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README.md

title description
GCP Google Kubernetes Engine Autopilot Cluster
Implement GCP Google Kubernetes Engine GKE Autopilot Cluster

Step-01: Introduction

  • Create GKE Autopilot Cluster
  • Understand in detail about GKE Autopilot cluster

Step-02: Pre-requisite: Verify if Cloud NAT Gateway created

  • Verify if Cloud NAT Gateway created in Region:us-central1 where you are planning to create GKE Autopilot Private Cluster
  • This is required for Workload in Private subnets to connect to Internet.
  • Primarily to Connect to Docker Hub to pull the Docker Images
  • Go to Network Services -> Cloud NAT

Step-03: Create GKE Autopilot Private Cluster

  • Go to Kubernetes Engine -> Clusters -> CREATE
  • Create Cluster -> GKE Autopilot -> CONFIGURE
  • Name: autopilot-cluster-private-1
  • Region: us-central1
  • Network access: Private Cluster
  • Access control plane using its external IP address: CHECK
  • Control plane ip range: 172.18.0.0/28
  • Enable control plane authorized networks: CHECK
  • Authorized networks:
    • Name: internet-access
    • Network: 0.0.0.0/0
    • Click on DONE
  • Network: default (LEAVE TO DEFAULTS)
  • Node subnet: default (LEAVE TO DEFAULTS)
  • Cluster default pod address range: /17 (LEAVE TO DEFAULTS)
  • Service Address range: /22 (LEAVE TO DEFAULTS)
  • Release Channel: Regular Channel (Default)
  • REST ALL LEAVE TO DEFAULTS
  • Click on CREATE

Step-04: Configure kubectl for kubeconfig

# Configure kubectl for kubeconfig
gcloud container clusters get-credentials CLUSTER-NAME --region REGION --project PROJECT-NAME

# Replace values CLUSTER-NAME, REGION, PROJECT-NAME
gcloud container clusters get-credentials autopilot-cluster-private-1 --region us-central1 --project kdaida123

# List Kubernetes Nodes
kubectl get nodes
kubectl get nodes -o wide

Step-05: Review Kubernetes Manifests

Step-05-01: 01-kubernetes-deployment.yaml

apiVersion: apps/v1
kind: Deployment 
metadata: #Dictionary
  name: myapp1-deployment
spec: # Dictionary
  replicas: 5 
  selector:
    matchLabels:
      app: myapp1
  template:  
    metadata: # Dictionary
      name: myapp1-pod
      labels: # Dictionary
        app: myapp1  # Key value pairs
    spec:
      containers: # List
        - name: myapp1-container
          image: stacksimplify/kubenginx:1.0.0
          ports: 
            - containerPort: 80  
          resources:
            requests:
              memory: "128Mi" # 128 MebiByte is equal to 135 Megabyte (MB)
              cpu: "200m" # `m` means milliCPU
            limits:
              memory: "256Mi"
              cpu: "400m"  # 1000m is equal to 1 VCPU core

Step-05-02: 02-kubernetes-loadbalancer-service.yaml

apiVersion: v1
kind: Service 
metadata:
  name: myapp1-lb-service
spec:
  type: LoadBalancer # ClusterIp, # NodePort
  selector:
    app: myapp1
  ports: 
    - name: http
      port: 80 # Service Port
      targetPort: 80 # Container Port

Step-06: Deploy Kubernetes Manifests

# Deploy Kubernetes Manifests
kubectl apply -f kube-manifests

# List Deployments
kubectl get deploy

# List Pods
kubectl get pods

# List Services
kubectl get svc

# Access Application
https://<EXTERNAL-IP-OF-GET-SERVICE-OUTPUT>

Step-07: Scale your Application

# Scale your Application
kubectl scale --replicas=15 deployment/myapp1-deployment

# List Pods
kubectl get pods

# List Nodes
kubectl get nodes

Step-08: Clean-Up

# Delete Kubernetes Resources
kubectl delete -f kube-manifests

# Delete GKE Autopilot Cluster 
# NOTE: Dont delete this cluster, as we are going to use this in next demo.
Go to Kubernetes Engine > Clusters -> autopilot-cluster-private-1 -> DELETE

References