Build a fault-tolerant Caching Pipeline
Last updated: September 14, 2025
Quick Overview
Design a fault-tolerant caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Lyft
September 14, 202550
6
4,536 solved
Design a fault-tolerant caching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Lyft asks this during the Onsite to assess your depth in software engineering. They want to see understanding of design patterns, system architecture, and the trade-offs involved in different technical approaches.
What the Interviewer Expects
- Explain the concept clearly with a practical example
- Discuss when and why to apply this principle
- Identify common mistakes and anti-patterns
- Compare with alternative approaches
Key Topics to Cover
How to Approach This
- Apply SOLID principles. Single Responsibility makes code testable, Open/Closed makes it extensible.
- Choose data structures based on access patterns, not familiarity.
- Prefer immutable data and message passing over shared mutable state for concurrency.
- Design APIs with RESTful conventions, versioning, meaningful errors, and pagination from day one.
Possible Follow-up Questions
- What are the security implications of this design?
- What testing strategy would you use for this component?
- How would this design change if the team size doubled?
- How would you measure the performance of this component in production?
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Core Design Principles
For building a fault-tolerant caching system at Lyft, the following core design principles are essential:
- CAP Theorem: This theorem states that a distributed system cannot provide all three gu...
Architecture
The architectural approach for the caching system at Lyft consists of a microservices-based architecture with the following components:
- Cache Layer: Utilize Redis or Memcached for the in-memor...
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