Migrating Legacy Python Applications to Kubernetes on Azure: A Step-by-Step Guide

Kubernetes, also known as K8s, is an open-source Container Orchestration platform, it manages the life-cycle of containerized applications effectively and efficiently, including deploying and scaling, and it’s one of the essential tools for digital transformation and cloud-native development. This article will guide you through migrating a legacy Python application to a Kubernetes cluster on Azure.

Prerequisites

Before you begin, you need to have:

1. A Microsoft Azure account with an active subscription
2. The Azure az CLI installed on your local machine
3. Docker installed on your local machine
4. Kubernetes CLI kubectl installed on your local machine
5. A Python application you want to migrate

Table of Contents

• Preparing Your Application
• Packaging Your Application in a Docker Container
• Setting Up an Azure Kubernetes Service (AKS)
• Deploying Your Application to AKS
• Testing Your Deployment
• Conclusion

Preparing Your Application

Firstly, ensure your Python application is designed to work with a 12-factor application methodology. Some of the key factors include:

• Codebase: Maintain a single codebase for your application, and use version control systems(VCS) like Git to track changes.
• Dependencies: Use package managers and dependency management tools to manage your application’s dependencies.
• Config: Use Kubernetes ConfigMaps and Secrets to store configuration data, such as environment variables and sensitive information.
• Backing services: Kubernetes makes connecting your application to backing services like databases, message queues, and caching systems easy through Services and Ingress resources.

Check out this guide to learn more about implementing the 12-factor application methodology using Kubernetes.

Packaging Your Application in a Docker Container

For Kubernetes to manage your application, it needs to be containerized. We will use Docker to achieve this. Here’s a simple Dockerfile example for a Python application:

# Use an official Python runtime as a parent image
FROM python:3.7-slim

# Set the working directory in the container
WORKDIR /app

# Add the current directory code to the working directory
ADD . /app

# Install the packages mentioned in the requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt

# Expose port 80
EXPOSE 80

# Run app.py when the container launches
CMD ["python", "app.py"]

Use the following command to build your Docker image:

docker build -t my-python-app .

Setting Up an Azure Kubernetes Service (AKS)

Now that you have a Docker container let’s set up AKS. Use the Azure CLI to create a resource group:

az group create --name myResourceGroup --location eastus

Create AKS cluster:

az aks create --resource-group myResourceGroup --name myAKSCluster --node-count 1 --enable-addons monitoring --generate-ssh-keys

Deploying Your Application to AKS

To deploy your Python app, create a Kubernetes deployment configuration. The configuration specifies how to create/update instances of your application. Here is a simple deployment configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-python-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-python-app
  template:
    metadata:
      labels:
        app: my-python-app
    spec:
      containers:
      - name: my-python-app
        image: <your-dockerhub-username>/my-python-app:v1
        ports:
        - containerPort: 80

Deploy your application using the kubectl command:

kubectl apply -f my-python-app-deployment.yaml

Testing Your Deployment

Now that your application is running on Kubernetes, you can interact with the Kubernetes cluster by using the kubectl command line tool:

kubectl get deployments

This command will display the list of deployments, and you should be able to see your application running.

Conclusion

Migrating a legacy Python application to Kubernetes on Azure can be straightforward with the proper steps. This guide gives you the foundations to start this journey. The migration provides the benefits of containerization, and with Azure Kubernetes Service, you can easily manage, scale and deploy your applications. Enjoy the journey toward a cloud-native future.