Continuous Deployment in GitHub Actions

Image by Chris Pagan

بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيْمِ

Setelah kita membuat script CI, waktunya kita membuat script untuk CD nya, tapi sebelum itu kita harus men-setting secret, yang nantinya akan kita gunakan untuk upload artifact docker images ke GitHub Container Registry dan melakukan remote SSH ke kubernetes master-nodes.

Pertama kita masuk ke setting repository :

Kemudian kita masuk kepada menu secrets

Dan jika kita telah menambahkan secrets kita bisa lanjut menuliskan CD scriptnya

Okeh kita langsung saja masuk ke script CD nya.

name: CD to Kubernetes Cluster
on:
  workflow_dispatch:
jobs:
  Deployment:
    env:
      PROJECT_ID: ${{ secrets.GKE_PROJECT }}
      GKE_CLUSTER: my-first-cluster-1
      GKE_ZONE: us-central1-c
      DEPLOYMENT_NAME: simple-go-api
    name: Deploy To Kubernetes
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v2
      - name: Authenticate to Google Cloud
        uses: google-github-actions/auth@v0.4.0
        with:
          credentials_json: '${{ secrets.GCP_CREDENTIALS }}'
      - name: Set up GKE credentials
        uses: google-github-actions/get-gke-credentials@v0.4.0
        with:
          cluster_name: ${{ env.GKE_CLUSTER }}
          location: ${{ env.GKE_ZONE }}
      - name: Deploy
        run: |-
          cd kubernetes/
          kubectl delete all --all -n experiment
          kubectl apply -f namespace.yaml
          kubectl apply -f deployment.yaml
          kubectl apply -f autoscale.yaml
          kubectl apply -f ingress.yaml
          kubectl get all -n experiment
          kubectl get services -o wide -n experiment

Deploy to Kubernetes Cluster

Dan pada tahap terakhir, kita akan melakukan deploy docker images kita ke Kubernetes Cluster yang sudah kita buat.

File namespace.yaml

apiVersion: v1
kind: Namespace
metadata:
  name: backend

File deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: simple-go-api
  namespace: backend
spec:
  selector:
    matchLabels:
      run: simple-go-api
  template:
    metadata:
      labels:
        run: simple-go-api
  spec:
    securityContext:
      runAsUser: 10001
    containers:
      - name: simple-go-api
      image: ghcr.io/mrofisr/simple:latest
      ports:
        - containerPort: 3000
      resources:
        limits:
          cpu: 500m
        requests:
          cpu: 200m

File autoscalling.yaml

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
  name: simple-go-api
  namespace: backend
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: simple-go-api
    minReplicas: 1
    maxReplicas: 5
    metrics:
      - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

setelah semua proses berhasil maka output atau hasilnya akan seperti ini :

Testing Horizontal Pod AutoScalling

Dan yang tahap terakhir kita akan melakukan testing pada HPA yang sudah kita buat, disini saya menggunakan Apache Benchmark untuk melakukan testing.

Testing :

mrofisr@mrofisr ~ % ab -n 10000 -c 1000 http://34.124.208.75:31000/quotes
This is ApacheBench, Version 2.3 <$Revision: 1879490 $>
Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
Licensed to The Apache Software Foundation, http://www.apache.org/
Benchmarking 34.124.208.75 (be patient)
Completed 1000 requests
Completed 2000 requests
Completed 3000 requests
Completed 4000 requests
Server Software:
Server Hostname:        34.124.208.75
Server Port:            31000
Document Path:          /quotes
Document Length:        224 bytes
Concurrency Level:      1000
Time taken for tests:   60.828 seconds
Complete requests:      4845
Failed requests:        4795
(Connect: 0, Receive: 0, Length: 4795, Exceptions: 0)
Total transferred:      1819471 bytes
HTML transferred:       1291366 bytes
Requests per second:    79.65 [#/sec] (mean)
Time per request:       12554.697 [ms] (mean)
Time per request:       12.555 [ms] (mean, across all concurrent requests)
Transfer rate:          29.21 [Kbytes/sec] received
Connection Times (ms)
min  mean[+/-sd] median   max
Connect:       28  404 468.1    288    3500
Processing:   305 10650 2602.1  11615   14534
Waiting:      305 10649 2601.9  11615   14534
Total:        352 11054 2581.8  11930   14895
Percentage of the requests served within a certain time (ms)
50%  11929
66%  12199
75%  12457
80%  12610
90%  12817
95%  13069
98%  13320
99%  14064
100%  14895 (longest request)

Before :

After :

After CPU usages normal :

Conclusion

Pada kasus ini saya membuat sebuah kondisi apabila penggunaan CPU melebihi 50% maka Kubernetes akan menggandakan podnya menjadi 4 buah pod agar proses aplikasi tetap berjalan dengan lancar.

About me

I’m Muhammad Abdur Rofi Maulidin, an aspiring DevOps Wannabe with a keen interest in Cloud Computing, CNCF technologies, and Automation. I am also the author of Jawara Cloud, a platform dedicated to sharing my experiences and insights in DevOps and Cloud Computing.

Feel free to keep in touch with me:

• LinkedIn: https://www.linkedin.com/in/mrofisr/
• Twitter: https://twitter.com/mrofisr_
• Telegram: https://t.me/@mrofisr
• Website: https://jawara.cloud

Free Palestine 🇵🇸