LRU缓存和内存缓存C＃有什么区别

Question

Please excuse my noob question as I am still a junior coder , whats the difference between LRU Caching using Dictionary and Linked list and Memory Caching C#, how would one implement a LRU list on say memory cache.

Thanks in advance.

Answer 1

LUR is a algorithm of expiring the cache and adding new Item to your cache. the this algorithm expires the least recently used Item in your cache when the cache is full. The MemoryCache is a class in .net 4 and after which is a way of implementing caching inside the heap memory. Caching can be categorise in different ways base on the media that you cache you can cache on hard drive or memory, based on the location of the memory you can categorize it as in-memory (inside the heap memory) and out-memory (a memory out side the heap for example on another server). MemoryCaching in c# uses in-memory and you have to be careful because it can use all the memory of your application. So its better not to use it if you have more than one node.

One another things you have to take into consideration is that you when you cache an object in an out-memory the object should be serializable. But the in-memory caching can cache any object without serializing.

Answer 2

Least-recently-used (LRU) evicts the key-value used-the-least when the cache is full and it needs to add a value. Whereas a MemoryCache evicts the oldest key-values, or those past their 'use-by-date' if they happen to have one.

Say if the first key-value you added is vital and you happened to read all the time, well in a LRU cache it would be kept, but in a memoryCache it would eventually disappear and need to be replaced. Sometimes though older key-values disappearing is what your after, so up-to-date values get pulled through from your backend (eg database).

Consider also if adding a existing key-value should be considered as being a 'used' (so recently updated stuff tends to stay around) or if 'used' is only when you read a key-value, so you just favour the things your reader likes. As always I would consider concurrency if you have more than one task or thread using it.