I spent way too long in clarification mode because my brain kept trying to map it onto demand-side concepts.
Start by clarifying the requirements and constraints of publisher-specific configuration rules, then propose a flexible data model that balances normalization and denormalization. Discuss trade-offs between different approaches (e.g., relational vs. document-based) and how the model supports scalability, versioning, and efficient retrieval.
Pro tip: Emphasize the importance of schema evolution and backward compatibility, as publisher configurations often change over time and must not break existing clients. Also, mention how you would handle conflicts and precedence when multiple rules apply.
Ask questions to understand the scope: What types of rules? How many publishers? What are the read/write patterns? Are rules hierarchical or flat? What are latency and consistency requirements?
Define entities like Publisher, ConfigurationRule, RuleSet, and their relationships. Consider whether rules are shared across publishers or unique, and how to model inheritance or overrides.
Evaluate options: normalized relational schema, document store (e.g., JSON), or key-value with composite keys. Discuss pros and cons based on query patterns, flexibility, and scalability.
Explain how the model supports efficient reads/writes at scale, including indexing, caching, partitioning, and handling hot publishers. Consider read replicas and eventual consistency if needed.
Describe versioning strategies, schema migration, validation, and access control. Discuss how to audit changes and roll back if necessary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.