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

Intermediate

May 2026

Summary

Google SWE coding round, one algorithmic problem about scheduling meetings. Pretty standard interval problem but the constraints were large enough that a naive solution would time out, so you had to think carefully.

Questions Asked (1)

Given a list of meetings with start and end times, find the minimum number of rooms needed to schedule all of them without conflicts. Meetings are treated as half-open intervals, so a meeting ending at time T does not block a new one starting at T.

Algorithms & Data Structures

Author's notes

My first instinct was to sort by start time and use a min-heap to track when rooms free up.

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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 concurrent meetings. Alternatively, use a min-heap to track end times of ongoing meetings. Analyze time and space complexity, and discuss potential optimizations or alternative approaches.

Pro tip: Mention that the problem is equivalent to finding the maximum number of overlapping intervals, which can be solved in O(n log n) time. Also, explicitly state that you assume the input is valid and meetings are half-open intervals, showing attention to detail.

1. Understand the problem

Restate the problem in your own words, confirm the half-open interval semantics, and ask clarifying questions about input size, constraints, and expected output.

2. Discuss brute force and inefficiencies

Briefly mention a naive O(n^2) approach that checks all pairs for conflicts, and explain why it's inefficient for large inputs.

3. Propose an optimal approach

Describe the two-pointer sweep after sorting start and end times, or the min-heap approach, and explain how it computes the minimum rooms needed.

4. Analyze complexity and edge cases

State the time and space complexity (O(n log n) time, O(n) space) and discuss edge cases like empty input, back-to-back meetings, and simultaneous start/end.

5. Test with examples

Walk through a small example to validate the algorithm, and if time permits, mention potential optimizations or variations.

Key Points to Mention

Half-open intervals: meetings ending at T do not conflict with those starting at T.
Sorting start and end times separately enables an O(n log n) solution.
Two-pointer technique to count maximum concurrent meetings.
Min-heap approach to track end times of ongoing meetings.
Time complexity: O(n log n) due to sorting; space complexity: O(n).
Edge cases: empty input, all meetings non-overlapping, all overlapping.

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