Optimal Integration of MongoDB in a Dockerized Python Project: Best Practices for Small-Scale Development Teams

MongoDB for Startups
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Currently, I am in the midst of integrating Docker with a Python project that is hosted on GitHub. This particular venture involves an application utilizing Jupyter Notebook and relies heavily upon MongoDB for its database functions. Since my team consists of only a few developers, it would be beneficial to receive guidance regarding how best to manage our MongoDB server within this development environment.

As we anticipate the size of our database will remain relatively small-scale, exploring strategies aimed at handling such instances seems prudent. With that said, there are several questions that require answers:

Firstly - what represents optimal practice when managing a limited number of developer-based project’s MongoDB instances? Our approach should strive towards balancing both simplicity and efficiency.

Secondly - do we need to consider implementing backup/restore procedures for safeguarding against data loss or unexpected issues?

Lastly – considering the compact nature of our current DB structure; might including backups as part-and-parcel codebase via version control prove advantageous? What benefits/drawbacks could arise from doing so?

Essentially I am seeking direction in efficiently overseeing the MongoDB aspect of our project, adhering to optimal procedures for small-scale development groups.

Dockerfile:

# Use the official Python image as the base
FROM python:3.9

# Set the working directory inside the container
WORKDIR /app

# Copy the requirements file into the container and install dependencies
COPY requirements.txt requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Copy the notebook files into the container
COPY notebooks notebooks

# Expose the port the Jupyter Notebook will run on
EXPOSE 8888

# Start the Jupyter Notebook when the container starts
CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--port=8888", "--no-browser", "--allow-root"]

docker-compose.yml (with MongoDB integrated):

version: '3.8'
services:
  jupyter:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: test_jupyter_container
    image: test-jupyter-image:latest
    ports:
      - "8888:8888"
    volumes:
      - ./notebooks:/app
    depends_on:
      - mongo

  mongo:
    container_name: test_mongo_container
    image: mongo:4.4.10
    ports:
      - "27017:27017"
    volumes:
      - ./mongodb_data:/data/db

volumes:
  test_mongodb_data: