Start by clarifying requirements: what operations are needed (get/set), data types, concurrency, persistence, and scale. Then propose a data model (e.g., sparse matrix or dictionary of cells) and discuss trade-offs for different access patterns. Finally, outline the API design, error handling, and potential optimizations like caching or indexing.
Pro tip: Demonstrate awareness of real-world spreadsheet challenges like formula dependencies, circular references, and concurrent edits, and suggest how to handle them (e.g., topological sort for recalculation, versioning for concurrency).
Ask about expected scale (number of cells, users), data types (numbers, strings, formulas), operations (get, set, delete, range queries), and non-functional requirements (latency, consistency, persistence).
Propose a storage structure: a 2D array for dense sheets or a hash map (dictionary) keyed by cell coordinates for sparse sheets. Discuss memory and access time trade-offs.
Specify the interface for get_cell(row, col) and set_cell(row, col, value), including error handling for invalid coordinates or types. Consider batch operations and range queries.
Discuss formula evaluation, dependency tracking, and recalculation strategies. Mention concurrency control (e.g., locking, optimistic concurrency) and persistence options.
Suggest optimizations like caching frequently accessed cells, indexing for range queries, and sharding for large sheets. Consider trade-offs between consistency and availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.