I went with a gap buffer approach after briefly considering a plain array and a linked list.
Start by clarifying requirements and constraints, then propose a balanced tree (e.g., rope or implicit treap) as the underlying representation, explaining how it supports segment operations efficiently. Walk through each operation with time complexity, and discuss trade-offs versus simpler structures like arrays or linked lists.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that Google often values scalability and cache efficiency; consider discussing how a B-tree or skip list might be used in practice for better memory locality, even if a balanced BST is the theoretical answer.
Ask about expected data size, frequency of operations, and whether elements are of fixed type. Confirm that operations are on segments (contiguous ranges) and that cursor position is a single index.
Propose a balanced binary search tree (e.g., implicit treap or rope) where each node stores a segment of elements and subtree sizes. Explain why arrays/linked lists are inefficient for insert/delete/move.
For each operation (insert, delete, move, reposition cursor), describe the algorithm using split/merge on the tree, and state the time complexity (typically O(log n) for balanced trees).
Compare with alternative structures (e.g., gap buffer, piece table) and mention potential optimizations like lazy propagation or block-based storage for cache efficiency.
Recap the chosen design, its complexities, and why it meets the requirements. Mention any assumptions made and possible extensions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.