Came in prepared on this one, which made a big difference.
Start by clarifying requirements (scale, read/write ratio, ranking rules, real-time vs. periodic updates) and then propose a high-level architecture. Focus on data modeling and trade-offs between different storage and ranking strategies, and discuss how to handle updates and queries efficiently.
Pro tip: Demonstrate awareness of the read-heavy nature of leaderboards and propose caching or precomputation to handle high query loads. Also, discuss how to handle ties and the importance of idempotent updates to avoid inconsistencies.
Ask questions to understand scale (number of players, updates per second, queries per second), ranking rules (global, regional, friends), update frequency (real-time vs. periodic), and consistency requirements.
Outline the main components: data ingestion (score updates), storage (database, cache), ranking computation, and query API. Consider using a combination of a fast in-memory store (e.g., Redis sorted sets) and a persistent database.
Choose appropriate data structures: sorted sets for rankings, hash maps for player scores, and possibly a time-series database for historical data. Discuss sharding and replication for scalability.
Explain how to compute rankings efficiently: use Redis ZSET for real-time leaderboards, or batch processing with MapReduce for periodic updates. For queries like 'top N' or 'player rank', describe algorithms and caching strategies.
Discuss trade-offs: real-time vs. eventual consistency, memory vs. disk, and cost. Propose optimizations like caching, precomputed leaderboards, and handling hot keys.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.