OAVOService Technical Deep Dive
This OpenAI spreadsheet system design question is a typical combination of graph theory and recursion examination, medium-high difficulty with numerous detail traps. Many candidates fail at dependency relationship handling, cycle detection, and cache optimization.
OAVOService Professional Reminder: This type of question appears to be a data structure problem but actually examines engineering thinking and system design capabilities. Our interview assistance team maintains a 98% pass rate on this question, with the key being comprehensive technical solutions and clear code implementation.
Complete Problem Statement
Design a simple spreadsheet system supporting inter-cell dependency calculations.
Core Requirements
Cell Types:
- Value Cells: Store fixed numerical values
- Dependency Cells: Reference other cells (maximum 2), value equals sum of referenced cells
System Interface:
setCell(key, cell)— Set cell contentgetCellValue(key)— Get computed cell value
Cell Object Definition
class Cell:
def __init__(self, value=None, child1=None, child2=None):
self.value = value # Numerical value (if constant cell)
self.child1 = child1 # First dependency cell key
self.child2 = child2 # Second dependency cell key
Example Scenarios
Basic Example:
A = B + C
B = 3
C = 5
getCellValue("A") → 8
Nested Dependencies:
A = B + C
B = D + E
C = 2
D = 4
E = 1
getCellValue("A") → 7 # (D+E) + C = (4+1) + 2 = 7
OAVOService Expert-Level Solutions
Solution 1: Basic DFS Recursive Implementation
class BasicSpreadsheet:
def __init__(self):
self.cells = {} # key -> Cell object
def setCell(self, key, cell):
"""Set cell"""
self.cells[key] = cell
def getCellValue(self, key):
"""Get cell value (DFS recursion)"""
if key not in self.cells:
raise KeyError(f"Cell {key} not found")
cell = self.cells[key]
# If value cell, return value directly
if cell.value is not None:
return cell.value
# If dependency cell, recursively calculate
result = 0
if cell.child1:
result += self.getCellValue(cell.child1)
if cell.child2:
result += self.getCellValue(cell.child2)
return result
Solution 2: Memoized Optimization Version
class OptimizedSpreadsheet:
def __init__(self):
self.cells = {}
self.cache = {} # Cache computation results
def setCell(self, key, cell):
"""Set cell and clear related cache"""
self.cells[key] = cell
# Clear affected cache
self._invalidateCache(key)
def getCellValue(self, key):
"""Get cell value (with cache optimization)"""
# Check cache
if key in self.cache:
return self.cache[key]
if key not in self.cells:
raise KeyError(f"Cell {key} not found")
cell = self.cells[key]
# Calculate value
if cell.value is not None:
# Value cell
result = cell.value
else:
# Dependency cell
result = 0
if cell.child1:
result += self.getCellValue(cell.child1)
if cell.child2:
result += self.getCellValue(cell.child2)
# Cache result
self.cache[key] = result
return result
def _invalidateCache(self, key):
"""Recursively clear affected cache"""
if key not in self.cache:
return
# Clear current key's cache
del self.cache[key]
# Find all cells depending on current key
for cell_key, cell in self.cells.items():
if (cell.child1 == key or cell.child2 == key):
self._invalidateCache(cell_key)
Solution 3: Complete Production Version (with Cycle Detection)
class ProductionSpreadsheet:
def __init__(self):
self.cells = {}
self.cache = {}
self.dependency_graph = {} # key -> [keys depending on it]
def setCell(self, key, cell):
"""Set cell (complete version)"""
# Update dependency graph
self._updateDependencyGraph(key, cell)
# Detect circular dependencies
if self._hasCycle(key):
raise ValueError(f"Circular dependency detected involving {key}")
# Set cell
self.cells[key] = cell
# Clear affected cache
self._invalidateCache(key)
def getCellValue(self, key):
"""Get cell value (production-grade version)"""
return self._getCellValueWithPath(key, set())
def _getCellValueWithPath(self, key, path):
"""Value calculation with path tracking (prevent runtime cycles)"""
if key in path:
raise ValueError(f"Circular dependency detected: {path} -> {key}")
# Check cache
if key in self.cache:
return self.cache[key]
if key not in self.cells:
raise KeyError(f"Cell {key} not found")
cell = self.cells[key]
path.add(key)
try:
if cell.value is not None:
# Value cell
result = cell.value
else:
# Dependency cell
result = 0
if cell.child1:
result += self._getCellValueWithPath(cell.child1, path)
if cell.child2:
result += self._getCellValueWithPath(cell.child2, path)
# Cache result
self.cache[key] = result
return result
finally:
path.remove(key)
def _updateDependencyGraph(self, key, cell):
"""Update dependency relationship graph"""
# Clear old dependencies
for dep_key in list(self.dependency_graph.keys()):
if key in self.dependency_graph[dep_key]:
self.dependency_graph[dep_key].remove(key)
if not self.dependency_graph[dep_key]:
del self.dependency_graph[dep_key]
# Add new dependencies
if cell.child1:
if cell.child1 not in self.dependency_graph:
self.dependency_graph[cell.child1] = []
self.dependency_graph[cell.child1].append(key)
if cell.child2:
if cell.child2 not in self.dependency_graph:
self.dependency_graph[cell.child2] = []
self.dependency_graph[cell.child2].append(key)
def _hasCycle(self, start_key):
"""Detect if cycle exists starting from specified node"""
def dfs(key, visited, rec_stack):
if key not in self.cells:
return False
visited.add(key)
rec_stack.add(key)
cell = self.cells[key]
children = [child for child in [cell.child1, cell.child2] if child]
for child in children:
if child not in visited:
if dfs(child, visited, rec_stack):
return True
elif child in rec_stack:
return True
rec_stack.remove(key)
return False
return dfs(start_key, set(), set())
def _invalidateCache(self, key):
"""Smart cache invalidation"""
if key not in self.cache:
return
# Use BFS to clear all affected cache
queue = [key]
invalidated = set()
while queue:
current = queue.pop(0)
if current in invalidated:
continue
invalidated.add(current)
# Clear current cache
self.cache.pop(current, None)
# Find all cells depending on current cell
if current in self.dependency_graph:
queue.extend(self.dependency_graph[current])
Common Interviewer Follow-ups & OAVOService Standard Responses
Q1: How to optimize performance for frequent queries?
Professional Answer:
- Global Cache: Compute once, reuse multiple times
- Incremental Updates: Only recalculate affected portions
- Topological Sort: Calculate in dependency order for batch updates
Q2: How to handle large-scale data?
System Design Approach:
- Sharded Storage: Distribute storage by cell regions
- Lazy Loading: Load relevant cells on demand
- Compression Algorithms: Sparse matrix compression storage
Q3: How to support more complex formulas?
Extension Solutions:
- Expression Parsing: Support A1 + 2 * B1 format
- Function Library: Built-in functions like SUM, AVG, MAX
- Type System: Support numbers, strings, booleans, etc.
Advanced Optimization: Reverse Dependency Graph Maintenance
class AdvancedSpreadsheet:
def __init__(self):
self.cells = {}
self.cache = {}
self.dependents = {} # key -> set of keys depending on it
self.dependencies = {} # key -> set of keys it depends on
def setCell(self, key, cell):
# Update bidirectional dependency graph
old_deps = self.dependencies.get(key, set())
new_deps = set()
if cell.child1:
new_deps.add(cell.child1)
if cell.child2:
new_deps.add(cell.child2)
# Remove old dependencies
for dep in old_deps:
if dep in self.dependents:
self.dependents[dep].discard(key)
# Add new dependencies
for dep in new_deps:
if dep not in self.dependents:
self.dependents[dep] = set()
self.dependents[dep].add(key)
self.dependencies[key] = new_deps
self.cells[key] = cell
# Smart cache invalidation
self._smartInvalidate(key)
def _smartInvalidate(self, key):
"""Smart cache invalidation strategy"""
to_invalidate = set()
queue = [key]
while queue:
current = queue.pop(0)
if current in to_invalidate:
continue
to_invalidate.add(current)
# Add all keys depending on current key
if current in self.dependents:
queue.extend(self.dependents[current])
# Batch clear cache
for k in to_invalidate:
self.cache.pop(k, None)
OAVOService Exclusive Interview Techniques
Code Implementation Key Points
- Error Handling: Non-existent keys, circular dependencies, and other exceptions
- Performance Analysis: Time and space complexity trade-offs
- Extensibility: How to support more operations and complex scenarios
Communication Strategies
- Overall then Details: Explain overall architecture first, then dive into implementation
- Proactive Optimization: Propose performance improvements without waiting for prompts
- Engineering Mindset: Consider edge conditions in real-world scenarios
Related Algorithm Problems
- Topological Sorting: Dependency ordering and batch computation
- Strongly Connected Components: Complex circular dependency detection
- Dynamic Programming: Optimal substructure caching strategies
Summary
Spreadsheet dependency calculation is a comprehensive system design question examining:
- Graph Theory Fundamentals: Dependency relationship modeling and traversal
- Recursive Thinking: DFS depth-first search applications
- Caching Strategies: Performance optimization and consistency guarantees
- Engineering Practices: Error handling and edge case management
OAVOService Interview Assistance Core Values: ✅ Thinking Guidance: Avoid design direction errors, ensure reasonable architecture ✅ Code Assistance: Real-time error correction and optimization suggestions, code quality guarantee ✅ Follow-up Response: Deep technical Q&A, demonstrate engineering experience ✅ Performance Tuning: Algorithm complexity analysis, system scalability considerations
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