Skip to main content

Kubernetes Cluster

Introduction​

A Kubernetes cluster is the foundation of Kubernetes architecture - a set of machines (physical or virtual) that work together to run containerized applications. These machines form a unified system that handles the deployment, scaling, and management of application containers.

Think of a Kubernetes cluster as an orchestra where each musician (node) plays their part under the direction of a conductor (control plane) to create beautiful music (running your applications reliably).

Cluster Architecture​

A Kubernetes cluster consists of two main components:

  1. Control Plane (formerly known as Master): The brain of the cluster
  2. Nodes: The workers that run your applications

Let's visualize this architecture:

Control Plane Components​

The Control Plane is responsible for global decisions about the cluster (scheduling, responding to cluster events, etc.) and detecting/responding to cluster events.

Let's examine each component:

API Server​

The API server (kube-apiserver) is the front-end of the control plane, exposing the Kubernetes API. It processes RESTful requests and updates the corresponding objects in etcd.

# Example: Accessing the API server to get nodes
kubectl get nodes

Output:

NAME       STATUS   ROLES    AGE   VERSION
node-1     Ready    <none>   10d   v1.26.1
node-2     Ready    <none>   10d   v1.26.1
node-3     Ready    <none>   10d   v1.26.1

etcd​

etcd is a consistent and highly-available key-value store used as Kubernetes' backing store for all cluster data. It stores the entire state of the cluster.

Controller Manager​

The Controller Manager (kube-controller-manager) runs controller processes that regulate the state of the system. These controllers include:

  • Node Controller: Notices and responds when nodes go down
  • Replication Controller: Maintains the correct number of pods
  • Endpoints Controller: Populates the Endpoints object
  • Service Account & Token Controllers: Create default accounts and API access tokens

Scheduler​

The Scheduler (kube-scheduler) watches for newly created pods with no assigned node and selects a node for them to run on based on resource requirements, constraints, and other factors.

Worker Node Components​

Nodes are the machines where your applications run. Each node contains:

kubelet​

The kubelet is an agent that runs on each node, ensuring containers are running in a pod. It takes a set of PodSpecs and ensures the containers described are running and healthy.

kube-proxy​

The kube-proxy maintains network rules on nodes, allowing network communication to your pods from inside or outside the cluster.

Container Runtime​

The container runtime (like Docker, containerd, or CRI-O) is responsible for running containers.

Setting Up a Kubernetes Cluster​

There are several ways to set up a Kubernetes cluster, depending on your needs:

Local Development Cluster​

For learning and development, tools like Minikube or kind (Kubernetes IN Docker) are perfect:

# Installing and starting Minikube
minikube start --driver=docker

# Verify the cluster is running
kubectl get nodes

Output:

NAME       STATUS   ROLES    AGE   VERSION
minikube   Ready    master   45s   v1.26.1

Production Clusters​

For production environments, you might use:

  1. Managed Kubernetes Services:

    • Amazon EKS (Elastic Kubernetes Service)
    • Google GKE (Google Kubernetes Engine)
    • Microsoft AKS (Azure Kubernetes Service)

  2. Self-managed Kubernetes:

    • Using kubeadm to bootstrap a cluster
    • Installing from scratch

Here's a simplified example of using kubeadm to initialize a cluster:

# On the control plane node
sudo kubeadm init --pod-network-cidr=10.244.0.0/16

# The output will provide a command to join worker nodes

Interacting with a Kubernetes Cluster​

The primary tool for interacting with a Kubernetes cluster is kubectl, the Kubernetes command-line tool.

Basic kubectl Commands​

# Get cluster information
kubectl cluster-info

# List all nodes
kubectl get nodes

# List all pods in all namespaces
kubectl get pods --all-namespaces

# Create resources from a YAML file
kubectl apply -f my-deployment.yaml

# Delete resources
kubectl delete deployment my-deployment

Deploying an Application to the Cluster​

Let's deploy a simple web application to our Kubernetes cluster:

  1. Create a deployment YAML file named web-app.yaml:
apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: web
  template:
    metadata:
      labels:
        app: web
    spec:
      containers:
      - name: web-app
        image: nginx:latest
        ports:
        - containerPort: 80
---
apiVersion: v1
kind: Service
metadata:
  name: web-app-service
spec:
  selector:
    app: web
  ports:
  - port: 80
    targetPort: 80
  type: LoadBalancer
  1. Apply the configuration:
kubectl apply -f web-app.yaml
  1. Check the deployment status:
kubectl get deployments

Output:

NAME      READY   UP-TO-DATE   AVAILABLE   AGE
web-app   3/3     3            3           45s
  1. Check the created pods:
kubectl get pods

Output:

NAME                      READY   STATUS    RESTARTS   AGE
web-app-5d4bbc7865-2xjpf  1/1     Running   0          50s
web-app-5d4bbc7865-9lknm  1/1     Running   0          50s
web-app-5d4bbc7865-tqd6k  1/1     Running   0          50s
  1. Access the service:
kubectl get services

Output:

NAME              TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)        AGE
kubernetes        ClusterIP      10.96.0.1       <none>        443/TCP        10d
web-app-service   LoadBalancer   10.96.45.123    <pending>     80:31234/TCP   55s

For Minikube, you can access the service with:

minikube service web-app-service

Namespaces​

Kubernetes supports multiple virtual clusters backed by the same physical cluster. These virtual clusters are called namespaces.

# List all namespaces
kubectl get namespaces

# Create a namespace
kubectl create namespace development

# Deploy resources to a specific namespace
kubectl apply -f web-app.yaml -n development

Cluster Scaling​

Kubernetes clusters can be scaled in two ways:

  1. Scaling the number of pods (application scaling):
# Scale deployment to 5 replicas
kubectl scale deployment web-app --replicas=5
  1. Scaling the number of nodes (cluster scaling):

For managed Kubernetes services, this is often as simple as adjusting a setting in the cloud provider console. For self-managed clusters, you would add or remove physical or virtual machines.

Cluster Health and Monitoring​

Monitoring your cluster is crucial for maintaining healthy applications:

# Get cluster events
kubectl get events

# Get detailed information about a specific node
kubectl describe node node-1

# Get logs from a pod
kubectl logs web-app-5d4bbc7865-2xjpf

For comprehensive monitoring, tools like Prometheus and Grafana are commonly used with Kubernetes.

Summary​

A Kubernetes cluster provides the foundation for running containerized applications at scale. It consists of a control plane that manages the cluster and worker nodes that run your applications.

Key concepts we've covered:

  • Cluster architecture (control plane and nodes)
  • Components of the control plane and worker nodes
  • Setting up a Kubernetes cluster
  • Interacting with the cluster using kubectl
  • Deploying applications to the cluster
  • Scaling applications and the cluster
  • Monitoring cluster health

Exercises for Practice​

  1. Install Minikube on your local machine and start a cluster.
  2. Deploy a simple application of your choice to the cluster.
  3. Scale the deployment up and down and observe the changes.
  4. Create a new namespace and deploy the same application in that namespace.
  5. Deliberately crash one of your pods and observe how Kubernetes handles it.

Additional Resources​

💡 Found a typo or mistake? Click "Edit this page" to suggest a correction. Your feedback is greatly appreciated!