Sort first, then walk through comparing each interval's start against the previous end.
Start by clarifying edge cases and assumptions, then propose sorting intervals by start time and merging in a single pass. Explain the O(n log n) time and O(n) space complexity, and walk through a concrete example to validate the logic.
Pro tip: At Amazon, interviewers value ownership and customer obsession—tie your solution to real-world scenarios like merging meeting times or consolidating server maintenance windows, and proactively discuss how you'd handle invalid input or large-scale data.
Ask about input format, whether intervals are inclusive, and how to handle empty lists, single intervals, or unsorted input. Confirm that the output should be sorted by start value.
Propose sorting intervals by start time, then iterating through them while merging overlapping intervals into a result list. Explain why sorting is necessary and how merging works.
State the time complexity O(n log n) due to sorting and space complexity O(n) for the output. Mention that if the input is already sorted, the time can be O(n).
Use a small example like [[1,3],[2,6],[8,10],[15,18]] to demonstrate the merging process step by step, showing how the result is built.
Cover cases like no overlaps, all overlaps, intervals with same start, and negative values. Explain how you would test the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.