Understanding terraform_remote_state data source: Explained with examples

Introduction

In the world of Infrastructure as Code (IaC), managing state is paramount, and Terraform by HashiCorp is a leading tool that practitioners use to achieve this. Terraform’s ability to manage the state of your infrastructure is one of its key features, facilitating collaboration and ensuring consistency across environments. One of the essential components in advanced Terraform usage is the terraform_remote_state data source. This guide aims to demystify the terraform_remote_state data source, providing clear explanations and practical examples to harness its potential fully.

Understanding terraform_remote_state

The terraform_remote_state data source is used to access the state of another Terraform project. It’s particularly useful in scenarios where your infrastructure is split across multiple Terraform projects and you need to access attributes of resources managed in another state file. This approach promotes modularity and reusability, enabling complex infrastructures to be managed more efficiently.

Configuring Remote State

The first step to leveraging the terraform_remote_state data source is to ensure that your project is configured to use a remote backend. The backend is where Terraform stores state information, making it accessible for queries. A popular choice is the S3 backend, combined with DynamoDB for state locking, ensuring that no concurrent operations can corrupt the state.

terraform {
  backend "s3" {
    bucket         = "my-terraform-state-bucket"
    key            = "path/to/my/statefile"
    region         = "us-east-1"
    dynamodb_table = "my-lock-table"
    encrypt        = true
  }
}

Accessing Remote State Data

Once your backend is configured, you can then access the remote state data of another Terraform project. Here’s an example of how to declare a terraform_remote_state data source to access resources from another state file:

data "terraform_remote_state" "vpc" {
  backend = "s3"
  config = {
    bucket = "my-terraform-state-bucket"
    key    = "network/terraform.tfstate"
    region = "us-east-1"
  }
}

This configuration allows you to query the state information of the network project stored in an S3 bucket. For instance, you may need to retrieve the ID of a VPC to create a peering connection:

resource "aws_vpc_peering_connection" "peer" {
  vpc_id        = data.terraform_remote_state.vpc.outputs.vpc_id
  peer_vpc_id   = var.peer_vpc_id
  auto_accept   = true
}

When to Use terraform_remote_state

Using the terraform_remote_state is advisable when you have clearly defined boundaries within your infrastructure that are managed by separate Terraform configurations. It enhances modularity by allowing projects to consume outputs from each other securely and efficiently. However, it’s crucial to use it sparingly, as over-reliance can introduce complexity, especially with state files depending on each other. Strategic planning of your infrastructure’s structure can mitigate such challenges.

Best Practices

• Secure your backends: Especially when using cloud-based storage, ensure that access is well-controlled, using IAM policies or similar mechanisms.
• Limit cross-project dependencies: While terraform_remote_state allows for the reuse of outputs across projects, it’s best to minimize these dependencies to avoid complex dependency graphs.
• Keep your Terraform versions aligned: Differences in Terraform versions between projects can lead to incompatibilities when accessing remote states.

Conclusion

The terraform_remote_state data source is a powerful feature within Terraform that facilitates modular infrastructure management. By breaking down your infrastructure into manageable components and utilizing the terraform_remote_state to share outputs between projects, teams can achieve more scalable and maintainable setups. This guide has walked you through the basic concepts and provided examples to get you started. Like any advanced feature, the key to success lies in understanding its use cases and applying best practices to avoid common pitfalls.