Build a distributed Rate Limiting Pipeline
Last updated: May 23, 2026
Quick Overview
Design a distributed rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
OpenAI
May 23, 20269
3
3,908 solved
Design a distributed rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Onsite at OpenAI. The interviewer expects you to demonstrate your ability to design large-scale distributed systems, make well-reasoned trade-offs, and communicate your thought process clearly. OpenAI values engineers who can think about scalability from day one.
What the Interviewer Expects
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you handle a region-wide outage?
- How would you migrate from a monolithic to a microservices architecture?
- How would you handle schema migrations with zero downtime?
- What monitoring and alerting would you set up on day one?
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Requirements
Functional Requirements
- Rate Limiting: The system must limit the number of requests from each user or API key to a predefined threshold (e.g., 100 requests per minute).
- **User Identifica...
Capacity Estimation
- Expected QPS: Assume OpenAI's APIs receive 10 million requests per day. This breaks down to approximately 115 requests per second (QPS).
- Scaling Factor: To account for peak loads, we ass...
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