Design a fault-tolerant Feature Flag System
Last updated: May 21, 2026
Quick Overview
Design a fault-tolerant feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Square/Block
May 21, 202634
4
1,405 solved
Design a fault-tolerant 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 Onsite at Square/Block. 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. Square/Block values engineers who can think about scalability from day one.
What the Interviewer Expects
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
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?
- How would you handle a 10x increase in traffic overnight?
- What happens if one of your database nodes goes down?
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Requirements
Functional Requirements
- Feature Management: Ability to create, update, delete, and retrieve feature flags.
- User Segmentation: Support targeting specific user segments based on attrib...
Capacity Estimation
Back-of-Envelope Calculations
Assuming Square/Block has around 10 million active users and each user triggers 10 feature flag evaluations per session, we estimate:
- Daily Requests: 10 million...
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