Start by clarifying requirements and constraints, then propose a high-level design that separates the two levels (e.g., L1 in-memory cache, L2 persistent store) and addresses each feature (expiry, prefix search, checkpointing, rollback) with appropriate data structures and algorithms. Discuss trade-offs (e.g., consistency vs. performance, memory vs. disk) and outline how you would implement and test each component.
Pro tip: Emphasize how you would handle failures and ensure atomicity during checkpointing and rollback, as this demonstrates production-level thinking. Also, proactively discuss how you would monitor and tune the system for performance.
Ask questions to understand expected scale (data size, QPS), latency requirements, consistency needs, and durability guarantees. Clarify what 'two-level' means (e.g., memory + disk, or two storage tiers) and the exact semantics of expiry, prefix search, checkpointing, and rollback.
Propose an architecture: e.g., L1 as an in-memory hash map with TTL and a trie for prefix search, L2 as a persistent key-value store (e.g., RocksDB or a custom log-structured store). Explain how data flows between levels and how each feature is implemented.
Detail the data structures and algorithms: for expiry, use lazy deletion with a min-heap or timing wheel; for prefix search, use a trie or sorted structure; for checkpointing, use write-ahead logging or snapshotting; for rollback, maintain versioned checkpoints or undo logs.
Compare design choices: e.g., eager vs. lazy expiry, in-memory vs. disk-based prefix search, full vs. incremental checkpointing, and rollback granularity. Discuss how to handle concurrency, consistency, and failure recovery.
Outline how you would implement the system in code (e.g., classes, interfaces) and test it: unit tests for each feature, integration tests for interactions, and stress tests for performance and correctness under failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.