Build a geo-distributed Image Processing Pipeline
Last updated: March 15, 2026
Quick Overview
Design a geo-distributed image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Booking.com
March 15, 2026401
5
1,018 solved
Design a geo-distributed image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This fundamentals question from Booking.com'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
- Explain the concept clearly with a practical example
- Discuss when and why to apply this principle
- Identify common mistakes and anti-patterns
- Compare with alternative approaches
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
- What testing strategy would you use for this component?
- How would you measure the performance of this component in production?
- How would you handle backward compatibility?
- How would you document this for other engineers?
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Core Design Principles
For the geo-distributed image processing pipeline at Booking.com, the core design principles include:
- Separation of Concerns: This ensures that different components of the system (image ingest...
Architecture
The architectural approach for the image processing pipeline will follow a microservices architecture deployed across multiple geographic regions. Key components include:
- API Gateway: This wil...
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