Build a geo-distributed Caching Pipeline
Last updated: December 29, 2025
Quick Overview
Design a geo-distributed caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Doordash
December 29, 20250
5
4,713 solved
Design a geo-distributed caching 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 Doordash. 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. Doordash 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 do you ensure data consistency across multiple services?
- What monitoring and alerting would you set up on day one?
- What would the deployment pipeline look like for this system?
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Requirements
- Functional Requirements:
- Cache millions of requests per second across multiple geographic regions to reduce latency for users.
- Support both read and write operations with a focus on ...
Capacity Estimation
- User Base: Assume 10 million active users.
- Request Rate: If each user makes about 10 requests per day, that results in 100 million requests per day or approximately 1,157 requests per sec...
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