Build a high-throughput Caching Pipeline
Last updated: July 2, 2025
Quick Overview
Design a high-throughput caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
xAI
July 2, 202510
12
433 solved
Design a high-throughput caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Technical Screen at xAI. 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. xAI values engineers who can think about scalability from day one.
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
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 monitoring and alerting would you set up on day one?
- What happens if one of your database nodes goes down?
- How would you implement rate limiting to protect the system?
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Requirements
- Functional Requirements:
- Handle high-throughput requests for caching data with low latency.
- Provide an API for storing, retrieving, and invalidating cache entries.
- Implement rate lim...
Capacity Estimation
To estimate capacity, let’s assume:
- User Requests: 1 million users, with each generating an average of 10 cache requests per second.
- Total Requests: 1 million users * 10 requests/user = 10...
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