Google·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Jul 2026

Summary

Google SWE coding round, just one algorithmic question about scheduling. Clean problem, classic setup, but the details matter more than you'd expect.

Questions Asked (1)

Given a list of meeting time intervals [start, end), find the minimum number of conference rooms needed so no two overlapping meetings share a room.

Algorithms & Data Structures

Author's notes

I'd seen this problem before so I wasn't panicking, but I fumbled explaining why a min-heap on end times actually works.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient algorithm such as sorting start and end times separately and using a two-pointer sweep to count maximum overlaps. Walk through a small example to validate the approach, and analyze time and space complexity.

Pro tip: Mention that the minimum number of rooms equals the maximum number of concurrent meetings, and that this can also be solved with a min-heap, but the two-pointer method is more space-efficient. This shows you understand the problem deeply and can compare trade-offs.

1. Clarify requirements and edge cases

Ask about input format, whether intervals are inclusive/exclusive, if the list can be empty, and if intervals are sorted. Confirm that overlapping means any intersection, even at endpoints.

2. Outline the approach

Explain that the minimum rooms needed is the maximum number of overlapping meetings at any point. Propose sorting start and end times separately and using two pointers to count active meetings.

3. Walk through an example

Choose a small set of intervals, e.g., [[0,30],[5,10],[15,20]], and demonstrate how the two-pointer sweep yields the answer 2. This validates the algorithm.

4. Analyze complexity and discuss alternatives

State that sorting takes O(n log n) time and O(n) space for the sorted arrays. Mention that a min-heap approach also works in O(n log n) time but uses O(n) space for the heap, and compare trade-offs.

5. Handle edge cases and conclude

Discuss empty input, single meeting, and meetings that touch at endpoints (non-overlapping). Summarize that the algorithm is optimal and scalable.

Key Points to Mention

Minimum rooms equals maximum number of concurrent meetings.
Sort start and end times separately, then use two pointers to count active meetings.
Time complexity O(n log n) due to sorting; space complexity O(n) for sorted arrays.
Alternative min-heap approach: sort by start time, push end times, pop when a meeting ends.
Edge cases: empty list, single meeting, meetings that only touch at endpoints.
Clarify interval inclusivity: [start, end) means end is exclusive, so meetings ending at time t and starting at t do not overlap.

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