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前言
前情回顾
上一讲我们讲了配置了feign.hystrix.enabled=true之后，默认的Targeter就会构建成HystrixTargter, 然后通过对应的HystrixInvocationHandler 生成对应的动态代理。
本讲目录
这一讲开始讲解Hystrix相关代码，当然还是基于上一个组件Feign的基础上开始讲解的，这里默认你已经对Feign有过大致了解。
目录如下：
- 线程池初始化过程
- HystrixCommand通过线程池执行原理
由于这里面代码比较多，所以我都是将一些主要核心代码发出来，这里后面会汇总一个流程图，可以参考流程图 自己一点点调试。
这里建议在回调的地方都加上断点，而且修改feign和hystrix超时时间，浏览器发送请求后，一步步debug代码。
源码分析
线程池初始化过程
上一讲已经讲过激活Hystrix后，构造的InvocationHandler为HystrixInvocationHandler，所以当调用FeignClient服务实例的时候，会先执行HystrixInvocationHandler.invoke()方法，这里我们先跟进这个方法：
final class HystrixInvocationHandler implements InvocationHandler {
@Override
public Object invoke(final Object proxy, final Method method, final Object[] args)
throws Throwable {
// 构建一个HystrixCommand
// HystrixCommand构造参数需要Setter对象
HystrixCommand<Object> hystrixCommand = new HystrixCommand<Object>(setterMethodMap.get(method)) {
@Override
protected Object run() throws Exception {
try {
// 执行SynchronousMethodHandler.invoke方法
return HystrixInvocationHandler.this.dispatch.get(method).invoke(args);
} catch (Exception e) {
throw e;
} catch (Throwable t) {
throw (Error) t;
}
}
}
// 省略部分代码...
return hystrixCommand.execute();
}
}
这里主要是构造HystrixCommand，我们先看看它的构造函数以及线程池池初始化的代码：
public abstract class HystrixCommand<R> extends AbstractCommand<R> implements HystrixExecutable<R>, HystrixInvokableInfo<R>, HystrixObservable<R> {
protected HystrixCommand(HystrixCommandGroupKey group) {
super(group, null, null, null, null, null, null, null, null, null, null, null);
}
}
abstract class AbstractCommand<R> implements HystrixInvokableInfo<R>, HystrixObservable<R> {
protected AbstractCommand(HystrixCommandGroupKey group, HystrixCommandKey key, HystrixThreadPoolKey threadPoolKey, HystrixCircuitBreaker circuitBreaker, HystrixThreadPool threadPool,
HystrixCommandProperties.Setter commandPropertiesDefaults, HystrixThreadPoolProperties.Setter threadPoolPropertiesDefaults,
HystrixCommandMetrics metrics, TryableSemaphore fallbackSemaphore, TryableSemaphore executionSemaphore,
HystrixPropertiesStrategy propertiesStrategy, HystrixCommandExecutionHook executionHook) {
this.commandGroup = initGroupKey(group);
this.commandKey = initCommandKey(key, getClass());
this.properties = initCommandProperties(this.commandKey, propertiesStrategy, commandPropertiesDefaults);
this.threadPoolKey = initThreadPoolKey(threadPoolKey, this.commandGroup, this.properties.executionIsolationThreadPoolKeyOverride().get());
this.metrics = initMetrics(metrics, this.commandGroup, this.threadPoolKey, this.commandKey, this.properties);
this.circuitBreaker = initCircuitBreaker(this.properties.circuitBreakerEnabled().get(), circuitBreaker, this.commandGroup, this.commandKey, this.properties, this.metrics);
// 初始化线程池
this.threadPool = initThreadPool(threadPool, this.threadPoolKey, threadPoolPropertiesDefaults);
// 省略部分代码...
}
private static HystrixThreadPool initThreadPool(HystrixThreadPool fromConstructor, HystrixThreadPoolKey threadPoolKey, HystrixThreadPoolProperties.Setter threadPoolPropertiesDefaults) {
if (fromConstructor == null) {
// get the default implementation of HystrixThreadPool
return HystrixThreadPool.Factory.getInstance(threadPoolKey, threadPoolPropertiesDefaults);
} else {
return fromConstructor;
}
}
}
public interface HystrixThreadPool {
final static ConcurrentHashMap<String, HystrixThreadPool> threadPools = new ConcurrentHashMap<String, HystrixThreadPool>();
static HystrixThreadPool getInstance(HystrixThreadPoolKey threadPoolKey, HystrixThreadPoolProperties.Setter propertiesBuilder) {
// 这个线程池的key就是我们feignClient定义的value名称，其他服务的projectName
// 在我们的demo中：key = serviceA
String key = threadPoolKey.name();
// threadPools是一个map，key就是serviceA
HystrixThreadPool previouslyCached = threadPools.get(key);
if (previouslyCached != null) {
return previouslyCached;
}
// 初始化线程池
synchronized (HystrixThreadPool.class) {
if (!threadPools.containsKey(key)) {
threadPools.put(key, new HystrixThreadPoolDefault(threadPoolKey, propertiesBuilder));
}
}
return threadPools.get(key);
}
}
public abstract class HystrixThreadPoolProperties {
/* defaults */
static int default_coreSize = 10;
static int default_maximumSize = 10;
static int default_keepAliveTimeMinutes = 1;
static int default_maxQueueSize = -1;
static boolean default_allow_maximum_size_to_diverge_from_core_size = false;
static int default_queueSizeRejectionThreshold = 5;
static int default_threadPoolRollingNumberStatisticalWindow = 10000;
static int default_threadPoolRollingNumberStatisticalWindowBuckets = 10;
// 省略部分代码...
}
这里主要是初始化线程池的逻辑，从HystrixCommand一直到HystrixThreadPoolProperties。这里的threadPools 是一个Map，一个serviceName会对应一个线程池。
线程池的默认配置都在HystrixThreadPoolProperties中。线程池的核心线程和最大线程数都是10，队列的大小为-1，这里意思是不使用队列。
HystrixCommand构造函数需要接收一个Setter对象，Setter中包含两个很重要的属性，groupKey和commandKey, 这里看下Setter是如何构造的：
final class HystrixInvocationHandler implements InvocationHandler {
HystrixInvocationHandler(Target<?> target, Map<Method, MethodHandler> dispatch,
SetterFactory setterFactory, FallbackFactory<?> fallbackFactory) {
this.target = checkNotNull(target, "target");
this.dispatch = checkNotNull(dispatch, "dispatch");
this.fallbackFactory = fallbackFactory;
this.fallbackMethodMap = toFallbackMethod(dispatch);
this.setterMethodMap = toSetters(setterFactory, target, dispatch.keySet());
}
static Map<Method, Setter> toSetters(SetterFactory setterFactory, Target<?> target,
Set<Method> methods) {
Map<Method, Setter> result = new LinkedHashMap<Method, Setter>();
for (Method method : methods) {
method.setAccessible(true);
result.put(method, setterFactory.create(target, method));
}
return result;
}
}
public interface SetterFactory {
HystrixCommand.Setter create(Target<?> target, Method method);
final class Default implements SetterFactory {
@Override
public HystrixCommand.Setter create(Target<?> target, Method method) {
// groupKey既是调用的服务服务名称：serviceA
String groupKey = target.name();
// commandKey即是方法的名称+入参定义等，一个commandKey能够确定这个类中唯一的一个方法
String commandKey = Feign.configKey(target.type(), method);
return HystrixCommand.Setter
.withGroupKey(HystrixCommandGroupKey.Factory.asKey(groupKey))
.andCommandKey(HystrixCommandKey.Factory.asKey(commandKey));
}
}
}
}
构建一个HystrixCommand时必须要传入这两个参数。
groupKey: 就是调用的服务名称，例如我们demo中的ServiceA，groupKey对应着一个线程池。commandKey: 一个FeignClient接口中的一个方法就是一个commandKey, 其组成为方法名和入参等信息。
groupkey和commandKey是一对多的关系，例如ServiceA中的2个方法，那么groupKey就对应着这个ServiceA中的2个commandKey。
groupKey -> target.name() -> ServiceA -> @FeignClient注解里设置的服务名称
commanKey -> ServiceAFeignClient#sayHello(String)
这里回调函数执行HystrixInvocationHandler.this.dispatch.get(method).invoke(args) 其实就是执行SynchronousMethodHandler.invoke() 方法了。但是什么时候才会回调回来呢？后面接着看吧。
HystrixCommand通过线程池执行原理
上面已经看了线程池的初始化过程，当一个服务第一次被调用的时候，会判断threadPools (数据结构为ConcurrentHashMap) 中是否存在这个serviceName对应的线程池，如果没有的话则会初始化一个对应的线程池。线程池默认配置属性在HystrixThreadPoolProperties中可以看到。
Hystrix线程池默认是不使用队列进行线程排队的，核心线程数为10。接下来我们看看创建HystrixCommand后，线程池是如何将HystrixCommand 命令提交的：
public abstract class HystrixCommand<R> extends AbstractCommand<R> implements HystrixExecutable<R>, HystrixInvokableInfo<R>, HystrixObservable<R> {
public R execute() {
try {
return queue().get();
} catch (Exception e) {
throw Exceptions.sneakyThrow(decomposeException(e));
}
}
public Future<R> queue() {
final Future<R> delegate = toObservable().toBlocking().toFuture();
final Future<R> f = new Future<R>() {
@Override
public boolean cancel(boolean mayInterruptIfRunning) {
if (delegate.isCancelled()) {
return false;
}
if (HystrixCommand.this.getProperties().executionIsolationThreadInterruptOnFutureCancel().get()) {
interruptOnFutureCancel.compareAndSet(false, mayInterruptIfRunning);
}
final boolean res = delegate.cancel(interruptOnFutureCancel.get());
if (!isExecutionComplete() && interruptOnFutureCancel.get()) {
final Thread t = executionThread.get();
if (t != null && !t.equals(Thread.currentThread())) {
t.interrupt();
}
}
return res;
}
@Override
public boolean isCancelled() {
return delegate.isCancelled();
}
@Override
public boolean isDone() {
return delegate.isDone();
}
@Override
public R get() throws InterruptedException, ExecutionException {
return delegate.get();
}
@Override
public R get(long timeout, TimeUnit unit) throws InterruptedException, ExecutionException, TimeoutException {
return delegate.get(timeout, unit);
}
};
if (f.isDone()) {
try {
f.get();
return f;
} catch (Exception e) {
Throwable t = decomposeException(e);
if (t instanceof HystrixBadRequestException) {
return f;
} else if (t instanceof HystrixRuntimeException) {
HystrixRuntimeException hre = (HystrixRuntimeException) t;
switch (hre.getFailureType()) {
case COMMAND_EXCEPTION:
case TIMEOUT:
// we don't throw these types from queue() only from queue().get() as they are execution errors
return f;
default:
// these are errors we throw from queue() as they as rejection type errors
throw hre;
}
} else {
throw Exceptions.sneakyThrow(t);
}
}
}
return f;
}
}
这里又是一堆的回调函数，我们可以在每个回调函数中打上断点，然后一点点调试。
这里主要是通过toObservable()方法构造了一个Future<R>, 然后包装此Future，添加了中断等逻辑，后面使用f.get() 阻塞获取线程执行结果，最后返回Future对象。
这里我们的重点在于寻找哪里将HystrixCommand丢入线程池，然后返回一个Future的。
接着往后跟进代码：
abstract class AbstractCommand<R> implements HystrixInvokableInfo<R>, HystrixObservable<R> {
public Observable<R> toObservable() {
// _cmd就是HystrixInvocationHandler对象
// 里面包含要请求的method信息，threadPool信息，groupKey，commandKey等信息
final AbstractCommand<R> _cmd = this;
final Func0<Observable<R>> applyHystrixSemantics = new Func0<Observable<R>>() {
@Override
public Observable<R> call() {
if (commandState.get().equals(CommandState.UNSUBSCRIBED)) {
return Observable.never();
}
return applyHystrixSemantics(_cmd);
}
};
// 省略部分回调函数代码...
return Observable.defer(new Func0<Observable<R>>() {
@Override
public Observable<R> call() {
// 是否使用请求缓存，默认为false
final boolean requestCacheEnabled = isRequestCachingEnabled();
// 请求缓存相关
final String cacheKey = getCacheKey();
// 省略部分代码...
Observable<R> hystrixObservable =
Observable.defer(applyHystrixSemantics)
.map(wrapWithAllOnNextHooks);
Observable<R> afterCache;
// put in cache
if (requestCacheEnabled && cacheKey != null) {
// 省略部分代码...
} else {
afterCache = hystrixObservable;
}
return afterCache
.doOnTerminate(terminateCommandCleanup)
.doOnUnsubscribe(unsubscribeCommandCleanup)
.doOnCompleted(fireOnCompletedHook);
}
});
}
}
toObservable()是比较核心的代码，这里也是定义了很多回调函数，上面代码做了精简，留下一些核心逻辑，在defer()中构造返回了一个Observable对象，这个Observable是包含上面的一些回调函数的。
通过debug代码，这里会直接执行到applyHystrixSemantics这个构造函数Func0中的call()方法中，通过语意 我们可以大致猜到这个函数的意思：应用Hystrix语义
接着往下跟进代码：
abstract class AbstractCommand<R> implements HystrixInvokableInfo<R>, HystrixObservable<R> {
private Observable<R> applyHystrixSemantics(final AbstractCommand<R> _cmd) {
executionHook.onStart(_cmd);
// 判断是否短路
if (circuitBreaker.attemptExecution()) {
final TryableSemaphore executionSemaphore = getExecutionSemaphore();
final AtomicBoolean semaphoreHasBeenReleased = new AtomicBoolean(false);
// 如果不使用Semaphore配置，那么tryAcquire使用的是TryableSemaphoreNoOp中的方法，返回true
if (executionSemaphore.tryAcquire()) {
try {
/* used to track userThreadExecutionTime */
executionResult = executionResult.setInvocationStartTime(System.currentTimeMillis());
return executeCommandAndObserve(_cmd)
.doOnError(markExceptionThrown)
.doOnTerminate(singleSemaphoreRelease)
.doOnUnsubscribe(singleSemaphoreRelease);
} catch (RuntimeException e) {
return Observable.error(e);
}
} else {
return handleSemaphoreRejectionViaFallback();
}
} else {
return handleShortCircuitViaFallback();
}
}
}
这里面我们默认使用的线程池的隔离配置，所以executionSemaphore.tryAcquire()都会返回true，这里有个重要的方法：executeCommandAndObserve(_cmd), 我们继续往后跟进这个方法：
abstract class AbstractCommand<R> implements HystrixInvokableInfo<R>, HystrixObservable<R> {
private Observable<R> executeCommandAndObserve(final AbstractCommand<R> _cmd) {
final HystrixRequestContext currentRequestContext = HystrixRequestContext.getContextForCurrentThread();
// 省略部分回调函数...
Observable<R> execution;
// 默认配置timeOutEnabled为true
if (properties.executionTimeoutEnabled().get()) {
// 执行指定的隔离执行命令
execution = executeCommandWithSpecifiedIsolation(_cmd)
.lift(new HystrixObservableTimeoutOperator<R>(_cmd));
} else {
execution = executeCommandWithSpecifiedIsolation(_cmd);
}
return execution.doOnNext(markEmits)
.doOnCompleted(markOnCompleted)
.onErrorResumeNext(handleFallback)
.doOnEach(setRequestContext);
}
}
对于Hystrix来说，默认是开启超时机制的，这里会执行executeCommandWithSpecifiedIsolation(), 返回一个执行的Observable.还是通过方法名我们可以猜测这个方法是：使用指定的隔离执行命令
继续往里面跟进：
abstract class AbstractCommand<R> implements HystrixInvokableInfo<R>, HystrixObservable<R> {
private Observable<R> executeCommandWithSpecifiedIsolation(final AbstractCommand<R> _cmd) {
if (properties.executionIsolationStrategy().get() == ExecutionIsolationStrategy.THREAD) {
// mark that we are executing in a thread (even if we end up being rejected we still were a THREAD execution and not SEMAPHORE)
return Observable.defer(new Func0<Observable<R>>() {
@Override
public Observable<R> call() {
executionResult = executionResult.setExecutionOccurred();
if (!commandState.compareAndSet(CommandState.OBSERVABLE_CHAIN_CREATED, CommandState.USER_CODE_EXECUTED)) {
return Observable.error(new IllegalStateException("execution attempted while in state : " + commandState.get().name()));
}
metrics.markCommandStart(commandKey, threadPoolKey, ExecutionIsolationStrategy.THREAD);
if (isCommandTimedOut.get() == TimedOutStatus.TIMED_OUT) {
return Observable.error(new RuntimeException("timed out before executing run()"));
}
if (threadState.compareAndSet(ThreadState.NOT_USING_THREAD, ThreadState.STARTED)) {
//we have not been unsubscribed, so should proceed
HystrixCounters.incrementGlobalConcurrentThreads();
threadPool.markThreadExecution();
// store the command that is being run
endCurrentThreadExecutingCommand = Hystrix.startCurrentThreadExecutingCommand(getCommandKey());
executionResult = executionResult.setExecutedInThread();
try {
executionHook.onThreadStart(_cmd);
executionHook.onRunStart(_cmd);
executionHook.onExecutionStart(_cmd);
return getUserExecutionObservable(_cmd);
} catch (Throwable ex) {
return Observable.error(ex);
}
} else {
//command has already been unsubscribed, so return immediately
return Observable.error(new RuntimeException("unsubscribed before executing run()"));
}
}
}).subscribeOn(threadPool.getScheduler(new Func0<Boolean>() {
@Override
public Boolean call() {
return properties.executionIsolationThreadInterruptOnTimeout().get() && _cmd.isCommandTimedOut.get() == TimedOutStatus.TIMED_OUT;
}
}));
}
}
}
这里就是我们千辛万苦需要找的核心方法了，里面仍然是一个回调函数，通过断点调试，这里会先执行：subscribeOn回调函数，执行threadPool.getScheduler方法，我们进一步往后跟进：
public interface HystrixThreadPool {
@Override
public Scheduler getScheduler(Func0<Boolean> shouldInterruptThread) {
touchConfig();
return new HystrixContextScheduler(HystrixPlugins.getInstance().getConcurrencyStrategy(), this, shouldInterruptThread);
}
private void touchConfig() {
final int dynamicCoreSize = properties.coreSize().get();
final int configuredMaximumSize = properties.maximumSize().get();
int dynamicMaximumSize = properties.actualMaximumSize();
final boolean allowSizesToDiverge = properties.getAllowMaximumSizeToDivergeFromCoreSize().get();
boolean maxTooLow = false;
// 动态调整最大线程池的数量
if (allowSizesToDiverge && configuredMaximumSize < dynamicCoreSize) {
//if user sets maximum < core (or defaults get us there), we need to maintain invariant of core <= maximum
dynamicMaximumSize = dynamicCoreSize;
maxTooLow = true;
}
// In JDK 6, setCorePoolSize and setMaximumPoolSize will execute a lock operation. Avoid them if the pool size is not changed.
if (threadPool.getCorePoolSize() != dynamicCoreSize || (allowSizesToDiverge && threadPool.getMaximumPoolSize() != dynamicMaximumSize)) {
if (maxTooLow) {
logger.error("Hystrix ThreadPool configuration for : " + metrics.getThreadPoolKey().name() + " is trying to set coreSize = " +
dynamicCoreSize + " and maximumSize = " + configuredMaximumSize + ". Maximum size will be set to " +
dynamicMaximumSize + ", the coreSize value, since it must be equal to or greater than the coreSize value");
}
threadPool.setCorePoolSize(dynamicCoreSize);
threadPool.setMaximumPoolSize(dynamicMaximumSize);
}
threadPool.setKeepAliveTime(properties.keepAliveTimeMinutes().get(), TimeUnit.MINUTES);
}
}
public class HystrixContextScheduler extends Scheduler {
public HystrixContextScheduler(HystrixConcurrencyStrategy concurrencyStrategy, HystrixThreadPool threadPool, Func0<Boolean> shouldInterruptThread) {
this.concurrencyStrategy = concurrencyStrategy;
this.threadPool = threadPool;
this.actualScheduler = new ThreadPoolScheduler(threadPool, shouldInterruptThread);
}
@Override
public Worker createWorker() {
// 构建一个默认的Worker
return new HystrixContextSchedulerWorker(actualScheduler.createWorker());
}
private static class ThreadPoolScheduler extends Scheduler {
private final HystrixThreadPool threadPool;
private final Func0<Boolean> shouldInterruptThread;
public ThreadPoolScheduler(HystrixThreadPool threadPool, Func0<Boolean> shouldInterruptThread) {
this.threadPool = threadPool;
this.shouldInterruptThread = shouldInterruptThread;
}
@Override
public Worker createWorker() {
// 默认的worker为：ThreadPoolWorker
return new ThreadPoolWorker(threadPool, shouldInterruptThread);
}
}
private class HystrixContextSchedulerWorker extends Worker {
// 执行schedule方法
@Override
public Subscription schedule(Action0 action) {
if (threadPool != null) {
if (!threadPool.isQueueSpaceAvailable()) {
throw new RejectedExecutionException("Rejected command because thread-pool queueSize is at rejection threshold.");
}
}
// 默认的worker为：ThreadPoolWorker
return worker.schedule(new HystrixContexSchedulerAction(concurrencyStrategy, action));
}
}
// 执行command的核心类
private static class ThreadPoolWorker extends Worker {
private final HystrixThreadPool threadPool;
private final CompositeSubscription subscription = new CompositeSubscription();
private final Func0<Boolean> shouldInterruptThread;
public ThreadPoolWorker(HystrixThreadPool threadPool, Func0<Boolean> shouldInterruptThread) {
this.threadPool = threadPool;
this.shouldInterruptThread = shouldInterruptThread;
}
@Override
public void unsubscribe() {
subscription.unsubscribe();
}
@Override
public boolean isUnsubscribed() {
return subscription.isUnsubscribed();
}
@Override
public Subscription schedule(final Action0 action) {
if (subscription.isUnsubscribed()) {
// don't schedule, we are unsubscribed
return Subscriptions.unsubscribed();
}
// This is internal RxJava API but it is too useful.
ScheduledAction sa = new ScheduledAction(action);
subscription.add(sa);
sa.addParent(subscription);
ThreadPoolExecutor executor = (ThreadPoolExecutor) threadPool.getExecutor();
FutureTask<?> f = (FutureTask<?>) executor.submit(sa);
sa.add(new FutureCompleterWithConfigurableInterrupt(f, shouldInterruptThread, executor));
return sa;
}
@Override
public Subscription schedule(Action0 action, long delayTime, TimeUnit unit) {
throw new IllegalStateException("Hystrix does not support delayed scheduling");
}
}
}
touchConfig() 方法主要是重新设置最大线程池actualMaximumSize的，这里默认的allowMaximumSizeToDivergeFromCoreSize是false。
在HystrixContextScheduler类中有HystrixContextSchedulerWorker、ThreadPoolScheduler、ThreadPoolWorker 这几个内部类。看看它们的作用：
-
HystrixContextSchedulerWorker: 对外提供schedule()方法，这里会判断线程池队列是否已经满，如果满了这会抛出异常：Rejected command because thread-pool queueSize is at rejection threshold。 如果配置的队列大小为-1 则默认返回true。 -
ThreadPoolScheduler：执行createWorker()方法，默认使用ThreadPoolWorker()类 -
ThreadPoolWorker：执行command的核心逻辑
private static class ThreadPoolWorker extends Worker {
private final HystrixThreadPool threadPool;
private final CompositeSubscription subscription = new CompositeSubscription();
private final Func0<Boolean> shouldInterruptThread;
@Override
public Subscription schedule(final Action0 action) {
if (subscription.isUnsubscribed()) {
return Subscriptions.unsubscribed();
}
ScheduledAction sa = new ScheduledAction(action);
subscription.add(sa);
sa.addParent(subscription);
// 获取线程池
ThreadPoolExecutor executor = (ThreadPoolExecutor) threadPool.getExecutor();
// 将包装后的HystrixCommand submit到线程池，然后返回FutureTask
FutureTask<?> f = (FutureTask<?>) executor.submit(sa);
sa.add(new FutureCompleterWithConfigurableInterrupt(f, shouldInterruptThread, executor));
return sa;
}
}
原来一个command就是在这里被提交到线程池的，再次回到AbstractCommand.executeCommandWithSpecifiedIsolation()方法中，这里会回调到这个回调函数的call()方法中，这里一路执行逻辑如下：
getUserExecutionObservable(_cmd)>getExecutionObservable()>hystrixCommand.run()==>SynchronousMethodHandler.invoke()
这里最后执行到HystrixInvocationHandler中的invoke()方法中的回调函数run()中，最后执行SynchronousMethodHandler.invoke()方法。
一个正常的feign请求，经过hystrix走一遍也就返回对应的response。
总结
上面一顿分析，不知道大家有没有对hystrix 线程池及command执行是否有些理解了？
这个是一个正向流程，没有涉及超时、熔断、降级等代码。关于这些异常降级的源码会在后面一篇文章涉及。
还是之前的建议，大家可以在每个相关的回调函数打上断点，然后一点点调试。
最后再总结一下简单的流程：
- 浏览器发送请求，执行HystrixTargter
- 创建HystrixCommand，根据serviceName构造线程池
- AbstractCommand中一堆回调函数，最后将command交由线程池submit处理
画一张流程图加深理解：
高清大图：https://www.processon.com/view/link/5e1c128ce4b0169fb51ce77e
申明
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