The open-ended API design part tripped me up more than the actual coding.
Start by clarifying requirements and constraints, then propose a simple design using a hash map for records and a hash map for each record's columns. Discuss trade-offs and potential optimizations, and outline basic operations like insert, get, update, and delete.
Pro tip: Emphasize that schema-less doesn't mean no schema; discuss how you would handle indexing and querying efficiently, and mention the importance of choosing appropriate data structures for performance.
Ask about expected operations, data volume, concurrency needs, and persistence requirements to scope the design appropriately.
Propose using a primary hash map to store records by key, where each record is itself a hash map of column names to values, allowing flexible schemas.
Outline APIs for insert, get, update, delete, and possibly query by column values, discussing time complexity for each.
Talk about memory overhead, indexing strategies for faster queries, and how to handle concurrent access if needed.
Mention how to add persistence, transactions, or more complex querying (e.g., secondary indexes) if requirements grow.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what types of filters (equality, range, etc.), how multiple filters combine (AND/OR), and the expected behavior for missing columns. Then propose a design that separates the query interface from the underlying data model, ensuring that missing columns are handled gracefully with a defined policy (e.g., treat as null or skip). Finally, discuss trade-offs between flexibility, performance, and complexity.
Pro tip: Demonstrate awareness of real-world data variability by suggesting a configurable policy for missing columns (e.g., strict mode vs. lenient mode) and mention how this impacts error handling and user experience.
Ask about filter types (equality, range, set membership), combination logic (AND/OR), and whether filters are applied at query time or precomputed. Also clarify the expected behavior when a column is missing.
Propose an API that accepts a list of filter conditions, each specifying column, operator, and value. Support logical composition (e.g., AND/OR) and consider a fluent builder or JSON-based query object.
Decide and document what happens when a filter references a non-existent column: options include treating as null (so equality fails, inequality passes), skipping the filter, or throwing an error. Recommend a default and allow configuration.
Discuss indexing, push-down of filters to storage, and how missing columns affect query planning. Consider caching or pre-filtering for common cases.
Weigh flexibility vs. strictness, performance vs. correctness, and propose a balanced approach with clear reasoning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.