Design Image Processing Infrastructure for microservices
Last updated: April 28, 2026
Quick Overview
Design a low-latency image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Meta
System Design
Product Manager
Meta
April 28, 2026Product Manager
Onsite
System Design
Easy
469
11
2,433 solved
Design a low-latency image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Meta 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
- 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
Database selection and data modeling
Partitioning and sharding strategies
Consistency models and replication
Load balancing and horizontal scaling
Monitoring, logging, and alerting
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 a 10x increase in traffic overnight?
- How do you ensure data consistency across multiple services?
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Requirements
- Functional Requirements:
- The system must accept image uploads via REST API and process them in real-time.
- It should support various image processing tasks such as resizing, filtering, an...
Capacity Estimation
- Daily Request Volume: 10 million requests/day
- Average Request Rate: 10 million / 24 hours = ~115,740 requests/hour = ~32 requests/second
- Processing Time per Image: Targeting 100ms fo...
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