Design a geo-distributed Logging System
Last updated: April 21, 2026
Quick Overview
Design a geo-distributed logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Splunk
April 21, 20264
14
4,972 solved
Design a geo-distributed logging system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Splunk asks this during the System Design Round to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
What the Interviewer Expects
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
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
- How would you implement rate limiting to protect the system?
- What monitoring and alerting would you set up on day one?
- How do you ensure data consistency across multiple services?
- How would you optimize costs as the system scales?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For geo-distributed Logging System, 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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