Architect a scalable Order Processing Engine
Last updated: November 23, 2025
Quick Overview
Design a scalable order processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Adobe
November 23, 2025150
7
1,473 solved
Design a scalable order processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Adobe goes beyond model selection. This Technical Screen question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
What the Interviewer Expects
- Define clear ML objectives with appropriate loss functions and metrics
- Design a comprehensive feature engineering pipeline
- Discuss model selection with trade-offs (complexity vs interpretability vs latency)
- Plan online and offline evaluation strategies including A/B testing
- Address serving infrastructure: batch vs real-time, latency requirements
- Consider data quality, labeling strategy, and feedback loops
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 is your model retraining strategy?
- How would you ensure fairness and reduce bias in the model?
- How would you run A/B tests on different model versions?
- How would you debug a model that works well offline but poorly online?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For scalable Order Processing Engine, key functional requirements include...
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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