Validate our deployment on GKE

In this guide, you’ll learn how to confirm that your containerized application is running correctly on Google Kubernetes Engine (GKE). We cover both the Google Cloud Console and the kubectl command-line interface.

1. Verifying via the GCP Console

Navigate to Kubernetes Engine > Clusters in the Google Cloud Console.
Select your cluster (for example, gcp-devops-project).
In the cluster overview, click Workloads in the left menu to see all Deployments.
Choose the correct namespace (e.g., gcp-devops-prod). You’ll see your Deployment, the number of ready pods, and its status.
Click the Deployment name for detailed metrics on CPU, memory, and more.

The image shows a Google Cloud Platform Kubernetes Engine dashboard displaying deployment details, including CPU, memory, and disk usage metrics. It also includes options for configuration and documentation links on the right side.

Warning

Avoid editing your Deployment manifest directly in the Console. Since changes aren’t tracked in your GitHub-backed CI/CD pipeline, this can lead to configuration drift.

Editing via the Console

When you click Edit, you’ll see a client-side apply patch like this:

apiVersion: apps/v1
manager: kubectl-client-side-apply
operation: Update
time: "2023-02-25T15:21:43Z"
fieldType: FieldsV1
fieldsV1:
  f:metadata:
    f:annotations:
      f:kubectl.kubernetes.io/last-applied-configuration: {}
  f:spec:
    f:template:
      f:spec:
        f:containers:
        - f:name: my-app
          f:image: gcr.io/my-project/my-image:latest
          f:ports:
          - f:containerPort: 5000
            f:protocol: TCP
          f:resources:
            f:requests:
              f:cpu: "100m"
              f:memory: "128Mi"

The image shows a Google Cloud Platform interface for editing a Kubernetes Engine deployment, displaying YAML configuration details.

Viewing Logs, Revisions, and Observability

Logs: Monitor container output filtered by severity or time.
Revision History: Track Deployment rollouts and rollbacks.
Observability: Access extended metrics, charts, and health checks.

The image shows a Google Cloud Platform Kubernetes Engine dashboard displaying deployment details and observability metrics for a project named "gcp-devops-gke." It includes sections for CPU and memory usage, container restarts, and documentation links.

Exposure & Actions

Action	Description
Scale Replicas	Increase or decrease the number of pod replicas.
Expose via LoadBalancer	Create a Service of type LoadBalancer for external access.
Configure Ingress	Set up Ingress rules for host/path-based routing.
Edit Configuration	Modify your Deployment manifest via CI/CD to avoid drift.

Note

Because no Service resource exists yet, the Exposure section will be empty.
We recommend using your CI/CD pipeline for all changes to ensure auditability.

2. Verifying via the Command Line

a. Configure `kubectl` Credentials

Open Cloud Shell (or your local terminal) and run:

gcloud container clusters get-credentials gcp-devops-project \
    --zone us-central1-c \
    --project kodekloud-gcp-training

This command retrieves cluster credentials and configures kubectl to use your GKE context.

b. Check Pods in the Default Namespace

By default, kubectl queries the default namespace:

kubectl get pods
# no resources found in default namespace.

Note

Most GKE applications run in a custom namespace, so the default namespace is often empty.

c. Check Pods in Your Namespace

List all pods in gcp-devops-prod:

kubectl get pods -n gcp-devops-prod

Example output:

NAME                             READY   STATUS    RESTARTS   AGE
gcp-devops-gke-7c7b74f68-tnz8z   1/1     Running   0          16m

From here, you can dig deeper:

kubectl logs gcp-devops-gke-7c7b74f68-tnz8z -n gcp-devops-prod
kubectl describe pod gcp-devops-gke-7c7b74f68-tnz8z -n gcp-devops-prod

Next Steps

Once your Deployment is validated:

Create a Service to expose your application externally.
Configure Ingress rules for HTTP routing and TLS termination.
Integrate monitoring and alerts with Cloud Monitoring and Cloud Logging.

Links and References

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