My first instinct was to just store a map of cell name to value and call it a day.
Start by clarifying requirements and scale, then model cells as nodes in a directed graph where edges represent dependencies. Explain how to store formulas, evaluate them with topological ordering, and handle updates via incremental recomputation while detecting cycles using DFS or Kahn's algorithm.
Pro tip: Mention that cycle detection can be integrated into the dependency graph construction, and that incremental updates should only recompute affected cells, not the entire sheet, to maintain performance at scale.
Ask about expected sheet size, update frequency, and whether real-time collaboration is needed. This informs choices like in-memory vs. persistent storage and incremental vs. full recomputation.
Represent each cell with its raw content (literal or formula) and a parsed dependency list. Use a graph (adjacency list) to track dependencies and reverse dependencies for efficient updates.
Evaluate cells using topological sorting to ensure dependencies are computed first. On change, mark affected cells dirty and recompute only those in topological order.
During dependency graph construction or evaluation, use DFS with recursion stack or Kahn's algorithm to detect cycles. Reject the update that introduces a cycle and notify the user.
Compare eager vs. lazy evaluation, incremental vs. full recomputation, and memory vs. speed trade-offs. Mention optimizations like memoization and batching updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current design and how formulas are represented, then propose extending the parser to recognize range syntax and the evaluator to handle ranges as collections. Discuss trade-offs between eager evaluation (expanding ranges into individual cells) and lazy evaluation (iterating over ranges on demand), and how to handle dependencies and updates.
Pro tip: Emphasize that ranges should be treated as first-class objects in the formula engine, enabling optimizations like vectorized operations and efficient dependency tracking, which is crucial for performance in a spreadsheet-like system.
Ask questions to understand the existing formula parsing and evaluation architecture, and confirm that ranges are not currently supported. Identify how cell references and formulas are represented.
Modify the grammar to recognize range syntax (e.g., A1:A10) and introduce a Range node in the abstract syntax tree. Ensure the parser can handle ranges in various contexts (e.g., function arguments, arithmetic).
Decide on a data structure to represent a range (e.g., start and end cell coordinates) and how to evaluate it. Consider whether to expand ranges into lists of cells or keep them as lazy iterators for efficiency.
Update the dependency graph to track ranges as dependencies. When a cell within a range changes, ensure dependent formulas are recalculated. Discuss strategies for efficient invalidation and recalculation.
Compare eager vs. lazy evaluation, memory usage, and performance. Mention potential optimizations like vectorized operations, caching, and handling large ranges without blowing up memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I felt most out of my depth.
Start by clarifying the context: what kind of recalculation is this (e.g., spreadsheet, data pipeline, UI rendering)? Then propose a dependency graph to track which computations depend on which inputs, so only affected nodes are recomputed. Finally, discuss implementation details like topological ordering, dirty marking, and incremental algorithms (e.g., dynamic programming, memoization).
Pro tip: Mention that incremental recomputation isn't just about performance—it also reduces the risk of inconsistent state and enables real-time updates. Also, be prepared to discuss trade-offs like memory overhead for dependency tracking and complexity of invalidation logic.
Ask questions to understand the system: what is being recalculated, how often changes occur, what are the latency and consistency requirements? This ensures your solution fits the context.
Represent computations as nodes and dependencies as edges. This allows you to identify which parts of the computation are affected by a change.
When an input changes, mark dependent nodes as dirty and recompute only those in topological order. Use techniques like memoization or dynamic programming to avoid redundant work.
Consider batching updates, cycle detection, and incremental algorithms for specific operations (e.g., incremental view maintenance). Discuss how to handle deletions or structural changes.
Compare with full recomputation: when is incremental worth the complexity? Mention memory overhead, invalidation cost, and potential for bugs. Suggest monitoring and fallback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the context—what kind of cells (e.g., spreadsheet formulas, database computed columns) and the scale/performance requirements. Then describe a caching strategy that includes cache key design, storage, and invalidation triggers, emphasizing correctness and safety. Finally, discuss trade-offs between different invalidation approaches and how you would handle edge cases like circular dependencies.
Pro tip: Demonstrate awareness of the 'cache invalidation is hard' problem by proposing a versioned or dependency-based invalidation system, and mention how you would monitor cache hit rates and staleness to detect issues in production.
Ask questions to understand the system: what are cells, how are they computed, what are the consistency and latency requirements, and what is the scale? This shows you avoid premature optimization.
Propose a cache key (e.g., cell ID + version or hash of dependencies), a storage mechanism (in-memory, Redis, etc.), and a strategy for populating the cache (lazy vs. eager).
Explain when to invalidate: when dependencies change, on a schedule, or via explicit invalidation. Discuss trade-offs between eager and lazy invalidation.
Address concurrency (e.g., locking or atomic operations), fallback to recomputation on cache miss, and handling of stale data. Mention versioning or timestamps to avoid race conditions.
Compare invalidation strategies (TTL vs. event-driven), and explain how you would monitor cache effectiveness and correctness (e.g., hit rate, staleness metrics).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Last follow-up and probably the one I handled worst.
Start by clarifying the current single-user design and the requirements for concurrent editing (e.g., real-time collaboration, conflict resolution, consistency). Then propose a shift to a distributed architecture with conflict-free replicated data types (CRDTs) or operational transformation (OT), and discuss trade-offs like latency, complexity, and offline support.
Pro tip: Mention that you would first consider the business context—Ramp's financial data requires strong consistency and auditability, so you might lean towards a server-authoritative model with OT rather than peer-to-peer CRDTs, and highlight how you'd handle conflicts with a clear merge strategy.
Ask about the expected number of concurrent users, latency tolerance, offline support, and consistency requirements (e.g., strong vs eventual). This ensures the design aligns with business needs.
Explain that concurrent edits introduce conflicts (e.g., two users editing the same cell) and require a mechanism to merge changes while preserving user intent and data integrity.
Compare approaches like locking (pessimistic), optimistic concurrency with versioning, operational transformation (OT), and conflict-free replicated data types (CRDTs). Discuss their trade-offs in terms of latency, complexity, and consistency.
Outline components: a real-time communication layer (WebSockets), a central server or peer-to-peer sync, a conflict resolution engine, and a data store that supports versioning or CRDTs. Consider scalability and fault tolerance.
Discuss handling offline edits, network partitions, undo/redo, and audit trails. Highlight how the chosen approach affects user experience and system complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.