Start by clarifying requirements (scale, consistency, latency, data model) and then propose a distributed architecture using consistent hashing for partitioning and replication for fault tolerance. Focus on how range scans are supported efficiently, likely via a sorted storage engine like LSM trees or B-trees, and discuss trade-offs between consistency, availability, and partition tolerance.
Pro tip: Emphasize that range scans require ordered data, so the partitioning strategy must preserve key order (e.g., range partitioning) or use a secondary index; this shows depth beyond basic hashing. Also, proactively discuss how you'd handle hot spots and rebalancing.
Ask about scale (data size, QPS), consistency needs (strong vs eventual), latency targets, and whether range scans are frequent or occasional. This shapes the entire design.
Propose a distributed system with partitioning (e.g., consistent hashing or range partitioning), replication (e.g., leader-follower or quorum), and a coordinator layer for routing requests. Mention using a sorted storage engine (LSM tree or B-tree) to support range scans.
Explain how keys are stored in sorted order to enable efficient range scans. Discuss using SSTables and memtables (LSM) or B-trees, and how tombstones handle deletes.
Detail how get, put, delete, and range scan are implemented across partitions. Discuss consistency models (e.g., quorum reads/writes) and how to handle conflicts (e.g., vector clocks or last-write-wins).
Describe how to scale (adding nodes, rebalancing), handle failures (replication, hinted handoff), and monitor performance. Mention trade-offs like CAP theorem and latency vs consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.