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Openai·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Interviewed for a software engineer role at OpenAI and got a reliability/systems question about duplicate network requests. Short and focused, mostly one meaty design problem.

Questions Asked (1)

Q1

If the same network request is accidentally sent twice, how do you ensure it only gets processed once?

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

Classic idempotency problem and I knew the concept but fumbled the specifics under pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the context: is this a client retry, a network duplicate, or a malicious replay? Then propose idempotency as the core principle, using idempotency keys for write operations and conditional requests for reads. Discuss trade-offs between client-generated keys, server-side deduplication windows, and exactly-once semantics in distributed systems.

Pro tip: Mention that true exactly-once delivery is impossible in distributed systems; instead, aim for effectively-once processing by making operations idempotent and using deduplication with a unique key and a reasonable time window. This shows you understand the theoretical limits and practical solutions.

1. Clarify the scenario and requirements

Ask whether the duplicate is from a client retry, network issue, or malicious replay, and whether the operation is read or write. Determine the required consistency and latency trade-offs.

2. Introduce idempotency keys

For write operations, have the client generate a unique idempotency key (e.g., UUID) and send it with the request. The server stores the key and the result, returning the same response for duplicate keys.

3. Implement server-side deduplication

Use a distributed cache or database with TTL to store idempotency keys and their associated responses. Ensure atomic check-and-set to avoid race conditions.

4. Handle reads and conditional requests

For reads, use conditional requests (ETags, If-None-Match) or versioning to avoid redundant processing. For writes, consider optimistic concurrency control with version numbers.

5. Discuss trade-offs and edge cases

Address key expiration, storage overhead, failure scenarios (e.g., key stored but response lost), and how to handle non-idempotent operations like payments.

Key Points to Mention

  • Idempotency keys (client-generated UUIDs) and their storage with TTL
  • Exactly-once vs. at-least-once vs. at-most-once semantics
  • Distributed locks or atomic operations for deduplication
  • Conditional requests (ETags, If-None-Match) for reads
  • Trade-offs: storage cost, latency, key expiration, and failure recovery
  • Real-world examples: Stripe's idempotency keys, AWS request deduplication

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