Set up complete MQTT broker infrastructure with RabbitMQ and Redis for transit vehicle tracking

[Copilot]

Overview

This PR implements a complete MQTT broker infrastructure for collecting real-time transit vehicle tracking and telemetry data. The system uses RabbitMQ as the MQTT broker and Redis as an in-memory database for fast data retrieval, all containerized with Docker for easy deployment.

Architecture

The implementation consists of three main containerized services:

Transit Vehicles → MQTT (1883) → RabbitMQ → Subscriber Service → Redis (6379)

1. RabbitMQ MQTT Broker: Receives MQTT messages from transit vehicles on port 1883, with a management UI on port 15672
2. Redis Database: Provides in-memory storage with sub-millisecond access times on port 6379
3. Python Subscriber Service: Bridges MQTT to Redis, automatically storing incoming vehicle data with TTL and timeline indexing

Key Features

• Docker-based deployment: All services orchestrated with Docker Compose for consistent deployment
• Health checks and dependencies: Services start in proper order with automatic health monitoring
• Automatic reconnection: Built-in retry logic for resilient service connections
• Data expiration: 1-hour TTL on vehicle records to manage memory usage
• Timeline indexing: Sorted set for efficient time-based data queries
• Environment-based configuration: Easy customization via .env file
• Comprehensive logging: Structured logging throughout for debugging and monitoring

Data Storage Schema

Vehicle data is stored in Redis with the following structure:

Key: "vehicle:{vehicle_id}"
Value: {
  "vehicle_id": "1234",
  "lat": 40.7128,
  "lon": -74.0060,
  "speed": 25,
  "heading": 180,
  "_timestamp": "2024-01-15T10:30:00.123456",
  "_topic": "transit/vehicles/bus/1234"
}
TTL: 3600 seconds

Additionally, a timeline index (vehicles:timeline) tracks all vehicles as a sorted set by timestamp for efficient time-based queries.

Files Added

Infrastructure & Configuration:

• docker-compose.yml - Orchestrates RabbitMQ, Redis, and subscriber services
• .env.example - Environment variable template
• .gitignore - Standard patterns for Python, Docker, and IDE files
• config/rabbitmq/enabled_plugins - Enables MQTT and management plugins
• config/rabbitmq/rabbitmq.conf - RabbitMQ MQTT configuration

Subscriber Service:

• subscriber/Dockerfile - Python 3.11 container
• subscriber/requirements.txt - Dependencies (paho-mqtt, redis)
• subscriber/subscriber.py - Main service with MQTT subscription and Redis storage logic

Examples & Testing:

• examples/publisher_example.py - Test MQTT publisher that generates simulated vehicle data
• examples/query_redis.py - Tool to query and display stored vehicle data
• examples/README.md - Documentation for example scripts

Documentation:

• README.md - Comprehensive guide with architecture, setup, configuration, and troubleshooting
• QUICKSTART.md - Simplified quick start guide for rapid deployment

Usage

Start the complete system with a single command:

docker-compose up -d

Test with the provided examples:

# Publish test vehicle data
python examples/publisher_example.py

# Query stored data
python examples/query_redis.py

Access the RabbitMQ management UI at http://localhost:15672 (admin/admin)

Configuration

Customize behavior via environment variables in .env:

• RABBITMQ_USER / RABBITMQ_PASS - Authentication credentials
• MQTT_TOPIC - Topic pattern to subscribe to (default: transit/vehicles/#)
• REDIS_DB - Redis database number (default: 0)

Technical Details

• RabbitMQ: Version 3 with management plugin, MQTT plugin enabled on port 1883
• Redis: Version 7 Alpine with AOF persistence enabled
• Python Subscriber: Python 3.11 with paho-mqtt 1.6.1 and redis 5.0.1
• Total Implementation: 847 lines of code and documentation across 13 files

The system is now ready for production deployment to collect and store real-time transit vehicle tracking data.

Original prompt

Please set up a repository for developing a MQTT broker using RabbitMQ within a Docker container. This broker will collect real-time transit vehicles tracking and telemetry data and store it in an external in-memory database (Redis) for quick data retrieval.

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