The half-open interval part is where I slipped up initially.
Clarify the requirements and constraints, then propose a solution using a balanced binary search tree (e.g., TreeMap) to store intervals sorted by start time. For each new booking, check for overlap with the immediate predecessor and successor intervals, and insert if no conflict. Discuss time and space complexity, and consider edge cases and potential optimizations.
Pro tip: Mention that using a TreeMap allows O(log n) insertion and overlap checking, but also discuss the trade-offs with other data structures like sorted lists or interval trees, showing you understand scalability for Uber's high-throughput systems.
Ask about expected number of bookings, concurrency needs, and whether intervals are half-open [start, end). Confirm return type and error handling.
Propose a balanced BST (e.g., TreeMap in Java) keyed by start time to maintain sorted intervals and enable efficient predecessor/successor queries.
For a new interval, find the floor and ceiling entries. Check if the new interval overlaps with either; if not, insert and return true, else return false.
State O(log n) time per booking and O(n) space. Discuss edge cases: empty calendar, adjacent intervals, exact duplicates, and intervals with zero duration.
Write clean code with helper methods for overlap checking. Walk through examples and consider unit tests for boundary conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.