Architect a high-throughput Ad Serving Engine
Last updated: February 4, 2026
Quick Overview
Design a high-throughput ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Rippling
February 4, 202610
12
2,425 solved
Design a high-throughput ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Rippling asks this during the Technical Screen to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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 optimize costs as the system scales?
- How would you handle a region-wide outage?
- How would you handle a 10x increase in traffic overnight?
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Requirements
- Functional Requirements:
- Handle millions of ad requests per second.
- Provide real-time targeting and personalization based on user data.
- Support A/B testing for ad performance.
- Re...
Capacity Estimation
Assuming Rippling serves ads to 10 million users, with an average of 10 ad impressions per user per day:
- Total Daily Requests: 10M users * 10 impressions = 100M requests/day.
- **Per Second R...
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