I started with Get and Put APIs which was fine, but then I spent way too long on partitioning before they nudged me toward quorums.
Start by clarifying requirements and scale, then propose a design that uses consistent hashing for partitioning and quorum-based replication for consistency. Walk through the write and read paths, explaining how quorums ensure consistency and how the system handles failures and conflicts. Conclude by discussing trade-offs and potential optimizations.
Pro tip: Amazon values customer obsession and operational excellence, so emphasize how your design meets latency and durability SLAs, and include monitoring and automated recovery mechanisms.
Ask questions to understand expected data size, read/write throughput, latency requirements, consistency needs, and geographic distribution. This ensures the design is appropriately tailored.
Specify the key-value store's API operations (e.g., get, put, delete) and the data model, including key size limits and value types. Consider versioning for conflict resolution.
Choose a partitioning strategy like consistent hashing to distribute data evenly and minimize rebalancing. Replicate each key across N nodes using a replication factor, and assign replicas to different failure domains.
Define consistency goals (e.g., strong, eventual) and configure quorum parameters (W for writes, R for reads) such that W + R > N for strong consistency. Explain how quorums balance consistency, availability, and latency.
Describe mechanisms for failure detection (e.g., heartbeats), recovery (e.g., hinted handoff, read repair), and conflict resolution (e.g., vector clocks, last-write-wins). Discuss how the system behaves during network partitions and how it recovers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.