In this part of the tutorial you’ll learn how to create your first layer definitions. First, we’ll create a base layer definition for our Kubernetes cluster. Then, we’ll create a layer definition containing Kibana and Elasticsearch pods to go on top of base layer instances. Before we start, make sure to install the layerform CLI. 
$ go install github.com/ergomake/layerform@latest

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Creating a base layer definition

To create a layer you must first create the Terraform files that belong to it. Then, you must a JSON file called layerform.json to be able to provision a layer definition.

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Creating the base layer’s Terraform files

First, let’s create terraform files for an EKS instance (AWS’s managed Kubernetes offering).
By convention, Layerform recommends you to encapsulate each layer into its own Terraform module. That way, it’s easier to understand each layer’s inputs and outputs, and which files belong to it.
Start by creating an eks folder within your terraform folder. Within that folder, you’ll add a vpc.tf file. 
layerform/
└─ eks/
   └─ vpc.tf
This vpc.tf file creates a VPC for the EKS cluster you’ll add next. 
# In eks.tf
provider "aws" {
  region = "us-east-1"
}

# We'll use the `random` resource to create different
# resource names every time someone spins up a new layer instance
# That way, we avoid naming conflicts
resource "random_string" "eks_name" {
  length  = 8
  special = false
}

locals {
  cluster_name = "preview-eks-${random_string.suffix.result}"
}

data "aws_availability_zones" "available" {}

# Each cluster will use a VPC like this
module "vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  version = "3.14.2"

  name = "vpc-${random_string.eks_name.result}"

  cidr = "10.0.0.0/16"
  azs  = slice(data.aws_availability_zones.available.names, 0, 3)
  map_public_ip_on_launch = false

  private_subnets = ["10.0.1.0/24", "10.0.2.0/24", "10.0.3.0/24"]
  public_subnets  = ["10.0.4.0/24", "10.0.5.0/24", "10.0.6.0/24"]

  enable_nat_gateway   = true
  single_nat_gateway   = true
  one_nat_gateway_per_az = false

  enable_dns_hostnames = true
  enable_dns_support = true

  public_subnet_tags = {
    "kubernetes.io/cluster/${local.cluster_name}" = "shared"
    "kubernetes.io/role/elb"                      = 1
  }

  private_subnet_tags = {
    "kubernetes.io/cluster/${local.cluster_name}" = "shared"
    "kubernetes.io/role/internal-elb"             = 1
  }

  tags = {
    Environment = "dev"
    Layerform   = "true"
  }
}
Now that you have a VPC, go ahead and create an eks.tf file within the eks folder. 
layerform/
└─ eks/
   ├─ vpc.tf
   └─ eks.tf
This eks.tf file will use the terraform-aws-modules/eks module to create an EKS instance which uses the VPC in vpc.tf. 
module "eks" "my_cluster" {
  source  = "terraform-aws-modules/eks/aws"
  version = "19.0.4"

  cluster_name    = "dev-eks-${random_string.eks_name.result}"
  cluster_version = "1.25"

  cluster_endpoint_public_access = true
  cluster_endpoint_private_access = true

  cluster_enabled_log_types = []

  cluster_addons = {
    coredns = {
      most_recent = true
    }
    kube-proxy = {
      most_recent = true
    }
    vpc-cni = {
      most_recent = true
    }
  }

  vpc_id     = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets

  enable_irsa = true

  eks_managed_node_group_defaults = {}

  eks_managed_node_groups = {
    default_pool = {
      name = "default-pool"
      ami_type       = "AL2_x86_64"
      instance_types = ["t3.large"]
      min_size     = 1
      max_size     = 2
      desired_size = 1
    }
  }

  tags = {
    Environment = "dev"
    Layerform   = "true"
  }
}
Finally, create an outputs.tf file to export your cluster’s cluster_id so that you can use it in the layers above. 
layerform/
└─ eks/
  ├─ eks.tf
  ├─ vpc.tf
  └─ outputs.tf <-- outputs values to be used in the elastic_stack layer
In this outputs.tf file, add the HCL code below. 
output "cluster_endpoint" {
  description = "Endpoint for EKS control plane"
  value       = module.eks.my_cluster.cluster_endpoint
}
The last thing you must do is add a eks.tf file outside the eks folder. 
layerform/
├─ eks/
│ ├─ eks.tf
│ ├─ vpc.tf
│ └─ outputs.tf
└─ eks.tf
In that outer eks.tf file, you should declare an instance of the eks module. 
module "eks" {
    source = "./modules/eks"
}
It’s up to you to define how you want this EKS cluster to be accessible.To make these examples work without too much effort, I recommend exposing services to the world using a NodePort or configuring nginx-ingress-controller so that your instances get an URL when you write an ingress for them in your Terraform files.

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Creating the base layer’s definition

To create a layer definition, create a layerform.json file in your project’s root folder. You’ll use this file to store all layer definitions. 
layerform.json
layerform/
├─ eks/
│ ├─ eks.tf
│ ├─ vpc.tf
│ └─ outputs.tf
└─ eks.tf
Within that file, create an object within the root key layers. This layer has a name base and a list of files which includes all files within the eks folder and the outer eks.tf file, which instantiates the module. 
{
    "layers": [
        {
            "name": "base",
            "files": [ "./eks.tf", "./eks/**" ],
        }
    ]
}
Now, configure the back-end in which this layer will be stored. 
currentContext: my-local-context
contexts:
    my-local-context:
        type: local
        # Make sure to create this folder
        dir: /Users/someone/project-layers
Now, run layerform configure in the outermost folder to create a layer definition in your machine.
The file containing the actual layer definition will be saved in the directory specified in your back-end.

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Creating a layer definition for Kibana and Elasticsearch

To create a layer definition for Kibana and Elasticsearch pods, we’ll follow a similar process. First, we’ll create a Terraform module and a file declaring it. Then, we’ll create a layer entry in layerform.json.

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Creating Terraform files for the Kibana and Elasticsearch layer

Start by creating an inputs.tf file to receive the EKS cluster_id you’ll use to configure the Kubernetes provider. 
layerform.json
layerform/
├─ elastic_stack/ <--- Kibana and Elasticsearch terraform files go here
│ └─ inputs.tf
├─ eks/
│ ├─ eks.tf
│ ├─ vpc.tf
│ └─ outputs.tf
└─ eks.tf
Within that file, add a variable for the cluster_id. 
variable "cluster_id" {
  description = "The cluster ID in which these pods will be provisioned"
  type        = string
}
Now, create a providers.tf file and configure the kubernetes provider to connect to the cluster whose cluster_id you will receive as an input. 
main.tf
layerform/
├─ elastic_stack/
│ ├─ providers.tf <--- the k8s provider goes here
│ └─ inputs.tf
├─ eks/
│ ├─ eks.tf
│ ├─ vpc.tf
│ └─ outputs.tf
└─ eks.tf
Within that file, add the data-source for the EKS cluster and instantiate the provider. 
data "aws_eks_cluster_auth" "cluster" {
  name = module.eks.my_cluster.cluster_id
}

provider "kubernetes" {
  host                   = data.aws_eks_cluster.cluster.endpoint
  cluster_ca_certificate = base64decode(data.aws_eks_cluster.cluster.certificate_authority[0].data)
  token                  = data.aws_eks_cluster_auth.cluster.token
}
Then, create a pods.tf file in which you’ll create a namespace for the Kibana and Elasticsearch pods and the pods themselves. 
layerform.json
layerform/
├─ elastic_stack/
│ ├─ pods.tf <--- your namespace and pods go here
│ ├─ providers.tf
│ └─ inputs.tf
├─ eks/
│ ├─ eks.tf
│ ├─ vpc.tf
│ └─ outputs.tf
└─ eks.tf
To create the namespace and pods, you’ll use the kubernetes_namespace and kubernetes_pod resources, as shown below. 
resource "random_string" "namespace_suffix" {
  length  = 8
  special = false
}

resource "kubernetes_namespace" "elastic_stack" {
  metadata {
    name = "elastic-stack-${namespace_suffix}"
  }
}

resource "kubernetes_pod" "elasticsearch" {
  metadata {
    name      = "elasticsearch"
    namespace = kubernetes_namespace.elastic_stack.metadata[0].name

    labels = {
      app = "elasticsearch"
    }
  }

  spec {
    container {
      image = "docker.elastic.co/elasticsearch/elasticsearch:7.17.0"

      env {
        name  = "discovery.type"
        value = "single-node"
      }
    }
  }
}

resource "kubernetes_pod" "kibana" {
  metadata {
    name      = "kibana"
    namespace = kubernetes_namespace.kibana.metadata[0].name

    labels = {
      app = "kibana"
    }
  }

  spec {
    container {
      image = "docker.elastic.co/kibana/kibana:7.10.1"

      env {
        name  = "ELASTICSEARCH_URL"
        value = "http://elasticsearch.${kubernetes_namespace.elastic_stack.metadata[0].name}:9200"
      }
    }
  }
}
Finally, you should go ahead and create an elastic_stack.tf file in the layerform folder which declares the module passing the necessary cluster_id variables to it. 
layerform.json
layerform/
├─ elastic_stack/
│ ├─ pods.tf
│ ├─ providers.tf
│ └─ inputs.tf
├─ eks/
│ ├─ eks.tf
│ ├─ vpc.tf
│ └─ outputs.tf
├─ elastic_stack.tf <--- declare the `elastic_stack` module here
└─ eks.tf
Within that module, you’ll use the output cluster_id from eks as an input to the elastic_stack module. 
module "elastic_stack" {
    source = "./modules/elastic_stack"
    cluster_id = modules.eks.cluster_id
}
The elastic_stack layer will go on top of the base layer. Therefore, it’s fine for elastic_stack to use values belonging to the Terraform files of the base layer below, like modules.eks.cluster_id.On the other hand, you can’t use values from layers above in the layers below. The base layer couldn’t reference values belonging to Terraform files of the elastic_stack layer, for example.

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Creating the actual layer definition for Kibana and Elasticsearch

Now, update your layerform.json to include a layer definition entry for the elastic_stack layer. This layer depends on base and contains the Terraform files in the elastic_stack folder as well as elastic_stack.tf. 
{
    "layers": [
        {
            "name": "base",
            "files": ["./layerform/eks.tf", "./layerform/eks/**"]
        },
        {
            "name": "elastic_stack",
            "files": [
                "./layerform/elastic_stack.tf",
                "./layerform/elastic_stack/**"
            ],
            "dependencies": ["base"]
        }
    ]
}
Finally, run layerform configure again to update your json definitions in the back-end.

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Testing your Terraform files

The files you just created are just plain Terraform files. Therefore, they should work just fine if you use terraform apply with a main.tf file that instantiates both module’s layers.