Design a large-scale Rate Limiting Platform
Last updated: July 6, 2025
Quick Overview
Design a fault-tolerant rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
DoorDash
System Design
Software Engineer
DoorDash
July 6, 2025Software Engineer
System Design Round
System Design
Medium
64
4
114 solved
Design a fault-tolerant rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at DoorDash 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
Partitioning and sharding strategies
Caching strategies (local, distributed, CDN)
Message queues and async processing
API design and rate limiting
Requirements gathering and capacity estimation
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 handle schema migrations with zero downtime?
- How would you implement rate limiting to protect the system?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements
Functional Requirements
- Rate Limiting: The system should allow users to set custom rate limits per API endpoint (e.g., 100 requests per minute).
- Dynamic Configuration: Administrators...
Capacity Estimation
Back-of-Envelope Calculations
- Expected QPS (Queries Per Second):
- Assume DoorDash serves 20 million users, with 5% making API calls at peak times.
- That leads to 1 million concurrent ...
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