinPoint.getSignature();
        //使用Java 反射技术获取方法上是否有@RedisRateLimiter 注解类
        RedisRateLimiter redisRateLimiter = signature.getMethod().getDeclaredAnnotation(RedisRateLimiter.class);
        if(redisRateLimiter == null){
            //正常执行方法,执行正常业务逻辑
            return proceedingJoinPoint.proceed();
        }
        //获取注解上的参数，获取配置的速率
        double value = redisRateLimiter.value();
        double time = redisRateLimiter.limit();


        //list设置lua的keys[1]
        //取当前时间戳到单位秒
        String key = "ip:"+ System.currentTimeMillis() / 1000;

        List<String> keyList = Lists.newArrayList(key);

        //用户Mpa设置Lua 的ARGV[1]
        //List<String> argList = Lists.newArrayList(String.valueOf(value));

        //调用脚本并执行
        List result = stringRedisTemplate.execute(redisScript, keyList, String.valueOf(value),String.valueOf(time));

        log.info("限流时间段内访问第：{} 次", result.toString());

        //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(StringUtils.equals(result.get(0).toString(),"0")){
            //服务降级
            fullback();
            return null;
        }

        // 没有限流，直接放行
        return proceedingJoinPoint.proceed();
    }

    /**
     * 服务降级方法
     */
    private  void  fullback(){
        response.setCharacterEncoding("UTF-8");
        response.setContentType("application/json; charset=utf-8");
        PrintWriter writer = null;
        try {
            writer= response.getWriter();
            JSONObject o = new JSONObject();
            o.put("status",500);
            o.put("msg","Redis限流：请求太频繁，请稍后重试！");
            o.put("data",null);
            writer.printf(o.toString()
            );

        }catch (Exception e){
            e.printStackTrace();
        }finally {
            if(writer != null){
                writer.close();
            }
        }
    }
}

6、在需要限流的类添加注解

import com.imooc.annotation.RedisRateLimiter;
import io.swagger.annotations.Api;
import io.swagger.annotations.ApiOperation;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;

import java.util.concurrent.TimeUnit;

@RestController
@Api(value = "限流", tags = {"限流测试接口"})
@RequestMapping("limiter")
public class LimiterController {

    @ApiOperation(value = "Redis限流注解测试接口",notes = "Redis限流注解测试接口", httpMethod = "GET")
    @RedisRateLimiter(value = 10, limit = 1)
    @GetMapping("/redislimit")
    public IMOOCJSONResult redislimit(){

        System.out.println("Redis限流注解测试接口");
        return IMOOCJSONResult.ok();
    }


}