Architect a event-driven Payment Engine
Last updated: February 9, 2026
Quick Overview
Design a event-driven payment system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Confluent
February 9, 202682
13
1,332 solved
Design a event-driven payment system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Confluent 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
- 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 monitoring and alerting would you set up on day one?
- How would you implement rate limiting to protect the system?
- How would you handle a region-wide outage?
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Requirements
Functional Requirements
- Transaction Processing: The system must process payment transactions (credit/debit) in real-time and support refunds.
- Event-Driven Architecture: Utilize Kafka...
Capacity Estimation
Capacity Estimation
Assuming Confluent's payment engine will handle 5 million transactions per day:
- Transactions per second (TPS): 5,000,000 transactions / 86,400 seconds = ~58 TPS
- **Peak ...
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