GSP188

Overview

Google Kubernetes Engine provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. The environment Kubernetes Engine provides consists of multiple machines (specifically, Compute Engine instances) grouped together to form a cluster.

Kubernetes provides the mechanisms through which you interact with your cluster. You use Kubernetes commands and resources to deploy and manage your applications, perform administration tasks and set policies, and monitor the health of your deployed workloads.

In this lab, you deploy the Quiz application into Kubernetes Engine, leveraging Google Cloud resources, including Cloud Build and Artifact Registry, and Kubernetes resources, such as Deployments, Pods, and Services.

What you'll learn

In this lab, you learn how to perform the following tasks:

• Create Dockerfiles to package up the Quiz application frontend and backend code for deployment.
• Harness Cloud Build to produce Docker images.
• Provision a Kubernetes Engine cluster to host the Quiz application.
• Employ Kubernetes deployments to provision replicated Pods into Kubernetes Engine.
• Leverage a Kubernetes service to provision a load balancer for the quiz frontend.

Setup and requirements

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 are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials 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 (recommended) or private browser window to run this lab. This prevents 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: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

2. Click through the following windows:

   • Continue through the Cloud Shell information window.
   • Authorize Cloud Shell to use your credentials to make Google Cloud API calls.

When you are connected, you are already authenticated, and the project is set to your Project_ID, . The output contains a line that declares the Project_ID for this session:

Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

3. (Optional) You can list the active account name with this command:

gcloud auth list

4. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

5. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Launch the Cloud Shell Editor

From Cloud Shell, click Open Editor to launch the code editor.

Note: The code editor launches in a separate tab of your browser, along with Cloud Shell.

Prepare the Quiz application

In this section, you access Cloud Shell, clone the git repository containing the Quiz application, configure environment variables, and run the application.

Clone source code in Cloud Shell

1. Click Open Terminal and clone the repository for the lab:

git clone https://github.com/GoogleCloudPlatform/training-data-analyst

2. Create a soft link as a shortcut to the working directory:

ln -s ~/training-data-analyst/courses/developingapps/v1.2/python/kubernetesengine ~/kubernetesengine

Configure the Quiz application

1. Change the directory that contains the sample files for this lab:

cd ~/kubernetesengine/start

2. To replace the default region with the lab-assigned region, run the following commands:

export APP_REGION={{{project_0.startup_script.app_region | APP_REGION}}} export REGION={{{project_0.default_region | REGION}}} sed -i -e 's/us-central1/'"$REGION"'/g' -e 's/us-central/'"$APP_REGION"'/g' -e 's/python3/'"python3.12"'/g' prepare_environment.sh

3. Configure the Quiz application:

. prepare_environment.sh

This script file:

• Creates a Google App Engine application.
• Exports environment variables GCLOUD_PROJECT and GCLOUD_BUCKET.
• Updates pip then runs pip install -r requirements.txt.
• Creates entities in Google Cloud Datastore.
• Creates a Google Cloud Pub/Sub topic.
• Creates a Cloud Spanner Instance, Database, and Table.
• Prints out the Project ID.

The Quiz application is configured when you see the following message:

Example output message

Creating Cloud Pub/Sub topic Created topic [projects/qwiklabs-gcp-92b7e5716e0cbf7e/topics/feedback]. Created subscription [projects/qwiklabs-gcp-92b7e5716e0cbf7e/subscriptions/worker-subscription]. Creating Cloud Spanner Instance, Database, and Table Creating instance...done. Creating database...done. Project ID: qwiklabs-gcp-92b7e5716e0cbf7e

Click Check my progress to verify the objective. Configure the Quiz application

Review the code

In this section you examine the application files.

To view and edit files, you can use the shell editors that are installed in Cloud Shell, such as nano or vim or the Cloud Shell Editor. This lab uses the Cloud Shell Editor.

Examine the code

• Navigate to training-data-analyst/courses/developingapps/v1.2/python/kubernetesengine/start.

The folder structure for the Quiz application reflects how it will be deployed in Kubernetes Engine.

The web application is in a folder called frontend.

The worker application code that subscribes to Cloud Pub/Sub and processes messages is in a folder called backend.

There are configuration files for Docker (a Dockerfile in the frontend and backend folder) and backend-deployment and frontend-deployment Kubernetes Engine .yaml files.

Create and connect to a Kubernetes Engine cluster

In this section you use Google Cloud console to create and then connect to a Kubernetes Engine cluster.

Create a Kubernetes Engine cluster

1. In the console, click Navigation menu ()> Kubernetes Engine > Clusters.

2. Click Create.

3. Click Configure for Standard demo. Set the following fields to the provided values, leave all others at the default value:

Property	Value
Name	quiz-cluster
Zonal
default Pool > Security > Access scopes	Select Allow full access to all Cloud APIs

4. Click Create. The cluster takes a few minutes to provision.

   Click Check my progress to verify the objective. Create Kubernetes engine cluster

Connect to the cluster

In this section you connect the Quiz application to the kubernetes cluster.

1. When the cluster is ready, click the Actions icon and select Connect.

2. In Connect to the cluster, click Run in Cloud Shell to populated Cloud shell with the command that resembles.

gcloud container clusters get-credentials quiz-cluster --zone {{{project_0.default_zone|ZONE}}} --project {{{project_0.project_id|Project ID}}}

Press ENTER to run the command in Cloud Shell.

3. Run the following command to list the Pods in the cluster:

kubectl get pods

The response should be No resources found in default namespace because there are no Pods in the cluster. It confirms that you have configured security to allow the kubectl command-line tool to perform operations against the cluster.

Create a Docker Repository on Artifact registry

Artifact Registry supports managing container images and language packages. Different artifact types require different specifications. For example, the requests for Maven dependencies are different from requests for Node dependencies.

To support the different API specifications, Artifact Registry needs to know what format you want the API responses to follow. To do this you will create a repository and pass in the --repository-format flag indicating the type of repository desired.

1. From Cloud Shell run the following command to create a repository for Docker images:

gcloud artifacts repositories create container-dev-repo --repository-format=docker \ --location={{{project_0.default_region | REGION}}} \ --description="Docker repository for Container Dev Workshop"

Click Authorize if the Cloud Shell authorization prompt appears.

2. In the Cloud console, go to Artifact Registry > Repositories and notice your newly created Docker repository named container-dev-repo. If you click on it you can see that it's empty at the moment.

Click Check my progress to verify the objective.

Create a Docker Repository

Build Docker images using Cloud Build

In this section, you create a Dockerfile for the application frontend and backend, and then employ Cloud Build to build images and store them in the Artifact Registry.

Create the Dockerfile for the frontend and backend

1. In the Cloud Shell Editor, open frontend/Dockerfile.
2. Now add a block of code that does the following:

• Enters the Dockerfile command to initialize the creation of a custom Docker image using Google's Python App Engine image as the starting point.
• Writes Dockerfile commands to activate a virtual environment.
• Writes the Dockerfile command to execute pip install as part of the build process.
• Writes the Dockerfile command to add the contents of the current folder to the /app path in the container.
• Completes the Dockerfile by entering the statement, gunicorn ..., that executes when the container runs. Gunicorn (Green Unicorn) is an HTTP server that supports the Python Web Server Gateway Interface (WSGI) specification.

Copy and paste the following to Dockerfile:

FROM gcr.io/google_appengine/python RUN virtualenv -p python3.7 /env ENV VIRTUAL_ENV /env ENV PATH /env/bin:$PATH ADD requirements.txt /app/requirements.txt RUN pip install -r /app/requirements.txt ADD . /app CMD gunicorn -b 0.0.0.0:$PORT quiz:app

3. Open the backend/Dockerfile file and copy and paste the following code:

FROM gcr.io/google_appengine/python RUN virtualenv -p python3.7 /env ENV VIRTUAL_ENV /env ENV PATH /env/bin:$PATH ADD requirements.txt /app/requirements.txt RUN pip install -r /app/requirements.txt ADD . /app CMD python -m quiz.console.worker

Build Docker images with Cloud Build

1. In Cloud Shell, make sure you are in the start folder:

cd ~/kubernetesengine/start

2. Run the following command to build the frontend Docker image:

gcloud builds submit -t {{{project_0.default_region | REGION}}}-docker.pkg.dev/{{{project_0.project_id|Project ID}}}/container-dev-repo/quiz-frontend:v1 ./frontend/

Here, Docker image is built and stored in the Artifact Registry. It takes a few minutes.

Ignore any incompatibility messages you see in the output messages.

3. Now run the following command to build the backend Docker image:

gcloud builds submit -t {{{project_0.default_region | REGION}}}-docker.pkg.dev/{{{project_0.project_id|Project ID}}}/container-dev-repo/quiz-backend:v1 ./backend/

When the backend Docker image is ready you see these last messages:

DONE --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ID: 97a3648f-5229-49ef-9c93-a33dbdb421f3 CREATE_TIME: 2024-12-16T13:29:57+00:00 DURATION: 2M10S SOURCE: gs://qwiklabs-gcp-03-2cae39dd1d7c_cloudbuild/source/1734355794.276254-1b5d85622afb4360910a164f9f29734d.tgz IMAGES: us-east1-docker.pkg.dev/qwiklabs-gcp-03-2cae39dd1d7c/container-dev-repo/quiz-backend:v1 STATUS: SUCCESS

4. In the console, from the Navigation menu menu, click Artifact Registry, then select container-dev-repo. You should see two Pods: quiz-frontend and quiz-backend.

5. Click quiz-frontend.

Note: You should see the image Name and Tags (latest).

Click Check my progress to verify the objective. Build Docker Images using Cloud Build

Create a Kubernetes deployment and service resources

In this section, you modify the template yaml files that contain the specification for Kubernetes Deployment and Service resources, and then create the resources in the Kubernetes Engine cluster.

Create a Kubernetes deployment file

1. In the Cloud Shell Editor, open the frontend-deployment.yaml file.

Note: The file skeleton has been created for you. Your job is to replace placeholders with values specific to your project.

2. Replace the placeholders in the frontend-deployment.yaml file using the following values:

Placeholder Name	Value
[GCLOUD_PROJECT]	Project ID (Display the Project ID by entering echo $GCLOUD_PROJECT in Cloud Shell)
[GCLOUD_BUCKET]	Cloud Storage bucket name for the media bucket in your project (Display the bucket name by entering echo $GCLOUD_BUCKET in Cloud Shell)
[FRONTEND_IMAGE_IDENTIFIER]	The frontend image identified in the form [REGION]-docker.pkg.dev/[Project_ID]/container-dev-repo/quiz-frontend:v1

Note: The quiz-frontend deployment provisions three replicas of the frontend Docker image in Kubernetes Pods, distributed across the three nodes of the Kubernetes Engine cluster.

3. Save the file.
4. Replace the placeholders in the backend-deployment.yaml file using the following values:

Placeholder Name	Value
[GCLOUD_PROJECT]	Project ID
[GCLOUD_BUCKET]	Cloud Storage bucket ID for the media bucket in your project
[BACKEND_IMAGE_IDENTIFIER]	The backend image identified in the form [REGION]-docker.pkg.dev/[Project_ID]/container-dev-repo/quiz-backend:v1

Note: The quiz-backend deployment provisions two replicas of the backend Docker image in Kubernetes Pods, distributed across two of the three nodes of the Kubernetes Engine cluster.

5. Save the file.
6. Review the contents of the frontend-service.yaml file.

Note: The service exposes the frontend deployment using a load balancer. The load balancer sends requests from clients to all three replicas of the frontend Pod.

Execute the deployment and service files

1. In Cloud Shell, provision the quiz frontend deployment:

kubectl create -f ./frontend-deployment.yaml

2. Provision the quiz backend deployment:

kubectl create -f ./backend-deployment.yaml

3. Provision the quiz frontend service:

kubectl create -f ./frontend-service.yaml Note: Each command provisions resources in the Kubernetes Engine. It takes a few minutes to complete the process.

Click Check my progress to verify the objective. Create Kubernetes Deployment and service resources

Test the Quiz application

In this section you review the deployed Pods and service and navigate to the Quiz application.

Review the deployed resources

1. In the console, in the Navigation menu, click Kubernetes Engine.
2. Click Workloads in the left menu.

Note: You should see two containers: quiz-frontend and quiz-backend. You may see that the status is OK or in the process of being created.

If the status of one or both containers is Does not have minimum availability, refresh the window.

3. Click quiz-frontend. In the Managed Pods section, there are three quiz-frontend Pods.

4. In the Exposing services section near the bottom, find the Endpoints section and copy the IP address and paste it into the URL field of a new browser tab or window:

5. This opens the Quiz application, which means you successfully deployed the application! You can end your lab here or use the remainder of the time to build some quizzes.

Note: If you get a pop-up that The connection to site is not secure click Continue to Site.

Congratulations!

This concludes the self-paced lab, App Dev: Deploying the Application into Kubernetes Engine - Python. You leveraged Google Cloud resources and Kubernetes resources to deploy a Quiz application.

Next steps / Learn more

Learn more about Kubernetes Engine.

Manual Last Updated December 17, 2024

Lab Last Tested December 17, 2024

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