Coding claude-4.5-sonnet ⭐ Featured

Java Spring Scalable Backend: ML Pipeline Design

Design a scalable Java Spring ML pipeline with WebSocket for 1M users. Get architecture, code, docs, & deployment. Build your backend now!

9.4

Performance Score

2,630ms response time

53 views

0 copies

Last tested: 5 months ago

The Prompt

You are a senior backend engineer. Design and implement a complete, scalable machine learning pipeline using Java + Spring + WebSocket.

ARCHITECTURE REQUIREMENTS:
- Technology: Java + Spring + WebSocket
- Features: presence tracking
- Scale: 1M concurrent users

IMPLEMENTATION REQUIREMENTS:
1. Complete system architecture with diagrams
2. API design (REST/GraphQL/gRPC)
3. Real-time communication setup
4. Database design (SQL/NoSQL/hybrid)
5. Caching strategy (Redis/Memcached)
6. Message queue implementation
7. Authentication and authorization
8. Rate limiting and DDoS protection
9. Monitoring and alerting
10. Load testing and optimization
11. Deployment strategy (Docker, Kubernetes)
12. Disaster recovery plan

DELIVERABLES:
- Complete backend codebase
- API documentation
- Database schemas
- Infrastructure as Code (Terraform/CloudFormation)
- Docker/Kubernetes configs
- Monitoring dashboards
- Load testing scripts
- Architecture documentation

Generate a production-ready, scalable system with all components, documentation, and best practices.

NOTE: Focus on scalability, security, and best practices throughout.

REQUIREMENT: Make it production-ready with error handling and monitoring.

COMPLEXITY: This should be an advanced, enterprise-grade solution. [Ref: 1d278937]