Architect a real-time Analytics Engine
Last updated: May 29, 2026
Quick Overview
Design a real-time analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
MongoDB
May 29, 20265
4
2,596 solved
Design a real-time analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Onsite at MongoDB. 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. MongoDB values engineers who can think about scalability from day one.
What the Interviewer Expects
- Clearly define functional and non-functional requirements
- Propose a reasonable high-level architecture with core components
- Choose appropriate data storage solutions with basic justification
- Discuss basic scaling strategies (horizontal scaling, caching)
- Identify potential bottlenecks and suggest simple solutions
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 happens if one of your database nodes goes down?
- What monitoring and alerting would you set up on day one?
- How would you optimize costs as the system scales?
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Requirements
- Functional Requirements:
- Real-time data ingestion from multiple sources (e.g., web applications, IoT devices).
- Support for complex analytical queries on large datasets.
- Provide real-...
Capacity Estimation
Assuming we need to handle 1 million requests per second:
- If each request generates 10 KB of data: 1,000,000 requests/s * 10 KB/request = 10 GB/s of data ingestion.
- For analytics, if we assume eac...
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