The basic setCell part was fine but the propagation piece tripped me up for a bit.
Start by clarifying requirements and constraints, then propose a graph-based dependency model where cells are nodes and references are edges. Explain how setCell updates the cell's value or formula, recalculates dependents via topological order, and handles cycles. Discuss trade-offs between eager and lazy evaluation, and mention optimizations like memoization and dirty marking.
Pro tip: Demonstrate awareness of real-world spreadsheet challenges: circular references, error propagation, and performance at scale. Mention that HarveyAI likely values robust, scalable solutions, so discuss how your approach handles large dependency graphs efficiently.
Ask about supported formula syntax, error handling, cycle detection, and performance expectations. Confirm whether updates should be immediate or lazy.
Represent cells with values/formulas, and maintain a dependency graph (e.g., adjacency lists) to track which cells depend on which. Consider reverse dependencies for efficient updates.
When setCell is called, update the cell, then recalculate all dependent cells in topological order. Use DFS/BFS to traverse dependents and detect cycles.
Detect circular references and propagate errors (e.g., #REF!, #CYCLE!). Ensure that invalid formulas don't crash the system and that errors propagate correctly.
Compare eager vs. lazy evaluation, discuss memoization, dirty marking, and incremental updates. Mention how to scale to large sheets with thousands of cells.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.