Design a scalable Ad Serving System
Last updated: October 10, 2025
Quick Overview
Design a scalable ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Snowflake
October 10, 202587
5
3,637 solved
Design a scalable ad serving 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 Snowflake. 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. Snowflake 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
- How would you optimize costs as the system scales?
- What monitoring and alerting would you set up on day one?
- How would you handle schema migrations with zero downtime?
- How would you implement rate limiting to protect the system?
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Requirements
Functional Requirements
- Ad Serving: The system should serve targeted ads to users based on their profiles and behavior in real-time.
- User Profile Management: Maintain user profiles t...
Capacity Estimation
Back-of-Envelope Calculations
- QPS: Assume 10 million requests per second at peak times.
- Storage: If we assume an average ad takes 10KB and we serve 1 billion ads per day, we would need...
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