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。
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。
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:
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
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)); } }
