Here's an integration guide for Python, Docker, and Kubernetes (k8s) to deploy a Python application:
1. Python Application
Start with a simple Flask application (app.py):
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello():
return "Hello from Python in Kubernetes!"
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5000)
Requirements:
Create a requirements.txt file:
flask==3.0.0
2. Docker Containerization
Create a Dockerfile to package the app:
# Use a lightweight Python base image
FROM python:3.11-alpine
# Set working directory
WORKDIR /app
# Copy requirements and install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy application code
COPY . .
# Expose port 5000 (Flask default)
EXPOSE 5000
# Run the application
CMD ["python", "app.py"]
Build the Docker Image:
docker build -t python-app:1.0 .
Run Locally:
docker run -p 5000:5000 python-app:1.0
3. Kubernetes Deployment
Create Kubernetes manifests:
Deployment (deployment.yaml)
Manages the app’s pods and scaling.
apiVersion: apps/v1
kind: Deployment
metadata:
name: python-app
spec:
replicas: 3 # Number of pods
selector:
matchLabels:
app: python-app
template:
metadata:
labels:
app: python-app
spec:
containers:
- name: python-app
image: python-app:1.0 # Use your Docker image
ports:
- containerPort: 5000
resources:
requests:
memory: "128Mi"
cpu: "100m"
limits:
memory: "256Mi"
cpu: "200m"
Service (service.yaml)
Exposes the app to external traffic.
apiVersion: v1
kind: Service
metadata:
name: python-app-service
spec:
type: LoadBalancer # Use NodePort for local clusters
selector:
app: python-app
ports:
- protocol: TCP
port: 80
targetPort: 5000
Deploy to Kubernetes:
kubectl apply -f deployment.yaml
kubectl apply -f service.yaml
Check Deployment:
kubectl get pods
kubectl get services
Key Concepts
- Docker: Containerizes the app for consistency across environments.
- Kubernetes: Orchestrates containers, handles scaling, self-healing, and load balancing.
- Pod: Smallest deployable unit in Kubernetes (1+ containers).
- Deployment: Manages pod replicas and updates.
- Service: Provides network access to pods.
Best Practices
- Image Optimization
Use small base images (e.g.,
python:alpine). -
Health Checks Add liveness/readiness probes to your Kubernetes deployment:
livenessProbe: httpGet: path: / port: 5000 initialDelaySeconds: 5 periodSeconds: 10 -
Environment Variables Use
ConfigMaporSecretsfor configuration:env: - name: FLASK_ENV value: "production" - CI/CD Pipeline Automate building and deploying with tools like GitHub Actions, Jenkins, or ArgoCD.
Local Testing with Minikube
-
Start Minikube:
minikube start -
Load Docker image into Minikube:
minikube image load python-app:1.0 -
Access the app:
minikube service python-app-service
This workflow allows you to develop a Python app locally, package it with Docker, and deploy it at scale using Kubernetes.
