Scaling smoothly: RevenueCat’s data-caching techniques for 1.2 billion daily API requests

RevenueCat manages over 1.2 billion daily requests by implementing a sophisticated caching system that ensures fast, reliable, and consistent data retrieval for their backend systems. The cache architecture relies on multiple servers with substantial RAM and high network capacity, utilizing key-value stores like memcached to maintain low latency and distribute load evenly. To ensure resilience, RevenueCat employs strategies such as low timeouts, fail-fast mechanisms, fallback and dedicated pools, and techniques for handling hot keys and "thundering herds." They also use consistent hashing to manage capacity increases and a client-driven migration process for server replacements. The company emphasizes cache consistency through techniques like write failure tracking and reducing TTLs before database operations, which help maintain an accurate reflection of data between cache and database. Additionally, RevenueCat has developed an open-source cache client that incorporates advanced features to support these practices, contributing to a robust and efficient infrastructure.