Redis实现IP限流的两种方式详解

gateway网关ip限流

通过reids实现

1. 限流的流程图

   

2. 在配置文件配置限流参数

   blackIP:
     # ip 连续请求的次数
     continue-counts: ${counts:3}
     # ip 判断的时间间隔，单位：秒
     time-interval: ${interval:20}
     # 限制的时间，单位：秒
     limit-time: ${time:30}

3. 编写全局过滤器类

   package com.ajie.gateway.filter;
   
   import com.ajie.common.enums.ResponseStatusEnum;
   import com.ajie.common.result.GraceJSONResult;
   import com.ajie.common.utils.CollUtils;
   import com.ajie.common.utils.IPUtil;
   import com.ajie.common.utils.JsonUtils;
   import com.ajie.common.utils.RedisUtil;
   import io.netty.handler.codec.http.HttpHeaderNames;
   import lombok.extern.slf4j.Slf4j;
   import org.springframework.beans.factory.annotation.Autowired;
   import org.springframework.beans.factory.annotation.Value;
   import org.springframework.cloud.gateway.filter.GatewayFilterChain;
   import org.springframework.cloud.gateway.filter.GlobalFilter;
   import org.springframework.core.Ordered;
   import org.springframework.core.io.buffer.DataBuffer;
   import org.springframework.http.HttpStatus;
   import org.springframework.http.server.reactive.ServerHttpRequest;
   import org.springframework.http.server.reactive.ServerHttpResponse;
   import org.springframework.stereotype.Component;
   import org.springframework.util.AntPathMatcher;
   import org.springframework.util.MimeTypeUtils;
   import org.springframework.web.server.ServerWebExchange;
   import reactor.core.publisher.Mono;
   
   import java.nio.charset.StandardCharsets;
   import java.util.List;
   import java.util.concurrent.TimeUnit;
   
   /**
    * @Description:
    * @Author: ajie
    */
   @Slf4j
   @Component
   public class IpLimitFilterJwt implements GlobalFilter, Ordered {
   
       @Autowired
       private UrlPathProperties urlPathProperties;
       @Value("${blackIP.continue-counts}")
       private Integer continueCounts;
       @Value("${blackIP.time-interval}")
       private Integer timeInterval;
       @Value("${blackIP.limit-time}")
       private Integer limitTime;
       private final AntPathMatcher antPathMatcher = new AntPathMatcher();
   
       @Override
       public Mono<Void> filter(ServerWebExchange exchange, GatewayFilterChain chain) {
           // 1.获取当前的请求路径
           String path = exchange.getRequest().getURI().getPath();
   
           // 2.获得所有的需要限流的url
           List<String> ipLimitUrls = urlPathProperties.getIpLimitUrls();
           // 3.校验并且排除excludeList
           if (CollUtils.isNotEmpty(ipLimitUrls)) {
               for (String url : ipLimitUrls) {
                   if (antPathMatcher.matchStart(url, path)) {
                       log.warn("IpLimitFilterJwt--url={}", path);
                       // 进行ip限流
                       return doLimit(exchange, chain);
                   }
               }
           }
           // 默认直接放行
           return chain.filter(exchange);
       }
   
       private Mono<Void> doLimit(ServerWebExchange exchange, GatewayFilterChain chain) {
           // 获取真实ip
           ServerHttpRequest request = exchange.getRequest();
           String ip = IPUtil.getIP(request);
   
           /**
            * 需求：
            * 判断ip在20秒内请求的次数是否超过3次
            * 如果超过，则限制访问30秒
            * 等待30秒以后，才能够恢复访问
            */
           // 正常ip
           String ipRedisKey = "gateway_ip:" + ip;
           // 被拦截的黑名单，如果存在，则表示该ip已经被限制访问
           String ipRedisLimitedKey = "gateway_ip:limit:" + ip;
           long limitLeftTime = RedisUtil.KeyOps.getExpire(ipRedisLimitedKey);
           if (limitLeftTime > 0) {
               return renderErrorMsg(exchange, ResponseStatusEnum.SYSTEM_ERROR_BLACK_IP);
           }
           // 在redis中获得ip的累加次数
           long requestTimes = RedisUtil.StringOps.incrBy(ipRedisKey, 1);
           // 如果访问次数为1，则表明是第一次访问，在redis设置倒计时
           if (requestTimes == 1) {
               RedisUtil.KeyOps.expire(ipRedisKey, timeInterval, TimeUnit.SECONDS);
           }
   
           // 如果访问次数超过限制的次数，直接将该ip存入限制的redis key，并设置限制访问时间
           if (requestTimes > continueCounts) {
               // 设置该ip需要被限流的时间
               RedisUtil.StringOps.setEx(ipRedisLimitedKey, ip, limitTime, TimeUnit.SECONDS);
               return renderErrorMsg(exchange, ResponseStatusEnum.SYSTEM_ERROR_BLACK_IP);
           }
           return chain.filter(exchange);
       }
   
       public Mono<Void> renderErrorMsg(ServerWebExchange exchange, ResponseStatusEnum statusEnum) {
           // 1.获得response
           ServerHttpResponse response = exchange.getResponse();
           // 2.构建jsonResult
           GraceJSONResult jsonResult = GraceJSONResult.exception(statusEnum);
           // 3.修改response的code为500
           response.setStatusCode(HttpStatus.INTERNAL_SERVER_ERROR);
           // 4.设定header类型
           if (!response.getHeaders().containsKey("Content-Type")) {
               response.getHeaders().add(HttpHeaderNames.CONTENT_TYPE.toString(), MimeTypeUtils.APPLICATION_JSON_VALUE);
           }
           // 5.转换json并且向response写入数据
           String jsonStr = JsonUtils.toJsonStr(jsonResult);
           DataBuffer dataBuffer = response.bufferFactory()
                   .wrap(jsonStr.getBytes(StandardCharsets.UTF_8));
           return response.writeWith(Mono.just(dataBuffer));
       }
   
       @Override
       public int getOrder() {
           return 1;
       }
   }

通过Lua+Redis实现

业务流程还是和上图差不多，只不过gateway网关不用再频繁和redis进行交互。整个限流逻辑放在redis层，通过Lua代码嵌套

1. Lua实现限流的代码

   --[[
   ipRedisLimitedKey：限流的redis key
   ipRedisKey：未被限流的redis key，通过此key计算访问次数
   timeInterval：访问时间间隔，在此时间内，访问到指定次数进行限流
   limitTime：限流的时长
   ]]
   -- 判断当前ip是否已经被限流
   if redis.call("ttl", ipRedisLimitedKey) > 0 then
       return 1
   end
   
   -- 如果没有被限流，就让当前ip在redis中的值累计1
   local requestTimes = redis.call("incrby", ipRedisKey, 1)
   -- 判断累加后的值
   if requestTimes == 1 then
       -- 如果累加后的值是1，说明是第一次请求，设置一个时间间隔
       redis.call("expire", ipRedisKey, timeInterval)
       return 0
   elseif requestTimes > continueCounts then
       --  如果累加后的值超过了设定的阈值，就对当前ip进行限流
       redis.call("setex", ipRedisLimitedKey, limitTime, ip)
       return 1
   end

2. java代码实现Lua和redis的整合

   package com.ajie.gateway.filter;
   
   import com.ajie.common.enums.ResponseStatusEnum;
   import com.ajie.common.result.GraceJSONResult;
   import com.ajie.common.utils.CollUtils;
   import com.ajie.common.utils.IPUtil;
   import com.ajie.common.utils.JsonUtils;
   import com.ajie.common.utils.RedisUtil;
   import com.google.common.collect.Lists;
   import io.netty.handler.codec.http.HttpHeaderNames;
   import lombok.extern.slf4j.Slf4j;
   import org.springframework.beans.factory.annotation.Autowired;
   import org.springframework.beans.factory.annotation.Value;
   import org.springframework.cloud.gateway.filter.GatewayFilterChain;
   import org.springframework.cloud.gateway.filter.GlobalFilter;
   import org.springframework.core.Ordered;
   import org.springframework.core.io.buffer.DataBuffer;
   import org.springframework.http.HttpStatus;
   import org.springframework.http.server.reactive.ServerHttpRequest;
   import org.springframework.http.server.reactive.ServerHttpResponse;
   import org.springframework.stereotype.Component;
   import org.springframework.util.AntPathMatcher;
   import org.springframework.util.MimeTypeUtils;
   import org.springframework.web.server.ServerWebExchange;
   import reactor.core.publisher.Mono;
   
   import java.nio.charset.StandardCharsets;
   import java.util.List;
   
   /**
    * @Description:
    * @Author: ajie
    */
   @Slf4j
   @Component
   public class IpLuaLimitFilterJwt implements GlobalFilter, Ordered {
   
       @Autowired
       private UrlPathProperties urlPathProperties;
       @Value("${blackIP.continue-counts}")
       private Integer continueCounts;
       @Value("${blackIP.time-interval}")
       private Integer timeInterval;
       @Value("${blackIP.limit-time}")
       private Integer limitTime;
       private final AntPathMatcher antPathMatcher = new AntPathMatcher();
   
       @Override
       public Mono<Void> filter(ServerWebExchange exchange, GatewayFilterChain chain) {
           // 1.获取当前的请求路径
           String path = exchange.getRequest().getURI().getPath();
   
           // 2.获得所有的需要限流的url
           List<String> ipLimitUrls = urlPathProperties.getIpLimitUrls();
           // 3.校验并且排除excludeList
           if (CollUtils.isNotEmpty(ipLimitUrls)) {
               for (String url : ipLimitUrls) {
                   if (antPathMatcher.matchStart(url, path)) {
                       log.warn("IpLimitFilterJwt--url={}", path);
                       // 进行ip限流
                       return doLimit(exchange, chain);
                   }
               }
           }
           // 默认直接放行
           return chain.filter(exchange);
       }
   
       private Mono<Void> doLimit(ServerWebExchange exchange, GatewayFilterChain chain) {
           // 获取真实ip
           ServerHttpRequest request = exchange.getRequest();
           String ip = IPUtil.getIP(request);
   
           /**
            * 需求：
            * 判断ip在20秒内请求的次数是否超过3次
            * 如果超过，则限制访问30秒
            * 等待30秒以后，才能够恢复访问
            */
           // 正常ip
           String ipRedisKey = "gateway_ip:" + ip;
           // 被拦截的黑名单，如果存在，则表示该ip已经被限制访问
           String ipRedisLimitedKey = "gateway_ip:limit:" + ip;
           // 通过redis执行lua脚本。返回1代表限流了，返回0代表没有限流
           String script = "if tonumber(redis.call('ttl', KEYS[2])) > 0 then return 1 end local" +
                   " requestTimes = redis.call('incrby', KEYS[1], 1) if tonumber(requestTimes) == 1 then" +
                   " redis.call('expire', KEYS[1], ARGV[2]) return 0 elseif tonumber(requestTimes)" +
                   " > tonumber(ARGV[1]) then redis.call('setex', KEYS[2], ARGV[3], ARGV[4])" +
                   " return 1 else return 0 end";
           Long result = RedisUtil.Helper.execute(script, Long.class,
                   Lists.newArrayList(ipRedisKey, ipRedisLimitedKey),
                   continueCounts, timeInterval, limitTime, ip);
           if(result == 1){
               return renderErrorMsg(exchange, ResponseStatusEnum.SYSTEM_ERROR_BLACK_IP);
           }
           return chain.filter(exchange);
       }
   
       public Mono<Void> renderErrorMsg(ServerWebExchange exchange, ResponseStatusEnum statusEnum) {
           // 1.获得response
           ServerHttpResponse response = exchange.getResponse();
           // 2.构建jsonResult
           GraceJSONResult jsonResult = GraceJSONResult.exception(statusEnum);
           // 3.修改response的code为500
           response.setStatusCode(HttpStatus.INTERNAL_SERVER_ERROR);
           // 4.设定header类型
           if (!response.getHeaders().containsKey("Content-Type")) {
               response.getHeaders().add(HttpHeaderNames.CONTENT_TYPE.toString(), MimeTypeUtils.APPLICATION_JSON_VALUE);
           }
           // 5.转换json并且向response写入数据
           String jsonStr = JsonUtils.toJsonStr(jsonResult);
           DataBuffer dataBuffer = response.bufferFactory()
                   .wrap(jsonStr.getBytes(StandardCharsets.UTF_8));
           return response.writeWith(Mono.just(dataBuffer));
       }
   
       @Override
       public int getOrder() {
           return 1;
       }
   }

注意事项

1. 在编写lua脚本的时候最好不要一次性写完去试，因为无法进行调试，最好进行拆解。

2. 在进行数字比较时建议加上tonumber()。如果是通过方法传参进来的一定要加，因为redisTemplate默认会把参数当做字符串传入

   

   如果不转数字就会出现上面的错误

3. 最后也是最重要的，lua代码逻辑一定要对，否则得不到自己想要的结果需要排查很久