Spring Cloud Gateway 6 限流

Spring Cloud Gateway 限流

限流的目的是通过对并发访问/请求进行限速或者对一个时间窗口内的请求进行限速来保护系统,一旦达到限制速率则可由拒绝服务,就是定向到错误页或友好的展示页,排队或等待

Gateway内置过滤器工厂限流

Spring Cloud Gateway官方就提供了RequestRateLimiterGatewayFilterFactory这个类,适用Redis和lua脚本实现了令牌桶的方式。具体实现逻辑在RequestRateLimiterGatewayFilterFactory类中

pom文件中引入gateway的起步依赖和redis的reactive依赖

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-gateway</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifatId>spring-boot-starter-data-redis-reactive</artifactId>
</dependency>

配置

server:
  port: 8081
spring:
  cloud:
    gateway:
      routes:
      - id: limit_route
        uri: http://httpbin.org:80/get
        predicates:
        - After=2017-01-20T17:42:47.789-07:00[America/Denver]
        filters:
        - name: RequestRateLimiter
          args:
            key-resolver: '#{@hostAddrKeyResolver}'
            redis-rate-limiter.replenishRate: 1
            redis-rate-limiter.burstCapacity: 3
  application:
    name: gateway-limiter
  redis:
    host: localhost
    port: 6379
    database: 0

配置文件,指定程序的端口为8081,配置了 redis的信息,并配置了RequestRateLimiter的限流过滤器,该过滤器需要配置三个参数:

  • burstCapacity,令牌桶总容量。
  • replenishRate,令牌桶每秒填充平均速率。
  • key-resolver,用于限流的键的解析器的 Bean 对象的名字。它使用 SpEL 表达式根据#{@beanName}从 Spring 容器中获取 Bean 对象。

KeyResolver需要实现resolve方法,比如根据Hostname进行限流,则需要用hostAddress去判断。实现完KeyResolver之后,需要将这个类的Bean注册到Ioc容器中

public class HostAddrKeyResolver implements KeyResolver {

    @Override
    public Mono<String> resolve(ServerWebExchange exchange) {
        return Mono.just(exchange.getRequest().getRemoteAddress().getAddress().getHostAddress());
    }

}
     @Bean
    public HostAddrKeyResolver hostAddrKeyResolver() {
        return new HostAddrKeyResolver();
    }

根据uri去限流,这时KeyResolver代码如下:

public class UriKeyResolver  implements KeyResolver {

    @Override
    public Mono<String> resolve(ServerWebExchange exchange) {
        return Mono.just(exchange.getRequest().getURI().getPath());
    }

}
     @Bean
    public UriKeyResolver uriKeyResolver() {
        return new UriKeyResolver();
    }

以用户的维度去限流

@Bean
    KeyResolver userKeyResolver() {
        return exchange -> Mono.just(exchange.getRequest().getQueryParams().getFirst("user"));
    }

自定义限流

Spring Cloud Gateway实现自定义限流,需要编写一个过滤器。Guava中的RateLimiter,Bucket4j,RateLimitJ限流都是基于令牌桶实现的。

下面使用Bucket4j实现限流。

pom

  <!-- Spring Cloud Gateway的依赖-->
        <dependency>
            <groupId>org.springframework.cloud</groupId>
            <artifactId>spring-cloud-starter-gateway</artifactId>
        </dependency>
        <!-- Bucket4j限流依赖-->
        <dependency>
            <groupId>com.github.vladimir-bukhtoyarov</groupId>
            <artifactId>bucket4j-core</artifactId>
            <version>4.0.0</version>
        </dependency>

自定义过滤器需要实现GatewayFilter,Ordered接口,实现对ip的限流

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;

/**
 * 自定义过滤器进行ip限流
 */
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() {
    }

    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();
        Bucket bucket = LOCAL_CACHE.computeIfAbsent(ip, k -> createNewBucket());
        log.debug("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
            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;
    }
}

代码配置

 @Bean
    public RouteLocator customerRouteLocator(RouteLocatorBuilder builder) {
        return builder.routes()
                .route(r -> r.path("/test/rateLimit")
                        .filters(f -> f.filter(new GatewayRateLimitFilterByIp(10,1,Duration.ofSeconds(1))))
                        .uri("http://localhost:8000/hello/rateLimit")
                        .id("rateLimit_route")
                ).build();
    }

基于CPU使用率进行限流

通过Spring Boot Actuator 提供的Metrics获取当前CPU的使用情况,进行限流

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.actuate.metrics.MetricsEndpoint;
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.stereotype.Component;
import org.springframework.web.server.ServerWebExchange;
import reactor.core.publisher.Mono;

import java.util.Objects;

/**
 * 根据CPU的使用情况限流
 **/
@Component
public class GatewayRateLimitFilterByCpu implements GatewayFilter, Ordered {

    private final Logger log = LoggerFactory.getLogger(GatewayRateLimitFilterByCpu.class);

    @Autowired
    private MetricsEndpoint metricsEndpoint;

    private static final String METRIC_NAME = "system.cpu.usage";

    private static final double MAX_USAGE = 0.50D;

    @Override
    public Mono<Void> filter(ServerWebExchange exchange, GatewayFilterChain chain) {
        //获取网关所在机器的CPU使用情况
        Double systemCpuUsage = metricsEndpoint.metric(METRIC_NAME, null)
                .getMeasurements()
                .stream()
                .filter(Objects::nonNull)
                .findFirst()
                .map(MetricsEndpoint.Sample::getValue)
                .filter(Double::isFinite)
                .orElse(0.0D);

        boolean isOpenRateLimit = systemCpuUsage >MAX_USAGE;
        log.debug("system.cpu.usage: {}, isOpenRateLimit:{} ",systemCpuUsage , isOpenRateLimit);
        if (isOpenRateLimit) {
            //当CPU的使用超过设置的最大阀值开启限流
            exchange.getResponse().setStatusCode(HttpStatus.TOO_MANY_REQUESTS);
            return exchange.getResponse().setComplete();
        } else {
            return chain.filter(exchange);
        }
    }

    @Override
    public int getOrder() {
        return 0;
    }

}

代码配置

    @Autowired
    private GatewayRateLimitFilterByCpu gatewayRateLimitFilterByCpu;

    @Bean
    public RouteLocator customerRouteLocator(RouteLocatorBuilder builder) {
        return builder.routes()
                .route(r -> r.path("/test/rateLimit")
                        .filters(f -> f.filter(gatewayRateLimitFilterByCpu))
                        .uri("http://localhost:8000/hello/rateLimit")
                        .id("rateLimit_route")
                ).build();
    }
posted @ 2020-06-17 14:55  路迢迢  阅读(249)  评论(0编辑  收藏