GKE Workload Optimization

“GKE Workload Optimization”

Salah satu dari banyak manfaat menggunakan Google Cloud adalah model penagihannya yang menagih Anda hanya untuk sumber daya yang Anda gunakan. Dengan mengingat hal itu, Anda tidak hanya harus mengalokasikan sumber daya dalam jumlah yang wajar untuk aplikasi dan infrastruktur Anda, tetapi juga memanfaatkannya secara efisien. Dengan GKE, ada sejumlah alat dan strategi yang tersedia untuk Anda yang dapat mengurangi penggunaan berbagai sumber daya dan layanan sekaligus meningkatkan ketersediaan aplikasi Anda.

Praktikum

Provision lab environment

• setting default zone

gcloud config set compute/zone us-central1-a

• buat 3 node cluster

gcloud container clusters create test-cluster --num-nodes=3  --enable-ip-alias

• buat manifest untuk gb-frontend pod

cat << EOF > gb_frontend_pod.yaml
apiVersion: v1
kind: Pod
metadata:
  labels:
    app: gb-frontend
  name: gb-frontend
spec:
    containers:
    - name: gb-frontend
      image: gcr.io/google-samples/gb-frontend:v5
      resources:
        requests:
          cpu: 100m
          memory: 256Mi
      ports:
      - containerPort: 80
EOF

• create manifest

kubectl apply -f gb_frontend_pod.yaml

Task 1. Container-native load balancing through ingress

• buat manifest cluster IP

cat << EOF > gb_frontend_cluster_ip.yaml
apiVersion: v1
kind: Service
metadata:
  name: gb-frontend-svc
  annotations:
    cloud.google.com/neg: '{"ingress": true}'
spec:
  type: ClusterIP
  selector:
    app: gb-frontend
  ports:
  - port: 80
    protocol: TCP
    targetPort: 80
EOF

• apply ke cluster

kubectl apply -f gb_frontend_cluster_ip.yaml

• Buat ingress untuk app

cat << EOF > gb_frontend_ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: gb-frontend-ingress
spec:
  defaultBackend:
    service:
      name: gb-frontend-svc
      port:
        number: 80
EOF

kubectl apply -f gb_frontend_ingress.yaml

• cek health status untuk backend service

BACKEND_SERVICE=$(gcloud compute backend-services list | grep NAME | cut -d ' ' -f2)

• get health status dari service

gcloud compute backend-services get-health $BACKEND_SERVICE --global

• retrieve

kubectl get ingress gb-frontend-ingress

Task 2. Load testing an application

• download docker image

gsutil -m cp -r gs://spls/gsp769/locust-image .

• build docker image

gcloud builds submit \
    --tag gcr.io/${GOOGLE_CLOUD_PROJECT}/locust-tasks:latest locust-image

• cek list docker image

gcloud container images list

• apply manifest

gsutil cp gs://spls/gsp769/locust_deploy_v2.yaml .
sed 's/${GOOGLE_CLOUD_PROJECT}/'$GOOGLE_CLOUD_PROJECT'/g' locust_deploy_v2.yaml | kubectl apply -f -

• cek IP external

kubectl get service locust-main

• cek web via -> [EXTERNAL_IP_ADDRESS]:8089

• Masukan 200 user , dan 20 hatch rate
• Click Start swarming
• Buka Navigation menu > Kubernetes Engine
• pilih workload
• klik gb-frontend pod

Task 3. Readiness and liveness probes

• buat demo

cat << EOF > liveness-demo.yaml
apiVersion: v1
kind: Pod
metadata:
  labels:
    demo: liveness-probe
  name: liveness-demo-pod
spec:
  containers:
  - name: liveness-demo-pod
    image: centos
    args:
    - /bin/sh
    - -c
    - touch /tmp/alive; sleep infinity
    livenessProbe:
      exec:
        command:
        - cat
        - /tmp/alive
      initialDelaySeconds: 5
      periodSeconds: 10
EOF

kubectl apply -f liveness-demo.yaml

• cek list pod

kubectl describe pod liveness-demo-pod

• delete file

kubectl exec liveness-demo-pod -- rm /tmp/alive

• cek pod event

kubectl describe pod liveness-demo-pod

Setting up a readiness probe

• buat single pod

cat << EOF > readiness-demo.yaml
apiVersion: v1
kind: Pod
metadata:
  labels:
    demo: readiness-probe
  name: readiness-demo-pod
spec:
  containers:
  - name: readiness-demo-pod
    image: nginx
    ports:
    - containerPort: 80
    readinessProbe:
      exec:
        command:
        - cat
        - /tmp/healthz
      initialDelaySeconds: 5
      periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: readiness-demo-svc
  labels:
    demo: readiness-probe
spec:
  type: LoadBalancer
  ports:
    - port: 80
      targetPort: 80
      protocol: TCP
  selector:
    demo: readiness-probe
EOF

kubectl apply -f readiness-demo.yaml

• cek service

kubectl get service readiness-demo-svc

• cek pod event

kubectl describe pod readiness-demo-pod

• generate file

kubectl exec readiness-demo-pod -- touch /tmp/healthz
kubectl describe pod readiness-demo-pod | grep ^Conditions -A 5

Task 4. Pod disruption budgets

• delete single app

kubectl delete pod gb-frontend

• edit jadi 5 replica

cat << EOF > gb_frontend_deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: gb-frontend
  labels:
    run: gb-frontend
spec:
  replicas: 5
  selector:
    matchLabels:
      run: gb-frontend
  template:
    metadata:
      labels:
        run: gb-frontend
    spec:
      containers:
        - name: gb-frontend
          image: gcr.io/google-samples/gb-frontend:v5
          resources:
            requests:
              cpu: 100m
              memory: 128Mi
          ports:
            - containerPort: 80
              protocol: TCP
EOF

kubectl apply -f gb_frontend_deployment.yaml

• drain node

for node in $(kubectl get nodes -l cloud.google.com/gke-nodepool=default-pool -o=name); do
  kubectl drain --force --ignore-daemonsets --grace-period=10 "$node";
done

• cek replica count

kubectl describe deployment gb-frontend | grep ^Replicas

• uncordoning node

for node in $(kubectl get nodes -l cloud.google.com/gke-nodepool=default-pool -o=name); do
  kubectl uncordon "$node";
done

kubectl describe deployment gb-frontend | grep ^Replicas

• buat pod disruption budget

kubectl create poddisruptionbudget gb-pdb --selector run=gb-frontend --min-available 4

• drain lagi

for node in $(kubectl get nodes -l cloud.google.com/gke-nodepool=default-pool -o=name); do
  kubectl drain --timeout=30s --ignore-daemonsets --grace-period=10 "$node";
done

kubectl describe deployment gb-frontend | grep ^Replicas

Penutup

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