Deploy Kubernetes Load Balancer Service with Terraform

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Deploy Kubernetes Load Balancer Service with Terraform

35 minutes 5 Credits


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In Terraform, a Provider is the logical abstraction of an upstream API. This lab will show you how to set up a Kubernetes cluster and deploy Load Balancer type NGINX service on it.


In this lab, you will learn how to:

  • Deploy a Kubernetes cluster along with a service using Terraform


For this lab, you should have experience with the following:

  • Familiarity with Kubernetes Services
  • Familiarity with kubectl CLI.

Setup and requirements

Before you click the Start Lab button

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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.

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 pop-up opens for you to select your payment method. On the left is the Lab Details panel 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 panel.

  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 panel.

  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 view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left. Navigation menu icon

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 Activate Cloud Shell icon at the top of the Google Cloud console.

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.

  1. (Optional) You can list the active account name with this command:
gcloud auth list
  1. Click Authorize.


ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:
gcloud config list project


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

Kubernetes services

A service is a grouping of pods that are running on the cluster. Services are "cheap" and you can have many services within the cluster. Kubernetes services can efficiently power a microservice architecture.

Services provide important features that are standardized across the cluster: load-balancing, service discovery between applications, and features to support zero-downtime application deployments.

Each service has a pod label query which defines the pods which will process data for the service. This label query frequently matches pods created by one or more replication controllers. Powerful routing scenarios are possible by updating a service's label query via the Kubernetes API with deployment software.

Why Terraform?

While you could use kubectl or similar CLI-based tools mapped to API calls to manage all Kubernetes resources described in YAML files, orchestration with Terraform presents a few benefits:

  • One language - You can use the same configuration language to provision the Kubernetes infrastructure and to deploy applications into it.
  • Drift detection - terraform plan will always present you the difference between reality at a given time and the config you intend to apply.
  • Full lifecycle management - Terraform doesn't just initially create resources, but offers a single command to create, update, and delete tracked resources without needing to inspect the API to identify those resources.
  • Synchronous feedback - While asynchronous behavior is often useful, sometimes it's counter-productive as the job of identifying operation results (failures or details of created resource) is left to the user. e.g. you don't have the IP/hostname of the load balancer until it has finished provisioning, hence you can't create any DNS record pointing to it.
  • Graph of relationships - Terraform understands relationships between resources which may help in scheduling - e.g. Terraform won't try to create a service in a Kubernetes cluster until the cluster exists.

Task 1. Clone the sample code

  1. In Cloud Shell, start by cloning the sample code:
gsutil -m cp -r gs://spls/gsp233/* .
  1. Navigate to the tf-gke-k8s-service-lb directory:
cd tf-gke-k8s-service-lb

Task 2. Understand the code

  1. Review the contents of the file:

Example output:

... variable "region" { type = string description = "Region for the resource." } variable "location" { type = string description = "Location represents region/zone for the resource." } variable "network_name" { default = "tf-gke-k8s" } provider "google" { region = var.region } resource "google_compute_network" "default" { name = var.network_name auto_create_subnetworks = false } resource "google_compute_subnetwork" "default" { name = var.network_name ip_cidr_range = "" network = google_compute_network.default.self_link region = var.region private_ip_google_access = true } ...
  • Variables are defined for region, zone, and network_name. These will be used to create the Kubernetes cluster.
  • The Google Cloud provider will let us create resources in this project.
  • There are several resources defined to create the appropriate network and cluster.
  • At the end, there are some outputs which you'll see after running terraform apply.
  1. Review the contents of the file:

Example output:

provider "kubernetes" { version = "~> 1.10.0" host = google_container_cluster.default.endpoint token = data.google_client_config.current.access_token client_certificate = base64decode( google_container_cluster.default.master_auth[0].client_certificate, ) client_key = base64decode(google_container_cluster.default.master_auth[0].client_key) cluster_ca_certificate = base64decode( google_container_cluster.default.master_auth[0].cluster_ca_certificate, ) } resource "kubernetes_namespace" "staging" { metadata { name = "staging" } } resource "google_compute_address" "default" { name = var.network_name region = var.region } resource "kubernetes_service" "nginx" { metadata { namespace = kubernetes_namespace.staging.metadata[0].name name = "nginx" } spec { selector = { run = "nginx" } session_affinity = "ClientIP" port { protocol = "TCP" port = 80 target_port = 80 } type = "LoadBalancer" load_balancer_ip = google_compute_address.default.address } } resource "kubernetes_replication_controller" "nginx" { metadata { name = "nginx" namespace = kubernetes_namespace.staging.metadata[0].name labels = { run = "nginx" } } spec { selector = { run = "nginx" } template { container { image = "nginx:latest" name = "nginx" resources { limits { cpu = "0.5" memory = "512Mi" } requests { cpu = "250m" memory = "50Mi" } } } } } } output "load-balancer-ip" { value = google_compute_address.default.address }
  • The script configures a Kubernetes provider with Terraform and creates the service, namespace and a replication_controller resource.
  • The script returns an nginx service IP as an output.

Task 3. Initialize and install dependencies

The terraform init command is used to initialize a working directory containing the Terraform configuration files.

This command performs several different initialization steps in order to prepare a working directory for use and is always safe to run multiple times, to bring the working directory up to date with changes in the configuration:

  1. Run terraform init:
terraform init

Example output:

... * version = "~> 3.8.0" * provider.kubernetes: version = "~> 1.10.0" Terraform has been successfully initialized! You may now begin working with Terraform. Try running `terraform plan` to see any changes that are required for your infrastructure. All Terraform commands should now work. If you ever set or change modules or backend configuration for Terraform, rerun this command to reinitialize your working directory. If you forget, other commands will detect it and remind you to do so if necessary.
  1. Run the terraform apply command, which is used to apply the changes required to reach the desired state of the configuration:
terraform apply -var="region={{{ project_0.default_region | "Region to be allocated" }}}" -var="location={{{ project_0.default_zone | "Zone to be allocated" }}}"
  1. Review Terraform's actions and inspect the resources which will be created.

  2. When ready, type yes to begin Terraform actions.

On completion, you should see similar output:

Example output:

Apply complete! Resources: 7 added, 0 changed, 0 destroyed. Outputs: cluster_name = tf-gke-k8s cluster_region = "{{{project_0.default_region|REGION}}}" cluster_zone = "{{{project_0.default_region|ZONE}}}" load-balancer-ip = network = subnetwork_name = tf-gke-k8s

Verify resources created by Terraform

  1. In the console, navigate to Navigation menu > Kubernetes Engine.
  2. Click on tf-gke-k8s cluster and check its configuration.
  3. In the left panel, click Services & Ingress and check the nginx service status.
  4. Click the Endpoints IP address to open the Welcome to nginx! page in a new browser tab.

Welcome to nginx! page

Click Check my progress to verify your performed task. If you have successfully deployed infrastructure with Terraform, you will see an assessment score.

Deploy infrastructure with Terraform


In this lab, you used Terraform to initialize, plan, and deploy a Kubernetes cluster along with a service.

Finish your quest

This self-paced lab is part of the Managing Cloud Infrastructure with Terraform and DevOps Essentials quests. A quest is a series of related labs that form a learning path. Completing a quest earns you a badge to recognize your achievement. You can make your badge or badges public and link to them in your online resume or social media account. Enroll in any quest that contains this lab and get immediate completion credit. See the Google Cloud Skills Boost catalog to see all available quests.

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Manual Last Updated September 28, 2023

Lab Last Tested September 28, 2023

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