Understanding precondition checks in Terraform

Introduction

Terraform, an open-source infrastructure as code software tool created by HashiCorp, enables users to define and provision a datacenter infrastructure using a high-level configuration language known as HashiCorp Configuration Language (HCL) or JSON. As you dive deeper into Terraform, you’ll understand the importance of precondition checks. This tutorial will guide you through the basics to more advanced examples of precondition checks in Terraform, essential for maintaining a stable and predictable infrastructure codebase.

Getting Started with Precondition Checks

Precondition checks are conditions or validations that you place within your Terraform configuration files to ensure that the correct and expected environment is in place before your infrastructure is provisioned or updated. These checks can prevent common issues like configuration drift, unexpected resource changes, or deployment failures due to misconfigured inputs.

Basic Precondition Example

Let’s start with a basic example where we check if a required version of the Terraform CLI is used:

terraform {
  required_version = ">= 0.14"
}

This precondition ensures that you’re using at least Terraform version 0.14, avoiding potential compatibility issues with your configuration.

Precondition Checks for Resource Attributes

Next, we delve into checks that validate specific resource attributes before proceeding with the provision:

resource "aws_instance" "example" {
  ami           = var.ami_id
  instance_type = var.instance_type
  
  precondition {
    condition     = contains(["t2.micro", "t2.small"], var.instance_type)
    error_message = "The instance type must be t2.micro or t2.small."
  }
}

This block checks that the EC2 instance type specified in var.instance_type is either t2.micro or t2.small, providing a clear error message if the condition is not met.

Advanced Usage: Combining Checks

Moving towards more advanced scenarios, you can combine multiple precondition checks to validate several aspects of your infrastructure before proceeding. For example:

resource "aws_vpc" "example" {
  cidr_block = var.cidr_block
  
  precondition {
    condition     = can(regex("^10.", var.cidr_block))
    error_message = "CIDR block must start with '10.'"
  }
  precondition {
    condition     = length(split(".", var.cidr_block)) == 4
    error_message = "CIDR block must be a valid IPv4 address."
  }
}

In this advanced example, two precondition blocks are used to ensure that the CIDR block for a VPC starts with ’10.’ and is a valid IPv4 address format. These intricate checks can significantly elevate the resilience and predictability of your infrastructure deployments.

Working with Outputs in Precondition Checks

While direct support for capturing output from precondition checks in Terraform is limited, you can utilize the diagnostic features of the tool to review the results of your preconditions. For instance:

# Example of interpreting precondition check outputs manually
output "precondition_check_result" {
  value = "Check the Terraform plan output for any precondition errors."
}

This approach requires manual review of Terraform plan or apply output, helping you understand whether your preconditions passed or failed.

Conclusion

Precondition checks in Terraform are instrumental in ensuring that your infrastructure as code deployments are predictable, stable, and in line with expected configurations. From simple version constraints to more complex attribute validations, mastering these checks can significantly improve the quality and reliability of your Terraform projects.