前期回顾：

【Java编程系列】Springcloud-gateway自带限流方案实践篇

1、实践中发生的问题

主要有以下几个问题：

1、限流返回的响应数据无法自定义

(LogFormatUtils.java:91) - [7b93af46-20] Completed 429 TOO_MANY_REQUESTS

返回后显示的情况如下：

2、默认的限流器（RequestRateLimiterGatewayFilterFactory）会发生异常

有一部分情况会出现：Error [java.lang.UnsupportedOperationException]

详细信息如下：

2023-04-25 12:27:10.628 [NGIEGzOnuguYdozn] ERROR (HttpWebHandlerAdapter.java:295) - [7533b14d-23] Error [java.lang.UnsupportedOperationException] for HTTP POST "***/activity/interaction", but ServerHttpResponse already committed (429 TOO_MANY_REQUESTS)
2023-04-25 12:27:10.641 [NGIEGzOnuguYdozn] ERROR (Loggers.java:319) - [id: 0x7533b14d, L:/0:0:0:0:0:0:0:1:18085 - R:/0:0:0:0:0:0:0:1:48745] Error starting response. Replying error status
java.lang.UnsupportedOperationException: nullat org.springframework.http.ReadOnlyHttpHeaders.add(ReadOnlyHttpHeaders.java:91)Suppressed: reactor.core.publisher.FluxOnAssembly$OnAssemblyException:
Error has been observed at the following site(s):|_ checkpoint ⇢ springfox.boot.starter.autoconfigure.SwaggerUiWebFluxConfiguration$CustomWebFilter [DefaultWebFilterChain]|_ checkpoint ⇢ org.springframework.cloud.gateway.filter.WeightCalculatorWebFilter [DefaultWebFilterChain]|_ checkpoint ⇢ HTTP POST "/***/***/interact" [Exception***Handler]
Stack trace:at org.springframework.http.ReadOnlyHttpHeaders.add(ReadOnlyHttpHeaders.java:91)at org.springframework.cloud.gateway.filter.factory.RequestRateLimiterGatewayFilterFactory.lambda$null$0(RequestRateLimiterGatewayFilterFactory.java:120)at reactor.core.publisher.MonoFlatMap$FlatMapMain.onNext(MonoFlatMap.java:118)at reactor.core.publisher.FluxMapFuseable$MapFuseableSubscriber.onNext(FluxMapFuseable.java:121)at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1705)at reactor.core.publisher.MonoReduceSeed$ReduceSeedSubscriber.onComplete(MonoReduceSeed.java:156)at reactor.core.publisher.Operators$MultiSubscriptionSubscriber.onComplete(Operators.java:1939)at reactor.core.publisher.FluxUsingWhen$UsingWhenSubscriber.deferredComplete(FluxUsingWhen.java:402)at reactor.core.publisher.FluxUsingWhen$CommitInner.onComplete(FluxUsingWhen.java:536)at reactor.core.publisher.MonoIgnoreElements$IgnoreElementsSubscriber.onComplete(MonoIgnoreElements.java:81)at reactor.core.publisher.FluxFlatMap$FlatMapMain.checkTerminated(FluxFlatMap.java:803)at reactor.core.publisher.FluxFlatMap$FlatMapMain.drainLoop(FluxFlatMap.java:589)at reactor.core.publisher.FluxFlatMap$FlatMapMain.drain(FluxFlatMap.java:569)at reactor.core.publisher.FluxFlatMap$FlatMapMain.onComplete(FluxFlatMap.java:455)at reactor.core.publisher.FluxArray$ArraySubscription.slowPath(FluxArray.java:137)at reactor.core.publisher.FluxArray$ArraySubscription.request(FluxArray.java:99)at reactor.core.publisher.FluxFlatMap$FlatMapMain.onSubscribe(FluxFlatMap.java:363)at reactor.core.publisher.FluxMerge.subscribe(FluxMerge.java:69)at reactor.core.publisher.Mono.subscribe(Mono.java:4110)at reactor.core.publisher.FluxUsingWhen$UsingWhenSubscriber.onComplete(FluxUsingWhen.java:394)at reactor.core.publisher.FluxMap$MapSubscriber.onComplete(FluxMap.java:136)at reactor.core.publisher.Operators$MultiSubscriptionSubscriber.onComplete(Operators.java:1939)at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.drain(FluxConcatMap.java:359)at reactor.core.publisher.FluxConcatMap$ConcatMapImmediate.onComplete(FluxConcatMap.java:268)at reactor.core.publisher.Operators$MultiSubscriptionSubscriber.onComplete(Operators.java:1939)at reactor.core.publisher.MonoFlatMapMany$FlatMapManyInner.onComplete(MonoFlatMapMany.java:252)at io.lettuce.core.RedisPublisher$ImmediateSubscriber.onComplete(RedisPublisher.java:895)at io.lettuce.core.RedisPublisher$State.onAllDataRead(RedisPublisher.java:673)at io.lettuce.core.RedisPublisher$State$3.read(RedisPublisher.java:587)at io.lettuce.core.RedisPublisher$State$3.onDataAvailable(RedisPublisher.java:544)at io.lettuce.core.RedisPublisher$RedisSubscription.onDataAvailable(RedisPublisher.java:313)at io.lettuce.core.RedisPublisher$RedisSubscription.onAllDataRead(RedisPublisher.java:328)at io.lettuce.core.RedisPublisher$SubscriptionCommand.complete(RedisPublisher.java:758)at io.lettuce.core.protocol.CommandHandler.complete(CommandHandler.java:654)at io.lettuce.core.protocol.CommandHandler.decode(CommandHandler.java:614)at io.lettuce.core.protocol.CommandHandler.channelRead(CommandHandler.java:565)at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:377)at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:363)at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:355)at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1410)at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:377)at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:363)at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:919)at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:163)at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:714)at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:650)at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:576)at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:493)at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:989)at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)at java.lang.Thread.run(Thread.java:748)

3、在nacos配置gateway的限流配置不生效问题

在nacos的配置中，配置gateway的限流配置，如下：

- id: ***serviceuri: 'lb://***service'order: 0filters: []predicates:- args:pattern: /xxx/**name: Path
- id: ***service-limiteruri: lb://***serviceorder: 0predicates:- Path= /xxx/activity/**filters:- name: GatewayRequestRateLimiterargs:key-resolver: "#{@remoteAddrKeyResolver}"redis-rate-limiter.replenishRate: 20 # 令牌桶填充的速率 秒为单位redis-rate-limiter.burstCapacity: 20 # 令牌桶总容量redis-rate-limiter.requestedTokens: 1 # 每次请求获取的令牌数

你会发现这样配置是不会生效的~~~，测试时都有点不理解。。。

2、解决方案

首先，我们分析一下2种情况导致的原因，

第一个问题，因为源码的过滤器RequestRateLimiterGatewayFilterFactory中，会将限流拦截的请求的http status code设置为429，但是具体的内容格式却不是JSON格式，导致我们看到的响应结果如上图所示。

第二个问题，通过报错内容提示，我们可以找到源码的120行：

at org.springframework.cloud.gateway.filter.factory.RequestRateLimiterGatewayFilterFactory.

lambda$null$0(RequestRateLimiterGatewayFilterFactory.java:120)

源码如下：

这里有往header里加参数，但是提示显示，是 ReadOnlyHttpHeaders.add，只读的headers，是不可以添加操作的，所以抛出了UnsupportedOperationException的异常：

所以，

这2个问题基本都是由于源代码的过滤器所导致，这里要解决问题，我们可以自定义一个过滤器替代一下，代码如下：

package ***.filters;import com.alibaba.fastjson.JSONObject;
import lombok.extern.slf4j.Slf4j;
import org.springframework.cloud.gateway.filter.GatewayFilter;
import org.springframework.cloud.gateway.filter.factory.RequestRateLimiterGatewayFilterFactory;
import org.springframework.cloud.gateway.filter.ratelimit.KeyResolver;
import org.springframework.cloud.gateway.filter.ratelimit.RateLimiter;
import org.springframework.cloud.gateway.route.Route;
import org.springframework.cloud.gateway.support.ServerWebExchangeUtils;
import org.springframework.core.io.buffer.DataBuffer;
import org.springframework.http.HttpStatus;
import org.springframework.http.server.reactive.ServerHttpResponse;
import org.springframework.stereotype.Component;
import reactor.core.publisher.Mono;import java.nio.charset.StandardCharsets;
import java.util.Map;/*** @Date: 2023/4/25 10:44* @Description gateway限流limiter，重写限流原过滤器RequestRateLimiterGatewayFilterFactory*/
@Component
@Slf4j
public class GatewayRequestRateLimiter extends RequestRateLimiterGatewayFilterFactory {private final RateLimiter defaultRateLimiter;private final KeyResolver defaultKeyResolver;public GatewayRequestRateLimiter(RateLimiter defaultRateLimiter, KeyResolver defaultKeyResolver) {super(defaultRateLimiter, defaultKeyResolver);this.defaultRateLimiter = defaultRateLimiter;this.defaultKeyResolver = defaultKeyResolver;}@Overridepublic GatewayFilter apply(Config config) {KeyResolver resolver = getOrDefault(config.getKeyResolver(), defaultKeyResolver);RateLimiter<Object> limiter = getOrDefault(config.getRateLimiter(), defaultRateLimiter);return (exchange, chain) -> resolver.resolve(exchange).flatMap(key -> {String routeId = config.getRouteId();if (routeId == null) {Route route = exchange.getAttribute(ServerWebExchangeUtils.GATEWAY_ROUTE_ATTR);routeId = route.getId();}String finalRouteId = routeId;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);}ServerHttpResponse httpResponse = exchange.getResponse();//修改code为500httpResponse.setStatusCode(HttpStatus.INTERNAL_SERVER_ERROR);if (!httpResponse.getHeaders().containsKey("Content-Type")) {httpResponse.getHeaders().add("Content-Type", "application/json");}//此处无法触发全局异常处理，手动返回JSONObject object = new JSONObject();object.put("status","429");object.put("message","请求已被限流");DataBuffer buffer = httpResponse.bufferFactory().wrap(object.toJSONString().getBytes(StandardCharsets.UTF_8));return httpResponse.writeWith(Mono.just(buffer));});});}private <T> T getOrDefault(T configValue, T defaultValue) {return (configValue != null) ? configValue : defaultValue;}
}

然后，替换掉原来的gateway中的配置项：

filters:- name: GatewayRequestRateLimiter   #替换原默认过滤器RequestRateLimiterargs:key-resolver: "#{@remoteAddrKeyResolver}"redis-rate-limiter.replenishRate: 20 # 令牌桶填充的速率 秒为单位redis-rate-limiter.burstCapacity: 20 # 令牌桶总容量redis-rate-limiter.requestedTokens: 1 # 每次请求获取的令牌数

然后再来看看效果，返回结果：

第三个问题，是因为该服务的限流配置的过滤器优先级，和要限流的服务本身的路由配置，两者之间的执行前后优先级一样或是限流的在服务本身配置之后了，简单说就是order的值如果更大，就会越迟执行，所以才导致限流配置没生效。。改成如下即可：

- id: ***service-limiteruri: lb://***serviceorder: -1   ##将优先级改小，即提高优先级predicates:- Path= /xxx/activity/**filters:- name: GatewayRequestRateLimiterargs:key-resolver: "#{@remoteAddrKeyResolver}"redis-rate-limiter.replenishRate: 20 # 令牌桶填充的速率 秒为单位redis-rate-limiter.burstCapacity: 20 # 令牌桶总容量redis-rate-limiter.requestedTokens: 1 # 每次请求获取的令牌数

好啦，至此限流的优化处理，基本完成啦。。。

最后，对于本文有疑问的地方，欢迎下方留言讨论，喜欢的朋友，请帮忙一键三连~~~

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