Summary of the docker_lab Project

The docker_lab project is designed to provide a structured environment for creating, managing, and deploying containerized applications using Docker and Docker Compose. It focuses on various backend services (databases, message queues, vector search, etc.) and provides a platform for experimenting with different application architectures.

Key Features of the docker_lab Project:

1. Backend Services:

   • The project includes multiple backend services, such as PostgreSQL, MySQL, MongoDB, Redis, Qdrant, Dgraph, and RabbitMQ.
   • Each service is configured using environment files (.env) located in docker/env/, and initialization instructions for each service are provided in the docs/services directory.

2. Multiple Test Cases:

   • The project features several test cases located under the tests/ directory. These test cases focus on different scenarios and application stacks:
     • Case 1: Focuses on a Node.js application that backs up PostgreSQL data to AWS S3.
     • Case 2: Contains two Node.js applications that depend on multiple backend services such as PostgreSQL, MongoDB, MySQL, and Redis.
     • Case 3: Implements a Retrieval-Augmented Generation (RAG) system using Qdrant, Dgraph, and PostgreSQL, with the application built in Python.

3. Docker Compose Configuration:

   • The project uses Docker Compose to manage multi-container setups, including backend services and application containers.
   • Each test case has its own Compose file, such as service.yml for backend services and app.yml for application-specific containers.
   • These Compose files provide scalability and easy orchestration of different services, enabling isolated and simultaneous execution of various applications.

4. Custom Dockerfiles:

   • Each application (Node.js or Python) has its own Dockerfile for building the application container:
     • For example, app1.docker, app2.docker, and app.docker are custom Dockerfiles for building the containers for different test cases.
   • These Dockerfiles ensure that each application is containerized with the correct dependencies and configuration.

5. Documentation:

   • The docs/ directory contains comprehensive documentation and guidelines for working with Docker and Docker Compose.
   • Key topics include setting up Docker for Node.js apps, using Docker with cloud services, and detailed instructions for initializing various backend containers (e.g., MySQL, PostgreSQL, Qdrant).

6. Code and Script Examples:

   • Application source code for different use cases is provided, such as TypeScript files for Node.js apps in index.ts (Case 1, Case 2) and Python scripts for the RAG system in app.py (Case 3).
   • The source files demonstrate how to interact with backend services, process data, and build APIs using Docker containers.

Use Cases Supported:

• Database Management: Multiple databases are supported, including relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis).
• Vector Search: Qdrant is used for high-performance vector-based search operations.
• Graph Relationships: Dgraph is leveraged to manage and query graph-based relationships.
• Message Queues: RabbitMQ provides message brokering capabilities.
• Cloud Service Integration: For example, AWS S3 is integrated for storing PostgreSQL backups (Case 1).

This project provides a versatile environment for learning, developing, and deploying complex applications with containerization, offering hands-on experience with different backend systems and Docker technologies.