This is the second episode of our Cloud Native DevOps on GCP series. In the previous chapter, we have built a multi-AZ GKE cluster with Terraform. This time, we’ll create a cloud native CI/CD pipeline leveraging our GKE cluster and Google DevOps tools such as Cloud Build and Google Container Registry (GCR). We’ll create a Cloud Build trigger by connecting to GitHub repository to perform automatic build, test and deployment of a sample micro-service app onto the GKE cluster.
For this demo, I have provided a simple NodeJS app which is already containerized and packaged as a Helm Chart for fast K8s deployment. You can find all the artifacts at my GitHub Repo, including the demo app, Helm template/chart, as well as the Cloud Build pipeline code.
WHAT YOU’LL NEED:
- Access to a GCP testing environment
- Install Git, Kubectl and Terrafrom on your client
- Install Docker on your client
- Install GCloud SDK
- Check the NTP clock & sync status on your client —> important!
- Clone or download the demo app repo at here
Step-1: Prepare the GCloud Environment
To begin, configure the GCloud environment variables and authentications.
gcloud init gcloud config set accessibility/screen_reader true gcloud auth application-default login
Register GCloud as a Docker credential helper — this is important so our Docker client will have privileged access to interact with GCR. (Later we’ll need to build and push a Helm client image to GCR as required for the pipeline deployment process)
gcloud auth configure-docker
Enable required GCP API services.
gcloud services enable compute.googleapis.com gcloud services enable servicenetworking.googleapis.com gcloud services enable cloudresourcemanager.googleapis.com gcloud services enable container.googleapis.com gcloud services enable cloudbuild.googleapis.com
Update Cloud Build service account with an editor role so it will have required permissions to access GKE and GCR within the project.
PROJECT_ID=`gcloud config get-value project` CLOUDBUILD_SA="$(gcloud projects describe $PROJECT_ID --format 'value(projectNumber)')@cloudbuild.gserviceaccount.com" gcloud projects add-iam-policy-binding $PROJECT_ID --member serviceAccount:$CLOUDBUILD_SA --role roles/editor
Step-2: Launch a GKE Cluster using Terraform
If you have been following the series and have already deployed a GKE cluster, you can skip this step and move on to the next. Otherwise you can follow this post to build a GKE cluster with Terraform.
Make sure to deploy an Ingress Controller as there is an Ingress service defined in our Helm Chart!
kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/controller-0.32.0/deploy/static/provider/cloud/deploy.yaml
Step-3: Initialize Helm for Application Deployment on GKE
As mentioned above, for this demo we have encapsulated our demo app into a Helm Chart. Helm is a package management system designed for simplifying and accelerating application deployment on the Kubernetes platform.
As of version 2, Helm consists of a local client and a Tiller server pod (deployed in K8s cluster) to interact with the Kube-apiserver for app deployment. In our example, we’ll first build a customised Helm client docker image and push it to GCR. This image will then be used by Cloud Build to interact with the Tiller server (deployed on GKE) for deploying the pre-packaged Helm chart — as illustrated in the below diagram.
First let’s configure a service account for Tiller and initialize Helm (server component) on our GKE cluster.
kubectl apply -f ./k8s-helm/tiller.yaml helm init --history-max 200 --service-account tiller
We’ll then build and push a customised Helm client image to GCR. This might take a few minutes.
cd ./k8s-helm/cloud-builders-community/helm docker build -t gcr.io/$PROJECT_ID/helm . docker push gcr.io/$PROJECT_ID/helm
On GCR confirm there is a new Helm (client) image has been pushed through.
Step-4: Review the (Cloud Build) Pipeline Code
Before we move forward, let’s take a moment to review the pipeline code (as defined in the cloudbuild.yaml). There is a total of 4 stages included in our Cloud Build pipeline:
- Build a docker image with our demo app
- Push the new image to GCR
- Deploy Helm chart (for our demo app) to GKE via GCR
- Integration Testing
The first two stages are straight forward, we’ll use the Google published Cloud Builder docker image to build the node app image and push it to the GCR repository.
# Build demo app image - name: gcr.io/cloud_builders/docker args: - build - -t - gcr.io/$PROJECT_ID/node-app:$COMMIT_SHA - . # Push demo app image to GCR - name: gcr.io/cloud-builders/docker args: - push - gcr.io/$PROJECT_ID/node-app:$COMMIT_SHA
Next we’ll leverage the (previously built) Helm client to interact with our GKE cluster and to deploy the Helm chart (for our node app), with the image repository pointing to the GCR path from the last pipeline stage.
# Deploy with Helm Chart - name: gcr.io/$PROJECT_ID/helm args: - upgrade - -i - node-app - ./k8s-helm/node-app - --set - image.repository=gcr.io/$PROJECT_ID/node-app,image.tag=$COMMIT_SHA - -f - ./k8s-helm/node-app/values.yaml env: - CLOUDSDK_COMPUTE_REGION=$_CUSTOM_REGION - CLOUDSDK_CONTAINER_CLUSTER=$_CUSTOM_CLUSTER - KUBECONFIG=/workspace/.kube/config - TILLERLESS=false - TILLER_NAMESPACE=kube-system
Lastly, we’ll run an integration test to verify the demo app status on our GKE cluster. For our node app there is a built-in heath-check URL configured at “/health“, and we’ll be leveraging another Cloud Builder curl image to ping this URL path and expect a return message of <“status”: “ok”> . Note: here we should be polling the internal DNS address for the k8s service (of the demo app) so there is no dependency on IP allocations.
# Integration Testing - name: gcr.io/cloud-builders/kubectl entrypoint: 'bash' args: - '-c' - | kubectl delete --wait=true pod curl kubectl run curl --restart=Never --image=gcr.io/cloud-builders/curl --generator=run-pod/v1 -- http://node-app.default.svc.cluster.local/health sleep 15 kubectl logs curl kubectl logs curl | grep OK env: - CLOUDSDK_COMPUTE_REGION=$_CUSTOM_REGION - CLOUDSDK_CONTAINER_CLUSTER=$_CUSTOM_CLUSTER - KUBECONFIG=/workspace/.kube/config
Step-4: Create a Cloud Build Trigger by Connecting to GitHub Repository
Now that we have our GKE cluster ready and Helm image pushed to GCR, the next step is to connect Cloud Build to the GitHub repository and create a CI trigger. On GCP console, go to Cloud Build —> Triggers, select the GitHub repo as below.
If this is the first time you are connecting to GitHub in Cloud Build, it will redirect you to an authorization page like below, accept it in order to access your repositories.
Select the demo app repository, which also includes the pipeline config (cloudbuild.yaml) file.
Create a push trigger in the next page and you should see a summary like this.
You can manually run the trigger now to kick off the CI build process. However we’ll be running more thorough testing to verify the end-to-end pipeline automation process in the next section.
Step-5: Test the CI/CD Pipeline
It’s time to test our CI/CD pipeline! First we’ll make a “cosmetic” version change (1.0.0 to 1.0.1) to the Helm chart for our demo app.
Commit the change and push to the Git repository.
This (push event) should have triggered our Cloud Build pipeline. You can jump on the GCP console to monitor the fully automated 4-stage process. The pipeline will be completed once the integration test has returned a status of OK.
On the GKE cluster we can see our Helm chart v-1.0.1 has been deployed successfully.
The deployment and node app are running as expected.
Retrieve the Ingress public IP and update the local host file for a quick testing. (Note the Ingress URL is defined as “node-app.local”)
[root@cloud-ops01 nodejs-cloudbuild-demo]# kubectl get ingresses NAME HOSTS ADDRESS PORTS AGE node-app node-app.local 18.104.22.168 80 15m [root@cloud-ops01 nodejs-cloudbuild-demo]# [root@cloud-ops01 nodejs-cloudbuild-demo]# echo "22.214.171.124 node-app.local" >> /etc/hosts
Now point your browser to “node-app.local” and you should see the demo app page like below. Congrats, you have just successfully deployed a cloud native CI/CD pipeline on GCP!