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Spring Cloud中Hystrix 线程隔离导致ThreadLocal数据丢失（续）

前言

上篇文章

《Spring Cloud中Hystrix 线程隔离导致ThreadLocal数据丢失》

我们对ThreadLocal数据丢失进行了详细的分析，并通过代码的方式复现了这个问题。

在上篇文章的末尾我也说了思路给大家提供了，如果需要能够在Hystrix 为线程隔离模式也能正确传递数据的话，需要我们自己去修改。

我这边以Zuul中自定义负载均衡策略来进行讲解，在Zuul中需要实现灰度发布的功能，需要在Filter中将请求的用户信息传递到自定的负载策略中，Zuul中整合了Hystrix，从Zuul Filter的请求到Ribbon的策略类中，线程已经发生了变化，变成了Hystrix提供的线程池来执行（配置隔离模式为线程）。这个时用ThreadLocal就会出问题了，数据传输会错乱。也就是我们前面分析的问题。

关于修改我说下自己分析问题的一些思路，ransmittable-thread-local可以解决这个问题，可以对线程或者线程池进行修饰，其实最终的原理就是对线程进行包装，在线程run之前和之后做一些处理来保证数据的正确传递。

https://blog.didispace.com/Spring-Cloud%E4%B8%ADHystrix-%E7%BA%BF%E7%A8%8B%E9%9A%94%E7%A6%BB%E5%AF%BC%E8%87%B4ThreadLocal%E6%95%B0%E6%8D%AE%E4%B8%A2%E5%A4%B1%EF%BC%88%E7%BB%AD%EF%BC%89/#%E6%94%B9%E9%80%A0%E6%80%9D%E8%B7%AF 改造思路

首先我想的就是改掉Hystrix中的线程池或者线程，只有这样才能让ransmittable-thread-local来接管线程中数据的传递。

通过调试的方式找到com.netflix.hystrix.HystrixThreadPool是Hystrix线程池的接口，里面定义了一个获取ExecutorService方法，代码如下：

public interface HystrixThreadPool {
    /**
     * Implementation of {@link ThreadPoolExecutor}.
     *
     * @return ThreadPoolExecutor
     */
    public ExecutorService getExecutor();
}

通过查找接口的实现类，发现只有一个默认的实现com.netflix.hystrix.HystrixThreadPool.HystrixThreadPoolDefault，实现也在接口中，是一个静态类。实现的方法如下：

@Override
 public ThreadPoolExecutor getExecutor() {
     touchConfig();
     return threadPool;
 }

threadPool是类中的一个变量，主要是通过touchConfig方法来设置线程的参数，touchConfig代码如下：

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;
      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;
      }
      //......
}

这是最外层获取线程池的地方，可以根据代码一步步进去看，最终获取线程池的代码在com.netflix.hystrix.strategy.concurrency.HystrixConcurrencyStrategy.getThreadPool方法中。

上面是线程池的源码分析，我们可以改造源码，将线程池用ransmittable-thread-local进行修饰。

https://blog.didispace.com/Spring-Cloud%E4%B8%ADHystrix-%E7%BA%BF%E7%A8%8B%E9%9A%94%E7%A6%BB%E5%AF%BC%E8%87%B4ThreadLocal%E6%95%B0%E6%8D%AE%E4%B8%A2%E5%A4%B1%EF%BC%88%E7%BB%AD%EF%BC%89/#%E6%94%B9%E9%80%A0%E7%BA%BF%E7%A8%8B%E6%96%B9%E5%BC%8F 改造线程方式

另外一种是改造线程的方式，在Hystrix将命令丢入线程池的时候对线程进行修饰也可以解决此问题，因为ransmittable-thread-local对线程池进行修饰，其原理也是改造了线程，通过源码可以看出：

public static ExecutorService getTtlExecutorService(ExecutorService executorService) {
        if (executorService == null || executorService instanceof ExecutorServiceTtlWrapper) {
            return executorService;
        }
        return new ExecutorServiceTtlWrapper(executorService);
}

class ExecutorServiceTtlWrapper extends ExecutorTtlWrapper implements ExecutorService {
    private final ExecutorService executorService;
    ExecutorServiceTtlWrapper(ExecutorService executorService) {
        super(executorService);
        this.executorService = executorService;
    }
    @Override
    public <T> Future<T> submit(Callable<T> task) {
        return executorService.submit(TtlCallable.get(task));
    }
    @Override
    public <T> Future<T> submit(Runnable task, T result) {
        return executorService.submit(TtlRunnable.get(task), result);
    }
    @Override
    public Future<?> submit(Runnable task) {
        return executorService.submit(TtlRunnable.get(task));
    }
    // ...........
}

重点在TtlRunnable.get()

改造Hystrix中线程的方式，可以通过HystrixContextScheduler进行入手，Hystrix通过HystrixContextScheduler的ThreadPoolScheduler把命令submit到ThreadPoolExecutor中去执行。

通过上面的分析，最终可以定位到提交命令的代码如下：

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");
        }
}

核心代码在schedule方法中，只需要将schedule中的sa进行修饰即可。

改造后的代码如下：

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(TtlRunnable.get(sa));
     sa.add(new FutureCompleterWithConfigurableInterrupt(f, shouldInterruptThread, executor));
     return sa;
}

改源码还涉及到重新打包等问题，每个项目都得用修改后的jar包，比较麻烦，最简单的做法就是在项目中建一个同样的HystrixContextScheduler类，包名也要和之前一样，让jvm优先加载，这样就能用这个修改的类来代替Hystrix原始的类。

最后我们来验证下这样的改动是否正确，首先我们在Zuul的Filter中进行值的传递：

RibbonFilterContextHolder是基于InheritableThreadLocal做的值传递,代码如下：

public class RibbonFilterContextHolder {
    private static final ThreadLocal<RibbonFilterContext> contextHolder = new InheritableThreadLocal<RibbonFilterContext>() {
        @Override
        protected RibbonFilterContext initialValue() {
            return new DefaultRibbonFilterContext();
        }
    };
    public static RibbonFilterContext getCurrentContext() {
        return contextHolder.get();
    }
    public static void clearCurrentContext() {
        contextHolder.remove();
    }
}

完整源码请参考:

https://github.com/yinjihuan/spring-cloud/blob/master/fangjia-common/src/main/java/com/fangjia/common/support/RibbonFilterContextHolder.java 
private static AtomicInteger ac = new AtomicInteger();
   @Override
   public Object run() {
       RequestContext ctx = RequestContext.getCurrentContext();
       RibbonFilterContextHolder.getCurrentContext().add("servers",ac.addAndGet(1)+"");
       return null;
   }

通过AtomicInteger 进行数字的累加操作，后面测试的时候用10个线程并发测试，如如果在Ribbon的自定义负载策略中接收的值是0-9的话表示正确，否则错误。

接下来定义一个负载策略类，输出接收的值：

public class GrayPushRule extends AbstractLoadBalancerRule {
    private AtomicInteger nextServerCyclicCounter;
    private static final boolean AVAILABLE_ONLY_SERVERS = true;
    private static final boolean ALL_SERVERS = false;
    private static Logger log = LoggerFactory.getLogger(RoundRobinRule.class);
    public GrayPushRule() {
        this.nextServerCyclicCounter = new AtomicInteger(0);
    }
    public GrayPushRule(ILoadBalancer lb) {
        this();
        this.setLoadBalancer(lb);
    }
    public Server choose(ILoadBalancer lb, Object key) {
        String servers = RibbonFilterContextHolder.getCurrentContext().get("servers");
        System.out.println(Thread.currentThread().getName()+":"+servers);  
        return null;
    }
    public Server choose(Object key) {
        return this.choose(this.getLoadBalancer(), key);
    }
    public void initWithNiwsConfig(IClientConfig clientConfig) {
    }
}

然后增加配置，使用自定义的策略，还需要将Hystrix的线程池数量改小一点，这样才可以线程复用

fsh-house.ribbon.NFLoadBalancerRuleClassName=com.fangjia.fsh.api.rule.GrayPushRule
# 线程隔离模式
zuul.ribbon-isolation-strategy=thread
hystrix.threadpool.default.coreSize=3

启动服务，用ab进行测试：

ab -n 10 -c 10 http://192.168.10.170:2103/fsh-house/house/1

输出结果如下:

hystrix-RibbonCommand-3:10
hystrix-RibbonCommand-2:3
hystrix-RibbonCommand-1:8
hystrix-RibbonCommand-3:10
hystrix-RibbonCommand-2:3
hystrix-RibbonCommand-1:8
hystrix-RibbonCommand-3:10
hystrix-RibbonCommand-2:3
hystrix-RibbonCommand-1:8
hystrix-RibbonCommand-3:10

很多数据都重复了，这就是线程复用导致的问题，接下来我们用上面讲的方式进行改造

需要将RibbonFilterContextHolder中的InheritableThreadLocal改成TransmittableThreadLocal

private static final TransmittableThreadLocal<RibbonFilterContext> contextHolder = new TransmittableThreadLocal<RibbonFilterContext>() {
    @Override
    protected RibbonFilterContext initialValue() {
        return new DefaultRibbonFilterContext();
    }
};

然后在项目中新建一个HystrixContextScheduler类，包名必须是com.netflix.hystrix.strategy.concurrency，代码就按上面贴的进行改，主要是对线程进行修饰：

FutureTask<?> f = (FutureTask<?>) executor.submit(TtlRunnable.get(sa));

再次启动服务，进行测试，结果如下：

hystrix-RibbonCommand-2:10
hystrix-RibbonCommand-1:1
hystrix-RibbonCommand-3:7
hystrix-RibbonCommand-3:8
hystrix-RibbonCommand-1:2
hystrix-RibbonCommand-2:4
hystrix-RibbonCommand-3:5
hystrix-RibbonCommand-1:9
hystrix-RibbonCommand-2:3
hystrix-RibbonCommand-3:6

现在的结果已经是正确的

https://blog.didispace.com/Spring-Cloud%E4%B8%ADHystrix-%E7%BA%BF%E7%A8%8B%E9%9A%94%E7%A6%BB%E5%AF%BC%E8%87%B4ThreadLocal%E6%95%B0%E6%8D%AE%E4%B8%A2%E5%A4%B1%EF%BC%88%E7%BB%AD%EF%BC%89/#%E6%94%B9%E9%80%A0%E7%BA%BF%E7%A8%8B%E6%B1%A0%E6%96%B9%E5%BC%8F 改造线程池方式

上面介绍了改造线程的方式，并且通过建一个同样的Java类来覆盖Jar包中的实现，感觉有点投机取巧，其实不用这么麻烦，Hystrix默认提供了HystrixPlugins类，可以让用户自定义线程池，下面来看看怎么使用：

在启动之前调用进行注册自定义实现的逻辑：

HystrixPlugins.getInstance().registerConcurrencyStrategy(new ThreadLocalHystrixConcurrencyStrategy());

ThreadLocalHystrixConcurrencyStrategy就是我们自定义的创建线程池的类，需要继承HystrixConcurrencyStrategy，前面也有讲到通过调试代码发现最终获取线程池的代码就在HystrixConcurrencyStrategy中。

我们只需要重写getThreadPool方法即可完成对线程池的改造，由于TtlExecutors只能修饰ExecutorService和Executor，而HystrixConcurrencyStrategy中返回的是ThreadPoolExecutor，我们需要对ThreadPoolExecutor进行包装一层，最终在execute方法中对线程修饰，也就相当于改造了线程池。

public class ThreadLocalHystrixConcurrencyStrategy extends HystrixConcurrencyStrategy {
    private final static Logger logger = LoggerFactory.getLogger(ThreadLocalHystrixConcurrencyStrategy.class);
    @Override
    public ThreadPoolExecutor getThreadPool(HystrixThreadPoolKey threadPoolKey, HystrixProperty<Integer> corePoolSize,
            HystrixProperty<Integer> maximumPoolSize, HystrixProperty<Integer> keepAliveTime, TimeUnit unit,
            BlockingQueue<Runnable> workQueue) {
        return this.doGetThreadPool(threadPoolKey, corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue);
    }
    @Override
    public ThreadPoolExecutor getThreadPool(HystrixThreadPoolKey threadPoolKey,
            HystrixThreadPoolProperties threadPoolProperties) {
        return this.doGetThreadPool(threadPoolKey, threadPoolProperties);
    }
}

在doGetThreadPool方法中就返回包装的线程池，代码如下：

return new ThreadLocalThreadPoolExecutor(dynamicCoreSize, dynamicMaximumSize, keepAliveTime.get(), unit, workQueue,
                    threadFactory);

最后就是ThreadLocalThreadPoolExecutor的代码：

public class ThreadLocalThreadPoolExecutor extends ThreadPoolExecutor {
    private static final RejectedExecutionHandler defaultHandler = new AbortPolicy();
    public ThreadLocalThreadPoolExecutor(int corePoolSize, int maximumPoolSize, long keepAliveTime, TimeUnit unit,
            BlockingQueue<Runnable> workQueue) {
        super(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue);
    }
    public ThreadLocalThreadPoolExecutor(int corePoolSize, int maximumPoolSize, long keepAliveTime, TimeUnit unit,
            BlockingQueue<Runnable> workQueue, ThreadFactory threadFactory) {
        super(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue, threadFactory, defaultHandler);
    }
    @Override
    public void execute(Runnable command) {
        super.execute(TtlRunnable.get(command));
    }
}
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