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PayPal·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jan 2023Remote

Summary

SQL screen for a Data Scientist role at PayPal. One question, interval overlap logic, felt straightforward on the surface but the self-join tripped me up for a minute.

Questions Asked (1)

Q1

You're given a sessions table with session_id, start_time, and end_time. Write a SQL query that returns the session with the most overlaps with other sessions, along with the overlap count.

Data ModelingAlgorithms & Data Structures
Author's notes

Took me longer than I'd like to admit to land on the self-join approach.

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AI HintsAI Generated

Suggested Approach

Clarify the definition of overlap (e.g., strict or inclusive) and whether a session can overlap with itself. Then use a self-join to count overlaps for each session, and select the session with the maximum count, handling ties appropriately.

Pro tip: Mention that you'd confirm the expected output for ties (e.g., return all tied sessions or just one) and discuss performance implications of the self-join on large datasets, suggesting an interval tree or sweep-line algorithm as an alternative.

1. Clarify requirements

Ask about the definition of overlap (strict vs. inclusive), whether a session can overlap with itself, and how to handle ties. Confirm the desired output format.

2. Design the query logic

Use a self-join on the sessions table where sessions overlap if one starts before the other ends and ends after the other starts. Count overlaps per session.

3. Write the SQL

Construct the query: SELECT s1.session_id, COUNT(*) AS overlap_count FROM sessions s1 JOIN sessions s2 ON s1.session_id != s2.session_id AND s1.start_time < s2.end_time AND s1.end_time > s2.start_time GROUP BY s1.session_id ORDER BY overlap_count DESC LIMIT 1;

4. Handle edge cases and ties

Discuss how to modify the query to return all sessions with the maximum overlap count (e.g., using a subquery or window function) and consider sessions with zero overlaps.

5. Optimize and discuss alternatives

Mention indexing on start_time and end_time, and note that for large datasets, a sweep-line algorithm can compute overlaps in O(n log n) time.

Key Points to Mention

  • Definition of overlap: strict inequality (start < other_end AND end > other_start) vs. inclusive (<=, >=).
  • Self-join to compare each session with every other session, excluding self-comparison.
  • Counting overlaps per session and selecting the maximum.
  • Handling ties: using RANK() or DENSE_RANK() window function to return all top sessions.
  • Performance considerations: indexing, and alternative algorithms like sweep-line for large data.
  • Edge cases: sessions with no overlaps, sessions with identical start/end times, and null values.

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