Use deploy parameters

Use deploy parameters

This page shows you how to use Cloud Deploy to deliver a sample application to two targets at the same time (a parallel deployment), while passing a different parameter value to each rendered manifest or service definition, using three different methods.

In this quickstart, you'll do the following:

1. Create two GKE clusters or two Cloud Run services.

   You can use GKE Enterprise clusters too, but this quickstart uses GKE and Cloud Run only.

2. Create a Skaffold configuration and either a Kubernetes manifest or a Cloud Run service definition.

   The manifest or service definition will be the same for both child targets, but at deploy time the rendered manifest or service definition for each child target will have different values for the specific parameters configured in this quickstart.

3. Define your Cloud Deploy delivery pipeline and deployment targets.

   This pipeline will have one multi-target, referencing two child targets, to deliver your app to the two clusters or two services.

4. Define deploy parameters in three different places:

   • In the pipeline progression
   • On the child targets
   • On the command line, when creating the release

5. Instantiate your delivery pipeline by creating a release, which automatically deploys to the two targets in parallel.

6. View the "controller rollout" and child rollouts in Google Cloud console.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the Cloud Deploy, Cloud Build, GKE, Cloud Run, and Cloud Storage APIs.

   Enable the APIs

5. Install the Google Cloud CLI.

6. To initialize the gcloud CLI, run the following command:

   gcloud init

   Note: You can run the gcloud CLI in the Google Cloud console without installing the Google Cloud CLI. To run the gcloud CLI in the Google Cloud console, use Cloud Shell.

7. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

8. Make sure that billing is enabled for your Google Cloud project.

9. Enable the Cloud Deploy, Cloud Build, GKE, Cloud Run, and Cloud Storage APIs.

   Enable the APIs

10. Install the Google Cloud CLI.

11. To initialize the gcloud CLI, run the following command:

    gcloud init

    Note: You can run the gcloud CLI in the Google Cloud console without installing the Google Cloud CLI. To run the gcloud CLI in the Google Cloud console, use Cloud Shell.

If you already have the CLI installed, make sure you're running the latest version:

gcloud components update

12. Make sure the default Compute Engine service account has sufficient permissions.

    The service account might already have the necessary permissions. These steps are included for projects that disable automatic role grants for default service accounts.

    1. First add the clouddeploy.jobRunner role:

       gcloud projects add-iam-policy-binding PROJECT_ID \
           --member=serviceAccount:$(gcloud projects describe PROJECT_ID \
           --format="value(projectNumber)")[e-mail] \
           --role="roles/clouddeploy.jobRunner"
       

    2. Add the developer role for your specific runtime.
    • For GKE:

      gcloud projects add-iam-policy-binding PROJECT_ID \
          --member=serviceAccount:$(gcloud projects describe PROJECT_ID \
          --format="value(projectNumber)")[e-mail] \
          --role="roles/container.developer"
      

    • For Cloud Run:

      gcloud projects add-iam-policy-binding PROJECT_ID \
          --member=serviceAccount:$(gcloud projects describe PROJECT_ID \
          --format="value(projectNumber)")[e-mail] \
          --role="roles/run.developer"
      

    3. Add the iam.serviceAccountUser role, which includes the actAspermission to deploy to the runtime:

       gcloud iam service-accounts add-iam-policy-binding $(gcloud projects describe PROJECT_ID \
           --format="value(projectNumber)")[e-mail] \
           --member=serviceAccount:$(gcloud projects describe PROJECT_ID \
           --format="value(projectNumber)")[e-mail] \
           --role="roles/iam.serviceAccountUser" \
           --project=PROJECT_ID
       

Create your runtime environments

If you're deploying to Cloud Run, you can skip this command.

For GKE, create two clusters: deploy-params-cluster-prod1 and deploy-params-cluster-prod2, with default settings. The clusters' Kubernetes API endpoints must be network-reachable from the public internet. GKE clusters are externally accessible by default.

gcloud container clusters create-auto deploy-params-cluster-prod1 \
                 --project=PROJECT_ID \
                 --region=us-central1 \
                 && gcloud container clusters create-auto deploy-params-cluster-prod2 \
                 --project=PROJECT_ID \
                 --region=us-west1

Prepare your Skaffold configuration and manifests

Cloud Deploy uses Skaffold to provide the details for what to deploy and how to deploy it properly for your separate targets.

In this quickstart, you create a skaffold.yaml file, which identifies the Kubernetes manifest or Cloud Run service definition to be used to deploy the sample app.

1. Open a terminal window.

2. Create a new directory and navigate into it.

   GKE

   mkdir deploy-params-gke-quickstart
   cd deploy-params-gke-quickstart
   

   Cloud Run

   mkdir deploy-params-run-quickstart
   cd deploy-params-run-quickstart
   

3. Create a file named skaffold.yaml with the following contents:

   GKE

   apiVersion: skaffold/v4beta7
   kind: Config
   manifests:
     rawYaml:
     - kubernetes.yaml
   deploy:
     kubectl: {}
   

   Cloud Run

   apiVersion: skaffold/v4beta7
   kind: Config
   manifests:
     rawYaml:
     - service.yaml
   deploy:
     cloudrun: {}
   

   This file is a minimal Skaffold config. For this quickstart, you create the file. But you can also have Cloud Deploy create one for you, for simple, non-production applications.

   See the skaffold.yaml reference for more information about this file.

4. Create the definition for your application—a service definition for Cloud Run or a Kubernetes manifest for GKE.

   GKE

   Create a file named kubernetes.yaml, with the following contents:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: my-deployment
   spec:
     replicas: 1 # from-param: ${replicaCount}
     selector:
       matchLabels:
         app: my-app
     template:
       metadata:
         labels:
           app: my-app
         annotations:
           commit: defaultShaValue # from-param: ${git-sha}
       spec:
         containers:
         - name: nginx
           image: my-app-image
           env:
           - name: envvar1
             value: default1 # from-param: ${application_env1}
           - name: envvar2
             value: default2 # from-param: ${application_env2}
   

   This file is a Kubernetes manifest, which is applied to the cluster to deploy the application. The container image to deploy is set here as a placeholder, my-app-image, which is replaced with the specific image when you create the release.

   Cloud Run

   Create a file named service.yaml, with the following contents:

   apiVersion: serving.knative.dev/v1
   kind: Service
   metadata:
     name: my-parallel-run-service
   spec:
     autoscaling.knative.dev/minScale: 1 # from-param: ${minInstances}
     selector:
       matchLabels:
         app: my-app
     template:
       metadata:
         annotations:
           commit: defaultShaValue # from-param: ${git-sha}
       spec:
         containers:
         - image: my-app-image
           env:
           - name: envvar1
             value: defaultValue1 # from-param: ${application_env1}
           - name: envvar2
             value: defaultValue2 # from-param: ${application_env2}
   

   This file is a basic Cloud Run service definition, which is used to deploy the application. The container image to deploy is set here as a placeholder, my-app-image, which is replaced with the specific image when you create the release.

Note: If you want to use different manifests per target, read this article about managing manifests to find out more about using Skaffold profiles.

Create your delivery pipeline and targets

You can define your pipeline and targets in one file or in separate files. In this quickstart, we create a single file.

1. Create your delivery pipeline and target definition:

   GKE

   In the deploy-params-gke-quickstart directory, create a new file: clouddeploy.yaml, with the following contents:

   apiVersion: deploy.cloud.google.com/v1
   kind: DeliveryPipeline
   metadata:
     name: my-params-demo-app-1
   description: main application pipeline
   serialPipeline:
     stages:
     - targetId: params-prod-multi
       deployParameters:
       - values:
           replicaCount: "2"
         # Apply the deploy parameter replicaCount: "2" to the target with this label
         matchTargetLabels:
           label1: label1
       - values:
           replicaCount: "3"
         # Apply the deploy parameter replicaCount: "3" to the target with this label
         matchTargetLabels:
           label2: label2
   ---
   
   apiVersion: deploy.cloud.google.com/v1
   kind: Target
   metadata:
     name: params-prod-multi
   description: production clusters
   multiTarget:
     targetIds: [params-prod-a, params-prod-b]
   ---
   
   apiVersion: deploy.cloud.google.com/v1
   kind: Target
   metadata:
     name: params-prod-a
     labels:
       label1: label1
   description: production cluster 1
   deployParameters:
     application_env1: "sampleValue1"
   gke:
     cluster: projects/PROJECT_ID/locations/us-central1/clusters/deploy-params-cluster-prod1
   ---
   
   apiVersion: deploy.cloud.google.com/v1
   kind: Target
   metadata:
     name: params-prod-b
     labels:
       label2: label2
   description: production cluster 2
   deployParameters:
     application_env2: "sampleValue2"
   gke:
     cluster: projects/PROJECT_ID/locations/us-west1/clusters/deploy-params-cluster-prod2
   

   Note: In this file, targets are included with the delivery pipeline, but you can define targets in a separate file or multiple separate files.

   Cloud Run

   In the deploy-params-run-quickstart directory, create a new file: clouddeploy.yaml, with the following contents:

   apiVersion: deploy.cloud.google.com/v1
   kind: DeliveryPipeline
   metadata:
     name: my-params-demo-app-1
   description: main application pipeline
   serialPipeline:
     stages:
     - targetId: params-prod-multi
       deployParameters:
       - values:
           minInstances: "2"
         # Apply the deploy parameter minInstances: "2" to the target with this label
         matchTargetLabels:
           label1: label1
       - values:
           minInstances: "3"
         # Apply the deploy parameter minInstances: "3" to the target with this label
         matchTargetLabels:
           label2: label2
   ---
   
   apiVersion: deploy.cloud.google.com/v1
   kind: Target
   metadata:
     name: params-prod-multi
   description: production clusters
   multiTarget:
     targetIds: [params-prod-a, params-prod-b]
   ---
   
   apiVersion: deploy.cloud.google.com/v1
   kind: Target
   metadata:
     name: params-prod-a
     labels:
       label1: label1
   description: production cluster 1
   deployParameters:
     application_env1: "sampleValue1"
   run:
     location: projects/PROJECT_ID/locations/us-central1
   ---
   
   apiVersion: deploy.cloud.google.com/v1
   kind: Target
   metadata:
     name: params-prod-b
     labels:
       label2: label2
   description: production cluster 2
   deployParameters:
     application_env2: "sampleValue2"
   run:
     location: projects/PROJECT_ID/locations/us-west1
   

   Note: In this file, targets are included with the delivery pipeline, but you can define targets in a separate file or multiple separate files.

2. Register your pipeline and targets with the Cloud Deploy service:

   gcloud deploy apply --file=clouddeploy.yaml --region=us-central1 --project=PROJECT_ID 
   

   You now have a pipeline, with one multi-target comprising two GKE or Cloud Run targets, ready to deploy your application.

3. Confirm your pipeline and targets:

   In the Google Cloud console, navigate to the Cloud Deploy Delivery pipelines page to view of list of your available delivery pipelines.

   Open the Delivery pipelines page

   The delivery pipeline you just created is shown. Notice that there is one target listed in the Targets column even though you configured three targets (one multi-target and two child targets) in your clouddeploy.yaml file.

   

   Notice that the only target listed is the multi-target params-prod-multi. Child targets are not shown.

Create a release

A release is the central Cloud Deploy resource representing the changes being deployed. The delivery pipeline defines the lifecycle of that release. See Cloud Deploy service architecture for details about that lifecycle.

GKE

Run the following command from the deploy-gke-parallel-quickstart directory to create a release resource that represents the container image to deploy:

 gcloud deploy releases create test-release-001 \
   --project=PROJECT_ID \
   --region=us-central1 \
   --delivery-pipeline=my-params-demo-app-1 \
   --images=my-app-image=gcr.io/google-containers/nginx@sha256:f49a843c290594dcf4d193535d1f4ba8af7d56cea2cf79d1e9554f077f1e7aaa \
   --deploy-parameters="git-sha=f787cac"

Notice the --images= flag, which you use to replace the placeholder (my-app-image) in the manifest with the specific, SHA-qualified image. Google recommends that you templatize your manifests this way, and that you use SHA-qualified image names at release creation.

Cloud Run

Run the following command from the deploy-run-parallel-quickstart directory to create a release resource that represents the container image to deploy:

 gcloud deploy releases create test-release-001 \
   --project=PROJECT_ID \
   --region=us-central1 \
   --delivery-pipeline=my-params-demo-app-1 \
   --images=my-app-image=us-docker.pkg.dev/cloudrun/container/hello@sha256:4a856b6f1c3ce723a456ddc2adfbb794cbfba93f727e2d96fcf6540bd0d6fff4 \
   --deploy-parameters="git-sha=f787cac"

Notice the --images= flag, which you use to replace the placeholder (my-app-image) in the service definition with the specific, SHA-qualified image. Google recommends that you templatize your service and job definitions this way, and that you use SHA-qualified image names at release creation.

As always, when you create a release, a rollout is created automatically for the first target in your pipeline (unless a specific target is specified using --to-target=). In this quickstart, this target is a multi-target, so the rollout is a "controller rollout" for two child targets, and there are no subsequent targets in the delivery pipeline. This means that your application is deployed everywhere upon rollout creation.

View the results in Google Cloud console

Now that you've created the release, and the controller rollout and child rollouts are created, those child rollouts are now deployed (or are in the process of being deployed) to their respective GKE clusters or Cloud Run services.

1. In the Google Cloud console, navigate to the Cloud Deploy Delivery pipelines page to view your my-parallel-demo-app-1 delivery pipeline.

   Open the Delivery pipelines page

2. Click the name of your delivery pipeline "my-parallel-demo-app-1".

   The pipeline visualization shows the app's deployment status. Because there's only one stage in the pipeline, the visualization shows only one node.

   

   And your release is listed on the Releases tab under Delivery pipeline details.

3. Click the release name, test-release-001.

   Your rollouts appear under Rollouts. You can click a rollout to view its details, including the deployment log.

   

4. Under Release details, select the Artifacts tab.

   The Deploy parameters table lists all the parameters you configured in your manifest and the values you provided for those parameters:

   GKE

   

   Cloud Run

   

   In addition to the parameters and values, the table shows which target each parameter applies to.

5. In the Release inspector column, click on View artifacts for either target.

6. Click Show diff, then select params-prod-a for one target, and params-prod-b for the other.

   A diff is shown, comparing the rendered manifests of the two targets, including the values you specified:

   GKE

   

   Cloud Run

   

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, follow these steps.

1. Delete the GKE clusters or Cloud Run services:

   GKE

   gcloud container clusters delete deploy-params-cluster-prod1 --region=us-central1 --project=PROJECT_ID \
   && gcloud container clusters delete deploy-params-cluster-prod2 --region=us-west1 --project=PROJECT_ID
   

   Cloud Run

   gcloud run services delete my-parallel-run-service --region=us-central1 --project=PROJECT_ID \
   && gcloud run services delete my-parallel-run-service --region=us-west1 --project=PROJECT_ID
   

2. Delete the delivery pipeline, multi-target, child targets, release, and rollouts:

   gcloud deploy delete --file=clouddeploy.yaml --force --region=us-central1 --project=PROJECT_ID
   

3. Delete the Cloud Storage buckets that Cloud Deploy created.

   One ends with _clouddeploy, and the other is [region].deploy-artifacts.[project].appspot.com.

   Open the Cloud Storage browser page

That's it, you completed this quickstart!

What's next

• Learn more about using deploy parameters.

• Read more about how to deploy to multiple targets at the same time.

• Learn more about Cloud Deploy.

• Learn the basics of deploying applications.

• Try out the Cloud Deploy walkthrough.

• Learn how to manage your manifests.

• Learn how to combine Google Cloud CI/CD tools to develop and deliver software effectively to GKE.