Design a Geolocation Service (IP to Location)

To design a reliable Geolocation Service, I would first clarify that we need sub-10ms latency for billions of daily requests while maintaining high accuracy. The core challenge is the tension between the static nature of IP blocks and the dynamic reality of user movement. My approach starts with a hybrid data model. We would ingest updates from trusted providers like MaxMind but also leverage Google's vast network telemetry for real-time validation. For the architecture, I'd implement a three-tier caching system. First, a local L1 cache on the application server using an efficient structure like a radix tree for O(1) lookups. Second, a distributed L2 cache layer using Redis with a consistent hashing strategy to handle hotspots. Finally, the persistent store would be a sharded NoSQL database optimized for range queries. To handle consistency, we'd use short TTLs combined with asynchronous background jobs to update the cache only when significant IP block changes occur. This ensures we don't serve stale locations during major ISP reassignments. If the cache misses, we fall back to the database, ensuring we never fail completely. This design balances the extreme read scalability required at Google scale with the need for precise geographic data.