Architect a high-throughput Ad Serving Engine
Last updated: April 11, 2026
Quick Overview
Design a high-throughput ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Slack
April 11, 2026108
5
4,949 solved
Design a high-throughput ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This fundamentals question from Slack's Onsite tests whether you can reason about software design at a deep level. The interviewer expects discussion of maintainability, testability, and operational considerations.
What the Interviewer Expects
- Design a complex system component applying multiple engineering principles
- Reason about system-level trade-offs: performance, reliability, developer experience
- Discuss advanced patterns: event sourcing, CQRS, distributed transactions
- Address cross-cutting concerns: observability, security, backward compatibility
- Demonstrate depth in both theoretical foundations and practical implementation
Key Topics to Cover
How to Approach This
- Apply SOLID principles. Single Responsibility makes code testable, Open/Closed makes it extensible.
- Choose data structures based on access patterns, not familiarity.
- Prefer immutable data and message passing over shared mutable state for concurrency.
- Design APIs with RESTful conventions, versioning, meaningful errors, and pagination from day one.
Possible Follow-up Questions
- How would this design change if the team size doubled?
- How would you handle backward compatibility?
- How would you document this for other engineers?
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Core Design Principles
For the ad serving engine, the following core design principles apply:
- Separation of Concerns: This principle ensures that different aspects of the system (ad selection, user targeting, and me...
Architecture
The architectural approach for the ad serving engine will utilize a microservices architecture, comprising the following components:
- Ad Selection Service: This service leverages machine learni...
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