Architect a high-throughput Rate Limiting Engine
Last updated: October 30, 2025
Quick Overview
Design a high-throughput rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Jane Street
October 30, 202556
12
803 solved
Design a high-throughput rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Jane Street typically last 45-60 minutes. You are expected to drive the conversation, starting from requirements gathering through to a detailed architecture. The interviewer will evaluate your ability to handle ambiguity and make practical engineering decisions.
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
- How would you handle a 10x increase in traffic overnight?
- What happens if one of your database nodes goes down?
- How would you handle schema migrations with zero downtime?
- How do you ensure data consistency across multiple services?
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Requirements
Functional Requirements:
- Rate Limiting Policies: Support multiple rate limiting strategies (e.g., fixed window, sliding window, token bucket).
- User Identification: Ability to identif...
Capacity Estimation
Assuming an initial estimation of 10 million users and an average of 100 requests/user/hour:
- Total Requests per Hour: 10 million users * 100 requests = 1 billion requests/hour.
- **Requests per ...
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