Start by clarifying requirements (read/write ratio, latency SLA, data size, update frequency) and then design a system that minimizes random disk I/O through caching, batching, and data layout optimizations. Focus on trade-offs between memory usage, disk access patterns, and consistency, and propose a layered architecture with a small in-memory cache, an append-only log, and a B-tree or LSM-tree on disk.
Pro tip: Emphasize that you would measure and optimize for the actual workload—most inventory lookups are reads, so prioritize read performance and use write batching to avoid random writes. Also, mention that you'd consider using memory-mapped files to let the OS manage caching efficiently.
Ask about data size, read/write ratio, latency requirements, consistency needs, and update frequency to understand the problem scope.
Store inventory data in a sorted, append-only format on disk to convert random reads into sequential reads, and use a small in-memory index to locate records.
Use a small LRU cache for hot items in RAM, and batch writes to reduce disk seeks. Consider write-ahead logging for durability.
Select on-disk structures like B-trees or LSM-trees that optimize for HDD characteristics, and use compact in-memory structures like Bloom filters to avoid unnecessary disk reads.
Discuss trade-offs between memory usage, latency, and consistency, and how to handle cache invalidation, disk failures, and recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.