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发表于 2020-01-16 09:00:01 | 查看: 462 | 回复: 0

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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。

image.png

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:

  1. 线程池初始化过程
  2. 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,groupKeycommandKey, 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。

  1. 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。
  2. 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。

groupkeycommandKey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。

HystrixContextScheduler类中有HystrixContextSchedulerWorkerThreadPoolSchedulerThreadPoolWorker 这几个内部类。看看它们的作用:

  1. 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。

  2. ThreadPoolScheduler:执行createWorker()方法,默认使用ThreadPoolWorker()

  3. 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:

  1. 浏览器发送请求,执行HystrixTargter
  2. 创建HystrixCommand,根据serviceName构造线程池
  3. 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:
Hystrix线程池创建过程及线程调用原理.jpg

高清大图:https://www.processon.com/view/link/5e1c128ce4b0169fb51ce77e

申明

本文章首发自本人博客:https://www.cnblogs.com/wang-meng 和公众号:壹枝花算不算浪漫,如若转载请标明来源!

感兴趣的小伙伴可关注个人公众号:壹枝花算不算浪漫

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