Not the most grueling OOD problem I've seen.
Start by clarifying the requirements and constraints, then design a class hierarchy that separates storage and lookup concerns. Use design patterns like Strategy and Factory to allow flexibility, and discuss trade-offs between consistency, latency, and scalability. Finally, relate the design to ML-specific needs such as feature stores or model artifact management.
Pro tip: Emphasize how your design supports ML workflows, such as versioning of models and features, and how it can integrate with existing ML pipelines. Show awareness of Box's content management focus by discussing metadata extraction and search.
Ask questions to understand the scope: file types, size, access patterns, consistency needs, and ML-specific requirements like versioning or feature retrieval.
Identify key objects such as File, StorageBackend, MetadataIndex, and LookupService. Define their responsibilities and interfaces.
Use Strategy for interchangeable storage backends (e.g., local, S3, HDFS) and Factory for creating storage instances. Consider Observer for metadata updates.
Discuss partitioning, replication, caching, and consistency models. Explain trade-offs between strong vs. eventual consistency and their impact on ML use cases.
Show how the system supports ML needs: storing models, features, and experiments; enabling efficient lookups for training and inference; and ensuring reproducibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.