Openai·Software Engineer·Onsite - Coding / Algorithms·Senior

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Jul 2026Remote

Summary

OpenAI SWE coding round, one meaty problem the whole time. The question was a distributed rate limiter with persistence and fallback behavior, and it went deeper than I expected with the follow-ups.

Questions Asked (1)

Design and implement a distributed rate limiter that supports multiple app servers sharing state, with persistence and fallback to local storage when the shared store is unavailable.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This started feeling manageable when I picked sliding window log with Redis sorted sets, but the follow-ups are where things got uncomfortable fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, latency, consistency) and then propose a design using a shared store like Redis with atomic operations for rate limiting. Discuss trade-offs between algorithms (e.g., token bucket vs. sliding window) and outline a fallback mechanism to local in-memory storage when the shared store is unavailable, ensuring graceful degradation.

Pro tip: Emphasize the importance of monitoring and observability: track rate limiter decisions and fallback activations to detect issues early. Also, consider using a hybrid approach where local limits are slightly more permissive to avoid double-counting during fallback.

1. Clarify Requirements and Constraints

Ask about expected throughput, latency requirements, consistency needs, and failure tolerance. Determine if strict global rate limiting is necessary or if approximate limits are acceptable.

2. Choose Rate Limiting Algorithm

Select an algorithm like token bucket, leaky bucket, fixed window, or sliding window. Discuss pros and cons, considering factors like burst handling, memory usage, and precision.

3. Design Shared State Management

Use a centralized store (e.g., Redis) with atomic operations (Lua scripts or transactions) to maintain counters. Ensure scalability and low latency via sharding or clustering.

4. Implement Fallback and Persistence

Design a fallback to local in-memory storage when the shared store is unavailable. Ensure persistence by periodically syncing local state or using write-ahead logs to avoid data loss.

5. Address Trade-offs and Edge Cases

Discuss consistency vs. availability, handling of clock skew, and reconciliation when the shared store recovers. Consider monitoring and alerting for fallback events.

Key Points to Mention

Atomic operations in Redis (e.g., Lua scripts) to prevent race conditions.
Choice of rate limiting algorithm and its impact on burst handling and memory.
Fallback strategy: local token bucket with periodic sync to shared store.
Persistence mechanisms: Redis persistence (AOF/RDB) or write-ahead logs.
Trade-offs between consistency and availability (CAP theorem).
Monitoring and observability for rate limiter decisions and fallback activations.

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