How Redis Caching Solves Database Overload but Introduces Consistency Risks

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Serving a dashboard to 500 concurrent users can trigger thousands of simultaneous database queries, making caching with tools like Redis a practical necessity. A software engineering lab explored using a Cache Aside pattern, where the application checks Redis first and falls back to PostgreSQL on a miss, keeping the database as the authoritative data source. While this reduces repeated work, it introduces consistency challenges such as stale data, cache invalidation race conditions, and behavior during Redis outages. The lab found that acceptable staleness varies by data type — dashboard metrics can tolerate minutes of delay, while wallet balances require real-time accuracy. Even a seemingly safe write sequence of deleting the cache before committing to the database can leave stale values if a reader repopulates the cache between those two operations.

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