"A cache bug will never page you at 3 AM - it will quietly cost you users. At some point in your career, you'll ship a feature that works perfectly - except users still see old data. The database is right, the API is fine, and nothing is throwing errors."
"So what's wrong? Did the frontend miss something? Is the read hitting the wrong endpoint? Is prod behaving differently than local?"
"Eventually, the uncomfortable answer shows up: it's the cache. The moment you cache data, you introduce multiple truths into your system - and cache invalidation becomes the only thing standing between you and silent data bugs."
Users can experience stale data even when the database and API are correct and no errors are thrown. Caching introduces multiple truths in a distributed system and can cause silent data inconsistencies that do not trigger alerts. Cache invalidation is the decision process for when cached data should be removed or refreshed to prevent stale reads. Naive invalidation approaches commonly fail in real-world systems. Effective engineering requires explicit invalidation strategies, coordination across services, and designing for correctness at scale to avoid quietly losing users and trust.
Read at Medium
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