Design a Rate Limiting Service
Last updated: April 29, 2026
Quick Overview
Design a scalable rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Confluent
April 29, 20261
13
4,240 solved
Design a scalable 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 Technical Screen at Confluent. 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. Confluent values engineers who can think about scalability from day one.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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 implement rate limiting to protect the system?
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Requirements
Functional Requirements
- Rate Limiting Policies: Support for various policies (e.g., per user, per IP, per application) with configurable limits.
- API Integration: Provide a RESTful AP...
Capacity Estimation
To estimate capacity, we consider the following:
- User Base: Assume 10 million users, each making an average of 100 requests per day.
- Peak Load: Peak traffic estimated at 10% of users mak...
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