GSP330

Overview

In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions, you will use the skills learned from the labs in the course to figure out how to complete the tasks on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.

When you take a challenge lab, you will not be taught new Google Cloud concepts. You are expected to extend your learned skills, like changing default values and reading and researching error messages to fix your own mistakes.

To score 100% you must successfully complete all tasks within the time period!

This lab is recommended for students enrolled in the Implement DevOps Workflows in Google Cloud course. Are you ready for the challenge?

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab---remember, once you start, you cannot pause a lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

Prerequisites

A GitHub account is required for this Lab. If you already have one, you can use it. If not, you will need to create a GitHub account

Challenge scenario

As a recent hire as a DevOps Engineer for Cymbal Superstore a few months ago, you have learned the ins and outs of how the company operates its e-commerce website. Specifically, the DevOps team is working on a large scale CI/CD pipeline that they would like your assistance with building. This allows the company to help developers automate tasks, collaborate more effectively with other teams, and release software more frequently and reliably. Your experience with Github, Artifact Registry, Docker, and Cloud Build is going to be a large help since Cymbal Superstore would like to use all native Google Cloud Services for their pipeline.

Before you start this project, the DevOps team would like you to demonstrate your new skills. As part of this demonstration, they have a list of tasks they would like to see you do in an allotted period of time in a sandbox environment.

Your challenge

Your tasks include the following:

• Creating a GKE cluster based on a set of configurations provided.
• Creating a Github Repository to host your Go application code.
• Creating Cloud Build Triggers that deploy a production and development application.
• Pushing updates to the app and creating new builds.
• Rolling back the production application to a previous version.

Overall, you are creating a simple CI/CD pipeline using Github Repositories, Artifact Registry, and Cloud Build.

Task 1. Create the lab resources

In this section, you initialize your Google Cloud project for the demo environment. You enable the required APIs, configure Git in Cloud Shell, create an Artifact Registry Docker repository, and create a GKE cluster to run your production and development applications on.

1. Run the following command to enable the APIs for GKE, Cloud Build, and Github Repositories:

gcloud services enable container.googleapis.com \ cloudbuild.googleapis.com

2. Add the Kubernetes Developer role for the Cloud Build service account:

export PROJECT_ID=$(gcloud config get-value project) gcloud projects add-iam-policy-binding $PROJECT_ID \ --member=serviceAccount:$(gcloud projects describe $PROJECT_ID \ --format="value(projectNumber)")@cloudbuild.gserviceaccount.com --role="roles/container.developer"

3. Run the following commands to configure Git and GitHub in Cloud Shell.

curl -sS https://webi.sh/gh | sh gh auth login gh api user -q ".login" GITHUB_USERNAME=$(gh api user -q ".login") git config --global user.name "${GITHUB_USERNAME}" git config --global user.email "${USER_EMAIL}" echo ${GITHUB_USERNAME}

Press Enter to accept the default options. Read the instructions in the CLI tool to Github Login with a web browser. If you have logged in successfully, it will show output your GitHub username.

4. Create an Artifact Registry Docker repository named my-repository in the region to store your container images.

5. Create a GKE Standard cluster named hello-cluster with the following configuration:

Setting	Value
Zone
Release channel	Regular
Cluster version	1.29 or newer
Cluster autoscaler	Enabled
Number of nodes	3
Minimum nodes	2
Maximum nodes	6

6. Create the prod and dev namespaces on your cluster.

Click Check my progress to verify the objective. Create the lab resources

Task 2. Create a repository in Github Repositories

In this task, you create a repository sample-app in Github Repositories and initialize it with some sample code. This repository holds your Go application code, and be the primary source for triggering builds.

1. Create an empty repository named sample-app in Github Repositories.

2. Clone the sample-app Github Repository in Cloud Shell.

3. Use the following command to copy the sample code into your sample-app directory:

cd ~ gsutil cp -r gs://spls/gsp330/sample-app/* sample-app

4. Run the following command, which will automatically replace the <your-region> and <your-zone> placeholders in the cloudbuild-dev.yaml and cloudbuild.yaml files with the assigned region and zone of your project:

export REGION="{{{project_0.default_region | REGION}}}" export ZONE="{{{project_0.default_zone | ZONE}}}" for file in sample-app/cloudbuild-dev.yaml sample-app/cloudbuild.yaml; do sed -i "s/<your-region>/${REGION}/g" "$file" sed -i "s/<your-zone>/${ZONE}/g" "$file" done

5. Create a Github repository with name sample-app

6. After creating repository make your first commit with the sample code added to your sample-app directory, and push the changes to the master branch.

7. Create a branch named dev. Make a commit with the sample code added to your sample-app directory and push the changes to the dev branch.

8. Verify you have the sample code and branches stored in the Github Repository.

The code you just cloned contains a simple Go application that has two entry points: Red and Blue. Each displays a simple colored square on the web page depending on the entry point you go to.

Task 3. Create the Cloud Build Triggers

In this section, you create two Cloud Build Triggers.

• The first trigger listens for changes on the master branch and builds a Docker image of your application, pushes it to Google Artifact Registry, and deploys the latest version of the image to the prod namespace in your GKE cluster.

• The second trigger listens for changes on the dev branch and build a Docker image of your application and push it to Google Artifact Registry, and deploy the latest version of the image to the dev namespace in your GKE cluster.

1. Create a Cloud Build Trigger named sample-app-prod-deploy that with the following configurations:

   • Event: Push to a branch
   • Source:
     • Connect to a new repository and select the source code management provider: GitHub (Cloud Build GitHub App)
     • Choose the Github repository: sample-app
   • Branch: ^master$
   • Cloud Build Configuration File: cloudbuild.yaml

2. Create a Cloud Build Trigger named sample-app-dev-deploy that with the following configurations:

   • Event: Push to a branch
   • Source, Choose the Github repository : sample-app
   • Branch: ^dev$
   • Cloud Build Configuration File: cloudbuild-dev.yaml

After setting up the triggers, any changes to the branches trigger the corresponding Cloud Build pipeline, which builds and deploy the application as specified in the cloudbuild.yaml files.

Click Check my progress to verify the objective. Create the Cloud Build Triggers

Task 4. Deploy the first versions of the application

In this section, you build the first version of the production application and the development application.

Build the first development deployment

1. In Cloud Shell, inspect the cloudbuild-dev.yaml file located in the sample-app directory to see the steps in the build process. In cloudbuild-dev.yaml file, replace the <version> on lines 9 and 13 with v1.0.

2. Navigate to the dev/deployment.yaml file and Update the <todo> on line 17 with the correct container image name. Also, replace the PROJECT_ID variable with actual project ID in the container image name.

Note: Make sure you have same container image name in dev/deployment.yaml and cloudbuild-dev.yaml file.

3. Make a commit with your changes on the dev branch and push changes to trigger the sample-app-dev-deploy build job.

4. Verify your build executed successfully in Cloud build History page, and verify the development-deployment application was deployed onto the dev namespace of the cluster.

5. Expose the development-deployment deployment to a LoadBalancer service named dev-deployment-service on port 8080, and set the target port of the container to the one specified in the Dockerfile.

6. Navigate to the Load Balancer IP of the service and add the /blue entry point at the end of the URL to verify the application is up and running. It should resemble something like the following: http://34.135.97.199:8080/blue.

Build the first production deployment

1. Switch to the master branch. Inspect the cloudbuild.yaml file located in the sample-app directory to see the steps in the build process. In cloudbuild.yaml file, replace the <version> on lines 11 and 16 with v1.0.

2. Navigate to the prod/deployment.yaml file and update the <todo> on line 17 with the correct container image name. Also, replace the PROJECT_ID variable with actual project ID in the container image name.

Note: Make sure you have same container image name in prod/deployment.yaml and cloudbuild.yaml file.

3. Make a commit with your changes on the master branch and push changes to trigger the sample-app-prod-deploy build job.

4. Verify your build executed successfully in Cloud build History page, and verify the production-deployment application was deployed onto the prod namespace of the cluster.

5. Expose the production-deployment deployment on the prod namespace to a LoadBalancer service named prod-deployment-service on port 8080, and set the target port of the container to the one specified in the Dockerfile.

6. Navigate to the Load Balancer IP of the service and add the /blue entry point at the end of the URL to verify the application is up and running. It should resemble something like the following: http://34.135.245.19:8080/blue.

Click Check my progress to verify the objective. Deploy the first versions of the application

Task 5. Deploy the second versions of the application

In this section, you build the second version of the production application and the development application.

Build the second development deployment

1. Switch back to the dev branch.

Note: Before proceeding, make sure you are on dev branch to create deployment for dev environment.

2. In the main.go file, update the main() function to the following:

func main() { http.HandleFunc("/blue", blueHandler) http.HandleFunc("/red", redHandler) http.ListenAndServe(":8080", nil) }

3. Add the following function inside of the main.go file:

func redHandler(w http.ResponseWriter, r *http.Request) { img := image.NewRGBA(image.Rect(0, 0, 100, 100)) draw.Draw(img, img.Bounds(), &image.Uniform{color.RGBA{255, 0, 0, 255}}, image.ZP, draw.Src) w.Header().Set("Content-Type", "image/png") png.Encode(w, img) }

4. Inspect the cloudbuild-dev.yaml file to see the steps in the build process. Update the version of the Docker image to v2.0.

5. Navigate to the dev/deployment.yaml file and update the container image name to the new version (v2.0).

6. Make a commit with your changes on the dev branch and push changes to trigger the sample-app-dev-deploy build job.

7. Verify your build executed successfully in Cloud build History page, and verify the development-deployment application was deployed onto the dev namespace of the cluster and is using the v2.0 image.

8. Navigate to the Load Balancer IP of the service and add the /red entry point at the end of the URL to verify the application is up and running. It should resemble something like the following: http://34.135.97.199:8080/red.

Note: it may take a couple of minutes for the updates to propagate to your load balancer.

Build the second production deployment

1. Switch to the master branch.

Note: Before proceeding, make sure you are on master branch to create deployment for master environment.

2. In the main.go file, update the main() function to the following:

func main() { http.HandleFunc("/blue", blueHandler) http.HandleFunc("/red", redHandler) http.ListenAndServe(":8080", nil) }

3. Add the following function inside of the main.go file:

func redHandler(w http.ResponseWriter, r *http.Request) { img := image.NewRGBA(image.Rect(0, 0, 100, 100)) draw.Draw(img, img.Bounds(), &image.Uniform{color.RGBA{255, 0, 0, 255}}, image.ZP, draw.Src) w.Header().Set("Content-Type", "image/png") png.Encode(w, img) }

4. Inspect the cloudbuild.yaml file to see the steps in the build process. Update the version of the Docker image to v2.0.

5. Navigate to the prod/deployment.yaml file and update the container image name to the new version (v2.0).

6. Make a commit with your changes on the master branch and push changes to trigger the sample-app-prod-deploy build job.

7. Verify your build executed successfully in Cloud build History page, and verify the production-deployment application was deployed onto the prod namespace of the cluster and is using the v2.0 image.

8. Navigate to the Load Balancer IP of the service and add the /red entry point at the end of the URL to verify the application is up and running. It should resemble something like the following: http://34.135.245.19:8080/red.

Note: it may take a couple of minutes for the updates to propagate to your load balancer.

Great! You have successfully created fully functioning production and development CI/CD pipelines.

Click Check my progress to verify the objective. Deploy the second versions of the application

Task 6. Roll back the production deployment

In this section, you roll back the production deployment to a previous version.

1. Roll back the production-deployment to use the v1.0 version of the application.

Hint: Using Cloud build history, you can easily rollback/rebuild the deployments with the previous versions.

2. Navigate to the Load Balancer IP of the service and add the /red entry point at the end of the URL of the production deployment and response on the page should be 404.

Click Check my progress to verify the objective. Roll back the production deployment

Congratulations!

Congratulations! In this lab you verified your skills on implementing DevOps workflows in Google Cloud. You first created a GKE cluster to run your application and a git repository to host your code base. You then created Cloud Build Triggers, modified the code and the templates, and pushed updates to the repo that created your first development and production application builds. You then pushed updates to the application to create new builds, and rolled back the production application to a previous version. You are now ready to start DevOps work in your own environment!

Earn your next skill badge

This self-paced lab is part of the Implement DevOps Workflows in Google Cloud course. Completing this skill badge course earns you the badge above, to recognize your achievement. Share your badge on your resume and social platforms, and announce your accomplishment using #GoogleCloudBadge.

This skill badge is part of Google Cloud’s Cloud Devops Engineer learning path. Continue your learning journey by enrolling in the Monitor and Log with Google Cloud Observability course.

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Manual Last Updated June 26, 2024

Lab Last Tested June 26, 2024

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