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In this tutorial, we’ll extend an existing Kubernetes Deployment by adding a Service of type LoadBalancer to expose your application on Google Kubernetes Engine (GKE). You’ll learn how to update your manifest, trigger a Cloud Build, and verify the Service in the GKE console.
The image shows a Google Cloud Platform interface displaying a Kubernetes Engine dashboard with details of a cluster named "gcp-devops-project" located in "us-central1-c" with 3 nodes, 6 vCPUs, and 12 GB of total memory.

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1. Create a New Git Branch

First, sync your main branch and create a feature branch for the Service manifest:

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2. Add the Service to gke.yaml

Open your gke.yaml and append the following Service definition beneath the existing Deployment:
If you’re targeting a production namespace, replace namespace: default with your environment (e.g., gcp-devops-prod).
Using LoadBalancer provisions a cloud load balancer and may incur additional costs.

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Kubernetes Resources Overview

Save your changes, then commit and push:

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3. Open a Pull Request & Trigger Cloud Build

  1. Go to your GitHub repository.
  2. Create a PR from feature/add-loadbalancer-service into main.
  3. Merge the PR to kick off the Cloud Build trigger configured on main.

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4. Verify the Build in Cloud Build

In the Google Cloud Console – Cloud Build, you’ll see logs for:
  • Building the Docker image
  • Pushing to Container Registry
  • Deploying to GKE
A successful build ends with a green checkmark:
The image shows a Google Cloud Build interface with a successful build summary, detailing steps and logs for a project deployment. The build includes Docker and GKE deployment steps.

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5. Inspect the Service in GKE

Navigate to Kubernetes Engine > Services & Ingress in the Cloud Console. You should see your gcp-devops-gke-service with an external IP:
The image shows a Google Cloud Kubernetes Engine dashboard displaying service details, including load balancer information, deployments, and serving pods. It also provides suggested next steps for managing the deployment.
Click the external IP to access your application. To monitor resource usage, switch to the Metrics tab:
The image shows a Google Cloud Platform Kubernetes Engine service details page, displaying metrics for CPU, memory, and disk usage, along with load balancer information and suggested next steps.
Congratulations! You’ve successfully deployed and exposed your application on GKE using a LoadBalancer Service—without modifying cloudbuild.yaml. Stay tuned for the next lesson.

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