# Test

In this section, you will try to configure and deploy a "problematic" workload to the cluster with the K8sGPT blueprint. We will then verify whether it was able to identify the issue and assist the user with a recommendation for resolution.

---

## Step 1: Create Namespace

In this step, you will create a namespace for the a sample workload that will be published to the cluster. The sample workload will be used to see how K8sGPT can scan and identify issues.

- Select Infrastructure -> Namespaces
- Click **New Namespace**
- Enter the name **sample-workload**
- Select **wizard** for the type
- Click **Save**

- Click **Save & Go to Placement**

- Select the cluster where the K8sGPT operator is installed
- Click **Save & Go to Publish**

- Click **Publish**

- Click **Exit**

---

## Step 2: Create Workload

In this step, you will create a workload which will be used to test the K8sGPT operator.

- Save the below YAML to a file named **sample-workload.yaml**

Note: This service contains a selector that does not map to an existing pod. This will be picked up by K8sGPT.

```yaml
apiVersion: v1
kind: Service
metadata:
  name: myapp
spec:
  type: ClusterIP
  ports:
  - name: myapp
    protocol: TCP
    port: 80
  selector:
    app: nonexistentapp
```

- Select Applications -> Workloads
- Click **New Workload -> Create New Workload**
- Enter a name for the workload
- Select **K8s YAML** for the package type
- Select **Upload files manually**
- Select **sample-workload** for the namespace
- Click **Continue**

- Click **Choose File** and select the previously saved sample-workload.yaml file
- Click **Save & Go to Placement**

- Select the cluster where the K8sGPT operator is installed
- Click **Save & Go to Publish**

- Click **Publish**

- Click **Exit**

---

## Step 3: View K8sGPT Results

In this step, you will view the results of the K8sGPT Operator.

- Execute the following command:

```
kubectl get results -A
```

You will see a result like the following providing details of the issue and possible resolution in simple terms.

```json
{
    "apiVersion": "v1",
    "items": [
        {
            "apiVersion": "core.k8sgpt.ai/v1alpha1",
            "kind": "Result",
            "metadata": {
                "creationTimestamp": "2023-05-09T19:55:28Z",
                "generation": 1,
                "name": "sampleworkloadmyapp",
                "namespace": "kube-system",
                "resourceVersion": "54316",
                "uid": "76646698-a0f1-4632-9aad-2b0de48b2b44"
            },
            "spec": {
                "details": "The service is looking for endpoints with the label \"app=nonexistentapp\", but it cannot find any. \n\nThe solution is to make sure that the label \"app=nonexistentapp\" is correctly assigned to one or more pods that are running the application. This can be done by checking the labels of the pods using the \"kubectl get pods --show-labels\" command and verifying that the \"app=nonexistentapp\" label is present. If it is not present, add it using the \"kubectl label pods <pod-name> app=nonexistentapp\" command. Once the label is correctly assigned, the endpoints should be discovered by the service and the error should be resolved.",
                "error": [
                    {
                        "sensitive": [
                            {
                                "masked": "XUBj",
                                "unmasked": "app"
                            },
                            {
                                "masked": "azUvWklzT0pEUU1lMjA=",
                                "unmasked": "nonexistentapp"
                            }
                        ],
                        "text": "Service has no endpoints, expected label app=nonexistentapp"
                    }
                ],
                "kind": "Service",
                "name": "sample-workload/myapp",
                "parentObject": ""
            }
        }
    ],
    "kind": "List",
    "metadata": {
        "resourceVersion": ""
    }
}
```

---

## Recap

Congratulations! You have successfully deployed the K8sGPT Operator on your managed Kubernetes cluster as an add-on in a custom cluster blueprint. You then tested the efficacy of the K8sGPT operator with a broken/misconfigured workload.