OpenAI interview Question #4 – Spreadsheet engine – VO help – VO support – interview proxy

Design and implement a tiny Spreadsheet engine.

• Each cell is addressed by an ID such as A1, B1, …
• A cell can store either:
  1. an integer literal, e.g., 42, or
  2. a formula of the form X + Y, where X and Y are cell IDs (no minus/multiply in the base version).
• Implement:
  • setCell(id, valueOrFormula) — set a cell to an integer or to a formula string like "A1 + B1".
  • getCellValue(id) — return the evaluated integer value of the cell.

Rules / constraints

• A formula’s value is the sum of its referenced cells.
• Cells may depend on other cells transitively (A1 = B1 + C1, B1 = D1 + 3, etc.).
• You may assume inputs are valid in Part 1 (no cycles); a simple DFS that follows dependencies is acceptable.

Follow-ups

1. There will be many queries per second (getCellValue called frequently). Optimize beyond naïve DFS.
2. Allow updates to cells. When a cell changes, affected dependent cells should be recomputed efficiently.
3. (Optional) Detect cycles and report an error.
4. (Optional) Extend formula grammar to allow integer literals inside expressions (A1 + 3) and more operators.

Example

setCell("A1", 3)
setCell("B1", 5)
setCell("C1", "A1 + B1")
getCellValue("C1")  → 8

setCell("B1", "A1 + 1")   # C1 depends on B1 which depends on A1
getCellValue("C1")  → 7    # (A1=3, B1=4, C1=7)

• Graph model: deps[cell] = [u, v] (operands); parents[child] for reverse edges.
• Evaluation: def eval(x): if x in memo: return memo[x] if is_int(x): return x a, b = deps[x] memo[x] = eval(a) + eval(b) return memo[x]
• Update (setCell):
  • remove old edges, add new edges;
  • BFS/DFS from id via parents to invalidate memo of all dependents.

This mirrors the interview: start with simple DFS, then show scalability via caching + dependency invalidation, and optionally cycle detection.

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