Design a large-scale Caching Platform
Last updated: January 26, 2026
Quick Overview
Design a event-driven caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
xAI
System Design
Software Engineer
xAI
January 26, 2026Software Engineer
Onsite
System Design
Easy
41
5
295 solved
Design a event-driven caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at xAI 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
- Clearly define functional and non-functional requirements
- Propose a reasonable high-level architecture with core components
- Choose appropriate data storage solutions with basic justification
- Discuss basic scaling strategies (horizontal scaling, caching)
- Identify potential bottlenecks and suggest simple solutions
Key Topics to Cover
Security and authentication
Monitoring, logging, and alerting
Consistency models and replication
Failure handling and fault tolerance
Load balancing and horizontal scaling
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?
- What would the deployment pipeline look like for this system?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements
- Functional Requirements:
- The system must cache responses to frequently requested data to reduce load on backend services.
- It should support event-driven data invalidation to ensure cache...
Capacity Estimation
- Assume that the system needs to handle 1 million requests per second (RPS) at peak times.
- Each request can be approximately 1 KB in size, resulting in 1 TB of data processed per second during peak...
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