Spring Cloud Feign组件实例解析
这篇文章主要介绍了Spring Cloud Feign组件实例解析,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下
采用Spring Cloud微服务框架后,经常会涉及到服务间调用,服务间调用采用了Feign组件。
由于之前有使用dubbo经验。dubbo的负载均衡策略(轮训、最小连接数、随机轮训、加权轮训),dubbo失败策略(快速失败、失败重试等等),
所以Feign负载均衡策略的是什么? 失败后是否会重试,重试策略又是什么?带这个疑问,查了一些资料,最后还是看了下代码。毕竟代码就是一切
Spring boot集成Feign的大概流程:
1、利用FeignAutoConfiguration自动配置。并根据EnableFeignClients 自动注册产生Feign的代理类。
2、注册方式利用FeignClientFactoryBean,熟悉Spring知道FactoryBean 产生bean的工厂,有个重要方法getObject产生FeignClient容器bean
3、同时代理类中使用hystrix做资源隔离,Feign代理类中 构造 RequestTemplate ,RequestTemlate要做的向负载均衡选中的server发送http请求,并进行编码和解码一系列操作。
下面只是粗略的看了下整体流程,先有整体再有细节吧,下面利用IDEA看下细节:
一、Feign失败重试
SynchronousMethodHandler的方法中的处理逻辑:
@Override public Object invoke(Object[] argv) throws Throwable { RequestTemplate template = buildTemplateFromArgs.create(argv); Retryer retryer = this.retryer.clone(); while (true) { try { return executeAndDecode(template); } catch (RetryableException e) { retryer.continueOrPropagate(e); if (logLevel != Logger.Level.NONE) { logger.logRetry(metadata.configKey(), logLevel); } continue; } } }
- 上面的逻辑很简单。构造 template 并去进行服务间的http调用,然后对返回结果进行解码
- 当抛出 RetryableException 后,异常逻辑是否重试? 重试多少次? 带这个问题,看了retryer.continueOrPropagate(e);
具体逻辑如下:
public void continueOrPropagate(RetryableException e) { if (attempt++ >= maxAttempts) { throw e; } long interval; if (e.retryAfter() != null) { interval = e.retryAfter().getTime() - currentTimeMillis(); if (interval > maxPeriod) { interval = maxPeriod; } if (interval < 0) { return; } } else { interval = nextMaxInterval(); } try { Thread.sleep(interval); } catch (InterruptedException ignored) { Thread.currentThread().interrupt(); } sleptForMillis += interval; }
- 当重试次数大于默认次数5时候,直接抛出异常,不在重试
- 否则每隔一段时间 默认值最大1ms 后重试一次。
这就Feign这块的重试这块的粗略逻辑,由于之前工作中一直使用dubbo。同样是否需要将生产环境中重试操作关闭?
思考:之前dubbo生产环境的重试操作都会关闭。原因有几个:
- 一般第一次失败,重试也会失败,极端情况下不断的重试,会占用大量dubbo连接池,造成连接池被打满,影响核心功能
- 也是比较重要的一点原因,重试带来的业务逻辑的影响,即如果接口不是幂等的,重试会带来业务逻辑的错误,引发问题
二、Feign负载均衡策略
那么负载均衡的策略又是什么呢?由上图中可知 executeAndDecode(template)
Object executeAndDecode(RequestTemplate template) throws Throwable { Request request = targetRequest(template); if (logLevel != Logger.Level.NONE) { logger.logRequest(metadata.configKey(), logLevel, request); } Response response; long start = System.nanoTime(); try { response = client.execute(request, options); // ensure the request is set. TODO: remove in Feign 10 response.toBuilder().request(request).build(); } catch (IOException e) { if (logLevel != Logger.Level.NONE) { logger.logIOException(metadata.configKey(), logLevel, e, elapsedTime(start)); } throw errorExecuting(request, e); } long elapsedTime = TimeUnit.NANOSECONDS.toMillis(System.nanoTime() - start); boolean shouldClose = true; try { if (logLevel != Logger.Level.NONE) { response = logger.logAndRebufferResponse(metadata.configKey(), logLevel, response, elapsedTime); // ensure the request is set. TODO: remove in Feign 10 response.toBuilder().request(request).build(); } if (Response.class == metadata.returnType()) { if (response.body() == null) { return response; } if (response.body().length() == null || response.body().length() > MAX_RESPONSE_BUFFER_SIZE) { shouldClose = false; return response; } // Ensure the response body is disconnected byte[] bodyData = Util.toByteArray(response.body().asInputStream()); return response.toBuilder().body(bodyData).build(); } if (response.status() >= 200 && response.status() < 300) { if (void.class == metadata.returnType()) { return null; } else { return decode(response); } } else if (decode404 && response.status() == 404 && void.class != metadata.returnType()) { return decode(response); } else { throw errorDecoder.decode(metadata.configKey(), response); } } catch (IOException e) { if (logLevel != Logger.Level.NONE) { logger.logIOException(metadata.configKey(), logLevel, e, elapsedTime); } throw errorReading(request, response, e); } finally { if (shouldClose) { ensureClosed(response.body()); } } }
概括的说主要做了两件事:发送HTTP请求,解码响应数据
想看的负载均衡应该在11行 response = client.execute(request, options); 而client的实现方式有两种 Default、LoadBalancerFeignClient
猜的话应该是LoadBalancerFeignClient,带这个问题去看源码(其实个人更喜欢带着问题看源码,没有目的一是看很难将复杂的源码关联起来,二是很容易迷失其中)
果然通过一番查找发现 Client 实例就是LoadBalancerFeignClient,而设置这个Client就是通过上面说的FeignClientFactoryBean的getObject方法中设置的,具体不说了
下面重点看LoadBalancerFeignClient execute(request, options)
@Override public Response execute(Request request, Request.Options options) throws IOException { try { URI asUri = URI.create(request.url()); String clientName = asUri.getHost(); URI uriWithoutHost = cleanUrl(request.url(), clientName); FeignLoadBalancer.RibbonRequest ribbonRequest = new FeignLoadBalancer.RibbonRequest( this.delegate, request, uriWithoutHost); IClientConfig requestConfig = getClientConfig(options, clientName); return lbClient(clientName).executeWithLoadBalancer(ribbonRequest, requestConfig).toResponse(); } catch (ClientException e) { IOException io = findIOException(e); if (io != null) { throw io; } throw new RuntimeException(e); } }
通过几行代码比较重要的点RibbonRequest ,原来Feign负载均衡还是通过Ribbon实现的,那么Ribbo又是如何实现负载均衡的呢?
public Observable<T> submit(final ServerOperation<T> operation) { final ExecutionInfoContext context = new ExecutionInfoContext(); if (listenerInvoker != null) { try { listenerInvoker.onExecutionStart(); } catch (AbortExecutionException e) { return Observable.error(e); } } final int maxRetrysSame = retryHandler.getMaxRetriesOnSameServer(); final int maxRetrysNext = retryHandler.getMaxRetriesOnNextServer(); // Use the load balancer Observable<T> o = (server == null ? selectServer() : Observable.just(server)) .concatMap(new Func1<Server, Observable<T>>() { @Override // Called for each server being selected public Observable<T> call(Server server) { context.setServer(server); final ServerStats stats = loadBalancerContext.getServerStats(server); // Called for each attempt and retry Observable<T> o = Observable .just(server) .concatMap(new Func1<Server, Observable<T>>() { @Override public Observable<T> call(final Server server) { context.incAttemptCount(); loadBalancerContext.noteOpenConnection(stats); if (listenerInvoker != null) { try { listenerInvoker.onStartWithServer(context.toExecutionInfo()); } catch (AbortExecutionException e) { return Observable.error(e); } } final Stopwatch tracer = loadBalancerContext.getExecuteTracer().start(); return operation.call(server).doOnEach(new Observer<T>() { private T entity; @Override public void onCompleted() { recordStats(tracer, stats, entity, null); // TODO: What to do if onNext or onError are never called? } @Override public void onError(Throwable e) { recordStats(tracer, stats, null, e); logger.debug("Got error {} when executed on server {}", e, server); if (listenerInvoker != null) { listenerInvoker.onExceptionWithServer(e, context.toExecutionInfo()); } } @Override public void onNext(T entity) { this.entity = entity; if (listenerInvoker != null) { listenerInvoker.onExecutionSuccess(entity, context.toExecutionInfo()); } } private void recordStats(Stopwatch tracer, ServerStats stats, Object entity, Throwable exception) { tracer.stop(); loadBalancerContext.noteRequestCompletion(stats, entity, exception, tracer.getDuration(TimeUnit.MILLISECONDS), retryHandler); } }); } }); if (maxRetrysSame > 0) o = o.retry(retryPolicy(maxRetrysSame, true)); return o; } }); if (maxRetrysNext > 0 && server == null) o = o.retry(retryPolicy(maxRetrysNext, false)); return o.onErrorResumeNext(new Func1<Throwable, Observable<T>>() { @Override public Observable<T> call(Throwable e) { if (context.getAttemptCount() > 0) { if (maxRetrysNext > 0 && context.getServerAttemptCount() == (maxRetrysNext + 1)) { e = new ClientException(ClientException.ErrorType.NUMBEROF_RETRIES_NEXTSERVER_EXCEEDED, "Number of retries on next server exceeded max " + maxRetrysNext + " retries, while making a call for: " + context.getServer(), e); } else if (maxRetrysSame > 0 && context.getAttemptCount() == (maxRetrysSame + 1)) { e = new ClientException(ClientException.ErrorType.NUMBEROF_RETRIES_EXEEDED, "Number of retries exceeded max " + maxRetrysSame + " retries, while making a call for: " + context.getServer(), e); } } if (listenerInvoker != null) { listenerInvoker.onExecutionFailed(e, context.toFinalExecutionInfo()); } return Observable.error(e); } }); }
通过上面代码分析,发现Ribbon和Hystrix一样都是利用了rxjava看来有必要掌握下rxjava了又。这里面 比较重要的就是17行,
selectServer() 方法选择指定的Server,负载均衡的策略主要是有ILoadBalancer接口不同实现方式:
- BaseLoadBalancer采用的规则为RoundRobinRule 轮训规则
- DynamicServerListLoadBalancer继承了BaseLoadBalancer,主要运行时改变Server列表
- NoOpLoadBalancer 什么操作都不做
- ZoneAwareLoadBalancer 功能主要是根据区域Zone分组的实例列表
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持我们。