Fullstack Python: Deploying Flask with Docker and Google Kubernetes Engine

Cloud-native development has made deploying scalable web applications easier than ever. This blog post will guide you through deploying a fullstack Python Flask application with Docker on Google Kubernetes Engine (GKE)—a managed Kubernetes service on Google Cloud Platform (GCP) that automates deployment, scaling, and management of containerized applications.


Why Use Docker and GKE for Flask?

Consistency: Docker ensures your Flask app behaves the same in development, testing, and production.

Scalability: GKE dynamically scales your application based on demand, giving you flexibility and cost efficiency.

Reliability: Kubernetes handles service discovery, self-healing, and rolling updates, reducing downtime and manual intervention.


1. Containerizing Your Flask Application

Begin by creating a Dockerfile in your Flask project:


text

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .

RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 8080

CMD ["flask", "run", "--host=0.0.0.0", "--port=8080"]

This script installs your dependencies, copies your application code, and launches the Flask server on port 8080, which is standard for running containers in GCP.


Build and tag your image:


bash

docker build -t gcr.io/[PROJECT_ID]/flask-app:latest .

Push your image to Google Container Registry or Artifact Registry:


bash

gcloud auth configure-docker

docker push gcr.io/[PROJECT_ID]/flask-app:latest


2. Setting Up the Kubernetes Cluster on GKE

Create a cluster with the GCP CLI:


bash

gcloud container clusters create flask-cluster --num-nodes=3 --zone=[COMPUTE_ZONE]

gcloud container clusters get-credentials flask-cluster --zone=[COMPUTE_ZONE]

These commands create a cluster and configure your kubectl context.


3. Writing Kubernetes Manifests

Deployment YAML (deployment.yaml):


text

apiVersion: apps/v1

kind: Deployment

metadata:

  name: flask-app

spec:

  replicas: 3

  selector:

    matchLabels:

      app: flask

  template:

    metadata:

      labels:

        app: flask

    spec:

      containers:

      - name: flask

        image: gcr.io/[PROJECT_ID]/flask-app:latest

        ports:

        - containerPort: 8080

Service YAML (service.yaml):


text

apiVersion: v1

kind: Service

metadata:

  name: flask-service

spec:

  type: LoadBalancer

  selector:

    app: flask

  ports:

    - protocol: TCP

      port: 80

      targetPort: 8080

The LoadBalancer service exposes your app on a public IP for external access.


4. Deploying to GKE

Apply your configurations:


bash

kubectl apply -f deployment.yaml

kubectl apply -f service.yaml

Monitor deployment status:


bash

kubectl get pods

kubectl get services

When the external IP is provisioned, you can access your Flask app from anywhere.


5. Automate with CI/CD

Integrate Google Cloud Build to automate your build and deployment process. Configure triggers to re-build and deploy on every push to your main branch, ensuring fast iteration and rollback capability.


Conclusion

With Docker and Google Kubernetes Engine, deploying Flask applications at scale becomes simple, consistent, and reliable. Embrace infrastructure-as-code, automation, and managed services to focus more on innovating and less on operations. Happy deploying

Learn FullStack Python Training

Read More : Fullstack Flask: Implementing Multi-Cloud Deployment for High Availability

Read More : Deploying Flask Apps with Kubernetes on Google Cloud

Read More : Fullstack Flask: Scaling Microservices with Kubernetes Horizontal Pod Autoscaling

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