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rippling·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Rippling SWE interview with a pretty gnarly algorithmic problem centered on streaming delivery data and concurrent driver tracking. The question had a lot of moving parts and I think I underestimated how much the edge cases around the time window would trip me up.

Questions Asked (1)

Q1

Given a stream of delivery intervals per driver (driver_id, start_time, end_time), implement a function that returns the maximum number of distinct drivers simultaneously active within the 24-hour window ending at a query time T. A driver counts as active if at least one of their intervals overlaps a given moment. You need to define your inclusivity rules, data structures, and analyze time and space complexity.

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

Spent the first few minutes just trying to nail down what 'simultaneously active' even meant since a driver can have multiple intervals.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and define inclusivity rules (e.g., intervals are half-open [start, end) and the query window is (T-24h, T]). Then propose an efficient solution using a sweep-line algorithm or interval tree to count distinct active drivers, and analyze time and space complexity.

Pro tip: Explicitly state your assumptions about inclusivity and edge cases (e.g., intervals touching boundaries) before diving into the algorithm; this shows attention to detail and prevents misunderstandings.

1. Clarify Requirements and Assumptions

Ask about data characteristics (e.g., interval frequency, driver count), define inclusivity rules for interval overlap and the query window, and confirm whether the stream is static or dynamic.

2. Choose Data Structures and Algorithm

Select an approach such as sweep-line with events (start/end) and a hash set for active drivers, or an interval tree for efficient overlap queries. Consider preprocessing if multiple queries are expected.

3. Handle Distinct Drivers and Overlaps

Ensure each driver is counted only once even if multiple intervals overlap the query moment. Use a set to track distinct active drivers during the sweep.

4. Analyze Complexity and Trade-offs

Derive time and space complexity for the chosen approach (e.g., O(N log N) time, O(N) space for sweep-line). Discuss trade-offs between preprocessing and query time.

5. Test with Edge Cases

Walk through examples including intervals exactly at boundaries, multiple intervals per driver, and no active drivers. Verify inclusivity rules are correctly applied.

Key Points to Mention

  • Inclusivity rules: define whether intervals are closed, open, or half-open, and how the query window boundaries are treated.
  • Sweep-line algorithm: process events (start/end) in sorted order, maintaining a set of active drivers.
  • Distinct driver counting: use a hash set to avoid double-counting drivers with multiple overlapping intervals.
  • Time and space complexity: O(N log N) time for sorting events, O(N) space for storing events and active set.
  • Handling multiple queries: consider preprocessing intervals into an interval tree or segment tree for efficient repeated queries.
  • Edge cases: intervals that start or end exactly at T or T-24h, and drivers with no intervals in the window.

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