Architect a multi-tenant Logging Engine

Last updated: December 10, 2025

Quick Overview

Design a multi-tenant logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Waymo
System Design
Software Engineer
Waymo
December 10, 2025
Software Engineer
System Design Round
System Design
Hard

32

6

1,220 solved


Design a multi-tenant logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Waymo asks this during the System Design Round 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
  • Design the full ML lifecycle from data collection to model monitoring
  • Address cold start, exploration/exploitation, and model freshness
  • Discuss multi-objective optimization and ranking systems
  • Plan for model debugging, fairness, and bias mitigation
  • Design the feature store and training pipeline for scale
  • Address model versioning, canary deployments, and rollback strategies
  • Discuss the data flywheel and long-term system evolution
Key Topics to Cover
ML objective formulation and metric selection
Monitoring and model degradation detection
A/B testing and experimentation
Feature engineering and feature stores
Model serving and latency optimization
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 handle the cold start problem?
  • How would you ensure fairness and reduce bias in the model?
  • How would you handle a 10x increase in prediction requests?
  • How would you debug a model that works well offline but poorly online?
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Sample Answer
Requirements

Functional Requirements:

  1. Multi-Tenant Support: The system must segregate logs from different tenants while allowing shared access to aggregate metrics.
  2. Real-Time Ingestion: The loggi...
Capacity Estimation

Assuming Waymo expects to handle 10 million logs per second during peak usage:

  • Each log entry is approximately 1 KB.
  • Daily logs: 10M logs/s * 60s * 60m * 24h = 864 billion logs/day.
  • Daily data ...

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