Architect a scalable Analytics Engine
Last updated: June 1, 2026
Quick Overview
Design a scalable analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Slack
System Design
Product Manager
Slack
June 1, 2026Product Manager
Onsite
System Design
Medium
546
7
4,173 solved
Design a scalable analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Slack asks this during the Onsite 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
High-level architecture and component design
API design and rate limiting
Monitoring, logging, and alerting
Partitioning and sharding strategies
Load balancing and horizontal scaling
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 monitoring and alerting would you set up on day one?
- How would you handle schema migrations with zero downtime?
- How would you handle a region-wide outage?
- How would you handle a 10x increase in traffic overnight?
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Requirements
Functional Requirements
- Real-time Analytics: The system should process and analyze user interactions (messages, file uploads, reactions) in real-time to provide insights.
- **Historical Da...
Capacity Estimation
Capacity Estimation
- User Base: Assume 10 million active users.
- Requests per User: Each user generates about 10 analytics events per hour (message sent, file uploaded, etc.).
- **QPS...
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