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 quest 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 in Google Cloud Quest. 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.

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 will allow the company to help developers automate tasks, collaborate more effectively with other teams, and release software more frequently and reliably. Your experience with Cloud Source Repositories, 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 will include the following:

• Creating a GKE cluster based on a set of configurations provided.
• Creating a Google Source 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 will be creating a simple CI/CD pipeline using Cloud Source Repositories, Artifact Registry, and Cloud Build.

Task 1. Create the lab resources

In this section, you will initialize your Google Cloud project for the demo environment. You will 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 Cloud Source Repositories:

gcloud services enable container.googleapis.com \ cloudbuild.googleapis.com \ sourcerepo.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 to configure Git in Cloud Shell, replacing <email> with your generated lab email address and <name> with your name.

git config --global user.email <email> git config --global user.name <name>

4. Create an Artifact Registry Docker repository named my-repository in the us-central1 region to store your container images.

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

Setting	Value
Zone	us-central1-a
Release channel	Regular
Cluster version	1.25.5-gke.2000 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 Cloud Source Repositories

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

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

2. Clone the sample-app Cloud Source 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. Make your first commit with the sample code added to your sample-app directory, and push the changes to the master branch.

5. 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.

6. Verify you have the sample code and branches stored in the Source Repository.

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

Click Check my progress to verify the objective. Create the repository in Cloud Source Repositories

Task 3. Create the Cloud Build Triggers

In this section you will 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 will listen 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 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 Repository: sample-app
   • Branch: ^dev$
   • Cloud Build Configuration File: cloudbuild-dev.yaml

After setting up the triggers, any changes to the branches will trigger the corresponding Cloud Build pipeline, which will build 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 will 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 to see the steps in the build process. Fill in the <version> on lines 10 and 15 with v1.0.

2. Navigate to the dev/deployment.yaml file and fill in the <todo> on line 17 with the correct container image name.

Hint: there are two containers that are built, one for the production app and one for the development app. Make sure to reference the correct one!

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, 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. Inspect the cloudbuild.yaml file to see the steps in the build process. Fill in the <version> on lines 10 and 15 with v1.0.

2. Navigate to the prod/deployment.yaml file and fill in the <todo> on line 17 with the correct container image name.

Note: your container image name cannot contain any variables, so you will need to use the full path of your image in Artifact Registry.

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, 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 will build the second version of the production application and the development application.

Build the second development deployment

1. Switch back to the dev branch.

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, 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.

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, 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 will roll back the production deployment to a previous version.

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

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 workflow 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 in Google Cloud skill badge quest. Completing this skill badge quest 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 Operations Suite quest.

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Manual Last Updated February 16, 2023

Lab Last Tested February 28, 2023

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