Terraform 1.9 Enhances Input Variable Validations

Amaresh Pelleti

Introduction

HashiCorp’s latest release, Terraform 1.9, introduces powerful enhancements to input variable validations, marking a significant advancement in infrastructure management. This improvement addresses one of the most requested features from the Terraform community while bolstering developer productivity and code reliability.

Cross-Object Reference: A Game-Changer for Input Validations

What’s New?

Prior to version 1.9, Terraform’s input validations were limited to self-referential checks. The new release transforms this limitation, allowing validations to reference multiple variables, data sources, and local values. This enhancement significantly improves infrastructure reliability and reduces deployment failures.

Practical Implementation

Let’s explore a real-world example of cross-object validation for a networking configuration:

variable "environment" {
  description = "Deployment environment (dev, staging, prod)"
  type        = string
  default     = "dev"

  validation {
    condition     = contains(["dev", "staging", "prod"], var.environment)
    error_message = "Environment must be either 'dev', 'staging', or 'prod'."
  }
}

variable "vpc_cidr" {
  description = "CIDR block for the VPC"
  type        = string
  default     = "10.0.0.0/16"

  validation {
    condition     = var.environment == "prod" ? can(regex("^10\\.0\\.", var.vpc_cidr)) : true
    error_message = "Production VPC CIDR must start with 10.0.x.x"
  }
}

This validation ensures proper CIDR block allocation based on the environment, preventing network overlap issues before deployment.

Advanced Validation with Data Sources

Here’s an example showing how to validate database configurations against available options:

data "aws_rds_engine_version" "postgresql" {
  engine  = "postgres"
  default = true
}

variable "db_parameters" {
  description = "Database configuration parameters"
  type = object({
    instance_class = string
    engine_version = string
    storage_type   = string
  })

  validation {
    condition = (
      contains(["db.t3.micro", "db.t3.small", "db.t3.medium"], var.db_parameters.instance_class) &&
      contains(data.aws_rds_engine_version.postgresql.valid_upgrade_targets[*].version, var.db_parameters.engine_version) &&
      contains(["gp2", "gp3", "io1"], var.db_parameters.storage_type)
    )
    error_message = "Invalid database configuration. Please check instance class, engine version, and storage type."
  }
}

Template Management with templatestring

Overview

The new templatestring function complements the enhanced validation capabilities by providing improved template handling. This function processes templates directly from string objects, eliminating the need for local file storage.

Practical Application

Here’s an example showing how to manage cloud-init configurations:

data "http" "init_script" {
  url = "https://templates.example.com/cloud-init-base.yaml"
}

locals {
  init_config = templatestring(data.http.init_script.response_body, {
    HOSTNAME          = "${var.environment}-${var.service_name}"
    MONITORING_LEVEL  = var.environment == "prod" ? "detailed" : "basic"
    BACKUP_RETENTION  = var.environment == "prod" ? "30" : "7"
    LOG_LEVEL        = var.environment == "prod" ? "INFO" : "DEBUG"
    METRICS_INTERVAL = var.environment == "prod" ? "60" : "300"
  })
}

resource "aws_instance" "application" {
  ami           = var.ami_id
  instance_type = var.instance_type
  user_data     = local.init_config
}

Best Practices for Enhanced Input Validations

Cross-Reference Validation Strategy

    • Implement validations between dependent variables
    • Use data source validations for dynamic requirements
    • Create clear, actionable error messages

    Complex Validation Scenarios

      • Combine multiple conditions for comprehensive validation
      • Include environment-specific validation rules
      • Validate against external data sources

      Error Message Design

        • Provide specific, actionable error messages
        • Include relevant variable values in error messages
        • Guide users toward correct configuration

        Additional Improvements in Terraform 1.9

        Resource Type Migration

        The release also includes improvements to existing features:

        • Seamless migration from null_resource to terraform_data
        • Enhanced support for destroy-time provisioners
        • Better handling of resource state during migrations

        Implementing Enhanced Validations in Your Infrastructure

        To effectively implement these new validation capabilities:

        Audit Existing Variables

          • Review current validation rules
          • Identify opportunities for cross-reference validations
          • Plan validation strategy for critical configurations

          Migration Strategy

            • Update existing validations progressively
            • Test validation rules thoroughly
            • Document new validation requirements

            Conclusion

            Terraform 1.9’s enhanced input variable validations represent a significant improvement in infrastructure management capabilities. These new features enable DevOps teams to create more robust and reliable infrastructure deployments while providing better feedback to users.

            Next Steps

            To leverage these new validation capabilities:

            1. Update to Terraform 1.9
            2. Review existing variable validations
            3. Implement cross-object validations where needed
            4. Update documentation to reflect new validation rules

            For detailed documentation and examples, visit the official HashiCorp documentation.