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Spring?Cloud?Gateway實(shí)現(xiàn)分布式限流和熔斷降級(jí)的示例代碼

 更新時(shí)間:2025年06月16日 08:46:35   作者:老寇開發(fā)分布式  
這篇文章主要介紹了Spring?Cloud?Gateway實(shí)現(xiàn)分布式限流和熔斷降級(jí)的示例代碼,本文通過(guò)實(shí)例代碼給大家介紹的非常詳細(xì),對(duì)大家的學(xué)習(xí)或工作具有一定的參考借鑒價(jià)值,需要的朋友參考下吧

一、限流

思考:為啥需要限流?

在一個(gè)流量特別大的業(yè)務(wù)場(chǎng)景中,如果不進(jìn)行限流,會(huì)造成系統(tǒng)宕機(jī),當(dāng)大批量的請(qǐng)求到達(dá)后端服務(wù)時(shí),會(huì)造成資源耗盡【CPU、內(nèi)存、線程、網(wǎng)絡(luò)帶寬、數(shù)據(jù)庫(kù)連接等是有限的】,進(jìn)而拖垮系統(tǒng)。

1.常見限流算法

  • 漏桶算法
  • 令牌桶算法

1.1漏桶算法(不推薦)

1.1.1.原理

將請(qǐng)求緩存到一個(gè)隊(duì)列中,然后以固定的速度處理,從而達(dá)到限流的目的

1.1.2.實(shí)現(xiàn)

將請(qǐng)求裝到一個(gè)桶中,桶的容量為固定的一個(gè)值,當(dāng)桶裝滿之后,就會(huì)將請(qǐng)求丟棄掉,桶底部有一個(gè)洞,以固定的速率流出。

1.1.3.舉例

桶的容量為1W,有10W并發(fā)請(qǐng)求,最多只能將1W請(qǐng)求放入桶中,其余請(qǐng)求全部丟棄,以固定的速度處理請(qǐng)求

1.1.4.缺點(diǎn)

處理突發(fā)流量效率低(處理請(qǐng)求的速度不變,效率很低)

1.2.令牌桶算法(推薦)

1.2.1.原理

將請(qǐng)求放在一個(gè)緩沖隊(duì)列中,拿到令牌后才能進(jìn)行處理

1.2.2.實(shí)現(xiàn)

裝令牌的桶大小固定,當(dāng)令牌裝滿后,則不能將令牌放入其中;每次請(qǐng)求都會(huì)到桶中拿取一個(gè)令牌才能放行,沒有令牌時(shí)即丟棄請(qǐng)求/繼續(xù)放入緩存隊(duì)列中等待

1.2.3.舉例

桶的容量為10w個(gè),生產(chǎn)1w個(gè)/s,有10W的并發(fā)請(qǐng)求,以每秒10W個(gè)/s速度處理,隨著桶中的令牌很快用完,速度又慢慢降下來(lái)啦,而生產(chǎn)令牌的速度趨于一致1w個(gè)/s

1.2.4.缺點(diǎn)

處理突發(fā)流量提供了系統(tǒng)性能,但是對(duì)系統(tǒng)造成了一定的壓力,桶的大小不合理,甚至?xí)嚎逑到y(tǒng)(處理1億的并發(fā)請(qǐng)求,將桶的大小設(shè)置為1,這個(gè)系統(tǒng)一下就涼涼啦)

2.網(wǎng)關(guān)限流(Spring Cloud Gateway + Redis實(shí)戰(zhàn))

2.1.pom.xml配置

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis-reactive</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.cloud</groupId>
            <artifactId>spring-cloud-starter-gateway</artifactId>
            <exclusions>
                <exclusion>
                    <groupId>org.springframework.boot</groupId>
                    <artifactId>spring-boot-starter-web</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
    <dependency>
        <groupId>org.apache.httpcomponents</groupId>
        <artifactId>httpclient</artifactId>
    </dependency>

2.2.yaml配置

spring:
  application:
    name: laokou-gateway
  cloud:
    gateway:
      routes:
        - id: LAOKOU-SSO-DEMO
          uri: lb://laokou-sso-demo
          predicates:
          - Path=/sso/**
          filters:
          - StripPrefix=1
          - name: RequestRateLimiter #請(qǐng)求數(shù)限流,名字不能亂打
            args:
              key-resolver: "#{@ipKeyResolver}"
              redis-rate-limiter.replenishRate: 1 #生成令牌速率-設(shè)為1方便測(cè)試
              redis-rate-limiter.burstCapacity: 1 #令牌桶容量-設(shè)置1方便測(cè)試
  redis:
    database: 0
    cluster:
      nodes: x.x.x.x:7003,x.x.x.x:7004,x.x.x.x:7005,x.x.x.x:7003,x.x.x.x:7004,x.x.x.x:7005
    password: laokou #密碼
    timeout: 6000ms #連接超時(shí)時(shí)長(zhǎng)(毫秒)
    jedis:
      pool:
        max-active: -1 #連接池最大連接數(shù)(使用負(fù)值表示無(wú)極限)
        max-wait: -1ms #連接池最大阻塞等待時(shí)間(使用負(fù)值表示沒有限制)
        max-idle: 10 #連接池最大空閑連接
        min-idle: 5 #連接池最小空間連接

2.3.創(chuàng)建bean

@Configuration
public class RequestRateLimiterConfig {

    @Bean(value = "ipKeyResolver")
    public KeyResolver ipKeyResolver(RemoteAddressResolver remoteAddressResolver) {
    	return exchange -> Mono.just(remoteAddressResolver.resolve(exchange).getAddress().getHostAddress());
    }

    @Bean
    public RemoteAddressResolver remoteAddressResolver() {
    	// 遠(yuǎn)程地址解析器
    	return XForwardedRemoteAddressResolver.trustAll();
    }

}

3.測(cè)試限流(編寫java并發(fā)測(cè)試)

@Slf4j
public class HttpUtil {
public static void apiConcurrent(String url,Map<String,String> params) {
        Integer count = 200;
        //創(chuàng)建線程池
        ThreadPoolExecutor pool = new ThreadPoolExecutor(5, 200, 0L, TimeUnit.SECONDS, new SynchronousQueue<>());
        //同步工具
        CountDownLatch latch = new CountDownLatch(count);
        Map<String,String> dataMap = new HashMap<>(1);
        dataMap.put("authorize","XXXXXXX");
        for (int i = 0; i < count; i++) {
            pool.execute(() -> {
                try {
                    //訪問(wèn)網(wǎng)關(guān)的API接口
                    HttpUtil.doGet("http://localhost:1234/sso/laokou-demo/user",dataMap);
                } catch (IOException e) {
                    e.printStackTrace();
                }finally {
                    latch.countDown();
                }
            });
        }
        try {
            latch.await();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
public static String doGet(String url, Map<String, String> params) throws IOException {
        //創(chuàng)建HttpClient對(duì)象
        CloseableHttpClient httpClient = HttpClients.createDefault();
        String resultString = "";
        CloseableHttpResponse response = null;
        try {
            //創(chuàng)建uri
            URIBuilder builder = new URIBuilder(url);
            if (!params.isEmpty()) {
                for (Map.Entry<String, String> entry : params.entrySet()) {
                    builder.addParameter(entry.getKey(), entry.getValue());
                }
            }
            URI uri = builder.build();
            //創(chuàng)建http GET請(qǐng)求
            HttpGet httpGet = new HttpGet(uri);
            List<NameValuePair> paramList = new ArrayList<>();
            RequestBuilder requestBuilder = RequestBuilder.get().setUri(new URI(url));
            requestBuilder.setEntity(new UrlEncodedFormEntity(paramList, Consts.UTF_8));
            httpGet.setHeader(new BasicHeader("Content-Type", "application/json;charset=UTF-8"));
            httpGet.setHeader(new BasicHeader("Accept", "*/*;charset=utf-8"));
            //執(zhí)行請(qǐng)求
            response = httpClient.execute(httpGet);
            //判斷返回狀態(tài)是否是200
            if (response.getStatusLine().getStatusCode() == 200) {
                resultString = EntityUtils.toString(response.getEntity(), "UTF-8");
            }
        } catch (Exception e) {
            log.info("調(diào)用失敗:{}",e);
        } finally {
            if (response != null) {
                response.close();
            }
            httpClient.close();
        }
        log.info("打?。簕}",resultString);
        return resultString;
    }
}

說(shuō)明這個(gè)網(wǎng)關(guān)限流配置是沒有問(wèn)題的

4.源碼查看

Spring Cloud Gateway RequestRateLimiter GatewayFilter Factory文檔地址

工廠 RequestRateLimiter GatewayFilter使用一個(gè)RateLimiter實(shí)現(xiàn)來(lái)判斷當(dāng)前請(qǐng)求是否被允許繼續(xù)。如果不允許,HTTP 429 - Too Many Requests則返回默認(rèn)狀態(tài)。

4.1.查看 RequestRateLimiterGatewayFilterFactory

@Override
	public GatewayFilter apply(Config config) {
		KeyResolver resolver = getOrDefault(config.keyResolver, defaultKeyResolver);
		RateLimiter<Object> limiter = getOrDefault(config.rateLimiter, defaultRateLimiter);
		boolean denyEmpty = getOrDefault(config.denyEmptyKey, this.denyEmptyKey);
		HttpStatusHolder emptyKeyStatus = HttpStatusHolder
				.parse(getOrDefault(config.emptyKeyStatus, this.emptyKeyStatusCode));
		return (exchange, chain) -> resolver.resolve(exchange).defaultIfEmpty(EMPTY_KEY).flatMap(key -> {
			if (EMPTY_KEY.equals(key)) {
				if (denyEmpty) {
					setResponseStatus(exchange, emptyKeyStatus);
					return exchange.getResponse().setComplete();
				}
				return chain.filter(exchange);
			}
			String routeId = config.getRouteId();
			if (routeId == null) {
				Route route = exchange.getAttribute(ServerWebExchangeUtils.GATEWAY_ROUTE_ATTR);
				routeId = route.getId();
			}
                 // 執(zhí)行限流
			return limiter.isAllowed(routeId, key).flatMap(response -> {
				for (Map.Entry<String, String> header : response.getHeaders().entrySet()) {
					exchange.getResponse().getHeaders().add(header.getKey(), header.getValue());
				}
				if (response.isAllowed()) {
					return chain.filter(exchange);
				}
				setResponseStatus(exchange, config.getStatusCode());
				return exchange.getResponse().setComplete();
			});
		});
	}

4.2.查看 RedisRateLimiter

@Override
	@SuppressWarnings("unchecked")
	public Mono<Response> isAllowed(String routeId, String id) {
		if (!this.initialized.get()) {
			throw new IllegalStateException("RedisRateLimiter is not initialized");
		}
        // 這里如何加載配置?請(qǐng)思考
		Config routeConfig = loadConfiguration(routeId);
        // 令牌桶每秒產(chǎn)生令牌數(shù)量
		int replenishRate = routeConfig.getReplenishRate();
        // 令牌桶容量
		int burstCapacity = routeConfig.getBurstCapacity();
        // 請(qǐng)求消耗的令牌數(shù)
		int requestedTokens = routeConfig.getRequestedTokens();
		try {
                  // 鍵
			List<String> keys = getKeys(id);
                  // 參數(shù)
			List<String> scriptArgs = Arrays.asList(replenishRate + "", burstCapacity + "", "", requestedTokens + "");
			// 調(diào)用lua腳本
			Flux<List<Long>> flux = this.redisTemplate.execute(this.script, keys, scriptArgs);
			return flux.onErrorResume(throwable -> {
				if (log.isDebugEnabled()) {
					log.debug("Error calling rate limiter lua", throwable);
				}
				return Flux.just(Arrays.asList(1L, -1L));
			}).reduce(new ArrayList<Long>(), (longs, l) -> {
				longs.addAll(l);
				return longs;
			}).map(results -> {
                          // 判斷是否等于1,1表示允許通過(guò),0表示不允許通過(guò)
				boolean allowed = results.get(0) == 1L;
				Long tokensLeft = results.get(1);
				Response response = new Response(allowed, getHeaders(routeConfig, tokensLeft));
				if (log.isDebugEnabled()) {
					log.debug("response: " + response);
				}
				return response;
			});
		}
		catch (Exception e) {
			log.error("Error determining if user allowed from redis", e);
		}
		return Mono.just(new Response(true, getHeaders(routeConfig, -1L)));
	}
	static List<String> getKeys(String id) {
		String prefix = "request_rate_limiter.{" + id;
		String tokenKey = prefix + "}.tokens";
		String timestampKey = prefix + "}.timestamp";
		return Arrays.asList(tokenKey, timestampKey);
	}

思考:redis限流配置是如何加載?

其實(shí)就是監(jiān)聽動(dòng)態(tài)路由的事件并把配置存起來(lái)

4.3.重點(diǎn)來(lái)了,令牌桶 /META-INF/scripts/request_rate_limiter.lua 腳本剖析

-- User Request Rate Limiter filter
-- See https://stripe.com/blog/rate-limiters
-- See https://gist.github.com/ptarjan/e38f45f2dfe601419ca3af937fff574d#file-1-check_request_rate_limiter-rb-L11-L34
-- 令牌桶算法工作原理
-- 1.系統(tǒng)以恒定速率往桶里面放入令牌
-- 2.請(qǐng)求需要被處理,則需要從桶里面獲取一個(gè)令牌
-- 3.如果桶里面沒有令牌可獲取,則可以選擇等待或直接拒絕并返回
-- 令牌桶算法工作流程
-- 1.計(jì)算填滿令牌桶所需要的時(shí)間(填充時(shí)間 = 桶容量 / 速率)
-- 2.設(shè)置存儲(chǔ)數(shù)據(jù)的TTL(過(guò)期時(shí)間),為填充時(shí)間的兩倍(存儲(chǔ)時(shí)間 = 填充時(shí)間 * 2)
-- 3.從Redis獲取當(dāng)前令牌的剩余數(shù)量和上一次調(diào)用的時(shí)間戳
-- 4.計(jì)算距離上一次調(diào)用的時(shí)間間隔(時(shí)間間隔 = 當(dāng)前時(shí)間 - 上一次調(diào)用時(shí)間)
-- 5.計(jì)算填充的令牌數(shù)量(填充令牌數(shù)量 = 時(shí)間間隔 * 速率)【前提:桶容量是固定的,不存在無(wú)限制的填充】
-- 6.判斷是否有足夠多的令牌滿足請(qǐng)求【 (填充令牌數(shù)量 + 剩余令牌數(shù)量) >= 請(qǐng)求數(shù)量 && (填充令牌數(shù)量 + 剩余令牌數(shù)量) <= 桶容量 】
-- 7.如果請(qǐng)求被允許,則從桶里面取出相應(yīng)數(shù)據(jù)的令牌
-- 8.如果TTL為正,則更新Redis鍵中的令牌和時(shí)間戳
-- 9.返回兩個(gè)兩個(gè)參數(shù)(allowed_num:請(qǐng)求被允許標(biāo)志。1允許,0不允許)、(new_tokens:填充令牌后剩余的令牌數(shù)據(jù))
-- 隨機(jī)寫入
redis.replicate_commands()
-- 令牌桶Key -> 存儲(chǔ)當(dāng)前可用令牌的數(shù)量(剩余令牌數(shù)量)
local tokens_key = KEYS[1]
-- 時(shí)間戳Key -> 存儲(chǔ)上次令牌刷新的時(shí)間戳
local timestamp_key = KEYS[2]
-- 令牌填充速率
local rate = tonumber(ARGV[1])
-- 令牌桶容量
local capacity = tonumber(ARGV[2])
-- 當(dāng)前時(shí)間
local now = tonumber(ARGV[3])
-- 請(qǐng)求數(shù)量
local requested = tonumber(ARGV[4])
-- 填滿令牌桶所需要的時(shí)間
local fill_time = capacity / rate
-- 設(shè)置key的過(guò)期時(shí)間(填滿令牌桶所需時(shí)間的2倍)
local ttl = math.floor(fill_time * 2)
-- 判斷當(dāng)前時(shí)間,為空則從redis獲取
if now == nil then
    now = redis.call('TIME')[1]
end
-- 獲取當(dāng)前令牌的剩余數(shù)量
local last_tokens = tonumber(redis.call("get", tokens_key))
if last_tokens == nil then
    last_tokens = capacity
end
-- 獲取上一次調(diào)用的時(shí)間戳
local last_refreshed = tonumber(redis.call('get', timestamp_key))
if last_refreshed == nil then
    last_refreshed = 0
end
-- 計(jì)算距離上一次調(diào)用的時(shí)間間隔
local delta = math.max(0, now - last_refreshed)
-- 當(dāng)前的令牌數(shù)量(剩余 + 填充 <= 桶容量)
local now_tokens = math.min(capacity, last_refreshed + (rate * delta))
-- 判斷是否有足夠多的令牌滿足請(qǐng)求
local allowed = now_tokens >= requested
-- 定義當(dāng)前令牌的剩余數(shù)量
local new_tokens = now_tokens
-- 定義被允許標(biāo)志
local allowed_num = 0
if allowed then
    new_tokens = now_tokens - requested
    -- 允許訪問(wèn)
    allowed_num = 1
end
-- ttl > 0,將當(dāng)前令牌的剩余數(shù)量和當(dāng)前時(shí)間戳存入redis
if ttl > 0 then
    redis.call('setex', tokens_key, ttl, new_tokens)
    redis.call('setex', timestamp_key, ttl, now)
end
-- 返回參數(shù)
return { allowed_num, new_tokens }

4.4.查看 GatewayRedisAutoConfiguration 腳本初始化

@Bean
	@SuppressWarnings("unchecked")
	public RedisScript redisRequestRateLimiterScript() {
		DefaultRedisScript redisScript = new DefaultRedisScript<>();
		redisScript.setScriptSource(
                          // 根據(jù)指定路徑獲取lua腳本來(lái)初始化配置
				new ResourceScriptSource(new ClassPathResource("META-INF/scripts/request_rate_limiter.lua")));
		redisScript.setResultType(List.class);
		return redisScript;
	}
	@Bean
	@ConditionalOnMissingBean
	public RedisRateLimiter redisRateLimiter(ReactiveStringRedisTemplate redisTemplate,
			@Qualifier(RedisRateLimiter.REDIS_SCRIPT_NAME) RedisScript<List<Long>> redisScript,
			ConfigurationService configurationService) {
		return new RedisRateLimiter(redisTemplate, redisScript, configurationService);
	}

思考:請(qǐng)求限流過(guò)濾器是如何開啟?

1.通過(guò)yaml配置開啟

spring:
  cloud:
    gateway:
      server:
        webflux:
          filter:
            request-rate-limiter:
              enabled: true

2.GatewayAutoConfiguration自動(dòng)注入bean

@Bean
@ConditionalOnBean({ RateLimiter.class, KeyResolver.class })
@ConditionalOnEnabledFilter
public RequestRateLimiterGatewayFilterFactory requestRateLimiterGatewayFilterFactory(RateLimiter rateLimiter,
       KeyResolver resolver) {
    return new RequestRateLimiterGatewayFilterFactory(rateLimiter, resolver);
}

重點(diǎn)來(lái)了,真正加載這個(gè)bean的是 @ConditionalOnEnabledFilter 注解進(jìn)行判斷

@Retention(RetentionPolicy.RUNTIME)
@Target({ ElementType.TYPE, ElementType.METHOD })
@Documented
@Conditional(OnEnabledFilter.class)
public @interface ConditionalOnEnabledFilter {
    // 這里value是用來(lái)指定滿足條件的某些類,換一句話說(shuō),就是這些類都加載或注入到ioc容器,這個(gè)注解修飾的自動(dòng)裝配類才會(huì)生效
    Class<? extends GatewayFilterFactory<?>> value() default OnEnabledFilter.DefaultValue.class;
}

我們繼續(xù)跟進(jìn)代碼,查看@Conditional(OnEnabledFilter.class)

眾所周知,@Conditional可以用來(lái)加載滿足條件的bean,所以,我們分析一下OnEnabledFilter

public class OnEnabledFilter extends OnEnabledComponent<GatewayFilterFactory<?>> {}

我分析它的父類,這里有你想要的答案!

public abstract class OnEnabledComponent<T> extends SpringBootCondition implements ConfigurationCondition {
    private static final String PREFIX = "spring.cloud.gateway.server.webflux.";
    private static final String SUFFIX = ".enabled";
    private ConditionOutcome determineOutcome(Class<? extends T> componentClass, PropertyResolver resolver) {
       // 拼接完整名稱
       // 例如 => spring.cloud.gateway.server.webflux.request-rate-limiter.enabled
       String key = PREFIX + normalizeComponentName(componentClass) + SUFFIX;
       ConditionMessage.Builder messageBuilder = forCondition(annotationClass().getName(), componentClass.getName());
       if ("false".equalsIgnoreCase(resolver.getProperty(key))) {
          // 不滿足條件不加載bean
          return ConditionOutcome.noMatch(messageBuilder.because("bean is not available"));
       }
       // 滿足條件加載bean
       return ConditionOutcome.match();
    }
}

5.優(yōu)化限流響應(yīng)[使用全限定類名直接覆蓋類]

小伙伴們,有沒有發(fā)現(xiàn),這個(gè)這個(gè)響應(yīng)體封裝的不太好,因此,我們來(lái)自定義吧,我們直接覆蓋類,代碼修改如下

@Getter
@ConfigurationProperties("spring.cloud.gateway.server.webflux.filter.request-rate-limiter")
public class RequestRateLimiterGatewayFilterFactory
       extends AbstractGatewayFilterFactory<RequestRateLimiterGatewayFilterFactory.Config> {
    private static final String EMPTY_KEY = "____EMPTY_KEY__";
    private final RateLimiter<?> defaultRateLimiter;
    private final KeyResolver defaultKeyResolver;
    /**
     * Switch to deny requests if the Key Resolver returns an empty key, defaults to true.
     */
    @Setter
    private boolean denyEmptyKey = true;
    /** HttpStatus to return when denyEmptyKey is true, defaults to FORBIDDEN. */
    @Setter
    private String emptyKeyStatusCode = HttpStatus.FORBIDDEN.name();
    public RequestRateLimiterGatewayFilterFactory(RateLimiter<?> defaultRateLimiter, KeyResolver defaultKeyResolver) {
       super(Config.class);
       this.defaultRateLimiter = defaultRateLimiter;
       this.defaultKeyResolver = defaultKeyResolver;
    }
    @Override
    public GatewayFilter apply(Config config) {
       KeyResolver resolver = getOrDefault(config.keyResolver, defaultKeyResolver);
       RateLimiter<?> limiter = getOrDefault(config.rateLimiter, defaultRateLimiter);
       boolean denyEmpty = getOrDefault(config.denyEmptyKey, this.denyEmptyKey);
       HttpStatusHolder emptyKeyStatus = HttpStatusHolder
          .parse(getOrDefault(config.emptyKeyStatus, this.emptyKeyStatusCode));
       return (exchange, chain) -> resolver.resolve(exchange).defaultIfEmpty(EMPTY_KEY).flatMap(key -> {
          if (EMPTY_KEY.equals(key)) {
             if (denyEmpty) {
                setResponseStatus(exchange, emptyKeyStatus);
                return exchange.getResponse().setComplete();
             }
             return chain.filter(exchange);
          }
          String routeId = config.getRouteId();
          if (routeId == null) {
             Route route = exchange.getAttribute(ServerWebExchangeUtils.GATEWAY_ROUTE_ATTR);
             Assert.notNull(route, "Route is null");
             routeId = route.getId();
          }
          return limiter.isAllowed(routeId, key).flatMap(response -> {
             for (Map.Entry<String, String> header : response.getHeaders().entrySet()) {
                exchange.getResponse().getHeaders().add(header.getKey(), header.getValue());
             }
             if (response.isAllowed()) {
                return chain.filter(exchange);
             }
             // 主要修改這行
             return responseOk(exchange, Result.fail("Too_Many_Requests", "請(qǐng)求太頻繁"));
          });
       });
    }
    private Mono<Void> responseOk(ServerWebExchange exchange, Object data) {
        return responseOk(exchange, JacksonUtils.toJsonStr(data), MediaType.APPLICATION_JSON);
    }
    private Mono<Void> responseOk(ServerWebExchange exchange, String str, MediaType contentType) {
        DataBuffer buffer = exchange.getResponse().bufferFactory().wrap(str.getBytes(StandardCharsets.UTF_8));
        ServerHttpResponse response = exchange.getResponse();
        response.setStatusCode(HttpStatus.OK);
        response.getHeaders().setContentType(contentType);
        response.getHeaders().setContentLength(str.getBytes(StandardCharsets.UTF_8).length);
        return response.writeWith(Flux.just(buffer));
    }
    private <T> T getOrDefault(T configValue, T defaultValue) {
       return (configValue != null) ? configValue : defaultValue;
    }
    public static class Config implements HasRouteId {
       @Getter
       private KeyResolver keyResolver;
       @Getter
       private RateLimiter<?> rateLimiter;
       @Getter
       private HttpStatus statusCode = HttpStatus.TOO_MANY_REQUESTS;
       @Getter
       private Boolean denyEmptyKey;
       @Getter
       private String emptyKeyStatus;
       private String routeId;
       public Config setKeyResolver(KeyResolver keyResolver) {
          this.keyResolver = keyResolver;
          return this;
       }
       public Config setRateLimiter(RateLimiter<?> rateLimiter) {
          this.rateLimiter = rateLimiter;
          return this;
       }
       public Config setStatusCode(HttpStatus statusCode) {
          this.statusCode = statusCode;
          return this;
       }
       public Config setDenyEmptyKey(Boolean denyEmptyKey) {
          this.denyEmptyKey = denyEmptyKey;
          return this;
       }
       public Config setEmptyKeyStatus(String emptyKeyStatus) {
          this.emptyKeyStatus = emptyKeyStatus;
          return this;
       }
       @Override
       public void setRouteId(String routeId) {
          this.routeId = routeId;
       }
       @Override
       public String getRouteId() {
          return this.routeId;
       }
    }
}

二、熔斷降級(jí)

思考:為什么需要熔斷降級(jí)?

當(dāng)某個(gè)服務(wù)發(fā)生故障時(shí)(超時(shí),響應(yīng)慢,宕機(jī)),上游服務(wù)無(wú)法及時(shí)獲取響應(yīng),進(jìn)而也導(dǎo)致故障,出現(xiàn)服務(wù)雪崩【服務(wù)雪崩是指故障像滾雪球一樣沿著調(diào)用鏈向上游擴(kuò)展,進(jìn)而導(dǎo)致整個(gè)系統(tǒng)癱瘓】

熔斷降級(jí)的目標(biāo)就是在故障發(fā)生時(shí),快速隔離問(wèn)題服務(wù)【快速失敗,防止資源耗盡】,保護(hù)系統(tǒng)資源不被耗盡,防止故障擴(kuò)散,保護(hù)核心業(yè)務(wù)可用性。

1.技術(shù)選型

1.1.熔斷降級(jí)框架選型對(duì)比表

對(duì)比維度Hystrix (Netflix)Sentinel (Alibaba)Resilience4j
當(dāng)前狀態(tài)? 停止更新 (維護(hù)模式)? 持續(xù)更新? 持續(xù)更新
熔斷機(jī)制滑動(dòng)窗口計(jì)數(shù)響應(yīng)時(shí)間/異常比例/QPS錯(cuò)誤率/響應(yīng)時(shí)間閾值
流量控制? 僅基礎(chǔ)隔離? QPS/并發(fā)數(shù)/熱點(diǎn)參數(shù)/集群流控? RateLimiter
隔離策略線程池(開銷大)/信號(hào)量并發(fā)線程數(shù)(無(wú)線程池開銷)信號(hào)量/Bulkhead
降級(jí)能力Fallback 方法Fallback + 系統(tǒng)規(guī)則自適應(yīng)Fallback + 自定義組合策略
實(shí)時(shí)監(jiān)控? Hystrix Dashboard原生控制臺(tái)(可視化動(dòng)態(tài)規(guī)則)? 需整合 Prometheus/Grafana
動(dòng)態(tài)配置? 依賴 Archaius控制臺(tái)實(shí)時(shí)推送? 需編碼實(shí)現(xiàn)(如Spring Cloud Config)
生態(tài)集成? Spring Cloud Netflix? Spring Cloud Alibaba/多語(yǔ)言網(wǎng)關(guān)? Spring Boot/響應(yīng)式編程
性能開銷高(線程池隔離)低(無(wú)額外線程)極低(純函數(shù)式)
適用場(chǎng)景遺留系統(tǒng)維護(hù)高并發(fā)控制/秒殺/熱點(diǎn)防護(hù)云原生/輕量級(jí)微服務(wù)
推薦指數(shù)?? (不推薦新項(xiàng)目)????? (Java高并發(fā)首選)????? (云原生/響應(yīng)式首選)

1.2選型決策指南

需求場(chǎng)景推薦方案原因
電商秒殺/API高頻調(diào)用管控Sentinel精細(xì)流量控制+熱點(diǎn)防護(hù)+實(shí)時(shí)看板
Kubernetes云原生微服務(wù)Resilience4j輕量化+無(wú)縫集成Prometheus+響應(yīng)式支持
Spring Cloud Netflix舊系統(tǒng)?? Hystrix兼容現(xiàn)存代碼(短期過(guò)渡)
多語(yǔ)言混合架構(gòu)(如Go+Java)Sentinel通過(guò)Sidecar代理支持非Java服務(wù)
響應(yīng)式編程(WebFlux)Resilience4j原生Reactive API支持

2.Resilience4j使用

Resilience4j官方文檔

Resilience4j 可以看作是 Hystrix 的替代品,Resilience4j支持 熔斷器單機(jī)限流

Resilience4j 是一個(gè)專為函數(shù)式編程設(shè)計(jì)的輕量級(jí)容錯(cuò)庫(kù)。Resilience4j 提供高階函數(shù)(裝飾器),可通過(guò)斷路器、速率限制器、重試或隔離功能增強(qiáng)任何函數(shù)式接口、lambda 表達(dá)式或方法引用。您可以在任何函數(shù)式接口、lambda 表達(dá)式或方法引用上堆疊多個(gè)裝飾器。這樣做的好處是,您可以只選擇所需的裝飾器,而無(wú)需考慮其他因素。

2.1.網(wǎng)關(guān)熔斷降級(jí)(Spring Cloud Gateway + Resilience4j實(shí)戰(zhàn))

2.1.1.pom依賴

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-circuitbreaker-reactor-resilience4j</artifactId>
</dependency>

2.1.2.yaml配置

spring:
  application:
    name: laokou-gateway
  cloud:
    gateway:
       server:
        webflux:
          routes:
            - id: LAOKOU-SSO-DEMO
              uri: lb://laokou-sso-demo
              predicates:
              - Path=/sso/**
              filters:
              - name: CircuitBreaker
                args:
                  name: default
                  fallbackUri: "forward:/fallback"
          filter:
            circuit-breaker:
              enabled: true

2.1.3.CircuitBreakerConfig配置

/**
 * @author laokou
 */
@Configuration
public class CircuitBreakerConfig {
    @Bean
    public RouterFunction<ServerResponse> routerFunction() {
       return RouterFunctions.route(
             RequestPredicates.path("/fallback").and(RequestPredicates.accept(MediaType.TEXT_PLAIN)),
             (request) -> ServerResponse.status(HttpStatus.SC_OK)
                .contentType(MediaType.APPLICATION_JSON)
                .body(BodyInserters.fromValue(Result.fail("Service_Unavailable", "服務(wù)正在維護(hù)"))));
    }
    @Bean
    public Customizer<ReactiveResilience4JCircuitBreakerFactory> reactiveResilience4JCircuitBreakerFactoryCustomizer() {
       return factory -> factory.configureDefault(id -> new Resilience4JConfigBuilder(id)
          // 3秒后超時(shí)時(shí)間
          .timeLimiterConfig(TimeLimiterConfig.custom().timeoutDuration(Duration.ofSeconds(3)).build())
          .circuitBreakerConfig(io.github.resilience4j.circuitbreaker.CircuitBreakerConfig.ofDefaults())
          .build());
    }
}

到此這篇關(guān)于Spring Cloud Gateway實(shí)現(xiàn)分布式限流和熔斷降級(jí)的文章就介紹到這了,更多相關(guān)Spring Cloud Gateway分布式限流和熔斷降級(jí)內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家!

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