Optimising Flask Dockerfiles: Best Practices for DevOps and Developers

? Introduction

Welcome to the world of DevOps! ? Today, we’re diving into an essential skill for any DevOps engineer: optimizing Dockerfiles for Flask applications. While beginner DevOps engineers often focus on mastering basic Dockerfile syntax, experienced engineers know that true expertise lies in optimization—crafting Dockerfiles that are efficient, secure, and production-ready.

In this blog, we’ll walk through the process of building a simple Flask application. First, we’ll create a basic Dockerfile, and then we’ll refine it into an optimized version, comparing the two to understand the difference. Whether you're a beginner or looking to sharpen your Dockerfile skills, this guide has something for everyone.

Let’s get started! ?️

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? Pre-Requisites

Before we dive into writing and optimizing Dockerfiles for a Flask application, ensure you have the following prerequisites in place:

1. Basic Understanding of Flask
   
   Familiarity with creating a simple Flask application will help you follow along seamlessly.

2. Docker Installed
   
   Make sure Docker is installed and running on your system. You can download it from the Docker website.

3. Python Environment Setup
   
   Python 3.x installed on your system, along with pip for managing Python packages.

4. Code Editor
   
   Use any code editor of your choice, such as Visual Studio Code, PyCharm, or Sublime Text.

5. Flask Installed
   
   Install Flask in your Python environment using the command:
   

   pip install flask

1. Sample Flask Application Have a simple Flask application ready or be prepared to create one as we proceed in the tutorial.

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? Creating the Flask Application

To start, we’ll create a simple Flask application and prepare it for containerization. Follow these steps:

1. Create the Project Directory
   
   Make a directory named basic-flask and navigate into it.

2. Create the Flask Application
   
   Inside the basic-flask directory, create a file named app.py with the following content:
   

   from flask import Flask

   app = Flask(__name__)

   @app.route("/")
   def HelloWorld():
       return "Hello World"

   if __name__ == "__main__":
       app.run()

You can run this application using the command:

   python3 app.py

Open your browser and go to http://localhost:5000. You should see Hello World displayed on the web page.

1. List the Dependencies To containerize the app, we first need to specify the required Python modules. Create a requirements.txt file by running:

   pip install flask

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? Creating Dockerfiles

Now, let’s create two versions of Dockerfiles: a basic version and an optimized version.

Basic Dockerfile

The basic Dockerfile is straightforward but lacks efficiency and security optimizations:

   from flask import Flask

   app = Flask(__name__)

   @app.route("/")
   def HelloWorld():
       return "Hello World"

   if __name__ == "__main__":
       app.run()

This Dockerfile is functional but leaves room for improvement in caching, size optimization, and security practices.

Optimized Dockerfile

The optimized Dockerfile follows multi-stage builds and incorporates best practices for efficiency, security, and modularity:

   python3 app.py

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? Building the Dockerfiles

Now that we have created both Dockerfiles, it’s time to build Docker images and observe the differences in their sizes. Follow these steps:

Build the Image from the Basic Dockerfile

1. Ensure the content of the basic Dockerfile is saved in a file named Dockerfile.
2. Build the image using the following command:

   pip3 freeze > requirements.txt

Build the Image from the Optimized Dockerfile

1. Save the content of the optimized Dockerfile in a separate file named Dockerfile.
2. Build the image using this command:

FROM python:3.9-slim

WORKDIR /app

COPY . /app

RUN pip install -r requirements.txt

CMD ["python3", "app.py"]

Compare the Built Images

Once the images are built, list all Docker images using:

# syntax=docker/dockerfile:1.4

# Stage 1: Build dependencies
FROM --platform=$BUILDPLATFORM python:3.10-alpine AS builder

WORKDIR /code

# Install build dependencies and cache pip files for efficiency
COPY requirements.txt /code
RUN --mount=type=cache,target=/root/.cache/pip \
    pip3 install --prefix=/install -r requirements.txt

COPY . /code

# Stage 2: Development environment setup
FROM python:3.10-alpine AS dev-envs

WORKDIR /code

# Copy application files and installed dependencies
COPY --from=builder /install /usr/local
COPY . /code

# Install additional tools for development (e.g., Git, Bash)
RUN apk update && apk add --no-cache git bash

# Create a non-root user for better security
RUN addgroup -S docker && \
    adduser -S --shell /bin/bash --ingroup docker vscode

# Set entrypoint and command for development purposes
ENTRYPOINT ["python3"]
CMD ["app.py"]

# Stage 3: Production-ready image
FROM python:3.10-alpine AS final

WORKDIR /app

# Copy only necessary application files and dependencies
COPY --from=builder /install /usr/local
COPY app.py /app

ENTRYPOINT ["python3"]
CMD ["app.py"]

You should notice a significant difference in the image sizes:

• Basic Dockerfile Image: Approximately 177MB
• Optimized Dockerfile Image: Approximately 59.2MB

Why the Optimized Image is Smaller

• Lightweight Base Image: The optimized Dockerfile uses python:3.10-alpine, which is significantly smaller than python:3.9-slim.
• Multi-Stage Build: Unnecessary build dependencies are excluded from the final image, keeping it minimal.
• Efficient Caching: The use of caching for pip installations avoids redundant downloads and reduces image layers.

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? Conclusion

Optimizing Dockerfiles is a crucial skill for DevOps engineers aiming to create efficient, secure, and production-ready containers. In this blog, we explored how to build a simple Flask application, containerize it using a basic Dockerfile, and then refine it with an optimized Dockerfile.

The differences in image size and structure demonstrate the impact of best practices like using multi-stage builds, lightweight base images, and caching mechanisms. While the basic Dockerfile served its purpose, the optimized version provided a leaner, more secure, and performant container, highlighting the importance of thoughtful design in containerization.

As you continue your DevOps journey, always strive to enhance your Dockerfiles by incorporating optimizations, considering security, and minimizing overhead. A well-optimized Dockerfile not only saves time and resources but also ensures smoother deployments and scalability in production.

Now it’s your turn—try applying these techniques to your own projects and see the difference optimization makes! ?

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Happy coding and automating! ?

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