Design a Recommendation for Databricks
Last updated: January 15, 2026
Quick Overview
Design a high-throughput recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Databricks
January 15, 202625
11
2,889 solved
Design a high-throughput recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This fundamentals question from Databricks'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
- 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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Solve on CodemiaSample Answer
Core Principles
Start by identifying which engineering principles are most relevant: **SOLID Principles**: Single Responsibility (one reason to change), Open/Closed ...
Design Approach
**API Design**: Define clear interfaces before implementation. Use RESTful conventions for HTTP APIs. Version your APIs from the start. Return meaning...
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