Design a fault-tolerant Order Processing System

Last updated: May 1, 2026

Quick Overview

Design a fault-tolerant order processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

CrowdStrike
System Design
Software Engineer
CrowdStrike
May 1, 2026
Software Engineer
Onsite
System Design
Easy

106

6

3,060 solved


Design a fault-tolerant order processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

CrowdStrike asks this during the Onsite to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.

What the Interviewer Expects
  • Map the business problem to a concrete ML objective
  • Propose reasonable features and a baseline model
  • Discuss basic model evaluation metrics
  • Outline a simple serving architecture
Key Topics to Cover
Model serving and latency optimization
Training pipeline and infrastructure
A/B testing and experimentation
ML objective formulation and metric selection
How to Approach This
  1. Start by clarifying functional and non-functional requirements with the interviewer.
  2. Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
  3. Draw a high-level architecture first, then deep dive into 1-2 critical components.
  4. Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
  5. Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
  • How would you ensure fairness and reduce bias in the model?
  • What would you do if model performance degrades over time?
  • How would you handle a 10x increase in prediction requests?
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Sample Answer
Requirements Clarification

Before diving into the architecture, clarify the scope with the interviewer. For fault-tolerant Order Processing System, key functional requirements i...

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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