spring cloud gateway（实现限流）

限流一般有两个实现方式，令牌桶和漏桶

令牌桶是初始化令牌（容器）的个数，通过拿走里边的令牌就能通过， 没有令牌不能报错，可以设置向容器中增加令牌的速度和最大个数

漏桶是向里边放入请求，当请求数量达到最大值后，丢弃，漏桶中的数据以一定速度流出，没有则不流出

令牌桶实现方式如下：

pom

<dependency>
 <groupId>com.github.vladimir-bukhtoyarov</groupId>
 <artifactId>bucket4j-core</artifactId>
 <version>4.0.0</version>
</dependency>

创建下边类并且继承下边类

package com.gla.datacenter.filter;
import io.github.bucket4j.Bandwidth;
import io.github.bucket4j.Bucket;
import io.github.bucket4j.Bucket4j;
import io.github.bucket4j.Refill;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.cloud.gateway.filter.GatewayFilter;
import org.springframework.cloud.gateway.filter.GatewayFilterChain;
import org.springframework.core.Ordered;
import org.springframework.http.HttpStatus;
import org.springframework.web.server.ServerWebExchange;
import reactor.core.publisher.Mono;
import java.time.Duration;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
/**
 * @Description: 自定义过滤器进行限流
 * @Author: zzh
 * @Modified By:
 * @Date: 2018/12/3 18:07
 */
public class GatewayRateLimitFilterByIP implements GatewayFilter, Ordered {
 private final Logger log = LoggerFactory.getLogger(GatewayRateLimitFilterByIP.class);
 /**
 * 单机网关限流用一个ConcurrentHashMap来存储 bucket，
 * 如果是分布式集群限流的话，可以采用 Redis等分布式解决方案
 */
 private static final Map<String, Bucket> LOCAL_CACHE = new ConcurrentHashMap<>();
 /**
 * 桶的最大容量，即能装载 Token 的最大数量
 */
 int capacity;
 /**
 * 每次 Token 补充量
 */
 int refillTokens;
 /**
 *补充 Token 的时间间隔
 */
 Duration refillDuration;
 public GatewayRateLimitFilterByIP() {
 }
 /**
 *
 * @param capacity 即能装载 Token 的最大数量.
 * @param refillTokens
 * @param refillDuration
 */
 public GatewayRateLimitFilterByIP(int capacity, int refillTokens, Duration refillDuration) {
 this.capacity = capacity;
 this.refillTokens = refillTokens;
 this.refillDuration = refillDuration;
 }
 private Bucket createNewBucket() {
 Refill refill = Refill.of(refillTokens, refillDuration);
 Bandwidth limit = Bandwidth.classic(capacity, refill);
 return Bucket4j.builder().addLimit(limit).build();
 }
 @Override
 public Mono<Void> filter(ServerWebExchange exchange, GatewayFilterChain chain) {
 String ip = exchange.getRequest().getRemoteAddress().getAddress().getHostAddress();
 //若ip不存在则创建一个Bucket（令牌桶）
 Bucket bucket = LOCAL_CACHE.computeIfAbsent(ip, k -> createNewBucket());
 log.info("IP:{} ,令牌通可用的Token数量:{} " ,ip,bucket.getAvailableTokens());
 if (bucket.tryConsume(1)) {
 return chain.filter(exchange);
 } else {
 //当可用的令牌书为0是，进行限流返回429状态码
 log.error("IP:{} ,限制访问:{} " ,ip,bucket.getAvailableTokens());
 exchange.getResponse().setStatusCode(HttpStatus.TOO_MANY_REQUESTS);
 return exchange.getResponse().setComplete();
 }
 }
 @Override
 public int getOrder() {
 return -1000;
 }
 public static Map<String, Bucket> getLocalCache() {
 return LOCAL_CACHE;
 }
 public int getCapacity() {
 return capacity;
 }
 public void setCapacity(int capacity) {
 this.capacity = capacity;
 }
 public int getRefillTokens() {
 return refillTokens;
 }
 public void setRefillTokens(int refillTokens) {
 this.refillTokens = refillTokens;
 }
 public Duration getRefillDuration() {
 return refillDuration;
 }
 public void setRefillDuration(Duration refillDuration) {
 this.refillDuration = refillDuration;
 }
}

配置路由

@Bean
	public RouteLocator customRouteLocator(RouteLocatorBuilder builder) {
		//生成比当前时间早一个小时的UTC时间
		ZonedDateTime minusTime = LocalDateTime.now().minusHours(1).atZone(ZoneId.systemDefault());
		return builder.routes()
				.route(r ->r.path("/demo/**")
						//过滤器
						.filters(f -> f.filter(new APIGatewayFilter())
								.filter(new GatewayRateLimitFilterByIP(10,1, Duration.ofSeconds(1))))
						.uri("http://192.168.26.113:8001/demo").order(0).id("demo_route"))
				.route(r ->r.path("/test")
								.uri("http://192.168.26.113/system/nav/login").id("jd_route")
				).build();

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