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Openai·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Jul 2026

Summary

Got a system design/coding hybrid at OpenAI for a SWE role. The problem was building a persistent key-value store from scratch, which sounds manageable until you start thinking about recovery and what happens when the process dies.

Questions Asked (1)

Q1

Design and implement a persistent key-value store that supports put, get, and delete operations, and can recover previously written data after a process restart.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

My first instinct was to just keep everything in a hashmap and call it a day.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (data size, read/write ratio, durability, performance) and then propose a simple design using an append-only log with an in-memory index, discussing trade-offs. Implement core operations and recovery, and consider optimizations like compaction and indexing.

Pro tip: Demonstrate awareness of real-world systems (e.g., Bitcask, LSM trees) and discuss how your design would scale or handle failures, showing you think beyond basic functionality.

1. Clarify Requirements

Ask about expected data volume, read/write patterns, durability guarantees, and performance targets to guide design decisions.

2. High-Level Design

Propose an architecture: append-only log for persistence, in-memory hash index for fast lookups, and periodic compaction to reclaim space.

3. Core Operations

Detail how put, get, and delete work: put appends a record and updates index; get reads from index and retrieves value; delete appends a tombstone and removes from index.

4. Recovery Mechanism

Explain how to rebuild the in-memory index by replaying the log from disk on startup, ignoring tombstones and older values.

5. Trade-offs and Optimizations

Discuss trade-offs (e.g., memory usage vs. speed) and potential optimizations like sparse indexes, SSTables, or LSM trees for larger scale.

Key Points to Mention

  • Append-only log for durability and sequential writes
  • In-memory hash index for O(1) reads
  • Tombstones for deletes and log compaction to reclaim space
  • Recovery by replaying the log and rebuilding the index
  • Trade-offs: memory footprint, write amplification, read performance
  • Comparison to real systems like Bitcask, LevelDB, or Redis persistence

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