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TripStack·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

TripStack software engineer interview with a meaty scheduling problem that went deeper than I expected. The algorithmic angle was straightforward enough but they kept pushing on design choices and edge case handling.

Questions Asked (1)

Q1

Given n tasks as half-open time intervals [start, end) in a single day, find the minimum number of servers needed so no two overlapping tasks share a server. Design an O(n log n) solution, describe your data structures, analyze complexity, handle equal endpoint edge cases, and discuss things like empty input or duplicate intervals. Bonus: return an actual assignment of tasks to servers.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

My first instinct was a sweep line and I went with that, sorting events and using a min-heap of end times to track which servers free up.

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AI HintsAI Generated

Suggested Approach

Clarify the problem and edge cases, then present the sweep-line algorithm: sort events (starts and ends) and track active tasks with a min-heap for assignment. Explain the O(n log n) complexity and how to handle equal endpoints by processing ends before starts.

Pro tip: Mention that the minimum number of servers equals the maximum number of overlapping tasks at any point, and that a min-heap of server end times efficiently assigns tasks to the earliest available server, which is optimal.

1. Clarify requirements and edge cases

Confirm interval semantics (half-open [start, end)), discuss empty input, duplicate intervals, and equal endpoints. State that tasks with end == start are non-overlapping.

2. Choose algorithm and data structures

Use a sweep-line approach: create events for starts and ends, sort them, and use a min-heap to track server availability. For assignment, store server IDs in the heap.

3. Process events and assign servers

Iterate through sorted events: for a start event, if the heap is non-empty and the earliest end time <= current start, reuse that server; otherwise allocate a new server. For an end event, push the server back into the heap.

4. Analyze complexity and correctness

Sorting takes O(n log n), heap operations O(log n) each, total O(n log n) time and O(n) space. Prove optimality by relating to maximum overlap.

5. Discuss extensions and trade-offs

Mention alternative approaches (e.g., difference array for count only) and trade-offs. For assignment, the heap method naturally provides it.

Key Points to Mention

  • Half-open intervals: [start, end) means tasks ending at time t and starting at time t do not overlap.
  • Minimum servers equals maximum number of overlapping tasks at any time (interval graph coloring).
  • Sweep-line with events: sort starts and ends, process ends before starts at same time to avoid false overlap.
  • Min-heap of server end times to efficiently find and reuse available servers.
  • Time complexity O(n log n) due to sorting and heap operations; space O(n).
  • Edge cases: empty input returns 0, duplicate intervals require separate servers, zero-length intervals need no server.

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