Architect a event-driven Load Balancing Engine
Last updated: December 22, 2025
Quick Overview
Design a event-driven load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Citadel
System Design
Software Engineer
Citadel
December 22, 2025Software Engineer
Onsite
System Design
Medium
78
7
3,812 solved
Design a event-driven load balancing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Citadel goes beyond model selection. This Onsite 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
Monitoring and model degradation detection
Training pipeline and infrastructure
Feature engineering and feature stores
Model serving and latency optimization
A/B testing and experimentation
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
- 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 handle the cold start problem?
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Requirements
Functional Requirements
- Event Processing: The system must process millions of incoming requests per second and route them to the appropriate ML model based on predefined rules.
- **Load Ba...
Capacity Estimation
To estimate capacity:
- Expected Requests: Assume we want to handle 10 million requests per second.
- Request Size: Average request size is 1 KB, leading to a bandwidth requirement of about 10...
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