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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Google interview with a scheduling/availability problem. Pretty classic but the edge cases sneak up on you if you're not careful.

Questions Asked (1)

Q1

Design an algorithm that finds all time slots where multiple people are simultaneously available.

Algorithms & Data StructuresSystem Design
Author's notes

My first instinct was to just do a brute-force overlap check and I started going down that path before realizing it gets messy fast with more than two people.

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AI HintsAI Generated

Suggested Approach

Clarify the input format (e.g., list of busy intervals per person) and the definition of a valid time slot. Then propose an algorithm that merges all busy intervals and finds gaps where everyone is free, or uses a sweep line to count overlapping busy intervals.

Pro tip: Discuss trade-offs between different approaches (e.g., merging vs. sweep line) and mention how to handle edge cases like zero-length intervals or time zones. Also, consider scalability for large inputs.

1. Clarify requirements and assumptions

Ask about input format (e.g., list of busy intervals per person), output format (e.g., list of free intervals), and constraints (e.g., time granularity, number of people).

2. Choose an algorithm

Decide between merging intervals or using a sweep line. Merging is simpler for few people; sweep line is efficient for many people or large data.

3. Outline the algorithm

For merging: combine all busy intervals, sort by start time, merge overlapping, then find gaps between merged intervals. For sweep line: create events for start/end of busy times, sort, and track count of busy people.

4. Analyze complexity and edge cases

Discuss time and space complexity (e.g., O(N log N) for sorting). Mention edge cases: no common free time, all free, intervals touching at endpoints, etc.

5. Optimize and extend

Suggest optimizations (e.g., using a heap for sweep line) or extensions (e.g., finding slots of minimum duration, handling recurring events).

Key Points to Mention

  • Input representation: list of busy intervals per person
  • Merging intervals vs. sweep line algorithm
  • Sorting and merging overlapping intervals
  • Time complexity: O(N log N) where N is total number of intervals
  • Edge cases: no overlap, all overlap, zero-length intervals
  • Scalability: handling many people or large time ranges

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