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

Intermediate

Jul 2026

Summary

Microsoft SWE interview with a classic interval merging problem. Pretty standard algorithmic round, nothing too surprising, but the follow-up discussion on complexity kept it from being totally routine.

Questions Asked (1)

Given an array of intervals, merge all overlapping ones and return the resulting non-overlapping set. Walk through an efficient approach and its time complexity.

Algorithms & Data Structures

Author's notes

Sort by start time, then walk through and merge greedily.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then propose sorting the intervals by start time and merging in a single pass. Explain the algorithm step-by-step, analyze time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Mention that sorting is the key to achieving O(n log n) time, and that in-place merging can save space if the input can be modified. Also, briefly discuss how to handle edge cases like empty input or intervals with the same start time.

1. Clarify the problem

Ask clarifying questions about input format, whether intervals are inclusive, and if the input can be modified. Confirm expected output format.

2. Propose sorting

Suggest sorting the intervals by their start times. This brings overlapping intervals together, simplifying the merging process.

3. Merge in one pass

Iterate through the sorted intervals, merging the current interval with the previous one if they overlap (i.e., current start <= previous end). Otherwise, add the previous interval to the result.

4. Analyze complexity

State that sorting takes O(n log n) time and the merge pass takes O(n) time, resulting in O(n log n) overall. Space complexity is O(n) for the output, or O(1) extra if merging in-place.

5. Discuss edge cases and optimizations

Mention handling empty input, single interval, and intervals with same start times. Optionally, discuss if the input is already sorted (then O(n) time) or if we can merge in-place to save space.

Key Points to Mention

Sorting by start time is crucial for efficiency.
Overlap condition: next.start <= current.end.
Time complexity: O(n log n) due to sorting, O(n) for merging.
Space complexity: O(n) for output, O(1) extra if in-place.
Edge cases: empty array, single interval, intervals with same start.
Potential optimization: if input is sorted, time is O(n).

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