Cloud Run is a service on Google Cloud Platform that supports serverless deployments of containers. You provide the container image and it runs it. Much like serverless platforms with App Engine, the infrastructure is hidden and is autoscaled to meet demand. In contrast to the restricted set of environments supported by App Engine, Cloud Run gives a developer the flexibility to run any environment that they can supply in a container, something that is highly desirable for those with a reasonable understanding of building containers for their applications. In this lab, we will deploy our guestbook application backed by Cloud Datastore to Cloud Run.

Cloud Run can run containers that are stored in public registries such as Docker Hub. However, it is often the case for performance and security reasons that you would want to build, push, and pull container images from a private container registry that is confined to the project you are running the containers in.

To support this, Google Cloud has two hosted services that can be used to automatically build and store container images used in a project. These services are helpful when constructing CI/CD pipelines. The first service, Cloud Build, constructs container images when given an application's source files and Dockerfile. The second service, Container Registry (, is a hosted container registry that is equivalent to Docker Hub that stores the container images in a Cloud Storage bucket.

Bring up Cloud Shell and go back to the source directory for the application that was used in the initial Cloud Datastore Guestbook codelab.

cd cs430-src/05_gcp_datastore

In the initial lab, we built the container via the docker CLI and pushed it to Docker Hub. Instead of running that image, we'll instead build the container using Cloud Build and then push it to the registry to host it. The command that does so is:

gcloud builds submit --timeout=900 --tag${GOOGLE_CLOUD_PROJECT}/gcp_gb

This command takes the Dockerfile and application source files in the current directory, uploads it to Cloud Build, instructs the service to build the container image, and then has the resulting container image stored in the project's private container registry using the image name gcp_gb.

We set the timeout value since this operation may take over the 10 minute default setting. The container will be identical to the one built in the initial Cloud Datastore codelab.

After executing the command, from the web console, visit the Cloud Build home page, click on History, then on the build you just executed. Scroll down to see that the docker command that Cloud Build has executed on your behalf resulted in the image being created. Take a screenshot that includes the output of the command and the time it took to execute.

Then, visit Container Registry and take a screenshot showing the container image and its virtual size

Finally, visit the Cloud Storage bucket that stores the Container Registry data at gs://artifacts.${GOOGLE_CLOUD_PROJECT} and see where the image layers are stored via their SHA-256 hash.

In order to deploy a container on Cloud Run, you will need to specify a service account that the container will use to access cloud resources. With the Guestbook application, this means a service account with the Cloud Datastore User role attached. In the initial Cloud Datastore codelab, we have already created this service account.


Given this service account and the container image in the previous step, we can now deploy our container serverlessly onto Cloud Run. To do so, run the following command:

gcloud run deploy gcpgb \
  --image${GOOGLE_CLOUD_PROJECT}/gcp_gb \
  --service-account guestbook@${GOOGLE_CLOUD_PROJECT}

When prompted, specify:

Upon completion, an https URL will be returned where your container can be accessed from. Click on it or copy and paste it into a browser.

Add an entry with the message "Hello Cloud Run!". Show your Guestbook app running in a browser. Make sure that your screenshot shows the URL Cloud Run has created for your site.

From the web console, visit Cloud Run, click on the service you created, then click on "Revisions". As the UI shows, one can create multiple revisions of a service and manage user traffic between them. This is helpful for A/B testing. View the "Details" section to the right and answer the following questions:

Although Cloud Run has a generous free tier, it is good practice to remove resources that are not being used. Go back to the Cloud Run landing page and delete the service you've created:

Then, visit Container Registry and delete the container image:

Alternatively, you can also delete these resources in Cloud Shell via:

gcloud run services delete gcpgb
gcloud container images delete${GOOGLE_CLOUD_PROJECT}/gcp_gb