Design a real-time Feature Flag System
Last updated: September 29, 2025
Quick Overview
Design a real-time feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
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Design a real-time feature flag 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 LinkedIn. 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. LinkedIn 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
- How would you optimize costs as the system scales?
- How would you handle a 10x increase in traffic overnight?
- What monitoring and alerting would you set up on day one?
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Requirements
Functional Requirements
- Create Feature Flags: Admins should be able to create, update, and delete feature flags through a web interface.
- Toggle Feature Flags: Users can enable or dis...
Capacity Estimation
Assuming LinkedIn has approximately 900 million members and an expected feature flag query rate of 10 requests per user per month:
- Monthly Requests: 900 million users * 10 requests/user = 9 bil...
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