This tutorial will guide you through setting up a K3s cluster with one master node and two worker nodes, deploying a simple "Hello-World" application, and utilizing various kubectl commands to manage and inspect Kubernetes resources.

Prerequisites

Three Linux machines (or VMs) with access to each other.
Basic understanding of Linux commands and Kubernetes concepts.

Step 1: Installing K3s

Install K3s on the Master Node

SSH into the Master Node:
```
ssh user@master-node-ip
```

Install K3s:

curl -sfL https://get.k3s.io |  INSTALL_K3S_VERSION=v1.27.12+k3s1 sh -s - --disable traefik --write-kubeconfig-mode 644 --node-name <NODE_NAME>

The command installs K3s version v1.27.12+k3s1 on a node named <NODE_NAME>, disables the default Traefik ingress controller, and sets the permissions for the kubeconfig file to 644.

Verify Installation:
```
kubectl get nodes
```
You should see the master node listed.

Install K3s on the Worker Nodes

SSH into each Worker Node:
```
ssh user@worker-node-ip
```

Get the token from the master node

cat /var/lib/rancher/k3s/server/node-token

Install K3s on Worker Nodes:

On each worker node, run:

curl -sfL https://get.k3s.io | INSTALL_K3S_VERSION=v1.27.12+k3s1  K3S_NODE_NAME=YOUR_NODE_NAME K3S_URL=https://master-node-ip:6443 K3S_TOKEN=YOUR_NODE_TOKEN sh -

You can find the YOUR_NODE_TOKEN value on the master node at /var/lib/rancher/k3s/server/node-token.

Verify Worker Nodes Join the Cluster:

On the master node, run:
```
kubectl get nodes
```
You should see all nodes (master and workers) listed.

Step 2: Verifying Node Status

To check the status of the nodes:

kubectl get nodes

You should see the master and worker nodes with the status Ready.

Step 3: Creating a Pod

Create a simple pod to ensure everything is working correctly.

Create a YAML file named hello-world-pod.yaml:

apiVersion: v1
kind: Pod
metadata:
name: hello-world
spec:
containers:
    - name: hello
    image: busybox  # Replace with your desired Docker image

Apply the YAML file to create the pod:
```
kubectl apply -f hello-world-pod.yaml
```
Verify the Pod is Running:
```
kubectl get pods
```
Describe the Pod:
```
kubectl describe pod hello-world
```

Step 4: Deleting the Pod

To delete the pod:

kubectl delete pod hello-world

Step 5: Deploying the Application

Creating a namespace

A namespace in Kubernetes is used to organize and manage resources within a cluster, providing isolation and a way to divide cluster resources among multiple users or teams.

The commands used in this process are:

View Existing Namespaces:

   kubectl get namespaces

   kubectl get ns

Create a New Namespace:

   kubectl create namespace demo

Verify the Creation of the Namespace:

   kubectl get namespaces

This process ensures you have successfully created the demo namespace and confirmed its presence in your Kubernetes cluster.

Deployment YAML File

A deployment in Kubernetes is a controller that manages the creation, scaling, and updating of a set of replicated Pods, ensuring the desired state of an application is maintained.

Create a file named hello-world-deployment.yaml with the following content:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: hello-world
  namespace: demo
spec:
  replicas: 5
  selector:
    matchLabels:
      app: hello-world
  template:
    metadata:
      labels:
        app: hello-world
    spec:
      containers:
        - name: hello-world
          image: bashofmann/rancher-demo:1.0.0
          imagePullPolicy: Always
          resources:
            requests:
              memory: "12Mi"
              cpu: "2m"
          ports:
            - containerPort: 8080
              name: web
              protocol: TCP
          env:
            - name: COW_COLOR
              value: purple
          readinessProbe:
            httpGet:
              path: /
              port: web
          livenessProbe:
            httpGet:
              path: /
              port: web

Explanation of the YAML File

apiVersion: Specifies the version of the Kubernetes API to use.
kind: Specifies the type of resource being created (Deployment).
metadata: Contains data that helps uniquely identify the object, including a name and namespace.
spec: Defines the desired state of the Deployment.
- replicas: Number of pod replicas to run.
- selector: Defines how the Deployment finds which Pods to manage.
- template: Defines the Pods that will be created.
- metadata: Labels for the Pods.
- spec: Specification of the containers within the Pods.
  - containers: List of containers in the Pod.
  - name: Name of the container.
  - image: Docker image to use for the container.
  - imagePullPolicy: Policy to use for image pulls.
  - ports: Ports to expose from the container.
  - env: Environment variables for the container.
  - readinessProbe: Defines a probe to check if the container is ready.
  - livenessProbe: Defines a probe to check if the container is alive.

This is a Go demo application used for demonstrating Kubernetes and load balancing.

It will create a colored icon for each replica within a ReplicaSet and indicate which one most recently served a response. Replicas which haven't been heard from will begin to fade out, until after a configurable number of seconds they will disappear.

For more information on using the container image, please click here.

Apply the Deployment

Apply the YAML file to create the Deployment:

kubectl apply -f hello-world-deployment.yaml

Verify the Deployment and Pods:

kubectl get deployments -n demo
kubectl get pods -n demo

Check the ReplicaSets:
```
kubectl get replicaset -n demo
```
For more information on replicas:
```
kubectl get pods -n demo -o wide
```
-o wide: Provides additional details about each pod, such as the node it's running on, the IP address, the container images, and more.

See the pods metrics

kubectl top pods -n demo

For nodes

kubectl top nodes

Step 6: Exposing the Application

Create a service to expose the Deployment.

A service in Kubernetes provides a stable network endpoint to access a set of pods, ensuring consistent access despite pod restarts or IP changes.

Types of Services:

ClusterIP: Exposes the service on an internal IP within the cluster, making it accessible only within the cluster.
NodePort: Exposes the service on each node's IP at a static port, making it accessible from outside the cluster using <NodeIP>:<NodePort>.
LoadBalancer: Exposes the service externally using a cloud provider's load balancer.
ExternalName: Maps the service to the contents of the externalName field (e.g., foo.bar.example.com), creating a DNS alias.

Create a ClusterIP Service:

kubectl expose deployment hello-world --type=ClusterIP --name=hello-world-service --port=8080 --target-port=8080 -n demo

- **expose deployment hello-world**: Creates a service to expose the deployment named `hello-world`.
- **`--type=ClusterIP`**: Specifies the type of service to create. `ClusterIP` means the service will be accessible only within the cluster.
- **`--name=hello-world-service`**: Assigns the name `hello-world-service` to the newly created service.
- **`--port=8080`**: Defines the port on which the service will be exposed.
- **`--target-port=8080`**: Specifies the port on the pod that the service should forward traffic to.
- **`-n demo`**: Limits the scope of the command to the `demo` namespace.

Verify the Service:
```
kubectl get svc -n demo
```

Step 7: Accessing the Application

To access the application from your local machine, use kubectl port-forward:

kubectl port-forward svc/hello-world-service 8080:8080 -n demo --address node_address

Open your browser and go to http://node_address:8080 to see the application in action.

Create a service of type LoadBalancer

kubectl expose deployment hello-world --type=LoadBalancer --name=hello-world-service-loadbalancing --port=8080 --target-port=8080 -n demo

Open your browser and go to http://any_node_address:PORT to see the application in action.
PORT here is generated by default, see the image below.

Application on the browser

Step 8: Inspecting Resources

Describing a Pod

To get detailed information about a specific pod:

kubectl describe pod <pod-name> -n demo

Describing a Deployment

To get detailed information about the deployment:

kubectl describe deployment hello-world -n demo

Viewing Pod Logs

To view the logs of a pod:

kubectl logs -f <pod-name> -n demo

Step 9: Cleaning Up

To delete all the resources created:

kubectl delete namespace demo

Conclusion

You've successfully set up a K3s cluster, deployed a simple application, and used various kubectl commands to manage and inspect Kubernetes resources. This foundation will help you explore Kubernetes further and leverage its capabilities.

Bonus

Scaling and Deploying a Horizontal Pod Autoscaler in Kubernetes

Step 1: Scaling Up/Down Manually

You can manually scale the number of replicas in a deployment using the kubectl scale command.

Scale Up

To scale the hello-world deployment to 10 replicas:

kubectl scale deployment hello-world --replicas=10 -n demo

Scale Down

To scale the hello-world deployment back down to 3 replicas:

kubectl scale deployment hello-world --replicas=3 -n demo

Step 2: Deploying a Horizontal Pod Autoscaler (HPA)

The Horizontal Pod Autoscaler automatically adjusts the number of pod replicas in a deployment based on observed CPU utilization (or other select metrics).

Prerequisites

Ensure that the Metrics Server is deployed in your cluster. The Metrics Server collects resource metrics from the Kubernetes nodes and pods and is required for HPA to function.

To install the Metrics Server:

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml

Deploy HPA for the `hello-world` Deployment

Create the HPA

Create the file hello-world-hpa.yaml

    apiVersion: autoscaling/v2
    kind: HorizontalPodAutoscaler
    metadata:
      name: hello-world-autoscaler
      namespace: demo
    spec:
      scaleTargetRef:
        apiVersion: apps/v1
        kind: Deployment
        name: hello-world
      minReplicas: 5
      maxReplicas: 20
      metrics:
      - type: Resource
        resource:
          name: cpu
          target:
            type: Utilization
            averageUtilization: 70
      behavior:
        scaleUp:
          stabilizationWindowSeconds: 10
          policies:
          - type: Pods
            value: 1
            periodSeconds: 5
        scaleDown:
          stabilizationWindowSeconds: 10
          policies:
          - type: Pods
            value: 1
            periodSeconds: 5

Apply

   kubectl apply -f hello-world-hpa.yaml

This command sets up an HPA for the hello-world deployment that:

Targets 70% CPU utilization.
Scales the number of pod replicas between 1 and 10 based on the observed CPU utilization.

Verify the HPA

To check the status of the HPA:

   kubectl get hpa -n demo

The output should show something like this:

   NAME          REFERENCE                TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
   hello-world   Deployment/hello-world   10%/50%   1         10        3          5m

Step 3: Testing the HPA

To test the HPA, you can simulate a load on the hello-world pods to observe the scaling behavior. Here's how you can do it:

Run a Load Generator Pod

Create a pod that continuously sends requests to the hello-world service. Create a file named load-generator.yaml with the following content:

   apiVersion: v1
   kind: Pod
   metadata:
     name: load-generator
     namespace: demo
   spec:
     containers:
     - name: busybox
       image: busybox
       args:
       - /bin/sh
       - -c
       - |
         while true; do wget -q -O- http://NODE_IP_ADDRESS:8080; done

Apply this YAML file:

   kubectl apply -f load-generator.yaml

Monitor the HPA

Keep an eye on the HPA status to see how it adjusts the number of replicas:

   watch -n3 kubectl get hpa,pod -n demo -o wide

You should see the TARGETS column indicating higher CPU utilization, and the REPLICAS column increasing as the HPA scales up the deployment.

Stop the Load Generator

Once you've observed the scaling behavior, delete the load generator pod:

   kubectl delete pod load-generator -n demo

Summary

Manual Scaling: Use kubectl scale to manually adjust the number of replicas.
Horizontal Pod Autoscaler (HPA): Automatically adjusts the number of replicas based on CPU utilization (or other metrics).
Metrics Server: Required for HPA to function, it collects resource metrics.
Testing HPA: Use a load generator to simulate traffic and observe the HPA scaling behavior.

This guide helps you understand and implement both manual and automatic scaling in Kubernetes, ensuring your applications can handle varying loads efficiently.

Congratulations !!!

Why did the Kubernetes developer go broke?
Because he lost all his containers! 🤣

Kubernetes Tutorial: Setting Up a K3s Cluster with a Hello-World Deployment

Prerequisites

Step 1: Installing K3s

Install K3s on the Master Node

Install K3s on the Worker Nodes

Step 2: Verifying Node Status

Step 3: Creating a Pod

Step 4: Deleting the Pod

Step 5: Deploying the Application

Creating a namespace

Deployment YAML File

Explanation of the YAML File

Apply the Deployment

Step 6: Exposing the Application

Types of Services:

Step 7: Accessing the Application

Step 8: Inspecting Resources

Describing a Pod

Describing a Deployment

Viewing Pod Logs

Step 9: Cleaning Up

Conclusion

Bonus

Scaling and Deploying a Horizontal Pod Autoscaler in Kubernetes

Step 1: Scaling Up/Down Manually

Scale Up

Scale Down

Step 2: Deploying a Horizontal Pod Autoscaler (HPA)

Prerequisites

Deploy HPA for the hello-world Deployment

Step 3: Testing the HPA

Summary

Deploy HPA for the `hello-world` Deployment