Uber's CacheFront system handles over 150 million reads per second while maintaining strong consistency. The architecture integrates Redis caching within its Docstore storage layer, achieving 99.9% cache hit rates for many operations.

The system solves critical latency issues that arise from querying databases directly. Traditional database reads create bottlenecks at scale, especially with Uber's massive user base making billions of daily requests. Caching provides microseconds response times versus milliseconds for direct database access.

Complexity arises during write operations where cache invalidation becomes challenging. Uber tackled this by moving beyond asynchronous Change Data Capture systems like Flux, which caused consistency delays. The company now focuses on tighter integration between cache and database layers.

Engineers addressed staleness issues by reducing reliance on TTL expiration alone. This ensures users don't experience outdated data after updates, particularly important for real-time features like trip tracking and driver locations.