DZone
Introduction
Caching facilitates faster access to data that is repeatedly being asked for. The data might have to be fetched from a database or have to be accessed over a network call or have to be calculated by an expensive computation. We can avoid multiple calls for these repeated data-asks by storing the data closer to the application (generally, in memory or local disc). Of course, all of this comes at a cost. We need to consider the following factors when cache has to be implemented:
- Additional memory is needed for applications to cache the data.
- What if the cached data is updated? How do you invalidate the cache? (Needless to say, now that caching works well, when the data is cached it does not need to be changed often.)
- We need to have Eviction Policies (LRU, LFU etc.) in place to delete the entries when cache grows bigger.
Caching becomes more complicated when we think of distributed systems. Let us assume we have our application deployed in a 3-node cluster:
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