I started with Piece and Puzzle and thought I was doing great, then the interviewer asked how I'd actually represent a tab vs a blank on an edge and I kind of froze.
Start by clarifying requirements and scope, then outline the core classes and their relationships. Focus on how pieces represent their sides and how matching is determined, and describe the board data structure and key API methods. Emphasize extensibility and performance considerations.
Pro tip: Demonstrate awareness of edge cases like border pieces and rotation, and discuss how your design supports features like hints or undo. This shows you think beyond the basic puzzle.
Ask questions to understand the puzzle size, whether pieces can rotate, if there are multiple players, and any performance constraints. This ensures your design meets the actual needs.
Identify main classes: Puzzle, Piece, Side, Board, and possibly Player. Describe their responsibilities and how they interact, using composition and inheritance where appropriate.
Explain how each piece has four sides, each with a type (e.g., flat, tab, blank) and an identifier. Describe how matching is determined by comparing side types and identifiers, and how rotation affects matching.
Propose a 2D grid or hash map to represent the assembled board, storing piece references. Discuss how to handle empty slots and validate placements.
List essential methods like placePiece(piece, position), removePiece(position), getPieceAt(position), isComplete(), and findMatches(piece). Explain their signatures and behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and I was still mentally stuck on the base design.
Start by clarifying the current design's assumptions and the new requirements, then propose a modular architecture that separates puzzle representation, solving logic, and collaboration layer. For each extension, discuss data model changes, algorithmic considerations, and trade-offs, emphasizing how to maintain backward compatibility and scalability.
Pro tip: Frame your answer around extensibility and separation of concerns, showing how each feature can be added without disrupting existing functionality. Mention that you'd validate assumptions with stakeholders and iterate based on user feedback, demonstrating product thinking.
Ask questions to understand the expected scale, performance needs, and user experience for each feature. Identify what 'non-rectangular' means (e.g., irregular polygons, holes) and the desired level of collaboration (real-time vs. asynchronous).
Propose a flexible data model, such as a graph or grid with arbitrary cell shapes, to support non-rectangular puzzles. Discuss how to adapt existing algorithms (e.g., constraint propagation) to this representation.
Outline a hint system that uses the solver's state to suggest next steps, and an auto-solve that leverages backtracking or constraint satisfaction. Consider performance optimizations like caching and incremental solving.
Design a real-time collaboration layer using WebSockets or CRDTs to sync puzzle state across clients. Address conflict resolution, presence, and offline support, ensuring consistency and low latency.
Discuss trade-offs between generality and performance, and how to scale the collaboration backend. Propose monitoring and testing strategies to ensure reliability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.