Design a large-scale Rate Limiting Platform
Last updated: September 10, 2025
Quick Overview
Design a high-throughput rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Amazon
September 10, 202590
3
3,471 solved
Design a high-throughput rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Amazon asks this during the Onsite 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?
- What happens if one of your database nodes goes down?
- How would you optimize costs as the system scales?
- What monitoring and alerting would you set up on day one?
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Requirements
Functional Requirements
- Rate Limiting: The system must allow API requests to be limited based on user-defined thresholds (e.g., 100 requests per minute).
- Burst Handling: Support sh...
Capacity Estimation
To estimate capacity, let's consider an example where we expect an average of 10 million users, each making an average of 100 requests per minute.
- Requests per Second (RPS):
- Total req...
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