I started with a basic trie structure for prefix matching and that was fine, but I got stuck when they pushed on scale.
Start by clarifying requirements: scale (queries per second, number of IPs), accuracy needs, update frequency, and latency. Then propose a data structure like a compressed trie or interval tree for efficient longest-prefix matching, and discuss how to distribute and cache the data for low-latency lookups at Google scale.
Pro tip: Mention that IP-to-geo mapping is inherently approximate and that you'd design for graceful degradation, e.g., falling back to country-level if city-level data is unavailable, and that you'd monitor accuracy metrics and update pipelines.
Ask about scale (QPS, data size), latency SLA, accuracy expectations, update frequency, and whether the system is read-heavy or write-heavy.
Propose a compressed trie (radix tree) or interval tree for efficient longest-prefix matching of IP addresses to ranges, and discuss memory vs. speed trade-offs.
Explain how to store the mapping (e.g., in-memory, distributed cache, or database) and how to replicate and shard it across regions for low latency and high availability.
Describe a pipeline for ingesting updated IP range data (e.g., from ISPs or commercial providers) and propagating changes without downtime, using versioning or atomic swaps.
Discuss caching strategies, precomputation, and monitoring for accuracy, latency, and hit rates; include fallback mechanisms for unknown IPs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.