Creating a Google Kubernetes Engine (GKE) Cluster with Terraform in a Custom VPC

In this tutorial, we will be looking at how to use to create a Kubernetes Engine (GKE) cluster within its VPC using Terraform. GKE is a managed, production-ready environment for deploying containerized applications on Google Cloud. Terraform Google Terraform is an open-source tool developed by HashiCorp. It's a cloud-agnostic tool used to automate and manage your infrastructure in a predictable and consistent manner. Terraform uses declarative language to describe your infrastructure, which makes it easier to understand and reason about. Infrastructure as Code (IaC) What are we going to create? In this blog, we will look at how to create the following resources in Azure Cloud. VPC and Subnet GKE Cluster Worker nodepools Connect to the GKE Cluster and Install helm chart to validate the functionality nginx We will skip going in detail on terraform modules as we have already covered those in detail in our blog . Create EKS cluster within its VPC The complete terraform code for what we will discuss below is in this . repository Prerequisites Before you get started, you need to have the following in place: A Google Cloud Platform (GCP) account and a project where you want to create the resources. Also create a Service Account for permissions needed for terraform to interact with GCP APIs. Please sections and below. Setup your GCP Project Create a Service Account for Terraform The Google Cloud SDK installed and initialized on your local machine Basic understanding of GKE and VPC and Subnets Terraform installed. kubectl compatible with the AKS version you are installing. terraform-docs if you want to auto-generate the documentation and tfswitch to manage multiple versions of terraform helm a package manager for Kubernetes manifests, we will use it to install nginx helm chart once the cluster is created. Setup your GCP Project First, we need to set up a project on GCP. This can be done through the . Note the as we will need this in Terraform scripts below. GCP console Project ID Create a Service Account for Terraform Terraform needs permission to interact with the GCP API. This is accomplished by creating a service account. In the GCP console, navigate to , provide a name, and grant it the and roles. IAM & Admin > Service Accounts > Create Service Account Kubernetes Engine Admin Service Account User Next, create a JSON key for this service account and download it. Keep this file safe and secure, as it provides administrative access to your GCP project. Creating and downloading a service account key can indeed be accomplished with Terraform. However, it's important to note that handling these keys is a sensitive operation, as they grant access to your Google Cloud resources. NOTE: You should handle these keys in a secure manner and avoid exposing them whenever possible. Storing secrets in Terraform state is considered an because the state is often stored in plaintext, so ensure you are managing these secrets according to your organization's best practices for secret management. anti-pattern Content Overview Module Structure Terraform Modules Setting Up the Environment Deploy and Validate Clean Up Conclusion References Author Notes Module Structure We have explained terraform modularization in detail in our previous blog hence we will not go over those details again. But, the following is the outline of the modules we are going to build in the sections below. Create EKS cluster within its VPC my-gke-tf/ # root directory . ├── cluster # scaffold module which invokes gke and vpc_and_subnets module │ ├── main.tf │ ├── outputs.tf │ └── variables.tf ├── modules │ ├── gke # module to create k8s cluster and worker nodepools │ │ ├── main.tf │ │ ├── outputs.tf │ │ └── variables.tf │ └── vpc_and_subnets # module to create vpc and subnets │ ├── main.tf │ ├── outputs.tf │ └── variables.tf ├── main.tf # invokes cluster module to create gke cluster in its vpc ├── outputs.tf ├── sample.tfvars # sample variables file └── variables.tf Terraform Modules The following are the terraform modules we will create in directory. You can refer the above section for the directory structure. We will look at respective terraform files below. Please note the terraform files may have been abbreviated for brevity, the complete code is available in this . my-gke-tf repository modules These are the APIs created by the Platform team, these modules can also be separated out to its dedicated repository in real world and can be imagined as being used as reference by remote modules prepared by the users wanting to claim the infrastructure. vpc_and_subnets This is an opinionated module created by the Platform team to create an . Create following files under directory. GCP VPC and Subnet modules/vpc_and_subnets file below locks down the google provider version we have validated this module with and also externalizes the vars like , , and where the resources need to be created. main.tf vpc_name subnet_name cidrBlock region The following file may have been abbreviated for brevity. # setup google terraform provider terraform { required_providers { google = { source = "hashicorp/google" version = "4.74.0" } } } # VPC - https://registry.terraform.io/providers/hashicorp/google/4.74.0/docs/resources/compute_network resource "google_compute_network" "vpc" { name = var.vpc_name description = var.vpc_description # the network is created in "custom subnet mode" # we will explicitly connect subnetwork resources below using google_compute_subnetwork resource auto_create_subnetworks = "false" } # Subnet - https://registry.terraform.io/providers/hashicorp/google/4.74.0/docs/resources/compute_subnetwork resource "google_compute_subnetwork" "subnet" { name = var.subnet_name description = var.subnet_description region = var.region network = google_compute_network.vpc.name ip_cidr_range = var.cidrBlock } The file mentions the variables being accepted as inputs from the user, which you can seeing being referred as in the above file. variables.tf var. main.tf The following may have been abbreviated for brevity. The complete working code can be found . variables.tf here variable "vpc_name" { type = string description = "Name of the resource. Provided by the client when the resource is created. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash." } variable "vpc_description" { type = string description = "An optional description of this resource. The resource must be recreated to modify this field." default = "" } variable "subnet_name" { type = string description = "The name of the resource, provided by the client when initially creating the resource. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash." } variable "subnet_description" { type = string description = "An optional description of this resource. Provide this property when you create the resource. This field can be set only at resource creation time." default = "" } variable "cidrBlock" { type = string description = "The range of internal addresses that are owned by this subnetwork. Provide this property when you create the subnetwork. For example, 10.0.0.0/8 or 192.168.0.0/16. Ranges must be unique and non-overlapping within a network. Only IPv4 is supported." } variable "region" { type = string description = "The GCP region for this subnetwork." } The file will output the necessary resource the user of this module might need to consume and probably use it as input to other modules. For example, we will need and as input to module below. outputs.tf vpc_self_link subnet_self_link gke output "vpc_self_link" { description = "The URI of the created resource" value = google_compute_network.vpc.self_link } output "subnet_self_link" { description = "The URI of the created resource" value = google_compute_subnetwork.subnet.self_link } output "vpc_name" { description = "vpc network name" value = google_compute_network.vpc.name } output "subnet_name" { description = "subnet name" value = google_compute_subnetwork.subnet.name } gke This is an opinionated module to create a GKE Cluster with an optional ability to create more worker nodepools. Create the following files under directory. modules/gke below file below locks down the azure provider version we have validated this module with and also externalizes the vars like , , config etc.. to create the GKE cluster. We are retrieving the first 3 availability zones using data resource , and these zones will be used to create the eks cluster and nodepools. We are also retrieving the latest from the provided by the user using data resource . main.tf cluster_name k8s_version nodepools google_compute_zones min_master_version k8s_version google_container_engine_versions To be able to successfully create the GKE cluster and Nodepools, we also need to create/enable project services , and . compute container cloudresourcemanager We are making sure that the is hard-coded to to avoid any automatic updates to the GKE cluster. This is a recommended practice if you want to control the k8s upgrades. We are also creating self-managed nodepools instead of default nodepools, such that we can easily add/remove nodepools as needed. release_channel UNSPECIFIED The complete working code can be found . here # setup google terraform provider terraform { required_providers { google = { source = "hashicorp/google" version = "4.74.0" } } } locals { zones = slice(data.google_compute_zones.available.names, 0, 3) # we only care about compute and container service here hence only enabling these project services # without cloudresourcemanager we get errors services = toset(["compute", "container", "cloudresourcemanager"]) # we will pick the latest k8s version master_version = data.google_container_engine_versions.main.valid_master_versions[0] } # allows management of a single API service for a Google Cloud Platform project. # official documentation - https://registry.terraform.io/providers/hashicorp/google/4.74.0/docs/resources/google_project_service resource "google_project_service" "main" { for_each = local.services service = "${each.value}.googleapis.com" disable_on_destroy = false } # to extract the UP available compute zones for the provided region # official documentation - https://registry.terraform.io/providers/hashicorp/google/4.74.0/docs/data-sources/compute_zones data "google_compute_zones" "available" { region = var.region status = "UP" depends_on = [google_project_service.main] } # to retrieve the latest k8s version supported for the provided k8s version in a region # official documentation - https://registry.terraform.io/providers/hashicorp/google/4.74.0/docs/data-sources/container_engine_versions data "google_container_engine_versions" "main" { location = var.region # Since this is just a string match, it's recommended that you append a . after minor versions # to ensure that prefixes such as 1.1 don't match versions like 1.12.5-gke.10 accidentally. version_prefix = "${var.k8s_version}." depends_on = [google_project_service.main] } # GKE cluster - https://registry.terraform.io/providers/hashicorp/google/4.74.0/docs/resources/container_cluster resource "google_container_cluster" "gke" { name = var.cluster_name location = var.region node_locations = local.zones min_master_version = local.master_version # to prevent automatic updates to cluster release_channel { channel = "UNSPECIFIED" } # cluster cannot be created without a nodepool but since we want to use self managed nodes # we will instruct to remove the default node pool on successful cluster creation remove_default_node_pool = true initial_node_count = 1 network = var.network subnetwork = var.subnetwork } # self managed nodepool # official documentation - https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/container_node_pool resource "google_container_node_pool" "nodepools" { for_each = var.nodepools name = each.value.name location = var.region cluster = var.cluster_name node_count = each.value.node_count autoscaling { min_node_count = "0" max_node_count = "100" } management { auto_repair = true auto_upgrade = false } node_config { machine_type = each.value.machine_type labels = each.value.node_labels } lifecycle { ignore_changes = [ initial_node_count, node_count, node_config, node_locations ] } version = local.master_version depends_on = [google_container_cluster.gke] } The file allows the user to configure the subnet where the gke cluster and nodepools needs to be created along with configurations for the nodepools. These configurations are referred in as . variables.tf main.tf var. variable "cluster_name" { type = string description = "The name of the cluster, unique within the project and location." } variable "region" { type = string description = "The location (region or zone) in which the cluster master will be created, as well as the default node location. If you specify a zone (such as us-central1-a), the cluster will be a zonal cluster with a single cluster master. If you specify a region (such as us-west1), the cluster will be a regional cluster with multiple masters spread across zones in the region, and with default node locations in those zones as well" } variable "network" { type = string description = "The name or self_link of the Google Compute Engine network to which the cluster is connected. For Shared VPC, set this to the self link of the shared network." } variable "subnetwork" { type = string description = "The name or self_link of the Google Compute Engine subnetwork in which the cluster's instances are launched." } variable "k8s_version" { type = string description = "kubernetes version" default = "1.27" } variable "nodepools" { description = "Nodepools for the Kubernetes cluster" type = map(object({ name = string node_count = number node_labels = map(any) machine_type = string })) default = { worker = { name = "worker" node_labels = { "worker-name" = "worker" } machine_type = "n1-standard-1" node_count = 1 } } } will output the variables which may be useful to the end user. You may observe that we , and variables are marked as , which prevents from printing any sensitive information on stdout, though it doesn't prevent it from being stored in file. outputs.tf client_certificate client_key cluster_ca_certificate sensitive = true tfstate The complete working code can be found . here output "cluster_id" { description = "an identifier for the resource with format projects/{{project}}/locations/{{zone}}/clusters/{{name}}" value = google_container_cluster.gke.id } output "cluster_master_version" { description = "The current version of the master in the cluster. This may be different than the min_master_version set in the config if the master has been updated by GKE." value = google_container_cluster.gke.master_version } output "client_certificate" { description = "Base64 encoded public certificate used by clients to authenticate to the Kubernetes cluster." value = google_container_cluster.gke.master_auth.0.client_certificate sensitive = true } output "client_key" { description = "Base64 encoded private key used by clients to authenticate to the cluster endpoint." value = google_container_cluster.gke.master_auth.0.client_key sensitive = true } output "cluster_ca_certificate" { description = "Base64 encoded public certificate that is the root certificate of the cluster." value = google_container_cluster.gke.master_auth.0.cluster_ca_certificate sensitive = true } output "endpoint" { description = "The IP address of this cluster's Kubernetes master." value = google_container_cluster.gke.endpoint } cluster modules In the sections above we have created the modules/APIs, it's time to invoke these modules in a consolidated module named . You can imagine this module being written by the client of the platform team which can be any application team wanting to claim infrastructure resources. This module will further be opinionated catering to the needs of the application team. cluster below accepts as input and uses the same name for , and . You can also observe that module uses (vpc unique url) and (subnet unique url) from modules output, this puts an indirect dependency on module. This means module will wait for module to finish before executing. main.tf cluster_name vpc_name subnet_name cluster_name gke_with_node_group vpc_self_link subnet_self_link vpc_with_subnets vpc_with_subnets gke_with_node_group vpc_with_subnets # invoking vpc and subnets modules module "vpc_with_subnets" { # invoke vpc_and_subnets module under modules directory source = "../modules/vpc_and_subnets" # create vpc and subnet with the same name as cluster name vpc_name = var.cluster_name subnet_name = var.cluster_name # region where the resources need to be created region = var.region cidrBlock = var.cidrBlock } # invoking gke module to create gke cluster and node group module "gke_with_node_group" { # invoke gke module under modules directory source = "../modules/gke" cluster_name = var.cluster_name k8s_version = var.k8s_version region = var.region nodepools = var.nodepools network = module.vpc_with_subnets.vpc_self_link subnetwork = module.vpc_with_subnets.subnet_self_link } file accepts less number of parameters than what we saw in vpc and gke modules earlier, as you can see above is written in an opinionated manner catering to the needs of a team. Each team can write their own version of the module. variables.tf main.tf The complete working code can be found . here variable "cluster_name" { type = string description = "vpc, subnet and gke cluster name" } variable "k8s_version" { type = string description = "kubernetes version" default = "1.27" } variable "region" { type = string description = "gcp region where the gke cluster must be created, this region should match where you have created the vpc and subnet" } variable "cidrBlock" { type = string description = "The cidr block for subnet" default = "10.1.0.0/16" } variable "nodepools" { description = "Nodepools for the Kubernetes cluster" type = map(object({ name = string node_count = number node_labels = map(any) machine_type = string })) default = { worker = { name = "worker" node_labels = { "worker-name" = "worker" } machine_type = "n1-standard-1" node_count = 1 } } } file is only retrieving the variables the team may need. outputs.tf output "cluster_id" { description = "an identifier for the resource with format projects/{{project}}/locations/{{zone}}/clusters/{{name}}" value = module.gke_with_node_group.cluster_id } output "cluster_master_version" { description = "The current version of the master in the cluster. This may be different than the min_master_version set in the config if the master has been updated by GKE." value = module.gke_with_node_group.cluster_master_version } output "client_certificate" { description = "Base64 encoded public certificate used by clients to authenticate to the Kubernetes cluster." value = module.gke_with_node_group.client_certificate sensitive = true } output "client_key" { description = "Base64 encoded private key used by clients to authenticate to the cluster endpoint." value = module.gke_with_node_group.client_key sensitive = true } output "cluster_ca_certificate" { description = "Base64 encoded public certificate that is the root certificate of the cluster." value = module.gke_with_node_group.cluster_ca_certificate sensitive = true } output "endpoint" { description = "The IP address of this cluster's Kubernetes master." value = module.gke_with_node_group.endpoint } output "vpc_self_link" { description = "The URI of the created resource" value = module.vpc_with_subnets.vpc_self_link } output "subnet_self_link" { description = "The URI of the created resource" value = module.vpc_with_subnets.subnet_self_link } prepare to invoke the cluster module Now we are at the final stage, where members of the team may want to invoke the cluster module for various use cases. For example, we may want to create , and gke clusters. dev stage prod below is only overriding the , and vars in module we created above, and using other default values. main.tf cluster_name k8s_version region cluster Along with that it's setting the to store the file in s3. This backend is configured at the time of initializing using in the section below. We have explained about this in our earlier blog on how to . terraform backend tfstate terraform init Create EKS cluster within its VPC This also configures the , you will see in the section below that we are overriding the required parameters by setting some environment variables to make sure that terraform creates the resources in the desired gcp account/project. You will also notice that module invocation is pointing to the module we created in the section above. google provider cluster source cluster # to use s3 backend # s3 bucket is configured at command line terraform { backend "s3" {} } # setup google provider # the environment variables below will be set before invoking the module # GOOGLE_CREDENTIALS - path/to/service/account/credentials/file # GOOGLE_PROJECT - google project id where the resources need to be created provider "google" {} # invoke cluster module which creates vpc, subnet and gke cluter with a default worker nodepool module "cluster" { source = "./cluster" region = var.region cluster_name = var.cluster_name k8s_version = var.k8s_version } file and files are as follows. The actual files are here - and . variables.tf outputs.tf variables.tf outputs.tf variable "region" { type = string description = "gcp region where the resources are being created" } variable "cluster_name" { type = string description = "gke cluster name, same name is used for vpc and subnets" default = "platformwale" } variable "k8s_version" { type = string description = "k8s version" default = "1.27" } output "endpoint" { description = "The IP address of this cluster's Kubernetes master." value = module.cluster.endpoint } Now, we also need to create file. You can imagine this as the input file used while invoking the module, this way you can have different behaviors based on your requirement. For example, as discussed earlier, you may have , and for our environment-specific clusters which may have distinguished configurations. The following is the which we will use in the sections below for provisioning the infrastructure. .tfvars dev.tfvars stage.tfvars prod.tfvars sample.tfvars # gcp region region = "us-east1" # gke cluster name, this is the same name used to create the resource group as well as vnet # hence this name must be unique cluster_name = "platformwale" With all these modules, we are all set to actually see the infrastructure for the GKE cluster come to life, please refer to the sections below for further instructions. Setting Up the Environment Create GCP Project and note the as we will need it below. Project ID Terraform needs permission to interact with the GCP API. This is accomplished by creating a service account. In the GCP console, navigate to , provide a name, and grant it the and roles. IAM & Admin > Service Accounts > Create Service Account Kubernetes Engine Admin Service Account User Next, create a JSON key for this service account and download it. Keep this file safe and secure, as it provides administrative access to your GCP project. We will need this file below. Before we dive into creating our resources, let's authenticate gcloud CLI with our GCP account: gcloud auth application-default login Set the following environment variables to prepare to create AKS cluster in a designated subscription in your azure account. export GOOGLE_CREDENTIALS="path/to/service/account/credentials/JSON from #2 above" export GOOGLE_PROJECT="GCP Project ID from #1 above" Deploy and Validate In this section, we will look at the details on how to execute the terraform modules we prepared above to create the GKE cluster within its VPC using terraform, connect to the cluster, and deploy helm chart to validate the functionality of the cluster. nginx Create an s3 bucket to store the tfstate file aws s3api create-bucket --bucket "your-bucket-name" --region "your-aws-region" Initialize terraform module, run this from the root of where you have prepared the terraform files to invoke module my-aks-tf cluster # tfstate file name tfstate_file_name=" " # tfstate s3 bucket name, this will have the tfstate file which you can use for further runs of this terraform module # for example to upgrade k8s version or add new node pools etc.. The bucket name must be unique as s3 is a global service. Terraform will create the s3 bucket if it doesn't exist tfstate_bucket_name="unique s3 bucket name you created above e.g. my-tfstate- " # initialize the terraform module terraform init -backend-config "key=${tfstate_file_name}" -backend-config "bucket=${tfstate_bucket_name}" -backend-config "region=us-east-1" Retrieve the , a preview of what will happen when you apply this terraform module. This is a best practice to understand the change. terraform plan terraform plan -var-file="path/to/your/terraform.tfvars" # example terraform plan -var-file="sample.tfvars" If you are satisfied with the plan above, this is the final step to apply the terraform and wait for the resources to be created. It will take about ~20 mins for all the resources to be created. terraform apply -var-file="path/to/your/terraform.tfvars" # example terraform apply -var-file="sample.tfvars" After successful cluster creation, retrieve the , connect to the GKE cluster and validate the is now pointing to the new cluster. kubeconfig kubeconfig context gcloud auth login gcloud container clusters get-credentials CLUSTER_NAME --zone ZONE_OR_REGION --project PROJECT_ID # example gcloud auth login gcloud container clusters get-credentials "platformwale" --zone "us-east1" --project "${GOOGLE_PROJECT}" # validate that the kubeconfig context is pointing to the new cluster kubectl config current-context Install helm chart, this will create a load balancer service which proves the functionality of the GKE cluster as nginx pods were able to come up successfully. nginx helm repo add bitnami https://charts.bitnami.com/bitnami helm install -n default nginx bitnami/nginx # validate nginx pod and load balancer service kubectl get pods -n default kubectl get svc -n default # example $ kubectl get pods -n default NAME READY STATUS RESTARTS AGE nginx-7c8ff57685-ck9pn 1/1 Running 0 3m31s $ kubectl get svc -n default nginx NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE nginx LoadBalancer 10.19.255.56 XX.XXX.XXX.XXX 80:32142/TCP 76s You will be able to put the in browser and will be able to see welcome page as below - http:// :80 nginx Clean Up When you're done with your resources, you can destroy them with the following commands. This is an extremely important step as otherwise you will see unexpected costs for the resources in your account. # uninstall nginx helm chart to make sure load balancer is deleted helm uninstall -n default nginx # destroy infrastructure terraform destroy -var-file="sample.tfvars" Make sure to delete the service account and GCP project created earlier from the GCP console. Conclusion There you have it! You've successfully created a GKE cluster within a VPC using Terraform. With the power of IaC, you can easily manage, replicate, and version control your infrastructure. Happy Terraforming! References Terraform Documentation Terraform Google Documentation Official GCP Documentation Kubernetes Documentation Please note that this tutorial is a basic guide, and best practices such as state management, data security, and others are not covered here. We recommend further study to understand and implement these practices for production-level projects. Author Notes Feel free to reach out with any concerns or questions you have, either on the GitHub repository or directly on this blog. I will make every effort to address your inquiries and provide resolutions. Stay tuned for the upcoming blog in this series dedicated to Platformwale (Engineers who work on Infrastructure Platform teams). Originally published on July 25, 2023. here