How to create a custom Hashicorp Terraform Provider?

If you are learning for Hashicorp Certified Terraform Associate Exam, then you should start learning how to create a custom Hashicorp terraform provider. In this article, you will be learning through some example codes, which are really helpful for developers ensuring an end to end consistency while deploying the resources using Terraform.

How to create a Terraform Provider?

Managing a service programmatically is very common in Ops. Though we can start a service via a UI, we’ll want more control over its configuration in the future, and Terraform providers allow us to do so.

Terraform is a fantastic tool that enables you to define your infrastructure using code. It’s a state machine that is extremely powerful and performs API queries within its shell.

You can therefore find out how to contribute to an existing Terraform provider or even start one from scratch by reading this guide.

So, what exactly is Terraform all about?

Terraform is a massive state machine. Terraform is a terrific state machine, regardless of how much we preach about “It’s infrastructure as code.” A good feature of Terraform is that it supports any resource backed by an API in the Terraform state file. Any API-backed resource can be used as a Terraform provider, including JSON, gRPC, and XML.

Terraform does nothing more than managing resources between a JSON payload and a Terraform struct when it comes down to it. Thus, if you’re looking for a tool that’s easy to use, Terraform is what you need! As previously mentioned, terraform providers can be generated from any resource with an API.

Terraform Custom Provider

A custom Terraform provider could be used in the following scenarios:

• The cloud is within an internal organization and is proprietary or of no use to open source communities.
• A provider is tested locally before entering into the registry as a “work in progress”.
• Expansion of a current service provider.

Terraform’s Initiator

Now that the API has been provided, the Terraform Provider logic must be written. To illustrate, take into consideration the following points:

• The API address must be known for the Provider to access the API.
• The Provider must understand how API requests and HCL code relate.
• Providers must understand how to flatten the incoming JSON in API responses before Terraform can display them to users.
• Concerning these concerns, we’ll demonstrate how they will be addressed programmatically during the implementation process.

Terraform Helper Libraries

Let’s begin by downloading Terraform’s helper libraries. Terraform’s Provider’s semantics can be defined significantly more efficiently with these libraries. The application’s entry point is straightforward, exposing only a single function to serve the plugin via the plugin. Serve().

func main() { plugin.Serve(plugin.ServeOpts{ ProviderFunc: cmdb.Provider}) }

 

Our Provider’s Definition

Providers in Terraform are defined by the input data specified in the schema for the Provider:

Schema: map[string]*schema.Schema{ “api_version”: { Type: schema.TypeString, Optional: true, Default: “”, },”hostname”: { Type: schema.TypeString, Required: true, },”headers”: { Type: schema.TypeMap, Optional: true, Elem: &schema.Schema{ Type: schema.TypeString, }, }, },

The previous line of code declares an API version, a hostname, and potentially headers (because the API we’re contacting should be versioned!). 

With optional headers, API calls can include custom data and authentication information.

HCL’s Provider initialization can be done as follows:

provider “cmdb” { api_version = “v1” hostname = “localhost” }

 

Our Provider’s Process for Defining Data and Resources

As part of Terraform’s structures, Terraform requires you to specify the functions you want to invoke at every stage of the resource or data lifecycle. The methods for data destruction are nearly the same as the methods we’re discussing here for data generation.

Our Provider’s name allocation data type is defined here.

So here’s how it works:

Schema: map[string]*schema.Schema{ “raw”: { Type: schema.TypeString, Computed: true, Elem: &schema.Schema{ Type: schema.TypeString, }, }, “name”: { Type: schema.TypeString, Computed: true, Elem: &schema.Schema{ Type: schema.TypeString, }, }, “region”: { Type: schema.TypeString, Required: true, }, “resource_type”: { Type: schema.TypeString, Required: true, }, },

 

Now the lifecycle methods:

Read: initNameDataSourceRead,

Simply a Read method is required for the lifecycle because we are not creating any resources, only data objects. As a subset of Terraform resources, only a Read method has been developed for Terraform data.

Take a look at the Operations section.

Calling out to an API and returning a flattened API answer are two separate tasks that a single Read action must handle. We’ve included a very basic HTTP client in client.go for demonstration reasons. Then, in data source allocation, we call that client to make an API call and receive its response.

The following is a definition of the full function call needed to do a Read:

func initNameDataSourceRead(d *schema.ResourceData, meta interface{}) (err error) { provider := meta.(ProviderClient) client := provider.Clientheader := make(http.Header) headers, exists := d.GetOk(“headers”) if exists { for name, value := range headers.(map[string]interface{}) { header.Set(name, value.(string)) } }resourceType := d.Get(“resource_type”).(string) if resourceType == “” { return fmt.Errorf(“Invalid resource type specified”) } region := d.Get(“region”).(string) if region == “” { return fmt.Errorf(“Invalid region specified”) } b, err := client.doAllocateName(client.BaseUrl.String(), resourceType, region) if err != nil { return } outputs, err := flattenNameAllocationResponse(b) if err != nil { return } marshalData(d, outputs)return }

 

Remember the call to flattenNameAllocationResponse(b), which flattens the API response sent back. It takes the bytes of the API response as input and unmarshals the JSON inside them. This results in extracting JSON data from the bytes into key-value pairs on a map.

func flattenNameAllocationResponse(b []byte) (outputs map[string]interface{}, err error) { var data map[string]interface{} err = json.Unmarshal(b, &data) if err != nil { err = fmt.Errorf(“Cannot unmarshal json of API response: %v”, err) return } else if data[“result”] == “” { err = fmt.Errorf(“missing result key in API response: %v”, err) return }outputs = make(map[string]interface{}) outputs[“id”] = time.Now().UTC().String() outputs[“raw”] = string(b) outputs[“name”] = data[“Name”]return }

 

Finally, we take the map and put its information into the *schema. To use Terraform, you need to know what resources it needs:

func marshalData(d *schema.ResourceData, vals map[string]interface{}) { for k, v := range vals { if k == “id” { d.SetId(v.(string)) } else { str, ok := v.(string) if ok { d.Set(k, str) } else { d.Set(k, v) } } } }

When a developer comes from a language that doesn’t use meta interfaces, the use of that argument might seem weird. For example, Golang has the theory of interfaces, which are contracts that say various methodologies. By meeting the terms of the contract, a sort is said to use the interface.

The interface is called this same empty interface because it doesn’t have any methods. This means that every type can use interfaces. In other languages, people who have a background in C or C++ may think that interface and void pointers in other languages look a lot like each other.

The interface is used to pass us our Provider object without telling us what type it is. This is how it works: This lets us have a fixed function signature in the Terraform library, but we can change it at runtime to match the type of our Provider object.

It means that the ProviderStruct that we already have in our source code is called “meta interface” in this case. This lets us see all of the properties on our Provider while taking care of our data object’s lifecycle.

Data are a part of resources.

The process of setting up the resource block on a Provider is almost the same as setting up a data block. This is because Terraform only thinks of data as a read-only part of a resource. In other words, it follows the same rules about how to write lifecycle methods and put them on the Provider. However, this is different from other objects: resource objects can be created, updated, and deleted.

The Essential Factors to Consider

1. Infrastructure-aware code

Declarative, high-level configuration languages let you describe infrastructure using Terraform. You’ll get a blueprint that can be used in multiple contexts by doing so.

2. Action Plan

You can analyze the changes before Terraform creates, updates, or destroys infrastructure. Then, terraform creates an execution plan and asks for your approval before changing your infrastructure.

3. A Process for Automating Change

There is no better way to make large-scale changes than Terraform requires minimal human effort. In addition, Terraform’s execution plans can be edited while the execution plan is still being analyzed.

The following are some Terraforms service providers:

A total of 90 Terraform providers can be used at the same time. Here’s an illustration to help illustrate the point. 

At some point in their lives, everyone has come across HCL. As a result, you have full control over your servers and staff. 

You may also manage Kubernetes with a Terraform provider for Kubernetes. Kubernetes services may now be specified in HCL rather than YAML, which is a huge improvement. It’s easy to follow because there isn’t a lot of repetition or nesting.

One of the most useful features of the GitHub API is performing a wide range of infrastructure and service-related operations. We have a Terraform provider in place to deal with this resource. This resource can be changed when it is used with data resources. 

Anything you don’t want to edit but would like to import as a read-only file is an example of a data resource. If you use the terraform destroy authority after importing a resource, that resource will be destroyed. It’s like having a permanent, read-only thing.

To conclude!

Terraform streamlines our processes and allows us to maintain a fluid flow between our data centers, cloud providers, and project sites. In addition, much different Continuous Integration (CI) solutions can be integrated to Terraform Enterprise, which allows businesses to customize deployments to match their requirements.

Summary:

Hope you have enjoyed learning how to create a custom terraform provider using real-time code samples. Getting a certification in Terraform will help you in gaining industry-accepted certification which tests your knowledge in DevOps and Terraform -Infrastructure as a Service (IaC) tool.

You can also have a look at our free terraform associate exam questions to fasten up your preparation level. To pass the Terraform associate exam on the first attempt, your preparation level should be good by learning through video courses and trying out practice tests.