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One of the main benefits of Infrastructure as Code (IAC) is reusing parts of the defined infrastructure. In Terraform, you can use modules to encapsulate logically connected components into one entity and customize them using input variables you define. By using modules to define your infrastructure at a high level, you can separate development, staging, and production environments by only passing in different values to the same modules, which minimizes code duplication and maximizes conciseness.
You are not limited to using only your custom modules. Terraform Registry is integrated into Terraform and lists modules and providers that you can incorporate in your project right away by defining them in the required_providers
section. Referencing public modules can speed up your workflow and reduce code duplication. If you have a useful module and would like to share it with the world, you can look into publishing it on the Registry for other developers to use.
In this tutorial, you’ll explore some of the ways to define and reuse code in Terraform projects. You’ll reference modules from the Terraform Registry, separate development and production environments using modules, learn about templates and how they are used, and specify resource dependencies explicitly using the depends_on
meta argument.
terraform-reusability
, instead of loadbalance
. During Step 2, do not include the pvt_key
variable and the SSH key resource.droplet-lb
module available under modules
in terraform-reusability
. Follow the How to Build a Custom Module tutorial and work through it until the droplet-lb
module is functionally complete. (That is, until the cd ../..
command in the Creating a Module section.)Note: This tutorial has been tested using Terraform 1.0.2
.
In this section, you’ll use modules to separate your target deployment environments. You’ll arrange these according to the structure of a more complex project. You’ll create a project with two modules: one will define the Droplets and Load Balancers, and the other will set up the DNS domain records. Afterward, you’ll write configuration for two different environments (dev
and prod
), which will call the same modules.
dns-records
ModuleAs part of the prerequisites, you set up the initial project under terraform-reusability
and created the droplet-lb
module in its own subdirectory under modules
. You’ll now set up the second module, called dns-records
, containing variables, outputs, and resource definitions. From the terraform-reusability
directory, create dns-records
by running:
- mkdir modules/dns-records
Navigate to it:
- cd modules/dns-records
This module will contain the definitions for your domain and the DNS records that you’ll later point to the Load Balancers. You’ll first define the variables, which will become inputs that this module will expose. You’ll store them in a file called variables.tf
. Create it for editing:
- nano variables.tf
Add the following variable definitions:
variable "domain_name" {}
variable "ipv4_address" {}
Save and close the file. You’ll now define the domain and the accompanying A
and CNAME
records in a file named records.tf
. Create and open it for editing by running:
- nano records.tf
Add the following resource definitions:
resource "digitalocean_domain" "domain" {
name = var.domain_name
}
resource "digitalocean_record" "domain_A" {
domain = digitalocean_domain.domain.name
type = "A"
name = "@"
value = var.ipv4_address
}
resource "digitalocean_record" "domain_CNAME" {
domain = digitalocean_domain.domain.name
type = "CNAME"
name = "www"
value = "@"
}
First, you add the domain name to your DigitalOcean account. The cloud will automatically add the three DigitalOcean nameservers as NS
records. The domain name you supply to Terraform must not already be present in your DigitalOcean account, or Terraform will show an error during infrastructure creation.
Then, you define an A
record for your domain, routing it (the @
as value
signifies the true domain name, without subdomains) to the IP address supplied as the variable ipv4_address
. The actual IP address will be passed in when you initialize an instance of the module. For the sake of completeness, the CNAME
record that follows specifies that the www
subdomain should also point to the same domain. Save and close the file when you’re done.
Next, you’ll define the outputs for this module. The outputs will show the FQDN (fully qualified domain name) of the created records. Create and open outputs.tf
for editing:
- nano outputs.tf
Add the following lines:
output "A_fqdn" {
value = digitalocean_record.domain_A.fqdn
}
output "CNAME_fqdn" {
value = digitalocean_record.domain_CNAME.fqdn
}
Save and close the file when you’re done.
With the variables, DNS records, and outputs defined, the last thing you’ll need to specify are the provider requirements for this module. You’ll specify that the dns-records
module requires the digitalocean
provider in a file called provider.tf
. Create and open it for editing:
- nano provider.tf
Add the following lines:
terraform {
required_providers {
digitalocean = {
source = "digitalocean/digitalocean"
version = "~> 2.0"
}
}
}
When you’re done, save and close the file. Now that the digitalocean
provider has been defined, the dns-records
module is functionally complete.
The current structure of the terraform-reusability
project will look similar to this:
terraform-reusability/
├─ modules/
│ ├─ dns-records/
│ │ ├─ outputs.tf
│ │ ├─ provider.tf
│ │ ├─ records.tf
│ │ ├─ variables.tf
│ ├─ droplet-lb/
│ │ ├─ droplets.tf
│ │ ├─ lb.tf
│ │ ├─ outputs.tf
│ │ ├─ provider.tf
│ │ ├─ variables.tf
├─ provider.tf
So far, you have two modules in your project: the one you just created (dns-records
) and the one you created as part of the prerequisites (droplet-lb
).
To facilitate different environments, you’ll store the dev
and prod
environment config files under a directory called environments
, which will reside in the root of the project. Both environments will call the same two modules, but with different parameter values. The advantage of this is when the modules change internally in the future, you’ll only need to update the values you are passing in.
First, navigate to the root of the project by running:
- cd ../..
Then, create the dev
and prod
directories under environments
at the same time:
- mkdir -p environments/dev && mkdir environments/prod
The -p
argument orders mkdir
to create all directories in the given path.
Navigate to the dev
directory, as you’ll first configure that environment:
- cd environments/dev
You’ll store the code in a file named main.tf
, so create it for editing:
- nano main.tf
Add the following lines:
module "droplets" {
source = "../../modules/droplet-lb"
droplet_count = 2
group_name = "dev"
}
module "dns" {
source = "../../modules/dns-records"
domain_name = "your_dev_domain"
ipv4_address = module.droplets.lb_ip
}
Here you call and configure the two modules, droplet-lb
and dns-records
, which will together result in the creation of two Droplets. They’re fronted by a Load Balancer, and the DNS records for the supplied domain are set up to point to that Load Balancer. Remember to replace your_dev_domain
with your desired domain name for the dev
environment, then save and close the file.
Next, you’ll configure the DigitalOcean provider and create a variable for it to be able to accept the personal access token you’ve created as part of the prerequisites. Open a new file, called provider.tf
, for editing:
- nano provider.tf
Add the following lines:
terraform {
required_providers {
digitalocean = {
source = "digitalocean/digitalocean"
version = "~> 2.0"
}
}
}
variable "do_token" {}
provider "digitalocean" {
token = var.do_token
}
In this code, you require the digitalocean
provider to be available and to pass in the do_token
variable to its instance. Save and close the file.
Initialize the configuration by running:
- terraform init
You’ll receive the following output:
OutputInitializing modules...
- dns in ../../modules/dns-records
- droplets in ../../modules/droplet-lb
Initializing the backend...
Initializing provider plugins...
- Finding digitalocean/digitalocean versions matching "~> 2.0"...
- Installing digitalocean/digitalocean v2.10.1...
- Installed digitalocean/digitalocean v2.10.1 (signed by a HashiCorp partner, key ID F82037E524B9C0E8)
Partner and community providers are signed by their developers.
If you'd like to know more about provider signing, you can read about it here:
https://www.terraform.io/docs/cli/plugins/signing.html
Terraform has created a lock file .terraform.lock.hcl to record the provider
selections it made above. Include this file in your version control repository
so that Terraform can guarantee to make the same selections by default when
you run "terraform init" in the future.
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.
The configuration for the prod
environment is similar. Navigate to its directory by running:
- cd ../prod
Create and open main.tf
for editing:
- nano main.tf
Add the following lines:
module "droplets" {
source = "../../modules/droplet-lb"
droplet_count = 5
group_name = "prod"
}
module "dns" {
source = "../../modules/dns-records"
domain_name = "your_prod_domain"
ipv4_address = module.droplets.lb_ip
}
The difference between this and your dev
code is that there will be five Droplets deployed. Furthermore, the domain name, which you should replace with your prod
domain name, will be different. Save and close the file when you’re done.
Then, copy over the provider configuration from dev
:
- cp ../dev/provider.tf .
Initialize this configuration as well:
- terraform init
The output of this command will be the same as the previous time you ran it.
You can try planning the configuration to see what resources Terraform would create by running:
- terraform plan -var "do_token=${DO_PAT}"
The output for prod
will be the following:
Output...
Terraform used the selected providers to generate the following execution plan. Resource actions are
indicated with the following symbols:
+ create
Terraform will perform the following actions:
# module.dns.digitalocean_domain.domain will be created
+ resource "digitalocean_domain" "domain" {
+ id = (known after apply)
+ name = "your_prod_domain"
+ urn = (known after apply)
}
# module.dns.digitalocean_record.domain_A will be created
+ resource "digitalocean_record" "domain_A" {
+ domain = "your_prod_domain"
+ fqdn = (known after apply)
+ id = (known after apply)
+ name = "@"
+ ttl = (known after apply)
+ type = "A"
+ value = (known after apply)
}
# module.dns.digitalocean_record.domain_CNAME will be created
+ resource "digitalocean_record" "domain_CNAME" {
+ domain = "your_prod_domain"
+ fqdn = (known after apply)
+ id = (known after apply)
+ name = "www"
+ ttl = (known after apply)
+ type = "CNAME"
+ value = "@"
}
# module.droplets.digitalocean_droplet.droplets[0] will be created
+ resource "digitalocean_droplet" "droplets" {
...
+ name = "prod-0"
...
}
# module.droplets.digitalocean_droplet.droplets[1] will be created
+ resource "digitalocean_droplet" "droplets" {
...
+ name = "prod-1"
...
}
# module.droplets.digitalocean_droplet.droplets[2] will be created
+ resource "digitalocean_droplet" "droplets" {
...
+ name = "prod-2"
...
}
# module.droplets.digitalocean_droplet.droplets[3] will be created
+ resource "digitalocean_droplet" "droplets" {
...
+ name = "prod-3"
...
}
# module.droplets.digitalocean_droplet.droplets[4] will be created
+ resource "digitalocean_droplet" "droplets" {
...
+ name = "prod-4"
...
}
# module.droplets.digitalocean_loadbalancer.www-lb will be created
+ resource "digitalocean_loadbalancer" "www-lb" {
...
+ name = "lb-prod"
...
Plan: 9 to add, 0 to change, 0 to destroy.
...
This would deploy five Droplets with a Load Balancer. It would also create the prod
domain you specified with the two DNS records pointing to the Load Balancer. You can try planning the configuration for the dev
environment as well—you’ll note that two Droplets would be planned for deployment.
Note: You can apply this configuration for the dev
and prod
environments with the following command:
- terraform apply -var "do_token=${DO_PAT}"
To destroy it, run the following command and input yes
when prompted:
- terraform destroy -var "do_token=${DO_PAT}"
The following demonstrates how you have structured this project:
terraform-reusability/
├─ environments/
│ ├─ dev/
│ │ ├─ main.tf
│ │ ├─ provider.tf
│ ├─ prod/
│ │ ├─ main.tf
│ │ ├─ provider.tf
├─ modules/
│ ├─ dns-records/
│ │ ├─ outputs.tf
│ │ ├─ provider.tf
│ │ ├─ records.tf
│ │ ├─ variables.tf
│ ├─ droplet-lb/
│ │ ├─ droplets.tf
│ │ ├─ lb.tf
│ │ ├─ outputs.tf
│ │ ├─ provider.tf
│ │ ├─ variables.tf
├─ provider.tf
The addition is the environments
directory, which holds the code for the dev
and prod
environments.
The benefit of this approach is that further changes to modules automatically propagate to all areas of your project. Barring any possible customizations to module inputs, this approach is not repetitive and promotes reusability as much as possible, even across deployment environments. Overall, this reduces clutter and allows you to trace the modifications using a version-control system.
In the final two sections of this tutorial, you’ll review the depends_on
meta argument and the templatefile
function.
While planning actions, Terraform automatically tries to identify existing dependencies and builds them into its dependency graph. The main dependencies it can detect are clear references; for example, when an output value of a module is passed to a parameter on another resource. In this scenario, the module must first complete its deployment to provide the output value.
The dependencies that Terraform can’t detect are hidden—they have side effects and mutual references not inferable from the code. An example of this is when an object depends not on the existence, but on the behavior of another one, and does not access its attributes from code. To overcome this, you can use depends_on
to manually specify the dependencies in an explicit way. Since Terraform 0.13
, you can also use depends_on
on modules to force the listed resources to be fully deployed before deploying the module itself. It’s possible to use the depends_on
meta argument with every resource type. depends_on
will also accept a list of other resources on which its specified resource depends.
depends_on
accepts a list of references to other resources. Its syntax looks like this:
resource "resource_type" "res" {
depends_on = [...] # List of resources
# Parameters...
}
Remember that you should only use depends_on
as a last-resort option. If used, it should be kept well documented, because the behavior that the resources depend on may not be immediately obvious.
In the previous step of this tutorial, you haven’t specified any explicit dependencies using depends_on
, because the resources you’ve created have no side effects not inferable from the code. Terraform is able to detect the references made from the code you’ve written, and will schedule the resources for deployment accordingly.
In Terraform, templating is substituting results of expressions in appropriate places, such as when setting attribute values on resources or constructing strings. You’ve used it in the previous steps and the tutorial prerequisites to dynamically generate Droplet names and other parameter values.
When substituting values in strings, the values are specified and surrounded by ${}
. Template substitution is often used in loops to facilitate customization of the created resources. It also allows for module customization by substituting inputs in resource attributes.
Terraform offers the templatefile
function, which accepts two arguments: the file from the disk to read and a map of variables paired with their values. The value it returns is the contents of the file rendered with the expression substituted—just as Terraform would normally do when planning or applying the project. Because functions are not part of the dependency graph, the file cannot be dynamically generated from another part of the project.
Imagine that the contents of the template file called droplets.tmpl
is as follows:
%{ for address in addresses ~}
${address}:80
%{ endfor ~}
Longer declarations must be surrounded with %{}
, as is the case with the for
and endfor
declarations, which signify the start and end of the for
loop respectively. The contents and type of the droplets
variable are not known until the function is called and actual values provided, like so:
templatefile("${path.module}/droplets.tmpl", { addresses = ["192.168.0.1", "192.168.1.1"] })
This templatefile
call will return the following value:
Output192.168.0.1:80
192.168.1.1:80
This function has its use cases, but they are uncommon. For example, you could use it when part of the configuration must exist in a proprietary format, but is dependent on the rest of the values and must be generated dynamically. In the majority of cases, it’s better to specify all configuration parameters directly in Terraform code, where possible.
In this article, you’ve maximized code reuse in an example Terraform project. The main way is to package often-used features and configurations as a customizable module and use it whenever needed. By doing so, you do not duplicate the underlying code (which can be error prone) and enable faster turnaround times, since modifying the module is almost all you need to do to introduce changes.
You’re not limited to your own modules. As you’ve seen, Terraform Registry provides third-party modules and providers that you can incorporate in your project.
This tutorial is part of the How To Manage Infrastructure with Terraform series. The series covers a number of Terraform topics, from installing Terraform for the first time to managing complex projects.
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Terraform is a popular open source Infrastructure as Code (IAC) tool that automates provisioning of your infrastructure in the cloud and manages the full lifecycle of all deployed resources, which are defined in source code. Its resource-managing behavior is predictable and reproducible, so you can plan the actions in advance and reuse your code configurations for similar infrastructure.
In this series, you will build out examples of Terraform projects to gain an understanding of the IAC approach and how it’s applied in practice to facilitate creating and deploying reusable and scalable infrastructure architectures.
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This is a great article! I have a couple of questions:
dev
andprod
are both terraform working directories becauseinit
is called in each of them. Wouldn’t that mean that they would manage their state separately?dev
andprod
shared some infrastructure? For example if they shared a VPC in AWS? Would changing such shared resource in one environment render the state of the other out of sync? What could be done to mitigate such a problem? Creating a thirdshared
environment, maybe?Thanks!