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Initial commit: Northern Thailand Ping River Monitor v3.1.0
Features:
- Real-time water level monitoring for Ping River Basin (16 stations)
- Coverage from Chiang Dao to Nakhon Sawan in Northern Thailand
- FastAPI web interface with interactive dashboard and station management
- Multi-database support (SQLite, MySQL, PostgreSQL, InfluxDB, VictoriaMetrics)
- Comprehensive monitoring with health checks and metrics collection
- Docker deployment with Grafana integration
- Production-ready architecture with enterprise-grade observability

 CI/CD & Automation:
- Complete Gitea Actions workflows for CI/CD, security, and releases
- Multi-Python version testing (3.9-3.12)
- Multi-architecture Docker builds (amd64, arm64)
- Daily security scanning and dependency monitoring
- Automated documentation generation
- Performance testing and validation

 Production Ready:
- Type safety with Pydantic models and comprehensive type hints
- Data validation layer with range checking and error handling
- Rate limiting and request tracking for API protection
- Enhanced logging with rotation, colors, and performance metrics
- Station management API for dynamic CRUD operations
- Comprehensive documentation and deployment guides

 Technical Stack:
- Python 3.9+ with FastAPI and Pydantic
- Multi-database architecture with adapter pattern
- Docker containerization with multi-stage builds
- Grafana dashboards for visualization
- Gitea Actions for CI/CD automation
- Enterprise monitoring and alerting

 Ready for deployment to B4L infrastructure!
2025-08-12 15:40:24 +07:00

7.7 KiB

Thailand Water Monitor - Current Project Status

📁 Clean Project Structure

The project has been cleaned up and organized with the following structure:

water_level_monitor/
├── 📄 .gitignore                             # Git ignore rules
├── 📄 README.md                              # Main project documentation
├── 📄 requirements.txt                       # Python dependencies
├── 📄 config.py                              # Configuration management
├── 📄 water_scraper_v3.py                    # Main application (15-min scheduler)
├── 📄 database_adapters.py                   # Multi-database support
├── 📄 demo_databases.py                      # Database demonstration
├── 📄 Dockerfile                             # Container configuration
├── 📄 docker-compose.victoriametrics.yml     # VictoriaMetrics stack
├── 📚 Documentation/
│   ├── 📄 DATABASE_DEPLOYMENT_GUIDE.md       # Multi-database setup guide
│   ├── 📄 DEBIAN_TROUBLESHOOTING.md          # Linux deployment guide
│   ├── 📄 ENHANCED_SCHEDULER_GUIDE.md        # 15-minute scheduler guide
│   ├── 📄 GAP_FILLING_GUIDE.md              # Data gap filling guide
│   ├── 📄 HTTPS_CONFIGURATION.md            # HTTPS setup guide
│   └── 📄 VICTORIAMETRICS_SETUP.md          # VictoriaMetrics guide
└── 📁 grafana/                              # Grafana configuration
    ├── 📁 provisioning/
    │   ├── 📁 datasources/
    │   │   └── 📄 victoriametrics.yml        # VictoriaMetrics data source
    │   └── 📁 dashboards/
    │       └── 📄 dashboard.yml              # Dashboard provider config
    └── 📁 dashboards/
        └── 📄 water-monitoring-dashboard.json # Pre-built dashboard

🧹 Files Removed During Cleanup

Old Data Files

  • thailand_water_data_v2.csv - Old CSV export
  • water_monitor.log - Log file (regenerated automatically)
  • water_monitoring.db - SQLite database (recreated automatically)

Outdated Documentation

  • FINAL_SUMMARY.md - Contained references to non-existent v2 files
  • PROJECT_SUMMARY.md - Outdated project information

System Files

  • __pycache__/ - Python compiled files directory

Current Features

Enhanced 15-Minute Scheduler

  • Timing: Runs every 15 minutes (1:00, 1:15, 1:30, 1:45, 2:00, etc.)
  • Full Checks: At :00 minutes (gap filling + data updates)
  • Quick Checks: At :15, :30, :45 minutes (data fetch only)
  • Gap Filling: Automatically fills missing historical data
  • Data Updates: Updates existing records when values change

Multi-Database Support

  • VictoriaMetrics (Recommended) - High-performance time-series
  • InfluxDB - Purpose-built time-series database
  • PostgreSQL + TimescaleDB - Relational with time-series optimization
  • MySQL - Traditional relational database
  • SQLite - Local development and testing

Production Features

  • Docker Support: Complete containerization
  • Grafana Integration: Pre-built dashboards
  • HTTPS Configuration: Secure deployment options
  • Health Monitoring: Comprehensive logging and error handling
  • Gap Detection: Automatic identification of missing data
  • Retry Logic: Database lock handling and network error recovery

🚀 Quick Start

1. Basic Setup (SQLite)

cd water_level_monitor
pip install -r requirements.txt
python water_scraper_v3.py

2. VictoriaMetrics Setup

# Start VictoriaMetrics + Grafana
docker-compose -f docker-compose.victoriametrics.yml up -d

# Configure environment
export DB_TYPE=victoriametrics
export VM_HOST=localhost
export VM_PORT=8428

# Run monitor
python water_scraper_v3.py

3. Test Different Databases

# Test all supported databases
python demo_databases.py all

# Test specific database
python demo_databases.py victoriametrics

📊 Data Collection

Station Coverage

  • 16 Water Monitoring Stations across Thailand
  • Accurate Station Codes: P.1, P.20, P.21, P.4A, P.5, P.67, P.75, P.76, P.77, P.81, P.82, P.84, P.85, P.87, P.92, P.103
  • Bilingual Names: Thai and English station identification

Metrics Collected

  • 🌊 Water Level: Measured in meters (m)
  • 💧 Discharge: Measured in cubic meters per second (cms)
  • 📊 Discharge Percentage: Relative to station capacity
  • Timestamp: Hour 24 handling (midnight = 00:00 next day)

Data Frequency

  • Every 15 Minutes: Continuous monitoring
  • ~300+ Data Points: Per collection cycle
  • Automatic Gap Filling: Historical data recovery
  • Data Updates: Changed values detection and correction

🔧 Command Line Tools

Main Application

python water_scraper_v3.py                    # Run continuous monitoring
python water_scraper_v3.py --test             # Single test cycle
python water_scraper_v3.py --help             # Show help

Gap Management

python water_scraper_v3.py --check-gaps [days]  # Check for missing data
python water_scraper_v3.py --fill-gaps [days]   # Fill missing data gaps
python water_scraper_v3.py --update-data [days] # Update existing data

Database Testing

python demo_databases.py                      # SQLite demo
python demo_databases.py victoriametrics      # VictoriaMetrics demo
python demo_databases.py all                  # Test all databases

📈 Monitoring & Visualization

Grafana Dashboard

  • URL: http://localhost:3000 (when using docker-compose)
  • Username: admin
  • Password: admin_password
  • Features: Time series charts, status tables, gauges, alerts

VictoriaMetrics API

🛡️ Security & Production

HTTPS Configuration

  • Complete guide in HTTPS_CONFIGURATION.md
  • SSL certificate setup
  • Reverse proxy configuration
  • Security best practices

Deployment Options

  • Docker: Containerized deployment
  • Systemd: Linux service configuration
  • Cloud: AWS, GCP, Azure deployment guides
  • Monitoring: Health checks and alerting

📚 Documentation

Available Guides

  1. README.md - Main project documentation
  2. DATABASE_DEPLOYMENT_GUIDE.md - Multi-database setup
  3. ENHANCED_SCHEDULER_GUIDE.md - 15-minute scheduler details
  4. GAP_FILLING_GUIDE.md - Data integrity and gap filling
  5. DEBIAN_TROUBLESHOOTING.md - Linux deployment troubleshooting
  6. VICTORIAMETRICS_SETUP.md - VictoriaMetrics configuration
  7. HTTPS_CONFIGURATION.md - Secure deployment setup

Key Features Documented

  • Installation and configuration
  • Multi-database support
  • 15-minute scheduling system
  • Gap filling and data integrity
  • Production deployment
  • Monitoring and troubleshooting
  • Security configuration

🎯 Project Status: PRODUCTION READY

The Thailand Water Monitor is now:

  • Clean: All old and redundant files removed
  • Organized: Clear project structure with proper documentation
  • Enhanced: 15-minute scheduling with gap filling
  • Scalable: Multi-database support with VictoriaMetrics
  • Secure: HTTPS configuration and security best practices
  • Monitored: Comprehensive logging and Grafana dashboards
  • Documented: Complete guides for all features and deployment options

The project is ready for production deployment with professional-grade monitoring capabilities.