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What is the write behind, also called write back, caching pattern, and what risk does it introduce? How does the read through caching pattern work, and how is it different from cache aside from the application's perspective? 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?
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How do you implement cache invalidation correctly when the same underlying data is cached under multiple different keys?

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Answer

You need a strategy to track all the cache keys related to a specific piece of data, such as using a naming convention with tags or maintaining a set of related keys in Redis, so that when the underlying data changes, you can reliably find and delete or update every cache entry that depends on it, rather than leaving some entries stale.
redis.sadd(f'tags:product:{product_id}', 'homepage_cache', 'category_cache')
-- When the product changes, look up and invalidate
-- every cache key tagged with that product
Real-world example An e-commerce site tags every cache entry that includes a specific product's price, letting it reliably invalidate the homepage banner cache, the category page cache, and the product detail cache all together whenever that price actually changes.

Common follow-ups: What are the performance costs of maintaining these tag relationships in Redis?;Are there simpler alternatives to this tagging approach for smaller applications?

Keyspace Notifications;Redis Memory Optimization
All 7 Caching Patterns questions