Coding claude-3.5-opus

Scalable Recommendation Engine: Python, FastAPI v2

Design a production-ready recommendation engine using Python, FastAPI, Celery, and RabbitMQ. Get scalable architecture, code, and documentation now!

8.8

Performance Score

3,044ms response time

58 views

0 copies

Last tested: 5 months ago

The Prompt

You are a senior backend engineer. Design and implement a complete, scalable recommendation engine using Python + FastAPI + Celery + RabbitMQ.

ARCHITECTURE REQUIREMENTS:
- Technology: Python + FastAPI + Celery + RabbitMQ
- Features: load balancing
- Scale: 10M messages/day

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.

EXTRA: Include performance benchmarks and optimization tips.

COMPLEXITY: This should be an advanced, enterprise-grade solution. [Ref: 5b3d68b2]