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Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Google SWE interview with a classic scheduling problem. Nothing too exotic but the follow-up pressure on complexity was real.

Questions Asked (1)

Q1

Given an array of meeting time intervals, each with a start and end time, find the minimum number of meeting rooms needed so no two meetings overlap.

Algorithms & Data Structures
Author's notes

I knew this problem but still fumbled the explanation for a bit.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then present the sweep line algorithm: separate start and end times, sort them, and use two pointers to count concurrent meetings. Alternatively, use a min-heap to track end times, but the sweep line is more efficient and elegant.

Pro tip: Mention that the sweep line approach is optimal with O(n log n) time and O(n) space, and that it can be implemented without a heap by sorting starts and ends separately. This shows you understand the trade-offs and can optimize for simplicity.

1. Clarify requirements and edge cases

Ask about input format, whether intervals are inclusive/exclusive, and if the array can be empty. Confirm that overlapping means any shared time, including touching endpoints.

2. Discuss brute force and its complexity

Mention that a brute force approach would check all pairs for overlaps, leading to O(n^2) time, which is inefficient for large inputs.

3. Present the sweep line algorithm

Explain that you separate start and end times into two arrays, sort both, and use two pointers to count the number of active meetings. The maximum count is the answer.

4. Analyze time and space complexity

State that sorting takes O(n log n) time and the sweep takes O(n), so overall O(n log n) time. Space is O(n) for the separate arrays.

5. Test with examples and edge cases

Walk through a simple example like [[0,30],[5,10],[15,20]] to show the algorithm works. Also test empty input, single meeting, and all overlapping meetings.

Key Points to Mention

  • Sweep line algorithm: separate starts and ends, sort, and use two pointers.
  • Time complexity: O(n log n) due to sorting, space O(n).
  • Alternative min-heap approach: sort by start time, use heap to track end times, O(n log n) time, O(n) space.
  • Edge cases: empty input, meetings that touch at endpoints (e.g., [1,2] and [2,3] do not overlap).
  • Proof of correctness: the maximum number of concurrent meetings equals the minimum rooms needed.
  • Optimization: if intervals are given as integers within a small range, counting sort could achieve O(n + k) time.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.