नागराज-भक्षकः नकुलः

This is an advanced, scalable monitoring system built with Python and Tornado. It consists of a server that collects and stores metrics, and a client that gathers and sends metrics to the server.

• 100% Python implementation - absolutely no PHP or other legacy languages in sight
• Modern, from-the-ground-up design - not built on a 20-year-old core
• Server-client architecture for distributed monitoring
• Shared-secret signature-based communication between server and client
• Extensible metric collection through custom Python scripts
• All metrics are implemented natively in Python, allowing for easy customization and extension
• PostgreSQL database for robust and scalable storage of metrics
• In-memory queue system for efficient metric processing
• Client-side buffering for resilience against network issues
• Advanced data aggregation for handling thousands of hosts
• RESTful API for fetching latest metrics and historical data
• Automatic cleanup and aggregation of old data
• Configurable alert system with support for downtimes
• Interactive dashboard with real-time updates
• URL-based host selection for easy sharing and bookmarking
• Host tagging system for better organization
• Admin interface for managing clients and uploading new metrics
• Data simulation tool for testing and development
• Flexible data management, including the ability to selectively delete metrics when needed
• Pure Python implementation, making it easy to understand, modify, and extend the entire system
• Dynamic metric selection with perioid rediscovery and remote or local configuration
• End-to-end monitoring including Selenium, Autoit, PyAutoGUI supporting elements, positional clicking, bitmap synchronization (OCR)

• Python 3.7+
• Tornado web framework
• PostgreSQL database
• psycopg2-binary (PostgreSQL adapter for Python)
• Chart.js (for dashboard visualizations)

1. Clone this repository or download the source files.
2. Install the required packages:

   pip install tornado psycopg2-binary
   

3. Ensure you have PostgreSQL installed and running.

1. Server Setup:

   • Create a server_config.json file with your database and server settings.
   • Run the server using: python server.py

2. Client Setup:

   • Create a metrics directory in the same location as client.py
   • Add custom Python scripts to the metrics directory for each metric you want to collect
   • Configure client_config.json with appropriate settings (see Configuration section)
   • Run the client using: python client.py

Update your client_config.json file to include the following new fields:

{
    "client_id": "",
    "server_url": "http://localhost:8888",
    "default_interval": 60,
    "metrics_dir": "./metrics",
    "secret_key": "your_secret_key",
    "active_metrics": ["cpu_usage", "memory_usage", "disk_usage"],
    "metric_intervals": {
        "cpu_usage": 30,
        "memory_usage": 60,
        "disk_usage": 300
    },
    "tags": {
        "environment": "production",
        "role": "webserver"
    }
}

• active_metrics: List of metrics that should be collected. Only metrics in this list will be gathered and sent to the server.
• metric_intervals: Custom collection intervals for specific metrics (in seconds). If not specified, the default_interval will be used.

1. Create a new Python file in the metrics directory (e.g., custom_metric.py).

2. Implement a collect() function that returns the metric value:

   def collect():
       metrics = {}
   
       # Handling system_1
       try:
           metrics["system_1"] = {
               "value": 123
           }
       except Exception as e:
           metrics["system_1"] = {
               "value": None,
               "message": f"UnexpectedError: {str(e)}"
           }
   
       # Handling system_2
       try:
           metrics["system_2"] = {
               "value": 456
           }
       except Exception as e:
           metrics["system_2"] = {
               "value": None,
               "message": f"UnexpectedError: {str(e)}"
           }
   
       return metrics
   
                  

3. Add the metric name to the active_metrics list in client_config.json.

To enable or disable metrics without restarting the client:

1. Update the active_metrics list in client_config.json.
2. The client will automatically detect the change and adjust its metric collection accordingly on the next update cycle.

Access the dashboard at http://localhost:8888/dashboard. Features include:

• Real-time metric visualizations
• Host selection with URL-based sharing
• Alert configuration and management
• Downtime scheduling

Access the admin interface at http://localhost:8888/admin. Features include:

• Client configuration management
• Metric script uploading
• Host tag management
• Active metric configuration

• GET /: Check if the server is running
• POST /metrics: Submit metrics (used by the client)
• GET /fetch/latest: Get the latest metrics for all hosts
• GET /fetch/history/<hostname>/<metric_name>: Get historical data for a specific metric
• GET /fetch/hosts: Get a list of all hosts
• POST /alert_config: Configure alerts
• POST /alert_state: Update alert state
• GET /downtime: Get downtime information
• POST /downtime: Schedule a downtime
• GET /fetch/recent_alerts: Get recent alerts
• POST /aggregate: Trigger manual data aggregation
• POST /remove_host: Remove a host from the system
• POST /update_tags: Update tags for a host
• GET /client_config: Fetch client configuration
• POST /client_config: Register or update client configuration

Nakulos is available for commercial use under a separate commercial license. Companies interested in using Nakulos for their monitoring needs can contact us at cyphernormie@gmail.com to discuss pricing and support options. We offer flexible plans tailored to the specific requirements of businesses of all sizes.

Benefits of the commercial plan include:

• Priority support and dedicated account management
• Access to additional enterprise features and integrations
• SLA guarantees for uptime and performance
• Assistance with setup, migration, and customization
• Option for on-premises or private cloud deployment

Please note that commercial use of Nakulos without a valid commercial license is not permitted under the open-source license detailed below.

• If metrics are not being collected, check the active_metrics list in your client configuration.
• Ensure that all custom metric scripts have a collect() function.
• Check the client logs for any errors related to metric collection or script loading.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

For more details about this license, please visit: https://creativecommons.org/licenses/by-nc/4.0/