Climate System Monitor 🌍

A comprehensive monitoring system designed for climate infrastructure and ML pipelines. Track service health, collect metrics, monitor pipelines, and manage alerts - all in one place.

🚀 Features

System Health Monitoring

• Real-time service status tracking
• Resource utilization metrics (CPU, Memory, Disk)
• Response time monitoring
• Error rate tracking

ML/Data Pipeline Monitoring

• Pipeline execution tracking
• Training metrics collection
• Data processing status
• Model performance monitoring

Alert Management

• Multi-severity alert system
• Telegram notifications
• Alert acknowledgment
• Custom alert rules

Metrics Collection

• System performance metrics
• Custom metric support
• Time-series data storage
• Metric visualization

🛠 Tech Stack

• Backend: Django 4.2, Django REST Framework
• Task Queue: Celery 5.3
• Cache/Message Broker: Redis 5.0
• Database: SQLite (development), PostgreSQL (production)
• Monitoring: Built-in metrics collection
• Notifications: Telegram integration

📦 Installation

1. Clone the repository:

git clone https://github.com/your-org/climate-system-monitor.git
cd climate-system-monitor

2. Create and activate virtual environment:

python -m venv env
source env/bin/activate  # Linux/Mac

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration

5. Run migrations:

python manage.py migrate

6. Start the development server:

python manage.py runserver

🔌 Integration

Python Client

Install the official Python client:

pip install climate-monitor-client

Quick start:

from climate_monitor import SystemMonitorClient

monitor = SystemMonitorClient(
    base_url='http://your-monitor-host:8000',
    service_name='your-service'
)

# Update service status
monitor.update_status('up')

# Start collecting metrics
monitor.start_metric_collection(interval=60)

See Python Client Documentation for more details.

📊 Dashboard

Access the monitoring dashboard at http://your-host:8000/dashboard/

Features:

• Service health overview
• Real-time metrics visualization
• Pipeline status tracking
• Alert management interface

📚 Documentation

• API Integration Guide
• Client Libraries
• Deployment Guide
• Contributing Guidelines

🤝 Contributing

Contributions are welcome! Please read our Contributing Guidelines before submitting a Pull Request.

Development Setup

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Topics

• monitoring
• climate-tech
• machine-learning
• devops
• django
• python
• metrics
• observability
• pipeline-monitoring
• alert-management
• system-monitoring
• real-time-monitoring
• telegram-bot
• climate-infrastructure
• ml-ops

🔗 Related Projects

• Climate Data Pipeline
• ML Model Training Framework
• Climate API Service

📧 Support

• Create an issue for bug reports or feature requests
• Join our Discord community
• Follow us on Twitter

✨ Contributors

Thanks goes to these wonderful people:

🙏 Acknowledgments

• Thanks to all contributors who have helped shape this project
• Special thanks to the climate tech community for feedback and support
• Built with ❤️ for the climate tech community