This guide helps you understand and deploy theCloud SDK Client Library solution.

This solution lets you interact with Google Cloud by using theGoogle Cloud SDK Client Libraries to process and aggregate data, and then displays aradar visualization. Use this app to identify trends and observations based onthe aggregate data.

This solution helps you learn key skills for successfully making API calls.This solution uses the Google Cloud SDK Client Libraries to access Google Cloud APIsprogrammatically, and uses Google Cloud services (Cloud Run jobsand Cloud Storage) to reduce boilerplate code.

In this solution, the code parses a sample dataset(the2018 Central Park Squirrel Census) by using Cloud Run jobs andCloud Storage. The solution logs all of the Google Cloud SDK Client requests into Cloud Logging,using a common pattern to enable troubleshooting and observability so you cansee how long those requests take and where a process might encounter an error.This solution also guides you through how to run a Cloud Runjob to process and store the dataset.

APIs are the fundamental mechanism that developers use to interact withGoogle Cloud products and services. Google Cloud SDK provides language-specificCloud Client libraries that support eight different languages and theirconventions and styles. Use this solution to learn how to use Google Cloud SDK ClientLibraries to process data and deploy a frontend application where you can viewthe results.

Objectives

This solution guide helps you do the following:

• Learn how to use a client library for Google Cloud API calls.
• Deploy an interactive dataset using Cloud Run jobs and Cloud Storage.
• Explore Google Cloud API calls using Cloud Logging.
• View the Cloud Run application, service account configurations, andenabled APIs and their usage.

Architecture

This solution deploys the raw data to a bucket in Cloud Storage, configures aCloud Run job to process the data and store it in a separate bucket inCloud Storage, and deploys a frontend service in Cloud Run that lets youview and interact with the processed data.

The following diagram shows the architecture of the solution:

The following section describes the Google Cloud resources that are shownin the diagram.

Components and configuration

This solution processes requests as follows. The steps are numbered as shown in the architecture diagram.

1. Unprocessed data uploads to a Cloud Storage bucket.
2. A Cloud Run job transforms the raw data into a more structured format thatthe frontend service can understand. The Cloud Run job uploads the processeddata to a second Cloud Storage bucket.
3. The frontend, hosted as a Cloud Run service, pulls the processed data fromthe second Cloud Storage bucket.
4. The user can visit the web application served by the frontendCloud Run service.

Products used

The solution uses the following Google Cloud products:

• Cloud Storage: An enterprise-ready service that provides low-cost, unlimitedobject storage for diverse data types. Data is accessible from within andoutside of Google Cloud and is replicated geo-redundantly.
• Cloud Logging: A service that lets you store, search, analyze, monitor, andalert on logging data and events from Google Cloud and other clouds.
• Cloud Run: A fully managed service that lets you build and deployserverless containerized apps. Google Cloud handles scaling and otherinfrastructure tasks so that you can focus on the business logic of your code.

Cost

For an estimate of the cost of the Google Cloud resources that theCloud SDK Client Library solution uses, see the precalculated estimate in theGoogle Cloud Pricing Calculator.

Use the estimate as a starting point to calculate the cost of your deployment.You can modify the estimate to reflect any configuration changes that you planto make for the resources that are used in the solution.

The precalculated estimate is based on assumptions for certain factors,including the following:

• The Google Cloud locations where the resources are deployed.

• The amount of time that the resources are used.

• The Google Cloud locations where the resources are deployed.

• The amount of time that the resources are used.

Deploy the solution

The following sections guide you through the process of deploying the solution.

Create or choose a Google Cloud project

When you deploy the solution, you choose theGoogle Cloud project where the resources are deployed. You can either create a new project or use anexisting project for the deployment.

If you want to create a new project, do sobefore you begin the deployment.Using a new project can help avoid conflicts with previously provisionedresources, such as resources that are used for production workloads.

To create a project, complete the following steps:

1. Ensure that you have the Project Creator IAM role (roles/resourcemanager.projectCreator).Learn how to grant roles.

2. In the Google Cloud console, go to the project selector page.

   Go to project selector

3. ClickCreate project.

4. Name your project. Make a note of your generated project ID.

5. Edit the other fields as needed.

6. ClickCreate.

Get the required IAM permissions

To start the deployment process, you need the Identity and Access Management (IAM)permissions that are listed in the following table.

If you created a new project for this solution, then you have theroles/ownerbasic role in that project and have all the necessary permissions. If you don't have theroles/owner role, then ask your administrator to grant these permissions (orthe roles that include these permissions) to you.

About temporary service account permissions

If you start the deployment process through the console, Google creates aservice account to deploy the solution on your behalf (and to delete the deployment later if youchoose). This service account is assigned certain IAM permissionstemporarily; that is, the permissions are revoked automatically after thesolution deployment and deletion operations are completed. Google recommendsthat after you delete the deployment, you delete the service account, asdescribed later in this guide.

Choose a deployment method

To help you deploy this solution with minimal effort, a Terraform configurationis provided in GitHub. The Terraform configuration defines all theGoogle Cloud resources that are required for the solution.

You can deploy the solution by using one of the following methods:

• Through the console: Use this method if you wantto try the solution with the default configuration and see how it works.Cloud Build deploys all the resources that are required for thesolution. When you no longer need the deployed solution, you can delete itthrough the console. Any resources that you create afteryou deploy the solution might need to be deleted separately.

  To use this deployment method, follow the instructions inDeploy through the console.

• Using the Terraform CLI: Use this method if you want to customize thesolution or if you want to automate the provisioning and management of theresources by using the infrastructure as code (IaC) approach. Download theTerraform configuration from GitHub, optionally customize the code asnecessary, and then deploy the solution by using the Terraform CLI. Afteryou deploy the solution, you can continue to use Terraform to manage thesolution.

  To use this deployment method, follow the instructions inDeploy using the Terraform CLI.

Deploy through the console

Complete the following steps to deploy the preconfigured solution.

1. In the Google Cloud Jump Start Solutions catalog, go to theCloud SDK Client Library solution.

   Go to the Cloud SDK Client Library solution

2. Review the information that's provided on the page, such as the estimatedcost of the solution and the estimated deployment time.

3. When you're ready to start deploying the solution, clickDeploy.

   A step-by-step configuration pane is displayed.

4. Complete the steps in the configuration pane.

   Note the name that you enter for the deployment. This name is requiredlater when you delete the deployment.

   When you clickDeploy, theSolution deployments page is displayed.TheStatus field on this page showsDeploying.

5. Wait for the solution to be deployed.

   If the deployment fails, theStatus field showsFailed. You canuse the Cloud Build log to diagnose the errors. For moreinformation, seeErrors when deploying through the console.

   After the deployment is completed, theStatus field changes toDeployed.

6. To view the solution, return to theSolution deployments page in theconsole.

7. With this solution, you need to run the data processing job using Cloud Runjobs in order to transform and interact with the sample dataset.To follow step-by-step guidance for this task directly inGoogle Cloud console, clickStart the data processing job.

   Start the data processing job

8. To view the Google Cloud resources that are deployed and theirconfiguration, choose an interactive tour in your preferred language(Python,Node.js, orJava).

   Choose a tour

   Now that you've processed the sample dataset into a Cloud Storage bucket,you can continue to use the Cloud SDK Client Library solution to:

   • Interact with Google Cloud APIs.
   • Learn more how APIs are powered by Identity and Access Management.
   • Troubleshoot API issues in the Cloud Client API apps.

When you no longer need the solution, you can delete the deployment to avoidcontinued billing for the Google Cloud resources. For more information,seeDelete the deployment.

Deploy using the Terraform CLI

This section describes how you can customize the solution or automate theprovisioning and management of the solution by using the Terraform CLI.Solutions that you deploy by using the Terraform CLI are not displayed in theSolution deployments page in the Google Cloud console.

Set up the Terraform client

You can run Terraform either in Cloud Shell or on your local host. Thisguide describes how to run Terraform in Cloud Shell, which hasTerraform preinstalled and configured to authenticate with Google Cloud.

The Terraform code for this solution is available in a GitHub repository.

1. Clone the GitHub repository to Cloud Shell.

   

   A prompt is displayed to confirm downloading the GitHub repository toCloud Shell.

2. ClickConfirm.

   Cloud Shell is launched in a separate browser tab, and theTerraform code is downloaded to the$HOME/cloudshell_open directory ofyour Cloud Shell environment.

3. In Cloud Shell, check whether the current working directory is$HOME/cloudshell_open/terraform-cloud-client-api/infra. This is thedirectory that contains the Terraform configuration files for the solution.If you need to change to that directory, run the following command:

   cd $HOME/cloudshell_open/terraform-cloud-client-api/infra

4. Initialize Terraform by running the following command:

   terraform init

   Wait until you see the following message:

   Terraform has been successfully initialized!

Configure the Terraform variables

The Terraform code that you downloaded includes variables that you can use tocustomize the deployment based on your requirements. For example, you canspecify the Google Cloud project and theregion where you want the solution to bedeployed.

1. Make sure that the current working directory is$HOME/cloudshell_open/terraform-cloud-client-api/infra. If itisn't, go to that directory.

2. In the same directory, create a text file namedterraform.tfvars.

3. In theterraform.tfvars file, copy the following code snippet, and setvalues for the required variables.

   • Follow the instructions that are provided as comments in the codesnippet.
   • This code snippet includes only the variables for which youmust setvalues. The Terraform configuration includes other variables that havedefault values. To review all the variables and the default values, seethevariables.tf file that's available in the$HOME/cloudshell_open/terraform-cloud-client-api/infra directory.
   • Make sure that each value that you set in theterraform.tfvars filematches the variabletype as declared in thevariables.tf file. For example, if the type that'sdefined for a variable in thevariables.tf file isbool, then youmust specifytrue orfalse as the value of that variable in theterraform.tfvars file.

   # ID of the project in which you want to deploy the solutionproject_id = "PROJECT_ID"# Google Cloud region where you want to deploy the solution# Example: us-central1region = "REGION"# Programming language implementation to use# Example: pythonlanguage = "LANGUAGE"# Version of application image to use# Example: 0.4.0image_version = "IMAGE_VERSION"

For information about the values that you can assign to the required variables, see the following:

• project_id: Identifying projects.
• region: Available regions.
• language: Programming language implementation to use.
• image_version: Version of application image to use.

Validate and review the Terraform configuration

1. Make sure that the current working directory is$HOME/cloudshell_open/terraform-cloud-client-api/infra. If itisn't, go to that directory.

2. Verify that the Terraform configuration has no errors:

   terraform validate

   If the command returns any errors, make the required corrections in theconfiguration and then run theterraform validate command again. Repeatthis step until the command returns the following message:

   Success! The configuration is valid.

3. Review the resources that are defined in the configuration:

   terraform plan

4. If you didn't create theterraform.tfvars file asdescribed earlier, Terraform prompts you to enter values for the variablesthat don't have default values.Enter the required values.

   The output of theterraform plan command is a list of the resources thatTerraform provisions when you apply the configuration.

   If you want to make any changes, edit the configuration and then run theterraform validate andterraform plan commands again.

Provision the resources

When no further changes are necessary in the Terraform configuration, deploythe resources.

1. Make sure that the current working directory is$HOME/cloudshell_open/terraform-cloud-client-api/infra. If itisn't, go to that directory.

2. Apply the Terraform configuration:

   terraform apply

3. If you didn't create theterraform.tfvars file asdescribed earlier, Terraform prompts you to enter values for the variablesthat don't have default values.Enter the required values.

   Terraform displays a list of the resources that will be created.

4. When you're prompted to perform the actions, enteryes.

   Terraform displays messages showing the progress of the deployment.

   If the deployment can't be completed, Terraform displays the errors thatcaused the failure. Review the error messages and update the configurationto fix the errors. Then run theterraform apply command again. For helpwith troubleshooting Terraform errors, seeErrors when deploying the solution using the Terraform CLI.

   After all the resources are created, Terraform displays the followingmessage:

   Apply complete!

5. To view the solution, return to theSolution deployments page in theconsole.

6. With this solution, you need to run the data processing job using Cloud Runjobs to transform and interact with the sample dataset.To follow step-by-step guidance for this task directly inGoogle Cloud console, clickStart the data processing job.

   Start the data processing job

7. To view the Google Cloud resources that are deployed and theirconfiguration, choose an interactive tour in your preferred language(Python,Node.js, orJava).

   Choose a tour

   Now that you've processed the sample dataset into a Cloud Storage bucket,you can continue to use the Cloud SDK Client Library solution to explore more aboutinteracting with Google Cloud APIs,how APIs are powered by Identity and Access Management,and troubleshooting API issues in the Cloud Client API apps.

When you no longer need the solution, you can delete the deployment to avoidcontinued billing for the Google Cloud resources. For more information,seeDelete the deployment.

Delete the deployment

When you no longer need the solution, delete all resources to avoid continued billing for the resources you created.

Delete through the console

Use this procedure if you deployed the solution throughthe console.

1. In the Google Cloud console, go to theSolution deployments page.

   Go to Solution deployments

2. Select the project that contains the deployment that you want to delete.

3. Locate the deployment that you want to delete.

4. In the row for the deployment, clickmore_vertActionsand then selectDelete.

   You might need to scroll to seeActions in the row.

5. Enter the name of the deployment and then clickConfirm.

   TheStatus field showsDeleting.

   If the deletion fails, see the troubleshooting guidance inError when deleting a deployment.

When you no longer need the Google Cloud project that you used for the solution, youcan delete the project. For more information, seeOptional: Delete the project.

Delete using the Terraform CLI

Use this procedure if you deployed the solution by using the Terraform CLI.

1. In Cloud Shell, make sure that the current working directory is$HOME/cloudshell_open/terraform-cloud-client-api/infra. If itisn't, go to that directory.

2. Remove the resources that were provisioned by Terraform:

   terraform destroy

   Terraform displays a list of the resources that will be destroyed.

3. When you're prompted to perform the actions, enteryes.

   Terraform displays messages showing the progress. After all the resourcesare deleted, Terraform displays the following message:

   Destroy complete!

   If the deletion fails, see the troubleshooting guidance inError when deleting a deployment.

When you no longer need the Google Cloud project that you used for the solution, youcan delete the project. For more information, seeOptional: Delete the project.

Optional: Delete the project

If you deployed the solution in a new Google Cloud project, and if you no longerneed the project, then delete it by completing the following steps:

1. In the Google Cloud console, go to theManage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then clickDelete.
3. At the prompt, type the project ID, and then clickShut down.

If you decide to retain the project, then delete the service account that thissolution created (as described inDelete the service account).

Optional: Delete the service account

If you deleted the project that you used for the solution, then skip thissection.

As mentioned earlier in this guide, when you deployed the solution, a serviceaccount was created on your behalf. The service account was assigned certainIAM permissionstemporarily; that is, the permissions wererevoked automatically after the solution deployment and deletion operations werecompleted, but the service account isn't deleted. Google recommends thatyou delete this service account.

• If you deployed the solution through the Google Cloud console, go to theSolution deployments page. (If you're already on that page, refresh the browser.) A process istriggered in the background to delete the service account. No further actionis necessary.

• If you deployed the solution by using the Terraform CLI, complete thefollowing steps:

  1. In the Google Cloud console, go to theService accounts page.

     Go to Service accounts

  2. Select the project that you used for the solution.

  3. Select the service account that you want to delete.

     The email ID of the service account that was created for the solution isin the following format:

     goog-sc-DEPLOYMENT_NAME-NNN@PROJECT_ID.iam.gserviceaccount.com

     The email ID contains the following values:

     • DEPLOYMENT_NAME: the name of the deployment.
     • NNN: a random 3-digit number.
     • PROJECT_ID: the ID of the project in which youdeployed the solution.

  4. ClickDelete.

Troubleshoot errors

The actions that you can take to diagnose and resolve errors depend on the deployment method and the complexity of the error.

Errors when deploying through the console

If the deployment fails when you use the console, do thefollowing:

1. Go to theSolution deployments page.

   If the deployment failed, theStatus field showsFailed.

2. View the details of the errors that caused the failure:

   1. In the row for the deployment, clickmore_vertActions.

      You might need to scroll to seeActions in the row.

   2. SelectView Cloud Build logs.

3. Review the Cloud Build log and take appropriate action to resolvethe issue that caused the failure.

Errors when deploying using the Terraform CLI

If the deployment fails when you use Terraform, the output of theterraformapply command includes error messages that you can review to diagnose theproblem.

The examples in the following sections show deployment errors that you mightencounter when you use Terraform.

API not enabled error

If you create a project and then immediately attempt to deploy the solution inthe new project, the deployment might fail with an error like the following:

Error: Error creating Network: googleapi: Error 403: Compute Engine API has notbeen used in projectPROJECT_ID before or it is disabled. Enable it by visitinghttps://console.developers.google.com/apis/api/compute.googleapis.com/overview?project=PROJECT_IDthen retry. If you enabled this API recently, wait a few minutes for the actionto propagate to our systems and retry.

If this error occurs, wait a few minutes and then run theterraform applycommand again.

Error when deleting a deployment

In certain cases, attempts to delete a deployment might fail:

• After deploying a solution through the console, if youchange any resource that was provisioned by the solution, and if you then tryto delete the deployment, the deletion might fail. TheStatus field on theSolution deployments page showsFailed, and theCloud Build log shows the cause of the error.
• After deploying a solution by using the Terraform CLI, if you change anyresource by using a non-Terraform interface (for example,the console), and if you then try to delete the deployment,the deletion might fail. The messages in the output of theterraform destroycommand show the cause of the error.

Review the error logs and messages, identify and delete the resources thatcaused the error, and then try deleting the deployment again.

If a console-based deployment doesn't get deleted and if you can'tdiagnose the error by using the Cloud Build log, then you can deletethe deployment by using the Terraform CLI, as described in the next section.

Delete a console-based deployment by using the Terraform CLI

This section describes how to delete a console-based deployment iferrors occur when you try to delete it through the console. Inthis approach, you download the Terraform configuration for the deployment thatyou want to delete and then use the Terraform CLI to delete the deployment.

1. Identify the region where the deployment's Terraform code, logs, and otherdata are stored. This region might be different from the region thatyou selected while deploying the solution.

   1. In the Google Cloud console, go to theSolution deploymentspage.

      Go to Solution deployments

   2. Select the project that contains the deployment that you want to delete.

   3. In the list of deployments, identify the row for the deployment that youwant to delete.

   4. Clickexpand_moreView all rowcontent.

   5. In theLocation column, note thesecond location, as highlightedin the following example:

      

2. In the Google Cloud console, activate Cloud Shell.

   Activate Cloud Shell

   At the bottom of the Google Cloud console, aCloud Shell session starts and displays a command-line prompt. Cloud Shell is a shell environment with the Google Cloud CLI already installed and with values already set for your current project. It can take a few seconds for the session to initialize.

3. Create environment variables for the project ID, region, and name ofthe deployment that you want to delete:

   exportREGION="REGION"exportPROJECT_ID="PROJECT_ID"exportDEPLOYMENT_NAME="DEPLOYMENT_NAME"

   In these commands, replace the following:

   • REGION: the location that you noted earlier inthis procedure.
   • PROJECT_ID: the ID of the project where youdeployed the solution.
   • DEPLOYMENT_NAME: the name of the deploymentthat you want to delete.

4. Get the ID of the latest revision of the deployment that you wantto delete:

   exportREVISION_ID=$(curl\-H"Authorization: Bearer $(gcloud auth print-access-token)"\-H"Content-Type: application/json"\"https://config.googleapis.com/v1alpha2/projects/${PROJECT_ID}/locations/${REGION}/deployments/${DEPLOYMENT_NAME}"\|jq.latestRevision-r)echo$REVISION_ID

   The output is similar to the following:

   projects/PROJECT_ID/locations/REGION/deployments/DEPLOYMENT_NAME/revisions/r-0

5. Get the Cloud Storage location of the Terraform configuration forthe deployment:

   exportCONTENT_PATH=$(curl\-H"Authorization: Bearer $(gcloud auth print-access-token)"\-H"Content-Type: application/json"\"https://config.googleapis.com/v1alpha2/${REVISION_ID}"\|jq.applyResults.content-r)echo$CONTENT_PATH

   The following is an example of the output of this command:

   gs://PROJECT_ID-REGION-blueprint-config/DEPLOYMENT_NAME/r-0/apply_results/content

6. Download the Terraform configuration from Cloud Storage toCloud Shell:

   gcloud storage cp $CONTENT_PATH $HOME --recursivecd $HOME/content/infra

   Wait until theOperation completed message is displayed, as shown inthe following example:

   Operation completed over 45 objects/268.5 KiB

7. Initialize Terraform:

   terraform init

   Wait until you see the following message:

   Terraform has been successfully initialized!

8. Remove the deployed resources:

   terraform destroy

   Terraform displays a list of the resources that will be destroyed.

   If any warnings about undeclared variables are displayed, ignore thewarnings.

9. When you're prompted to perform the actions, enteryes.

   Terraform displays messages showing the progress. After all theresources are deleted, Terraform displays the following message:

   Destroy complete!

10. Delete the deployment artifact:

    curl-XDELETE\-H"Authorization:Bearer$(gcloudauthprint-access-token)"\-H"Content-Type:application/json"\"https://config.googleapis.com/v1alpha2/projects/${PROJECT_ID}/locations/${REGION}/deployments/${DEPLOYMENT_NAME}?force=true&delete_policy=abandon"

11. Wait a few seconds and then verify that the deployment artifact wasdeleted:

    curl-H"Authorization:Bearer$(gcloudauthprint-access-token)"\-H"Content-Type:application/json"\"https://config.googleapis.com/v1alpha2/projects/${PROJECT_ID}/locations/${REGION}/deployments/${DEPLOYMENT_NAME}"\|jq.error.message

    If the output showsnull, wait a few seconds and then run the commandagain.

    After the deployment artifact is deleted, a message as shown in thefollowing example is displayed:

    Resource 'projects/PROJECT_ID/locations/REGION/deployments/DEPLOYMENT_NAME' was not found

Submit feedback

Jump Start Solutions are for informational purposes only and are not officiallysupported products. Google may change or remove solutions without notice.

To troubleshoot errors, review the Cloud Build logs and the Terraformoutput.

To submit feedback, do the following:

• For documentation, in-console tutorials, or the solution, usetheSend Feedback button on the page.

• For unmodified Terraform code, create issues in theGitHub repository.GitHub issues are reviewed on a best-effort basis and are not intended forgeneral usage questions.

• For issues with the products that are used in the solution, contactCloud Customer Care.

What's next

To explore more using the Cloud SDK Client Library solution:

• Learn aboutAPIs that power Google Cloud and how to interact with them in your programming language of choice.
• Learn howAPIs are powered by Identity and Access Managementon Google Cloud.
• TroubleshootAPI issues in the Cloud Client API apps.

Contributors

Author:Kadeem Dunn | Technical Writer

Other contributor: Katie McLaughlin | Senior Developer Relations Engineer