I'd seen this problem before so I figured it'd be fine.
Start by clarifying requirements (data size, read/write ratio, latency, durability, consistency) and then propose a high-level design that separates the storage engine from the API layer. Discuss core components like indexing (e.g., B-tree or LSM-tree), persistence (write-ahead log, SSTables), and trade-offs between performance and durability. Finally, walk through a simple implementation sketch and how you would handle scaling and failures.
Pro tip: Demonstrate awareness of real-world systems by referencing existing key-value stores (e.g., RocksDB, LevelDB, Redis) and explaining why you would choose certain design choices for OpenAI's scale and latency requirements. Also, proactively discuss how you would test and monitor the system.
Ask questions to understand the expected data size, read/write patterns, latency and throughput targets, durability and consistency needs, and deployment environment.
Outline the main components: API, storage engine, indexing, persistence, and caching. Choose a storage engine approach (e.g., LSM-tree vs B-tree) based on requirements.
Explain how data is stored on disk (e.g., SSTables, WAL), how reads and writes are handled, and how indexing works. Discuss compaction, garbage collection, and recovery.
Discuss trade-offs between read/write performance, durability, and complexity. Mention optimizations like bloom filters, caching, and concurrency control.
Describe how to scale horizontally (sharding, replication) and handle failures (replication, consistency models). Mention monitoring and testing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.