I started with the API and data model which felt safe, but then spent way too long debating consistency models out loud before I'd even sketched the architecture.
Start by clarifying requirements and constraints, then propose a partitioned, replicated architecture using consistent hashing and tunable consistency. Focus on meeting latency and availability goals through data modeling, caching, and failure handling, and discuss trade-offs explicitly.
Pro tip: Emphasize how you would monitor and iteratively improve p95 latency in production, showing you understand real-world performance tuning beyond initial design.
Ask about data size, access patterns, consistency needs, and SLA details to scope the problem. Confirm assumptions like uniform key distribution and read/write ratio.
Propose a distributed system with partitioning (e.g., consistent hashing) and replication (e.g., N replicas) for scalability and availability. Choose a data model (e.g., LSM-tree or B-tree) and storage engine.
Explain how data is partitioned across nodes and replicated for fault tolerance. Discuss consistency models (e.g., eventual vs. strong) and quorum-based reads/writes to balance latency and consistency.
Describe techniques to achieve sub-10ms p95 latency: in-memory caching, SSD storage, efficient indexing, and minimizing network hops. Consider colocating related data and using async replication.
Outline mechanisms for high availability: replication, automatic failover, and handling network partitions (e.g., using gossip protocols). Discuss trade-offs between consistency and availability (CAP theorem).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.