GCP Google Kubernetes Engine 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
http://<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¶

https://cloud.google.com/kubernetes-engine/docs/concepts/autopilot-overview#default_container_resource_requests
https://cloud.google.com/kubernetes-engine/quotas#limits_per_cluster