Deploying Flask Apps with Kubernetes on Google Cloud

Deploying Flask applications using Kubernetes on Google Cloud Platform (GCP) combines the flexibility of Python web development with the robust scalability, high availability, and orchestration capabilities of Kubernetes, all powered by Google’s global infrastructure. Here’s a comprehensive guide to walk you from code to production in the cloud.


Why Choose Kubernetes on Google Cloud?

Google Kubernetes Engine (GKE) is a managed Kubernetes platform that greatly simplifies container management, scaling, and integration with other Google Cloud services. Using Kubernetes allows you to automate deployments, rollbacks, scaling, and monitoring, while Google Cloud provides world-class infrastructure, strong security, cost efficiency, and seamless integration with CI/CD tools.


Step-by-Step Deployment Workflow

Containerize Your Flask App

Begin by preparing your application for containerization. Create a Dockerfile that sets up Python, installs dependencies, copies your code, and exposes the required port. It might look like:


text

FROM python:3.9-slim

WORKDIR /app

COPY . .

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

EXPOSE 8080

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

Make sure your Flask app listens on the port expected by GCP (commonly 8080).


Build and Push the Docker Image

Use Docker to build your container image locally or with Google Cloud Build. Then, push it to Google Cloud Artifact Registry or Container Registry:


text

gcloud auth configure-docker

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

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

Set Up a GKE Cluster

Create a Kubernetes cluster using the Cloud Console or CLI:


text

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

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

This sets your local environment to interact with your new cluster.


Create Kubernetes Manifests

Write a deployment.yaml describing your app’s desired state:


text

apiVersion: apps/v1

kind: Deployment

metadata:

  name: flask-app

spec:

  replicas: 2

  selector:

    matchLabels:

      app: flask

  template:

    metadata:

      labels:

        app: flask

    spec:

      containers:

      - name: flask

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

        ports:

        - containerPort: 8080

Expose your app via a Kubernetes Service:


text

apiVersion: v1

kind: Service

metadata:

  name: flask-service

spec:

  type: LoadBalancer

  selector:

    app: flask

  ports:

  - protocol: TCP

    port: 80

    targetPort: 8080

Deploy and Monitor

Apply your manifests:


text

kubectl apply -f deployment.yaml

kubectl apply -f service.yaml

Monitor your pods and services:


text

kubectl get pods

kubectl get services

Once the external IP is provisioned, access your Flask app via the listed IP address.


Best Practices for Reliable Deployments

Leverage Auto-scaling: Enable Horizontal Pod Autoscaling for flexible scaling based on traffic.

Implement Resource Limits: Set CPU and memory limits to stabilize workloads and avoid resource contention.

Secure Your Cluster: Use Kubernetes network policies, GCP IAM roles, and secrets management for robust security.

Automate CI/CD: Integrate with Cloud Build and Cloud Source Repositories for automated, reproducible deployments.


Conclusion

Deploying Flask apps on Kubernetes via GCP gives you a future-proof, scalable, and production-grade solution for modern web services. By following these steps and harnessing best practices, you ensure your applications are reliable, secure, and ready for cloud-era workloads

Learn FullStack Python Training

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

Read More : Fullstack Python: Decentralized Authentication in Microservices with OAuth

Read More : Fullstack Python: Monitoring and Logging Microservices with ELK Stack

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