Listing Windows: Find All Time Windows Where Active Listings Exceed a Threshold
Interview Experience
Problem
You have a list of marketplace listings, each with a start_date and end_date. Find all contiguous time windows where the number of simultaneously active listings is strictly greater than a threshold k.
Return each window as (start, end) with the peak count.
python
def find_busy_windows(
listings: list[tuple[int, int]], # (start_day, end_day) inclusive
k: int
) -> list[dict]:
**Return** [{"start": int, "end": int, "peak": int}]
pass
Example:
listings = [(1,5),(2,8),(4,6),(9,12)]
k = 2
At day 2-5: 3 active (listings 1,2,3 overlap here at peak)
At day 6-8: 2 active
k=2 means strictly > 2, so only days 2-5 qualify with peak=3
Output: [{"start": 2, "end": 5, "peak": 3}]
Approach
Use a sweep line: create events (day, +1) for starts and (day, -1) for ends, sort by day, sweep to track active count, then merge contiguous windows above threshold.
Follow-ups
- What is the time complexity of the sweep line approach?
- How does your answer change if listings can have fractional (hourly) durations?
- If the threshold itself varies by day of week, how do you adapt the sweep?
- How would you express this query in SQL using window functions?
Full Details
Problem
You have a list of marketplace listings, each with a start_date and end_date. Find all contiguous time windows where the number of simultaneously active listings is strictly greater than a threshold k.
Return each window as (start, end) with the peak count.
python
def find_busy_windows(
listings: list[tuple[int, int]], # (start_day, end_day) inclusive
k: int
) -> list[dict]:
**Return** [{"start": int, "end": int, "peak": int}]
pass
Example:
listings = [(1,5),(2,8),(4,6),(9,12)]
k = 2
At day 2-5: 3 active (listings 1,2,3 overlap here at peak)
At day 6-8: 2 active
k=2 means strictly > 2, so only days 2-5 qualify with peak=3
Output: [{"start": 2, "end": 5, "peak": 3}]
Approach
Use a sweep line: create events (day, +1) for starts and (day, -1) for ends, sort by day, sweep to track active count, then merge contiguous windows above threshold.
Follow-ups
- What is the time complexity of the sweep line approach?
- How does your answer change if listings can have fractional (hourly) durations?
- If the threshold itself varies by day of week, how do you adapt the sweep?
- How would you express this query in SQL using window functions?
About This Question
This is a candidate experience report from a stubhub interview during the phone round.
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About Stubhub Interview Reports
This question was reported by a candidate who interviewed at Stubhub. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Stubhub are the higher-signal extractions to take from this report.
For broader preparation context, the Stubhub interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Stubhub reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Stubhub Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Stubhub reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.