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Terraform

Terraform Conditional Data Sources: A Complete Guide

By Filip on 10/05/2024

Learn how to use Terraform conditional data sources to dynamically fetch and utilize data based on predefined conditions in your infrastructure code.

Terraform Conditional Data Sources: A Complete Guide

Table of Contents

Introduction

In Terraform, you can use conditional logic to fetch data only when a specific condition is met. This is useful for scenarios where you might need to retrieve data based on variables, resource states, or other dynamic conditions.

Step-by-Step Guide

To handle situations where you might need to fetch data only if a certain condition is met, you can leverage Terraform's conditional logic within your data sources. Here's a breakdown of how to achieve this:

  1. Define your condition: Start by clearly defining the condition that will determine whether or not the data source should be queried. This condition is typically an expression that evaluates to either true or false. For instance, you might check if a variable is set to a specific value or if a resource exists.

  2. Utilize the count argument (prior to Terraform 0.12): In earlier Terraform versions (0.11 and before), the count meta-argument was the primary way to conditionally create resources or data sources. You would set the count to 1 if the condition was true, and 0 if false. This meant the data source would only be queried if count was 1.

  3. Embrace for_each for dynamic scenarios (Terraform 0.12 and later): From Terraform 0.12 onwards, the for_each meta-argument became the preferred method for dynamically creating multiple instances of a resource or data source. You can use it with a conditional expression to control whether the data source is included in the plan.

  4. Handle potentially empty results: Keep in mind that data sources might not always return results. If your condition prevents a data source from running and you try to access its output, you'll encounter an error. To mitigate this, use the try() function to gracefully handle cases where the data source doesn't provide any output.

  5. Example: Let's say you want to fetch an AWS AMI ID based on the value of a variable use_custom_ami:

    data "aws_ami" "example" {
  count = var.use_custom_ami ? 1 : 0
  filter {
    name   = "name"
    values = ["my-custom-ami"]
  }
}

resource "aws_instance" "example" {
  ami = var.use_custom_ami ? data.aws_ami.example[0].id : "ami-default"
  # ... other configurations
}

    In this example, the aws_ami data source is only queried if var.use_custom_ami is true. The aws_instance resource then uses either the fetched custom AMI ID or a default AMI ID based on the same condition.

Remember that using conditional logic effectively can make your Terraform code more adaptable and robust, allowing it to handle various scenarios gracefully.

Code Example

The code demonstrates conditional data fetching in Terraform using different methods. It shows how to use count in Terraform 0.11 and earlier, and for_each in Terraform 0.12 and later, to conditionally fetch data based on a boolean variable. It also illustrates how to handle potentially empty data source outputs using the try() function to avoid errors during infrastructure provisioning. The examples use fetching a custom AMI based on variables as a use case, highlighting how to choose the appropriate method and handle potential errors for different Terraform versions and scenarios.

1. Using count (Terraform 0.11 and earlier):

variable "use_custom_ami" {
  type = bool
  default = false
}

data "aws_ami" "example" {
  count = var.use_custom_ami ? 1 : 0
  filter {
    name   = "name"
    values = ["my-custom-ami"]
  }
}

resource "aws_instance" "example" {
  ami = var.use_custom_ami ? data.aws_ami.example[0].id : "ami-default"
  # ... other configurations
}

2. Using for_each (Terraform 0.12 and later):

variable "use_custom_ami" {
  type = bool
  default = false
}

data "aws_ami" "example" {
  for_each = var.use_custom_ami ? toset(["this"]) : toset([])
  filter {
    name   = "name"
    values = ["my-custom-ami"]
  }
}

resource "aws_instance" "example" {
  ami = var.use_custom_ami ? data.aws_ami.example["this"].id : "ami-default"
  # ... other configurations
}

3. Handling potentially empty results with try():

variable "environment" {
  type = string
  default = "dev"
}

data "aws_ami" "example" {
  for_each = var.environment == "prod" ? toset(["this"]) : toset([])
  filter {
    name   = "tag:environment"
    values = [var.environment]
  }
}

resource "aws_instance" "example" {
  ami = try(data.aws_ami.example["this"].id, "ami-default")
  # ... other configurations
}

Explanation:

  • Example 1 & 2: The aws_ami data source is only queried if var.use_custom_ami is true. The aws_instance resource then uses either the fetched custom AMI ID or a default AMI ID based on the same condition.
  • Example 3: This example demonstrates fetching an AMI based on the environment variable. If no AMI is found with the specified tag, the try() function gracefully handles the situation by using the default AMI ID ("ami-default").

These examples showcase different approaches to conditionally fetching data in Terraform. Choose the method that best suits your Terraform version and specific use case. Remember to handle potentially empty data source outputs to prevent errors in your infrastructure provisioning.

Additional Notes

  • Conditional logic in Terraform allows you to dynamically control the behavior of your infrastructure code based on various factors.
  • This dynamic behavior is crucial for creating adaptable and reusable modules and configurations.
  • Before Terraform 0.12, the count meta-argument was used to conditionally create resources and data sources. However, it had limitations, especially when dealing with complex scenarios.
  • From Terraform 0.12 onwards, the for_each meta-argument provides a more robust and flexible way to handle dynamic resource and data source creation.
  • When using conditional logic with data sources, it's essential to remember that they might not always return a result.
  • The try() function is invaluable for gracefully handling cases where a data source doesn't return any output, preventing unexpected errors during your Terraform runs.
  • Always consider the potential for empty data source results when using conditional logic and implement appropriate error handling using mechanisms like the try() function.
  • This practice ensures your Terraform code remains resilient and continues to function as expected even when dealing with dynamic and potentially unpredictable scenarios.
  • Using these techniques makes your Terraform code more adaptable, maintainable, and less prone to errors, ultimately leading to more reliable infrastructure provisioning.

Summary

This table summarizes how to use conditional logic to fetch data in Terraform:

Feature Description Terraform Version
Defining the Condition Use an expression that evaluates to true or false to determine if the data source should be queried. All versions
Conditional Data Fetching
count Set to 1 if the condition is true, 0 if false. Prior to 0.12
for_each Use with a conditional expression to dynamically include the data source. 0.12 and later
Handling Empty Results Use the try() function to avoid errors when accessing output from potentially empty data sources. All versions

Example:

data "aws_ami" "example" {
  count = var.use_custom_ami ? 1 : 0
  # ... filters ...
}

resource "aws_instance" "example" {
  ami = var.use_custom_ami ? data.aws_ami.example[0].id : "ami-default"
  # ... other configurations ...
}

This example demonstrates using the count argument (for versions prior to 0.12) to conditionally fetch a custom AMI ID based on the value of the use_custom_ami variable.

Conclusion

By using conditional logic in Terraform, you can create more efficient and adaptable infrastructure configurations. This allows you to fetch data only when needed, based on conditions like variable values or resource states. Whether you're using count in earlier Terraform versions or for_each from version 0.12 onwards, understanding how to implement conditional logic for data sources is crucial for creating dynamic and robust infrastructure as code. Remember to handle cases where data sources might not return results, using mechanisms like the try() function to prevent errors and ensure your Terraform code executes smoothly. By mastering these techniques, you can leverage the full power of Terraform to manage complex infrastructure deployments effectively.

References

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