I jumped straight into storage and ranking logic, which felt right, but I underestimated how much they wanted me to talk about read/write contention at scale.
Start by clarifying the requirements and scale, then propose a high-level architecture that separates read and write paths. Focus on data modeling and trade-offs between consistency, latency, and cost, and justify your choices with Amazon's leadership principles.
Pro tip: Emphasize that with 100,000 teams, the system is small enough to fit in memory on a single node, but design for horizontal scalability and fault tolerance to show you're thinking beyond the immediate scale. Discuss how you would handle peak loads during game events and ensure low-latency reads for real-time leaderboards.
Ask questions to understand functional and non-functional requirements: What is the update frequency? What is the read latency requirement? Is the leaderboard global or per-region? What are the consistency requirements? How often are scores updated?
Propose a high-level architecture: a write path that ingests score updates and a read path that serves leaderboard queries. Consider using a fast in-memory data store like Redis sorted sets for real-time ranking, with a persistent database like DynamoDB for durability.
Detail the data model: teams, scores, and timestamps. Discuss using Redis sorted sets for efficient rank queries (O(log N) inserts and O(log N + M) range queries). For persistence, consider DynamoDB with team ID as partition key and score as sort key, or a time-series database if historical trends are needed.
Explain how the system scales: sharding by region or game mode, using read replicas, and caching. Discuss trade-offs: strong vs. eventual consistency, cost of in-memory vs. disk-based storage, and complexity of maintaining multiple data stores.
Describe how to handle failures: replication for Redis, DynamoDB's built-in durability, and fallback mechanisms. Mention monitoring metrics like latency, error rates, and cache hit ratio, and how to alert on anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.