Spring Cloud 之feign源码阅读
feign 是什么
feign是声明式的web service客户端，它让微服务之间的调用变得更简单了，类似controller调用service。Spring Cloud集成了Ribbon和Eureka，可在使用Feign时提供负载均衡的http客户端。
怎么启用feign负载均衡
1.在调用方中开启基于feign的负载均衡注解
2.name=被调用feign的服务名称=配置文件中的spring.appliaction.name
feign是怎么实现负载均衡
1.在openfeign源码包中LoadBalancerFeignClient类有个execute方法，就是执行feign请求的。
@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);
//看到这里，其实feign在底层还是用ribbon
FeignLoadBalancer.RibbonRequest ribbonRequest = new FeignLoadBalancer.RibbonRequest(
this.delegate, request, uriWithoutHost);
IClientConfig requestConfig = getClientConfig(options, clientName);
//这里就是执行整个RPC请求
return lbClient(clientName)//clientName为feign中的name
.executeWithLoadBalancer(ribbonRequest, requestConfig).toResponse();
}
catch (ClientException e) {
IOException io = findIOException(e);
if (io != null) {
throw io;
}
throw new RuntimeException(e);
}
}
2.在上一步的源码中，有个executeWithLoadBalancer方法，该方法的描述：当调用方希望将请求分派到由
负载均衡器，而不是在请求的URI中指定服务器。这个方法是AbstractLoadBalancerAwareClient类中的，具体源码如下：
public T executeWithLoadBalancer(final S request, final IClientConfig requestConfig) throws ClientException {
//该方法是处理重试机制
LoadBalancerCommand command = buildLoadBalancerCommand(request, requestConfig);
try {
//调用的是LoadBalancerCommand类中的submit方法
return command.submit(
new ServerOperation() {
@Override
public Observable call(Server server) {
URI finalUri = reconstructURIWithServer(server, request.getUri());
S requestForServer = (S) request.replaceUri(finalUri);
try {
return Observable.just(AbstractLoadBalancerAwareClient.this.execute(requestForServer, requestConfig));
}
catch (Exception e) {
return Observable.error(e);
}
}
})
.toBlocking()
.single();
} catch (Exception e) {
Throwable t = e.getCause();
if (t instanceof ClientException) {
throw (ClientException) t;
} else {
throw new ClientException(e);
}
}
}
3.LoadBalancerCommand类中的submit方法：创建一个 Observable，该对象一旦订阅，就会与负载均衡器选择的服务器异步执行网络调用：
public Observable submit(final ServerOperation 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 o =
//这里面selectServer()返回一个可观察对象，该对象要么只发出单个请求服务器或在每次订阅时查询下一个服务器的负载平衡器
(server == null ? selectServer() : Observable.just(server))
.concatMap(new Func1>() {
@Override
// Called for each server being selected
public Observable call(Server server) {
context.setServer(server);
final ServerStats stats = loadBalancerContext.getServerStats(server);
// Called for each attempt and retry
Observable o = Observable
.just(server)
.concatMap(new Func1>() {
@Override
public Observable 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() {
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>() {
@Override
public Observable 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);
}
});
}
4.此时跟着源码selectServer（）阅读
private Observable selectServer() {
return Observable.create(new OnSubscribe() {
@Override
public void call(Subscriber next) {
try {
//获取负载均衡的服务，重头戏终于出现啦
Server server = loadBalancerContext.getServerFromLoadBalancer(loadBalancerURI, loadBalancerKey);
next.onNext(server);
next.onCompleted();
} catch (Exception e) {
next.onError(e);
}
}
});
}
5.继续步骤4，代码比较多，就截取其中重要的部分
// Various Supported Cases
// The loadbalancer to use and the instances it has is based on how it was registered
// In each of these cases, the client might come in using Full Url or Partial URL
ILoadBalancer lb = getLoadBalancer();
if (host == null) {
// Partial URI or no URI Case
// well we have to just get the right instances from lb - or we fall back
if (lb != null){
//根据key获取Server
Server svc = lb.chooseServer(loadBalancerKey);
if (svc == null){
throw new ClientException(ClientException.ErrorType.GENERAL,
"Load balancer does not have available server for client: "
+ clientName);
}
host = svc.getHost();
if (host == null){
throw new ClientException(ClientException.ErrorType.GENERAL,
"Invalid Server for :" + svc);
}
logger.debug("{} using LB returned Server: {} for request {}", new Object[]{clientName, svc, original});
return svc;
}
这里看到是默认实现ILoadBalancer的chooseServer（）方法
6.BaseLoadBalancer类：一个负载均衡器的基本实现，其中有一个任意列表服务器可以设置为服务器池。实现方法代码：
public Server chooseServer(Object key) {
if (counter == null) {
counter = createCounter();
}
counter.increment();
if (rule == null) {
return null;
} else {
try {
//默认规则选择负载均衡服务
return rule.choose(key);
} catch (Exception e) {
logger.warn("LoadBalancer [{}]: Error choosing server for key {}", name, key, e);
return null;
}
}
}
rule究竟是何方妖怪：
private final static IRule DEFAULT_RULE = new RoundRobinRule();
private final static SerialPingStrategy DEFAULT_PING_STRATEGY = new SerialPingStrategy();
private static final String DEFAULT_NAME = "default";
private static final String PREFIX = "LoadBalancer_";
protected IRule rule = DEFAULT_RULE;
此时可以看到RoundRobinRule类，终于见到黎明曙光啦。
7.RoundRobinRule：最广为人知和最基本的负载平衡策略：
public Server choose(ILoadBalancer lb, Object key) {
if (lb == null) {
log.warn("no load balancer");
return null;
}
Server server = null;
int count = 0;
//轮训重试10次
while (server == null && count++ < 10) {
List reachableServers = lb.getReachableServers();
List allServers = lb.getAllServers();
int upCount = reachableServers.size();
int serverCount = allServers.size();
if ((upCount == 0) || (serverCount == 0)) {
log.warn("No up servers available from load balancer: " + lb);
return null;
}
int nextServerIndex = incrementAndGetModulo(serverCount);
server = allServers.get(nextServerIndex);
if (server == null) {
/* Transient. */
Thread.yield();
continue;
}
if (server.isAlive() && (server.isReadyToServe())) {
return (server);
}
// Next.
server = null;
}
if (count >= 10) {
log.warn("No available alive servers after 10 tries from load balancer: "
+ lb);
}
return server;
}
最后ribbon中的负载均衡策略
RandomRule表示随机策略、RoundRobin表示轮询策略、WeightedResponseTimeRule表示加权策略、BestAvailableRule表示请求数最少策略.在实际项目中可以根据业务需求，采用不同的策略。
总结
阅读源码实现痛苦并快乐着，但又是一门基本功，知其然，并知其所以然，方能成神。