Start by clarifying the domain: 'machines' likely refers to compute instances or nodes (e.g., GPU machines) acquired for AI video processing. Then propose a data model that captures machine metadata, state, and lifecycle, and discuss storage options (relational DB, key-value store, or time-series DB) with trade-offs around consistency, scalability, and query patterns.
Pro tip: Emphasize that the choice of storage depends on access patterns and scale—e.g., if you need fast lookups by machine ID, a key-value store like Redis or DynamoDB works well; if you need complex queries and relationships, a relational DB like PostgreSQL is better. Also mention the importance of tracking machine state changes over time for auditing and debugging.
Ask questions to understand what 'machines' are (e.g., physical servers, VMs, containers), the scale (thousands to millions), and access patterns (read-heavy, write-heavy, queries by attributes).
Outline key entities and attributes: machine ID, type, specs (CPU, GPU, memory), acquisition date, status (active, idle, decommissioned), owner, location, and cost. Consider relationships to other entities like users or jobs.
Evaluate options: relational (PostgreSQL) for strong consistency and complex queries; NoSQL (MongoDB, DynamoDB) for scalability and flexible schema; time-series (InfluxDB) for metrics; or a hybrid approach. Discuss trade-offs.
Explain how to handle updates (e.g., status changes) and historical tracking. Consider event sourcing or audit tables to log changes over time for debugging and compliance.
Discuss partitioning/sharding, indexing strategies, caching, and read replicas to handle growth. Mention backup and recovery plans.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.