目录
- 通过reids实现
- 通过Lua+Redis实现
- 注意事项
- 总结
通过reids实现
限流的流程图
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: ${time:30}编写全局过滤器类
package com.ajie.gateway.filter; import com.ajie.common.enums.ResponseStatusEnum; import com.ajie.common.result.GracejsONResult; import com.ajie.common.utils.CollUtils; import com.ajie.common.utils.IPUtil; import com.ajie.common.utils.JsonUtils; import com.ajie.common.utils.RedisUtil; import io.netty.handler.codec.http.HttpHeaderNames; import lombok.extern.slf4j.Slf4j; import org.springframewo编程rk.beans.factory.annotation.Autowired; import org.springframework.beans.factory.annotation.Value; import org.springframework.cloud.gateway.filter.GatewayFilterChain; import org.springframework.cloud.gateway.filter.GlobalFilter; import org.springframework.core.Ordered; import org.springframework.core.io.buffer.DataBuffer; import org.springframework.http.HttpStatus; import org.springframework.http.server.reactive.ServerHttpRequest; import org.springframework.http.server.reactive.ServerHttpResponse; import org.springframework.stereotype.Component; import org.springframework.util.AntPathMatcher; import org.springframework.util.MimeTypeUtils; import org.springframework.web.server.ServerWebExchange; import reactor.core.publisher.Mono; import Java.nio.charset.StandardCharsets; import java.util.List; import java.util.concurrent.TimeUnit; /** * @Description: * @Author: ajie */ @Slf4j @Component public class IpLimitFilterJwt implements GlobalFilter, Ordered { @Autowired private UrlPathProperties urlPathProperties; @Value("${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(ipRhttp://www.devze.comedisLimitedKey, 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
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 endjava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.springframew编程客栈ork.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 php * 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www.devze.com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
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。
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
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
总结
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