My first instinct was to sort and binary search within each room's meeting list to find the insertion point, then just check the neighbors for overlap.
Clarify the problem constraints (e.g., number of rooms, meetings per room, whether intervals are sorted, and if we need to return any room or optimize for utilization). Then propose an efficient algorithm: for each room, check if the new meeting overlaps with any existing meeting using interval overlap logic; if not, return that room. Discuss time complexity and possible optimizations like sorting intervals or using interval trees.
Pro tip: Mention that in real systems like Uber, you'd likely need to handle concurrency and scale, so you might use a database with proper indexing or a distributed lock, but for this coding problem, focus on the algorithmic solution and clearly state assumptions.
Ask about input size, whether intervals are sorted, if rooms have capacities or other attributes, and if we need to optimize for any metric (e.g., earliest available room).
Two intervals [s1, e1] and [s2, e2] overlap if s1 < e2 and s2 < e1. Use this to check against each existing meeting.
Iterate through each room; for each, check all existing meetings for overlap. If none overlap, return room ID. If no room fits, return null.
Time complexity O(R * M) where R is number of rooms and M is average meetings per room. Discuss optimizations: sort intervals per room and use binary search, or use interval trees for O(log M) per room.
Handle empty room list, meetings that touch at endpoints (non-overlap), and consider extensions like recurring meetings, time zones, or concurrency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.