📚 Expanse - Distributed Online Learning Platform

👥 Team Name:

Survey Corps

---

📝 Project Summary

Expanse is an online learning platform designed to make education accessible, interactive, and personalized. It offers:

• Course management for students and educators.
• Collaborative learning through discussion forums.
• Real-time quizzes with automated assessments.
• Secure user authentication and role-based access control.
• Efficient file storage and retrieval.
• Real-time analytics for performance tracking.

Built with a scalable microservices architecture, Expanse ensures seamless interactions, reliable communication, and consistent user experience across services.

---

⚙️ How to Compile and Run the Project

Prerequisites:

• Node.js (v14+)
• Python (v3.8+)
• Docker & Docker Compose
• PostgreSQL & MongoDB

Backend (FastAPI + gRPC)

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install backend dependencies:

   pip install -r requirements.txt

4. Run the backend server:

   uvicorn main:app --reload

Frontend and Authentication Service (Next.js + TailwindCSS + GoogleAuth + NextAuth + Prisma ORM)

1. Navigate to the frontend and Authentication service directory:

   cd frontend-with-auth

2. Create a .env file by referencing from .env.example.
   • For Google Client ID and Secret head over to this URL

     https://console.cloud.google.com/apis/credentials

     Setup OAuth consent screen and Create OAuth client ID. Once done you will get Google Client ID and Secret.
     
3. Install frontend and Authentication Service dependencies:

   npm install
   npm install -g prisma

4. Setup Prisma for Authentication Service and userDB setup

   npx prisma generate

5. Start the development server:

   npm run dev

6. Access the application at:

   http://localhost:3000
   

Docker Deployment (Optional)

1. Navigate to the root directory:

   docker-compose up --build

2. Access the app via:

   http://localhost:3000
   

Kubernetes Deployment (Optional)

1. Navigate to the root directory:

   minikube start

2. Run the below command:

   kubectl apply -f kubernetes/config/ -f kubernetes/deployments/ -f kubernetes/hpa/ -f kubernetes/services/

3. Check the status of the pods:

   kubectl get pods -o wide

4. Once the pods are running, access the app via:

   http://localhost:3000
   

---

Test Kubernetes HPA (Optional)

1. Navigate to the root directory:

   minikube start

2. Run the below command:

   kubectl apply -f kubernetes/config/ -f kubernetes/deployments/ -f kubernetes/hpa/ -f kubernetes/services/

3. Check the status of the pods:

   kubectl get pods -o wide

4. Once the pods are running, port forward each service:

   kubectl port-forward service/courses-topics 8080:8080
   kubectl port-forward service/discussion-forum 8081:8081
   kubectl port-forward service/quiz-service 8082:8082

5. Install metrics-server

   kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

6. Verify if metrics server is running

   kubectl get pods -n kube-system

7. Edit the metrics server deployment yaml

   kubectl get deployment metrics-server -n kube-system -o yaml > metrics-server.yaml

8. Open it up on VSCode

   code metrics-server.yaml

9. Apply the new metrics server configurations

   kubectl apply -f metrics-server.yaml

10. Run the below command to check CPU utilization

kubectl get hpa -w

11. Run the Apache Benchmark to simulate load

ab -n 10000 -c 100 http://localhost:8082/get-score

---

📑 Project Report

The full project report is available in the root folder:
Project Report

---

🎥 Project Demo Video

Watch the video showcasing the project here:
Project Demo Video

---