Design a large-scale Rate Limiting Platform
Last updated: December 7, 2025
Quick Overview
Design a scalable rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Neon
December 7, 202526
13
4,848 solved
Design a scalable rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Neon asks this during the Technical Screen to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
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
- What would the deployment pipeline look like for this system?
- How would you migrate from a monolithic to a microservices architecture?
- What happens if one of your database nodes goes down?
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Requirements
Functional Requirements
- Handle rate limiting for multiple APIs with varying limits (e.g., 100 requests/minute for API A, 500 requests/hour for API B).
- Provide a flexible configuration system...
Capacity Estimation
Capacity Estimation
Assuming we expect to serve 1 million users, each making an average of 10 requests per hour:
- Total requests = 1,000,000 users * 10 requests/user/hour = 10,000,000 requests/ho...
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