The caching requirement is what trips you up first.
Start by clarifying requirements and constraints, then propose a design using a dependency graph and topological order for updates. Explain how caching ensures O(1) reads and how set_cell propagates changes efficiently, discussing trade-offs and edge cases.
Pro tip: Mention that you would use a reverse dependency graph to track which cells depend on a changed cell, enabling efficient propagation. Also, discuss handling cycles and the trade-off between eager and lazy evaluation.
Ask about expected scale, update frequency, formula complexity, and whether cycles are allowed. Confirm that get_cell must be O(1) and set_cell should propagate updates.
Propose storing cell values in a hash map for O(1) access. For formulas, maintain a dependency graph (forward and reverse) to track relationships between cells.
get_cell returns cached value from the map. set_cell updates the cell, then propagates changes to dependents using topological order, updating cached values.
Discuss cycle detection, error handling, and potential optimizations like lazy evaluation or batching updates. Consider memory vs. speed trade-offs.
Explain time complexity: get_cell O(1), set_cell O(k) where k is number of affected cells. Discuss trade-offs between eager and lazy propagation, and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Explain that you detect cycles using a directed graph of cell dependencies and a DFS-based cycle detection algorithm, and that you reject set_cell calls that would introduce a cycle. Emphasize that you ensure atomicity by validating the entire change before mutating any state, so the engine remains consistent if the call is rejected.
Pro tip: Mention that you perform cycle detection incrementally on the affected subgraph rather than the whole graph for performance, and that you log rejected calls with enough context to debug user errors without compromising engine integrity.
Represent cells as nodes and formula references as directed edges. This allows cycle detection and impact analysis.
When set_cell is called, simulate the update and run DFS or topological sort on the affected subgraph to check for cycles.
If a cycle is detected, reject the set_cell call without modifying any cell values or dependency edges, ensuring the engine remains in its previous valid state.
Return a clear error to the caller and optionally log the cycle path. Ensure that no partial updates occur and that the engine's state is unchanged.
Use incremental cycle detection on the affected subgraph, and consider caching or memoization to avoid full-graph scans on every update.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.