SpringCache - 请求级别缓存的简易实现

前言

在SpringCache缓存初探中我们研究了如何利用spring cache已有的几种实现快速地满足我们对于缓存的需求。这一次我们有了新的更个性化的需求，想在一个请求的生命周期里实现缓存。

需求背景是：一次数据的组装需要调用多个方法，然而在这多个方法里又会调用同一个IO接口，此时多浪费了一次IO的资源。首先想到的解决方案是将这次IO接口提出来调用，然后将结果作为参数传递到多个方法中，但是这样一来，每个调用这些方法的地方都得添加额外的代码。那么第二个方案就是，我们还是分别调用，只不过将这个结果缓存起来，就像我们之前做的那样。

这时候问题来了，这个数据结果我们希望尽可能实时，即使只缓存了一秒，导致在不同的请求里用了同一份数据也不太好，又或者缓存效率非常低下，可能就这个请求会查几次。看来不得不自己实现一个只保持在一次请求过程中的缓存了。

方案分析

要将数据缓存在一次请求周期内，那我们先得区分是什么环境下的请求，以分析我们如何存储数据。

1. Web

Web环境下的有个绝佳的数据存储位置 HttpServletRequest的Attribute 。调用setAttribute和getAttribute方法就能轻易地将我们的数据用key-value的形式存储在请求上，而且每次请求都自动拥有一个干净的Request 。想要获取到HttpServletRequest 也非常简单，在web请求中随时随地调用((ServletRequestAttributes) RequestContextHolder.getRequestAttributes()).getRequest() 即可。

2. RPC框架

我司所使用的rpc框架是基于finagle自研的，对外提供服务时使用线程池进行处理请求，即对于一次完整的请求，会使用同一个线程进行处理。首先想到的办法还是改动这个rpc框架服务端，增加一个可以对外暴露的、可以key-value存储的请求上下文。为了能在方便的地方获取到这个请求上下文，得将其存储在ThreadLocal中。

---

综合这两种环境考虑，我们最好还是实现一个统一的方案以减少维护和开发成本。Spring的RequestContextHolder.getRequestAttributes()其实也是使用ThreadLocal来实现的，那我们可以统一将数据存到ThreadLocal<Map<Object,Object>>，自己来维护缓存的清理。

存储位置有了，接下来实现SpringCache思路就比较清晰了。

实现SpringCache

要实现SpringCache需要一个CacheManager，接口定义如下

public interface CacheManager {
           Cache getCache(String name);
           Collection<String> getCacheNames();
}

可以看到其实只需要实现Cache接口就行了。 在上一篇文章中提到的SimpleCacheManager，它的Cache实现ConcurrentMapCache内部的存储是依赖ConcurrentMap<Object, Object>。我们的实现跟它非常类似，最主要的不同是我们需要使用ThreadLocal<Map<Object, Object>> 下面给出几处关键的实现，其他部分简单看下ConcurrentMapCache就能明白。

1 extends

我们选择不直接继承Cache而是AbstractValueAdaptingCache，其被大多数缓存实现所继承，它的作用主要是包装value值以区分是没有命中缓存还是缓存的null值。

2 store

private final ThreadLocal<Map<Object, Object>> store = ThreadLocal.withInitial(() -> new HashMap<>(128));

我们的缓存数据存储的地方，ThreadLocal保证缓存只会存在于这一个线程中。同时又因为只有一个线程能够访问，我们简单地使用HashMap即可。

3 get

public <T> T get(Object key, Callable<T> valueLoader) {
    return (T) fromStoreValue(this.store.get().computeIfAbsent(key, r -> {
        try {
            return toStoreValue(valueLoader.call());
        } catch (Throwable ex) {
            throw new ValueRetrievalException(key, valueLoader, ex);
        }
     }));
 }   

至此我们即将大功告成，只差一个步骤，ThreadLocal的清理：使用AOP实现即可。

   @After("bean(server)")
    public void clearThreadCache() {
        threadCacheManager.clear();
    }

记得将Cache的clear方法通过我们自定义的CacheManager暴露出来。同时也要确保切面能覆盖每个请求的结束。

总结与扩展

从以上一个简单的ThreadLocalCacheManager实现，我们对CacheManager又有了更多的理解。

同时可能也会有更多的疑问。

1. 我们实现的这些方法，从方法名和逻辑上看起来都很简单，那他们是如何配合使用的？跟@Cacheable上的sync又有什么关系呢？

再回顾Spring Cache为我们提供的@Cacheable中的sync的注释，它提到此功能的作用是: 同步化对被注解方法的调用，使得多个线程试图调用此方法时，只有一个线程能够成功调用，其他线程直接取这次调用的返回值。同时也提到这仅仅只是个hint，是否真的能成还是要看缓存提供者。

我们找到Spring Cache处理缓存调用的关键方法org.springframework.cache.interceptor.CacheAspectSupport#execute(org.springframework.cache.interceptor.CacheOperationInvoker, java.lang.reflect.Method, org.springframework.cache.interceptor.CacheAspectSupport.CacheOperationContexts) (spring-context-5.1.5.RELEASE)

经过分析，当sync = true 时, 只会调用如下代码

return wrapCacheValue(method, cache.get(key, () -> unwrapReturnValue(invokeOperation(invoker))))

即我们上文实现的T get(Object key, Callable<T> valueLoader) 方法，回头一看一切都清晰了。 只要我们的this.store.get().computeIfAbsent是同步的，那这个sync = true就起作用了。 当然我们这里使用的HashMap不支持，但是我们如果换成ConcurrentMap就能够实现同步化的功能。另外简单粗暴地让方法同步也是可以的（RedisCache就是这样做的）。

当sync = false时，会组合Cache中其他的方法进行缓存的处理。逻辑较为简单清晰，自行阅读源码即可。

2. 用ThreadLocal严格来说实现的只是线程内的缓存，万一一次请求中有异步操作怎么办？

异步操作分两种情况，直接创建线程或者使用线程池。对于第一种情况我们可以简单地使用java.lang.InheritableThreadLocal 来替代ThreadLocal，创建的子进程会自然而然地共享父进程的InheritableThreadLocal；第二种情况就相对比较复杂了，建议可以参考 alibaba/transmittable-thread-local ，它实现了线程池下的ThreadLocal值传递功能。

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