Architect a low-latency Logging Engine

Last updated: December 31, 2025

Quick Overview

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

Dropbox
System Design
Software Engineer
Dropbox
December 31, 2025
Software Engineer
Onsite
System Design
Easy

2

12

2,134 solved


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

ML system design at Dropbox 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
  • 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 selection and architecture
Feature engineering and feature stores
Data collection and labeling strategy
A/B testing and experimentation
Training pipeline and infrastructure
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
  • What would you do if model performance degrades over time?
  • What is your model retraining strategy?
  • How would you handle the cold start problem?
  • How would you ensure fairness and reduce bias in the model?
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Sample Answer
Requirements

Functional Requirements

  1. Real-time Logging: The system should accept and process millions of log entries per second with minimal latency.
  2. Search and Filter: Users should be able to qu...
Capacity Estimation

Assuming Dropbox handles approximately 300 million file uploads daily, with each upload generating 2 log entries (one for start, one for completion), we can estimate:

  1. Log entries per day: 300 m...

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