Quickstart: Deploy Azure AI Search service using Terraform

Article
08/28/2024

This article shows how to use Terraform to create an Azure AI Search service using Terraform.

Terraform enables the definition, preview, and deployment of cloud infrastructure. Using Terraform, you create configuration files using HCL syntax. The HCL syntax allows you to specify the cloud provider - such as Azure - and the elements that make up your cloud infrastructure. After you create your configuration files, you create an execution plan that allows you to preview your infrastructure changes before they're deployed. Once you verify the changes, you apply the execution plan to deploy the infrastructure.

In this article, you learn how to:

Create a random pet name for the Azure resource group name using random_pet
Create an Azure resource group using azurerm_resource_group
Create a random string using random_string
Create an Azure AI Search service using azurerm_search_service

Prerequisites

Install and configure Terraform

Implement the Terraform code

Note

See more articles and sample code showing how to use Terraform to manage Azure resources

Create a directory in which to test and run the sample Terraform code and make it the current directory.

Create a file named main.tf and insert the following code:

resource "random_pet" "rg_name" {
  prefix = var.resource_group_name_prefix
}

resource "azurerm_resource_group" "rg" {
  name     = random_pet.rg_name.id
  location = var.resource_group_location
}

resource "random_string" "azurerm_search_service_name" {
  length  = 25
  upper   = false
  numeric = false
  special = false
}

resource "azurerm_search_service" "search" {
  name                = random_string.azurerm_search_service_name.result
  resource_group_name = azurerm_resource_group.rg.name
  location            = azurerm_resource_group.rg.location
  sku                 = var.sku
  replica_count       = var.replica_count
  partition_count     = var.partition_count
}

Create a file named outputs.tf and insert the following code:

output "resource_group_name" {
  value = azurerm_resource_group.rg.name
}

output "azurerm_search_service_name" {
  value = azurerm_search_service.search.name
}

Create a file named providers.tf and insert the following code:

terraform {
  required_version = ">=1.0"
  required_providers {
    azurerm = {
      source  = "hashicorp/azurerm"
      version = "~>3.0"
    }
    random = {
      source  = "hashicorp/random"
      version = "~>3.0"
    }
  }
}
provider "azurerm" {
  features {}
}

Create a file named variables.tf and insert the following code:

variable "resource_group_location" {
  type        = string
  description = "Location for all resources."
  default     = "eastus"
}

variable "resource_group_name_prefix" {
  type        = string
  description = "Prefix of the resource group name that's combined with a random ID so name is unique in your Azure subscription."
  default     = "rg"
}

variable "sku" {
  description = "The pricing tier of the search service you want to create (for example, basic or standard)."
  default     = "standard"
  type        = string
  validation {
    condition     = contains(["free", "basic", "standard", "standard2", "standard3", "storage_optimized_l1", "storage_optimized_l2"], var.sku)
    error_message = "The sku must be one of the following values: free, basic, standard, standard2, standard3, storage_optimized_l1, storage_optimized_l2."
  }
}

variable "replica_count" {
  type        = number
  description = "Replicas distribute search workloads across the service. You need at least two replicas to support high availability of query workloads (not applicable to the free tier)."
  default     = 1
  validation {
    condition     = var.replica_count >= 1 && var.replica_count <= 12
    error_message = "The replica_count must be between 1 and 12."
  }
}

variable "partition_count" {
  type        = number
  description = "Partitions allow for scaling of document count as well as faster indexing by sharding your index over multiple search units."
  default     = 1
  validation {
    condition     = contains([1, 2, 3, 4, 6, 12], var.partition_count)
    error_message = "The partition_count must be one of the following values: 1, 2, 3, 4, 6, 12."
  }
}

Initialize Terraform

Run terraform init to initialize the Terraform deployment. This command downloads the Azure provider required to manage your Azure resources.

terraform init -upgrade

Key points:

The -upgrade parameter upgrades the necessary provider plugins to the newest version that complies with the configuration's version constraints.

Create a Terraform execution plan

Run terraform plan to create an execution plan.

terraform plan -out main.tfplan

Key points:

The terraform plan command creates an execution plan, but doesn't execute it. Instead, it determines what actions are necessary to create the configuration specified in your configuration files. This pattern allows you to verify whether the execution plan matches your expectations before making any changes to actual resources.
The optional -out parameter allows you to specify an output file for the plan. Using the -out parameter ensures that the plan you reviewed is exactly what is applied.

Apply a Terraform execution plan

Run terraform apply to apply the execution plan to your cloud infrastructure.

terraform apply main.tfplan

Key points:

The example terraform apply command assumes you previously ran terraform plan -out main.tfplan.
If you specified a different filename for the -out parameter, use that same filename in the call to terraform apply.
If you didn't use the -out parameter, call terraform apply without any parameters.

Verify the results

Get the Azure resource name in which the Azure AI Search service was created.
```
resource_group_name=$(terraform output -raw resource_group_name)
```

Get the Azure AI Search service name.

azurerm_search_service_name=$(terraform output -raw azurerm_search_service_name)

Run az search service show to show the Azure AI Search service you created in this article.

az search service show --name $azurerm_search_service_name \
                       --resource-group $resource_group_name

Clean up resources

When you no longer need the resources created via Terraform, do the following steps:

Run terraform plan and specify the destroy flag.
```
terraform plan -destroy -out main.destroy.tfplan
```
Key points:
- The terraform plan command creates an execution plan, but doesn't execute it. Instead, it determines what actions are necessary to create the configuration specified in your configuration files. This pattern allows you to verify whether the execution plan matches your expectations before making any changes to actual resources.
- The optional -out parameter allows you to specify an output file for the plan. Using the -out parameter ensures that the plan you reviewed is exactly what is applied.
Run terraform apply to apply the execution plan.
```
terraform apply main.destroy.tfplan
```

Troubleshoot Terraform on Azure

Troubleshoot common problems when using Terraform on Azure

Next steps

Create an Azure AI Search index using the Azure portal

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Quickstart: Deploy Azure AI Search service using Terraform

Prerequisites

Implement the Terraform code

Initialize Terraform

Create a Terraform execution plan

Apply a Terraform execution plan

Verify the results

Clean up resources

Troubleshoot Terraform on Azure

Next steps

Feedback

Additional resources