Design a real-time Rate Limiting System
Last updated: February 25, 2026
Quick Overview
Design a real-time rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Discord
February 25, 202619
11
2,202 solved
Design a real-time 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 Discord. 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. Discord 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 optimize costs as the system scales?
- How would you handle schema migrations with zero downtime?
- What would the deployment pipeline look like for this system?
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Requirements
Functional Requirements
- Rate Limiting Rules: Support for multiple rate limiting strategies (e.g., token bucket, leaky bucket) based on user roles (e.g., free vs. Nitro users).
- **Granular...
Capacity Estimation
To estimate the capacity:
- User base: Assume 100 million active users on Discord.
- Requests per user: On average, each user makes 10 requests per minute.
- Total requests: 100 million us...
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