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What is the cache aside pattern, and how does it work with Redis? What is the write through caching pattern, and how does it differ from cache aside? What is the write behind, also called write back, caching pattern, and what risk does it introduce? How do you decide which caching pattern, cache aside, write through, or write behind, is right for a specific application feature? How would you prevent a cache stampede, where many requests simultaneously try to regenerate the same expired cache entry at once? How do you implement cache invalidation correctly when the same underlying data is cached under multiple different keys?
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How does the read through caching pattern work, and how is it different from cache aside from the application's perspective?

Intermediate

Answer

In read through caching, the caching layer itself is responsible for fetching data from the database when it is missing from the cache, meaning the application always just asks the cache for data without needing its own logic to check the database, unlike cache aside where the application explicitly handles the fallback to the database itself.
-- With read through, the cache client or library
-- automatically fetches from the database on a cache miss
-- and the application code only ever talks to the cache
Real-world example A company uses a caching library that automatically loads missing data from the database behind the scenes, simplifying their application code since it only ever needs to call the cache directly.

Common follow-ups: What libraries or tools provide built-in read through caching support for Redis?;Is read through caching harder to implement than cache aside?

Caching Patterns;Redis Modules Overview
All 7 Caching Patterns questions