This one covers a lot of ground and I think I underestimated how many sub-problems are buried inside it.
Start by clarifying requirements and scale (e.g., writes per second, read latency, number of players). Then propose a high-level architecture that separates write ingestion from read serving, using a fast in-memory store for real-time rankings and a durable store for persistence. Finally, dive into data modeling and trade-offs for global vs. friends rankings and multiple time windows.
Pro tip: Emphasize that leaderboards are read-heavy and latency-sensitive, so you should decouple writes from reads using a streaming pipeline (e.g., Kafka) and maintain pre-aggregated views. Also, discuss how to handle rank queries efficiently by using sorted sets with score-based pagination.
Ask questions to understand the expected write QPS, read QPS, latency requirements, number of players, and update frequency. Clarify what 'friends-based rankings' means (e.g., mutual friends, one-way follows) and how time windows are defined.
Propose a system that ingests score updates via a message queue (e.g., Kafka) and processes them with a stream processor (e.g., Flink) to update multiple leaderboards. Use a fast in-memory data store (e.g., Redis) for serving rankings and a durable store (e.g., Cassandra) for persistence and recovery.
Model each leaderboard as a sorted set in Redis, with player ID as member and score as value. For time windows, use separate keys with TTL or periodic snapshots. For friends rankings, maintain per-player friend lists and compute rankings on the fly or precompute using a graph store.
Use Redis ZREVRANK to get a player's rank and ZRANGE to fetch nearby players. For scalability, consider sharding by leaderboard ID or using a tiered approach where top N are cached and the rest are computed on demand.
Discuss trade-offs between consistency and latency (e.g., eventual consistency for friends rankings), cost of maintaining multiple time windows, and strategies for hot keys (e.g., sharding popular leaderboards). Mention monitoring and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.