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Understanding the Cache Abstraction

Cache vs Buffer

The terms, “buffer” and “cache,” tend to be used interchangeably. Note, however, that they represent different things. Traditionally, a buffer is used as an intermediate temporary store for data between a fast and a slow entity. As one party would have to wait for the other (which affects performance), the buffer alleviates this by allowing entire blocks of data to move at once rather than in small chunks. The data is written and read only once from the buffer. Furthermore, the buffers are visible to at least one party that is aware of it.

A cache, on the other hand, is, by definition, hidden, and neither party is aware that caching occurs. It also improves performance but does so by letting the same data be read multiple times in a fast fashion.

You can find a further explanation of the differences between a buffer and a cache here.

At its core, the cache abstraction applies caching to Java methods, thus reducing the number of executions based on the information available in the cache. That is, each time a targeted method is invoked, the abstraction applies a caching behavior that checks whether the method has been already invoked for the given arguments. If it has been invoked, the cached result is returned without having to invoke the actual method. If the method has not been invoked, then it is invoked, and the result is cached and returned to the user so that, the next time the method is invoked, the cached result is returned. This way, expensive methods (whether CPU- or IO-bound) can be invoked only once for a given set of parameters and the result reused without having to actually invoke the method again. The caching logic is applied transparently without any interference to the invoker.

This approach works only for methods that are guaranteed to return the same output (result) for a given input (or arguments) no matter how many times they are invoked.

The caching abstraction provides other cache-related operations, such as the ability to update the content of the cache or to remove one or all entries. These are useful if the cache deals with data that can change during the course of the application.

As with other services in the Spring Framework, the caching service is an abstraction (not a cache implementation) and requires the use of actual storage to store the cache data — that is, the abstraction frees you from having to write the caching logic but does not provide the actual data store. This abstraction is materialized by the org.springframework.cache.Cache and org.springframework.cache.CacheManager interfaces.

Spring provides a few implementations of that abstraction: JDK java.util.concurrent.ConcurrentMap based caches, Gemfire cache, Caffeine, and JSR-107 compliant caches (such as Ehcache 3.x). See Plugging-in Different Back-end Caches for more information on plugging in other cache stores and providers.

The caching abstraction has no special handling for multi-threaded and multi-process environments, as such features are handled by the cache implementation.

If you have a multi-process environment (that is, an application deployed on several nodes), you need to configure your cache provider accordingly. Depending on your use cases, a copy of the same data on several nodes can be enough. However, if you change the data during the course of the application, you may need to enable other propagation mechanisms.

Caching a particular item is a direct equivalent of the typical get-if-not-found-then-proceed-and-put-eventually code blocks found with programmatic cache interaction. No locks are applied, and several threads may try to load the same item concurrently. The same applies to eviction. If several threads are trying to update or evict data concurrently, you may use stale data. Certain cache providers offer advanced features in that area. See the documentation of your cache provider for more details.

To use the cache abstraction, you need to take care of two aspects:

  • Caching declaration: Identify the methods that need to be cached and their policies.

  • Cache configuration: The backing cache where the data is stored and from which it is read.