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Netflix·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jul 2026

Summary

Netflix SWE interview, got a versioned key-value store design question. Pretty classic but there are enough edge cases to trip you up if you're not careful.

Questions Asked (1)

Q1

Design and implement an in-memory versioned key-value store that supports put(key, value, timestamp) and get(key, timestamp), where get returns the value at the largest timestamp less than or equal to the query timestamp, or null if none exists. Same-timestamp overwrites should take the latest write. Discuss time and space complexity.

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

I started with a plain hashmap mapping keys to a list of (timestamp, value) pairs, which felt obvious.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a design using a hash map from keys to lists of (timestamp, value) pairs, with binary search for get. Discuss how to handle same-timestamp overwrites and analyze time/space complexity, mentioning potential optimizations like tree maps or versioned data structures.

Pro tip: Mention that you would use binary search on the timestamp list for O(log n) get, and that same-timestamp overwrites can be handled by replacing the last entry if timestamps match. Also, proactively discuss trade-offs between different data structures (e.g., hash map + list vs. tree map) to show depth.

1. Clarify requirements and edge cases

Ask about expected operations, data size, concurrency, and whether timestamps are monotonically increasing. Clarify behavior for same-timestamp overwrites and missing keys.

2. Propose data structure

Suggest a hash map where each key maps to a list of (timestamp, value) pairs, kept sorted by timestamp. Alternatively, consider a tree map for each key to allow efficient range queries.

3. Implement put and get

For put, append or replace if timestamp matches the last entry. For get, binary search the timestamp list to find the largest timestamp ≤ query timestamp, returning the corresponding value or null.

4. Analyze complexity

Put: O(1) amortized for append, O(log n) if using tree map. Get: O(log n) for binary search. Space: O(total number of versions).

5. Discuss trade-offs and optimizations

Compare list vs. tree map, consider memory overhead, and mention possible optimizations like compression or pruning old versions if needed.

Key Points to Mention

  • Use a hash map from keys to sorted lists of (timestamp, value) pairs.
  • Binary search for get to achieve O(log n) time.
  • Handle same-timestamp overwrites by replacing the last entry if timestamps match.
  • Time complexity: put O(1) amortized (or O(log n) with tree map), get O(log n).
  • Space complexity: O(total number of versions) across all keys.
  • Consider trade-offs: list vs. tree map, memory vs. speed, and potential concurrency issues.

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