# Cloud Notes App

## Description

Cloud Notes App is a containerized cloud-based web application for storing notes online.  
The application allows users to add and manage notes using a modern web interface connected to a MongoDB database.  
The project was developed as part of a Cloud Technologies deployment assignment.

---

## Technologies Used

- Node.js
- Express.js
- MongoDB
- Nginx
- Docker
- Docker Compose
- Railway Cloud Platform
- GitHub

---

## Application Architecture

The application uses a multi-container architecture consisting of:

1. **Node.js Backend Container**
   - Handles API requests
   - Connects to MongoDB
   - Serves application logic

2. **MongoDB Database Container**
   - Stores user notes persistently
   - Uses persistent storage volumes

3. **Nginx Reverse Proxy Container**
   - Handles HTTP traffic
   - Proxies requests to Node.js backend

---

## Features

- Add cloud notes
- Persistent database storage
- Responsive user interface
- Dockerized deployment
- Public cloud hosting
- HTTPS support
- MongoDB integration
- Reverse proxy using Nginx
- Environment variable configuration
- Automated deployment scripts

---

## Cloud Platform

The application is deployed publicly using the **Railway** cloud platform.

Railway provides:
- Public HTTPS access
- Container hosting
- Cloud networking
- Managed MongoDB service

---

## Public Access

The application is accessible publicly through HTTPS using a generated Railway domain.

**Example:**
```
https://your-domain.up.railway.app
```

---

## Persistent Storage

MongoDB data is stored using persistent volumes to ensure that notes remain saved after application restarts.

---

## Deployment Instructions

### Run Application
```bash
docker compose up -d --build
```

### Stop Application
```bash
docker compose down -v
```

---

## Scripts

### `prepare-app.sh`
Used to:
- Build containers
- Start services
- Deploy the application

### `remove-app.sh`
Used to:
- Stop containers
- Remove services
- Clean deployment resources

---

## Environment Variables

The application uses environment variables for secure configuration.

**Example:**
```env
MONGO_URL=mongodb://mongodb:27017/notes
```

> Sensitive data is not stored directly in GitHub.

---

## Backup Instructions

MongoDB database can be backed up using:
```bash
mongodump
```

Backup files can later be restored using:
```bash
mongorestore
```

---

## Viewing Logs

Docker logs can be viewed using:
```bash
docker logs container_name
```

Railway deployment logs are available through the Railway dashboard.

---

## Cost Analysis

Estimated cloud deployment cost for:
- **1,000 users/day**
- **50 GB database/storage**

### Estimated Resources

| Resource             | Estimated Cost    |
|----------------------|-------------------|
| Railway Web Service  | $5 – $10/month    |
| MongoDB Storage      | $5 – $15/month    |
| Network Traffic      | $2 – $5/month     |

### Estimated Annual Cost

Approximately **$150 – $300 per year**, depending on traffic and storage usage.

---

## Repository Structure

```
sk1/
│
├── app/
│   ├── public/
│   │   └── index.html
│   ├── Dockerfile
│   ├── package.json
│   └── server.js
│
├── nginx/
│   └── nginx.conf
│
├── docker-compose.yml
├── prepare-app.sh
├── remove-app.sh
└── README.md
```

---

## Configuration Description

### Docker Compose
Defines:
- Application containers
- Networking
- Persistent volumes

### Nginx Configuration
Used as reverse proxy forwarding requests to the Node.js application.

### Dockerfile
Builds the Node.js application container.

---

## Requirements for Running

The following software is required:
- Docker Desktop
- Docker Compose
- Git
- Internet connection

**Supported operating systems:**
- Windows
- Linux
- macOS

---

## External Resources

The following resources were used during development:
- [Docker Documentation](https://docs.docker.com)
- [MongoDB Documentation](https://www.mongodb.com/docs)
- [Node.js Documentation](https://nodejs.org/en/docs)
- [Railway Documentation](https://docs.railway.app)
- [GitHub Documentation](https://docs.github.com)

> Generative AI assistance was used for debugging, deployment guidance, frontend improvements, and README preparation.