Thursday, January 15, 2026
Designing a Distributed Rate Limiter
Design a scalable rate limiting system for a high-traffic API gateway handling 5 million requests per second.
00The Situation
You're interviewing for a Principal Engineer role at a company that operates a high-traffic API gateway. The gateway currently handles 5 million requests per second across 200+ microservices.
The current rate limiting solution is causing problems:
- Rate limits are enforced per-instance, not globally
- During traffic spikes, some users get blocked while others bypass limits
- The team has tried Redis-based solutions but hit performance bottlenecks
- Business wants per-user, per-API, and per-tenant rate limiting with different tiers
The interviewer wants to understand how you'd design a distributed rate limiter that can:
- Handle 5M+ RPS with <5ms latency overhead
- Provide accurate global rate limiting
- Scale horizontally
- Support multiple rate limiting strategies (fixed window, sliding window, token bucket)
- Be operationally simple
Before proceeding, take a moment to think about the core tradeoffs between accuracy and latency at this scale.
Requirements Clarification
5 min
You know the scale (5M RPS) and latency target (<5ms). But production systems have nuances not in the initial brief. Principal engineers probe for hidden requirements and tradeoffs that fundamentally change the design.
Think about this first
What deeper questions would you ask beyond the stated requirements?
High-Level Architecture
10 min
Design the core architecture that addresses the latency and scale requirements.
Think about this first
How would you architect a system that adds near-zero latency while maintaining global accuracy?
Failure Modes & Operational Concerns
10 min
Production systems must handle failures gracefully. Discuss how your design degrades under various failure scenarios.
Think about this first
What happens when components of your rate limiting system fail?