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.
- 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 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 result (failures or details of created resource) is left to the user. e.g. you don't have IP/hostname of 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.
k8s.tf & main.tf
- main.tf
variable "region" {
default = "us-west1"
}
variable "location" {
default = "us-west1-b"
}
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 = "10.127.0.0/20"
network = google_compute_network.default.self_link
region = var.region
private_ip_google_access = true
}
data "google_client_config" "current" {
}
data "google_container_engine_versions" "default" {
location = var.location
}
- k8s.tf
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
}
-
terraform init
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 -
terraform plan
to see any changes that are required for your infrastructure. -
terraform apply
to apply the changes required to reach the desired state of the configuration.