Introduction

Scaling Flask applications efficiently is crucial for handling increased traffic and ensuring reliability. By using Docker for containerization and Kubernetes for orchestration, you can deploy scalable, portable, and resilient Flask applications. This guide will cover how to containerize Flask with Docker, deploy it on Kubernetes, and ensure scalability.

Containerizing Flask with Docker

Creating a Dockerfile

A Dockerfile defines the environment and dependencies for running a Flask application.

# Use the official Python image
FROM python:3.9

# Set the working directory
WORKDIR /app

# Copy project files
COPY . .

# Install dependencies
RUN pip install -r requirements.txt

# Expose port 5000
EXPOSE 5000

# Start Flask application
CMD ["python", "app.py"]

Building and Running the Docker Image

Run the following commands to build and run your Flask container:

# Build the Docker image
docker build -t flask-app .

# Run the container
docker run -p 5000:5000 flask-app

Now, your Flask application runs inside a container and is accessible at http://localhost:5000.

Deploying Flask with Kubernetes

Writing a Kubernetes Deployment

To deploy Flask in Kubernetes, create a Deployment YAML file.

apiVersion: apps/v1
kind: Deployment
metadata:
name: flask-app
spec:
replicas: 3
selector:
matchLabels:
app: flask-app
template:
metadata:
labels:
app: flask-app
spec:
containers:
- name: flask-app
image: flask-app
ports:
- containerPort: 5000

This configuration deploys three replicas of the Flask application for load balancing.

Exposing the Flask App with a Service

To make the Flask app accessible externally, create a Kubernetes Service:

apiVersion: v1
kind: Service
metadata:
name: flask-service
spec:
type: LoadBalancer
selector:
app: flask-app
ports:
- protocol: TCP
port: 80
targetPort: 5000

Apply the configurations using:

kubectl apply -f deployment.yaml
kubectl apply -f service.yaml

Scaling the Flask Application

Horizontal Scaling with Kubernetes

To scale up your Flask deployment, run:

kubectl scale deployment flask-app --replicas=5

This increases the number of running instances, improving availability and handling higher traffic loads.

Auto-Scaling with Kubernetes HPA

For dynamic scaling, enable the Horizontal Pod Autoscaler (HPA):

kubectl autoscale deployment flask-app --cpu-percent=50 --min=2 --max=10

This ensures Kubernetes automatically adjusts the number of replicas based on CPU utilization.

Conclusion

By containerizing Flask with Docker and deploying it on Kubernetes, you ensure scalability, portability, and high availability. Kubernetes orchestrates deployments, load balancing, and auto-scaling, making it a robust choice for production-ready Flask applications.

Start scaling your Flask applications today! 🚀