I went with a list of characters in Python because I knew a rope implementation would take forever to code under pressure.
Start by clarifying requirements and constraints, then propose a balanced binary search tree (e.g., a rope or implicit treap) that supports efficient insertions and deletions. Walk through the time complexities for each operation, and discuss trade-offs with simpler approaches like arrays or linked lists.
Pro tip: Mention that real-world text editors often use piece tables or gap buffers, and explain why you might choose one over a tree for specific use cases. This shows practical awareness beyond textbook data structures.
Ask about expected text size, frequency of operations, and whether concurrent access is needed. This ensures your solution aligns with the actual use case.
Suggest a balanced BST like a rope or implicit treap, where each node represents a substring and stores the size of its subtree. This allows efficient splits and merges.
Detail how insert and delete are implemented via split and merge, achieving O(log n) time. Retrieving the full text is O(n) by in-order traversal.
Compare with arrays (O(n) insert/delete), linked lists (O(n) search), and gap buffers (amortized O(1) for local edits but O(n) for random access). Highlight when each is preferable.
Reiterate the chosen approach's strengths and acknowledge any limitations, such as memory overhead or implementation complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.