Question

A retail company runs an e-commerce API on Azure App Service backed by Azure SQL Database. Product pricing data is read very frequently but updated only a few times per day by a back-office system. The team adds Azure Cache for Redis to reduce database load. However, when prices are updated, customers occasionally see stale prices for extended periods because cached entries only expire after a long TTL. The architect must ensure customers see updated prices immediately after a back-office change while still minimizing read load on the database. Which caching approach should be implemented?

Accepted Answer

Use a cache-aside (lazy loading) read pattern, and have the back-office update path explicitly invalidate or update the affected cache keys when a price changes

Answer

Keep the cache-aside pattern with the long TTL and rely on eventual expiration to refresh prices

Answer

Disable Redis and query Azure SQL Database directly on every request to guarantee fresh prices

Answer

Reduce the TTL on all cache entries to one second so prices refresh almost immediately