TON Validator Monitoring Platform

The Validator Monitoring Platform is designed to simplify and optimize validator operations. It provides real-time monitoring of validator efficiency and offers automated notifications for abnormal behavior, helping teams maintain and expand the validator pool while reducing operational overhead.

Key Features

• Cycle and Scoreboard Monitoring: Continuously tracks voting cycles and evaluates validator efficiency.
• Real-Time Notifications: Automatically sends alerts through Telegram when validator performance drops below a configurable threshold.
• Historical Data Aggregation: Aggregates data over extended periods, supporting hourly and daily queries for long-term analysis.
• Data Visualization: Displays validator efficiency metrics through dynamic charts powered by Redis caching and Chart.js.
• ClickHouse Integration: Stores cycle and scoreboard data in ClickHouse, with partitioned tables for optimized storage and querying.
• Customizable Environment: All hosts and parameters are configurable via environment variables.

Validators health:

Efficiency chart:

Architecture Overview

1. Scrapper Module: Retrieves voting cycles and scoreboard data from external APIs and saves it to ClickHouse.
2. Checker Module: Monitors validator efficiency against a defined threshold and tracks state changes.
3. Notifier Module: Manages notifications and subscriptions via the Telegram API

Installation and Setup

1. Prerequisites:

   • Docker
   • Redis
   • ClickHouse

2. Environment Variables: Configure the environment variables for hosts, ports, and thresholds in a .env file.

3. Deployment:

   • Build and deploy the Go backend in Kubernetes.
   • Set up the Ingress and load balancers for external access.
   • Deploy Redis and ClickHouse.

4. Migrations: The platform runs migrations automatically on startup to set up necessary tables in ClickHouse.

Usage

Monitoring Validator Efficiency

1. Threshold-based Alerts: The Checker module monitors validator performance and sends alerts if efficiency falls below a defined threshold.
2. State Change Tracking: Only sends notifications on state changes (e.g., ok to not ok), reducing notification noise.
3. Historical Data: Provides aggregated metrics for long-term trend analysis.

Contributing

Contributions are welcome! Please open issues or pull requests for new features, improvements, or bug fixes.

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For any questions or support, please contact the development team.