Design a large-scale Caching Platform
Last updated: December 13, 2025
Quick Overview
Design a geo-distributed caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Uber
December 13, 202565
4
877 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 System Design Round at Uber. 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. Uber 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
- What happens if one of your database nodes goes down?
- How would you optimize costs as the system scales?
- How would you handle a region-wide outage?
- How would you handle schema migrations with zero downtime?
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Requirements
- Functional Requirements:
- Geo-distributed Caching: The caching system must support multiple regions to minimize latency for global users.
- Cache Operations: Support for standar...
Capacity Estimation
Assuming Uber has approximately 100 million active users globally, and each user generates an average of 10 cacheable requests per minute:
- Total Requests per Minute (RPM):
100 million use...
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