Caching Deep Dive

Caching comes up in most system design interviews because many systems serve the same data repeatedly. A feed, product page, profile page, URL redirect, or rate limiter can often avoid expensive work by reusing a recent result.

The important part is not saying "add Redis." It is knowing what you are caching, where the cache sits, how stale the data can be, what happens on a miss, and how the system behaves when the cache is slow or unavailable.

This chapter covers the caching decisions that come up most in interviews: cache placement, read and write strategies, eviction, distributed caching, invalidation, stampede prevention, failure handling, and common design patterns.