Architect a geo-distributed Search Engine
Last updated: May 20, 2026
Quick Overview
Design a geo-distributed search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
OpenAI
May 20, 2026375
12
777 solved
Design a geo-distributed search system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
OpenAI 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 migrate from a monolithic to a microservices architecture?
- How would you handle a 10x increase in traffic overnight?
- How would you optimize costs as the system scales?
- What would the deployment pipeline look like for this system?
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Requirements
- Functional Requirements:
- Support full-text search queries with relevance scoring.
- Geo-distributed data centers for low-latency access to users worldwide.
- Real-time indexing ...
Capacity Estimation
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Estimating QPS:
Assuming we have 10 million daily active users, with each user performing 5 searches per day:
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