Architect a distributed Ad Serving Engine
Last updated: May 6, 2026
Quick Overview
Design a distributed ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Anduril
May 6, 202630
11
222 solved
Design a distributed 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 Anduril. 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. Anduril 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
- What monitoring and alerting would you set up on day one?
- What would the deployment pipeline look like for this system?
- How would you optimize costs as the system scales?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For distributed Ad Serving Engine, key functional requirements include: w...
Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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