/Quick-MING

Quick - Preconfigured MING Stack

Primary LanguageJavaScriptMIT LicenseMIT

Quick MING Setup

This repository provides a rapid deployment setup for the MING stack: MQTT, InfluxDB, Node-RED, and Grafana. Designed for simplicity, this environment is containerized using Docker and orchestrated with docker-compose.

Prerequisites

Before you begin, ensure you have the following:

  • Docker and Docker Compose installed on your system.
  • A fundamental understanding of Docker and containerization concepts.

Project Structure

Quick MING/
│
├── mosquitto               # MQTT Broker configurations
│   ├── Dockerfile
│   └── ...
│
├── node-red                # Node-RED configurations and flows
│   ├── Dockerfile
│   └── ...
│
├── .env                    # Environment variables for services
└── docker-compose.yaml     # Docker Compose configuration

Configuration

  1. Clone the repository:

    git clone https://github.com/DMDuFresne/Quick-MING.git
  2. Rename example.env to .env and configure the environment variables according to your setup.

  3. Ensure ports in docker-compose.yaml are not in use or change them accordingly.

Usage

  1. Start the environment by running:

    docker-compose up -d --build
  2. Use the stack to build whatever you want.

Services

The stack includes:

  • Mosquitto: MQTT broker on port 1883.

  • InfluxDB: Time-series database on port 8086.

  • Node-RED: Wiring tool for devices and online services on port 1880.

  • Grafana: Monitoring and observability platform on port 3000.

  • Telegraf: An agent for collecting, processing, aggregating, and writing metrics.

Attribution

This project is possible thanks to the use and support from the following open-source projects:

  • Eclipse Mosquitto: A reliable and lightweight MQTT broker.

  • InfluxDB: A purpose-built database designed to handle time-stamped data at scale.

  • Node-RED: A visual tool for wiring the Internet of Things.

  • Grafana: An analytics platform for all your metrics.

  • Telegraf: A server agent to collect metrics.

  • mpous/ming: The MING stack as used in this project is inspired by the work done by Marc Pous.

Each tool brings unique capabilities to any project, and their combination allows for a robust data monitoring solution. For their individual licensing terms, please refer to their respective websites or documentation.