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 Clarification
Before diving into the architecture, clarify the scope with the interviewer. For distributed Rate Limiting Pipeline, key functional requirements inclu...
Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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