Final Project — Production-Grade Task Manager API¶

🎯 Learning Goals¶

By the end of this lesson, you will be able to:

• Deploy your FastAPI Task Manager API to a cloud provider.
• Configure monitoring and scaling for production workloads.
• Review the full architecture and plan next steps for extending your project.

⚡ Step 1: Deploy to the Cloud¶

You’ve already containerized your app with Docker and set up CI/CD. Now you’ll deploy it.

Option A: Render¶

• Push your code to GitHub.
• Create a new Web Service in Render.

• Connect your repo and set the build command:

  docker build -t fastapi-taskmanager .
  

• Render automatically deploys on every push.

Option B: Fly.io¶

• Install Fly CLI:

  curl -L https://fly.io/install.sh | sh
  

• Initialize app:

  fly launch
  

• Deploy:

  fly deploy
  

Option C: AWS ECS (Elastic Container Service)¶

• Push Docker image to Amazon ECR.
• Create ECS service with Fargate.
• Configure load balancer and auto-scaling.

⚡ Step 2: Monitor and Scale¶

Logging¶

• Use structured logging (JSON) and ship logs to CloudWatch (AWS), Loki (Grafana), or ELK stack.

Metrics¶

• Expose /metrics with prometheus-fastapi-instrumentator.
• Scrape with Prometheus, visualize in Grafana.
• Track request rate, latency, error rate, and task creation counts.

Tracing¶

• Use OpenTelemetry to trace requests.
• Export spans to Jaeger or Tempo for distributed tracing.

Scaling¶

• Horizontal scaling: run multiple Gunicorn workers or replicas.
• Vertical scaling: increase CPU/memory for heavy workloads.
• Cloud auto-scaling: configure rules (e.g., scale up when CPU > 70%).

⚡ Step 3: Final Review¶

Your production-grade Task Manager API now includes:

• ✅ Authentication & Authorization (JWT, admin-only endpoints)
• ✅ Task Management (per-user ownership, CRUD)
• ✅ Background Tasks (notifications, logging)
• ✅ WebSockets (real-time updates)
• ✅ Testing (unit + integration with pytest & TestClient)
• ✅ Deployment (Docker + Gunicorn/Uvicorn workers)
• ✅ CI/CD (GitHub Actions for tests + Docker builds)
• ✅ Monitoring (logging, metrics, tracing with Prometheus/Grafana)
• ✅ Scaling (cloud auto-scaling, load balancing)

⚡ Step 4: Next Steps¶

• Add a frontend: Build a React or Vue dashboard that consumes your API and listens to WebSocket updates.
• Enhance notifications: Integrate with real email/SMS providers (SendGrid, Twilio).
• Role-based access control (RBAC): Add more granular permissions.
• Database migrations: Use Alembic for schema evolution.
• Security hardening: Add HTTPS, rate limiting, and audit logs.
• Deploy globally: Use CDNs and multi-region deployments for low latency.

🧪 Practice Challenge¶

1. Deploy your Task Manager API to Render or Fly.io.
2. Configure Prometheus + Grafana to monitor request rate and error rate.
3. Simulate load (e.g., with locust or ab) and watch your app auto-scale.
4. Tag your release as v1.0.0 and push to GitHub to trigger CI/CD.

🧠 Recap¶

In this final project, you:

• Deployed your FastAPI app to the cloud.
• Configured monitoring and scaling for production.
• Reviewed the full architecture of a production-grade Task Manager API.
• Planned next steps to extend and harden your system.