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NVIDIA·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

NVIDIA Linux Engineer interview that was basically one meaty design problem about a key-value store with a tricky O(1) constraint. Not a lot of fluff, just got thrown into the deep end pretty fast.

Questions Asked (1)

Q1

Design and implement an object-oriented key-value store with set(key, value), get(key), and setAll(value) operations, where setAll applies to all current and future keys. All three must run in amortized O(1) time.

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

The set and get parts were fine, standard hashmap stuff.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, especially the amortized O(1) for setAll. Then propose a design using a versioned timestamp approach where each key stores a version and the store maintains a global version with a default value. Finally, discuss implementation details, trade-offs, and potential edge cases.

Pro tip: Emphasize that setAll is O(1) by lazily updating keys only when accessed, and highlight that this design is used in real systems like Redis. Also, mention that you would consider thread-safety for production use.

1. Clarify Requirements

Ask about expected data types, concurrency needs, memory constraints, and whether setAll should affect keys set before or after the call. Confirm that amortized O(1) means occasional O(n) operations are acceptable if rare.

2. Design Core Data Structures

Propose a hash map for key-value storage, where each entry stores the value and a version number. Maintain a global version counter and a global default value. On setAll, increment the global version and update the default value.

3. Implement Operations

For set(key, value), store the value with the current global version. For get(key), if the key's version is less than the global version, return the global default; otherwise return the stored value. For setAll(value), just update the global version and default value.

4. Analyze Complexity and Trade-offs

Explain that set and setAll are O(1), and get is O(1) amortized because it may need to update the key's version and value to the global default (lazy update). Discuss memory overhead of storing versions and potential for stale entries.

5. Discuss Extensions and Edge Cases

Mention handling of missing keys, concurrency (e.g., using locks or atomic operations), and possible optimizations like periodic cleanup of stale entries. Also, consider if setAll should affect future keys (it does by design).

Key Points to Mention

  • Use of versioning or timestamps to track when a key was last set relative to setAll.
  • Lazy update strategy: only update a key's value when accessed after a setAll.
  • Amortized O(1) analysis: setAll is O(1), get may occasionally do extra work but amortizes to O(1).
  • Trade-offs: memory overhead for version numbers, potential for stale data if not cleaned up.
  • Concurrency considerations: thread-safety with locks or atomic operations.
  • Real-world example: Redis's setAll-like functionality or similar patterns in caching systems.

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