Architect a fault-tolerant Analytics Engine
Last updated: September 15, 2025
Quick Overview
Design a fault-tolerant analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Snowflake
September 15, 2025141
13
3,110 solved
Design a fault-tolerant analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during System Design Round at Snowflake. The interviewer expects you to demonstrate your ability to design large-scale distributed systems, make well-reasoned trade-offs, and communicate your thought process clearly. Snowflake values engineers who can think about scalability from day one.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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
- What would the deployment pipeline look like for this system?
- How would you optimize costs as the system scales?
- How would you handle a 10x increase in traffic overnight?
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Requirements
Functional Requirements:
- Query Handling: The system should process millions of analytical queries per second with support for SQL.
- Data Ingestion: Seamless ingestion of data from var...
Capacity Estimation
Assuming Snowflake needs to handle 10 million queries per day:
- QPS Calculation: 10 million queries per day / 86400 seconds = ~115.74 queries per second.
- Data Size: If each query operates o...
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