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The overlap condition tripped me up for a second.
Clarify the half-open interval semantics and edge cases first, then propose a balanced binary search tree (e.g., TreeMap) to store bookings sorted by start time. For each new booking, find the immediate predecessor and successor to check for overlaps in O(log n) time, and insert if no conflict.
Pro tip: Emphasize that half-open intervals [start, end) allow adjacent bookings (e.g., [1,2) and [2,3)) to coexist, and explicitly state that you'll handle edge cases like start >= end by returning false. This shows attention to detail and practical robustness.
Confirm the half-open interval semantics, discuss invalid inputs (start >= end), and whether zero-length intervals are allowed. Also clarify if bookings can be modified or cancelled.
Select a balanced binary search tree (e.g., TreeMap in Java) keyed by start time to maintain sorted order and enable O(log n) lookups. Explain why a simple list would be O(n) per operation.
For a new interval [s, e), find the floor entry (largest start <= s) and ceiling entry (smallest start >= s). Check if the floor's end > s or the ceiling's start < e; if either, conflict exists.
If no conflict, insert the new interval into the tree and return true; otherwise return false. Ensure the insertion maintains the tree's sorted order.
State that book runs in O(log n) time and O(n) space. Walk through examples including adjacent intervals, overlapping intervals, and invalid inputs to verify correctness.
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