Meeting Rooms
Given an array of meeting time intervals, determine if a person could attend all meetings. Sort the intervals and check for overlaps.
Why Interviewers Ask This
Spotify asks this question to evaluate a candidate's ability to optimize data structures for resource management, mirroring their need for efficient scheduling systems. It tests fundamental algorithmic thinking, specifically the application of sorting to solve interval overlap problems, while assessing clarity in communicating logic under pressure.
How to Answer This Question
1. Clarify Requirements: Confirm if intervals are inclusive or exclusive and handle edge cases like empty arrays immediately. 2. Propose Strategy: Explicitly state that sorting by start time is the optimal approach to linearize the problem. 3. Walk Through Logic: Explain how iterating through the sorted list allows you to compare each meeting's start time with the previous one's end time to detect conflicts. 4. Analyze Complexity: Discuss Time Complexity (O(N log N) due to sorting) and Space Complexity (O(1) or O(N) depending on sort implementation). 5. Implement Cleanly: Write code with clear variable names, ensuring you handle boundary conditions where meetings touch but do not overlap.
Key Points to Cover
- Explicitly choosing sorting as the primary optimization strategy
- Correctly defining the overlap condition (start < previous_end)
- Clearly articulating O(N log N) time complexity reasoning
- Demonstrating awareness of edge cases like empty arrays
- Connecting the algorithmic efficiency to real-world scalability needs
Sample Answer
To determine if a person can attend all meetings, we first need to understand the input structure. We have an array of intervals representing start and end times. The core challenge is identifying any overlaps efficiently. My approach starts by sorting these intervals based on their start times. This step is crucial because it transforms the problem from a complex search into a simple linear scan. Once sorted, we iterate through the array starting from the second meeting. For each meeting, we compare its start time against the end time of the immediately preceding meeting. If the current start time is less than the previous end time, an overlap exists, meaning attendance is impossible, and we return false immediately. If we complete the loop without finding such a conflict, we return true. Regarding complexity, sorting dominates the runtime, making it O(N log N), while the iteration is O(N), resulting in an overall O(N log N) time complexity. Space complexity is typically O(1) if we sort in place, though some languages may require O(N) auxiliary space. This solution mirrors Spotify's focus on scalable algorithms; just as they manage millions of user streams efficiently, this logic ensures resource allocation checks remain performant even with large datasets. By handling edge cases like empty inputs upfront, we demonstrate robust engineering practices expected at scale.
Common Mistakes to Avoid
- Failing to sort the intervals first, leading to an inefficient O(N^2) nested loop comparison
- Incorrectly handling boundary conditions where one meeting ends exactly when another starts
- Neglecting to analyze time and space complexity after writing the solution
- Overlooking edge cases such as null inputs or single-element arrays
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