The core of it is cycle detection in a directed graph, which sounds clean until you're mid-implementation and realize you need to track not just current deps but transitive ones too.
Model the spreadsheet as a directed graph where cells are nodes and dependencies are edges. When setting a cell's value, perform a depth-first search (DFS) from that cell to detect cycles; if a cycle is found, reject the update. Otherwise, update the cell and propagate changes to dependents.
Pro tip: Discuss how to handle dynamic updates efficiently, such as using topological sorting or memoization to avoid redundant cycle checks, and mention that cycle detection can be integrated with evaluation order.
Ask about the scope: are formulas limited to simple references or can they include arithmetic? How often will cells be updated? This helps tailor the solution.
Represent each cell with its raw value (e.g., formula string) and a list of cells it depends on (precedents) and cells that depend on it (dependents). Use a graph structure to track dependencies.
When setting a cell's value, parse its formula to extract dependencies. Then run a DFS from the cell to check if any dependency path leads back to it. If a cycle is detected, reject the update and return an error.
If no cycle, update the cell and recursively update all dependent cells, ensuring they are evaluated in topological order to avoid stale values.
Mention optimizations like caching evaluation results, incremental cycle detection, or using Tarjan's algorithm for strongly connected components. Discuss trade-offs between update latency and memory usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.