How to upgrade a Python application with Kubernetes?

Table of Contents

• Introduction
• Performing a Rolling Update in Kubernetes
  • Step 1: Update the Deployment Manifest
  • Step 2: Apply the Changes
  • Step 3: Monitor the Update
• Canary Deployment for Incremental Testing
  • Step 4: Create a Canary Deployment
  • Step 5: Set Up Traffic Splitting
  • Step 6: Monitor Canary Performance
• Blue-Green Deployment for Zero-Downtime Upgrades
  • Step 7: Deploy the New Version (Green)
  • Step 8: Switch Traffic to the New Version
  • Step 9: Delete the Old Version (Blue)
• Conclusion

Introduction

Upgrading a Python application in Kubernetes involves deploying a new version of the application while ensuring minimal disruption to users. Kubernetes provides several deployment strategies to safely upgrade applications, such as rolling updates, canary deployments, and blue-green deployments. This guide walks through the steps to upgrade your Python application in Kubernetes using these methods.

Performing a Rolling Update in Kubernetes

A rolling update is the default deployment strategy in Kubernetes, where the new version of the application is gradually deployed by replacing old pods with new ones.

Step 1: Update the Deployment Manifest

To perform a rolling update, you need to modify your deployment configuration (YAML file) to point to the new version of the Python application.

In this example, we change the image tag to the new version (my-python-app:v2). This is the key to upgrading your Python application.

Step 2: Apply the Changes

Once the deployment manifest has been updated, apply the changes to your Kubernetes cluster:

Kubernetes will automatically start a rolling update. It gradually replaces the old pods with new ones, ensuring that there is always a certain number of pods running to avoid downtime.

Step 3: Monitor the Update

You can monitor the status of the rolling update using:

If something goes wrong, you can roll back the deployment:

This command reverts the deployment to the previous version, ensuring the stability of the application.

Canary Deployment for Incremental Testing

Canary deployments allow you to release the new version of the Python application to a subset of users for testing, while the majority of users continue using the old version.

Step 4: Create a Canary Deployment

To perform a canary deployment, create a second deployment for the canary version of your application with fewer replicas.

This deployment uses the new version (my-python-app:v2) but serves only a small portion of traffic by having fewer replicas.

Step 5: Set Up Traffic Splitting

To manage traffic distribution between the old and canary deployments, you can use a service mesh like Istio or an Ingress controller with traffic-splitting capabilities.

For example, using Istio, you can create a VirtualService to split traffic between the old and new versions:

This setup directs 10% of traffic to the new version (v2) and 90% to the old version (v1). You can adjust these percentages as needed.

Step 6: Monitor Canary Performance

Monitor the canary deployment’s performance and stability. If successful, you can gradually increase the traffic to the canary version until all users are on the new version.

Blue-Green Deployment for Zero-Downtime Upgrades

Blue-green deployment is another strategy for upgrading your Python application with zero downtime. You deploy the new version alongside the old one and switch traffic to the new version once it's stable.

Step 7: Deploy the New Version (Green)

First, create a separate deployment for the new version of your Python app:

This deployment runs the new version (my-python-app:v2) of your application in parallel with the old version.

Step 8: Switch Traffic to the New Version

Once the new version (green) is deployed and tested, switch the traffic from the old version (blue) to the new version using a Kubernetes service.

This service will now route traffic to the new version of your Python application.

Step 9: Delete the Old Version (Blue)

Once the traffic is successfully switched, and the new version is stable, you can safely delete the old version:

Now, your Python application is upgraded using a blue-green deployment.

Conclusion

Upgrading a Python application in Kubernetes can be done efficiently with minimal disruption by using strategies such as rolling updates, canary deployments, or blue-green deployments. Each method has its own advantages depending on the level of control, testing, and traffic management you require. Kubernetes' flexibility makes it easier to ensure seamless updates to your Python app with reduced risks and downtime.