Beginner's Guide to Terragrunt

Table of Contents

1. Introduction: The Promise and Pitfalls of Terragrunt
2. Configuration Conundrums: Keeping It DRY, or Drowning in HCL?
   • The include and find_in_parent_folders Maze
   • HCL Parsing: Speed Bumps at Scale
   • Remote Backend Blues: Automation vs. Control
3. The Performance Puzzle: When run-all Becomes Run...eventually
   • Provider Proliferation and Cache Considerations
   • The Cost of Fetching Outputs
   • run-all Under Pressure
4. Dependency Dramas: Untangling the Web
   • run-all plan: What You See Isn't Always What You Get
   • The run-all destroy Dilemma
   • Making Sense of run-all show -json
5. Operational Obstacles: From Local Setups to CI/CD Nightmares
   • The Usual Suspects: Path Errors and File System Foibles
   • CI/CD Integration: The Terraform Cloud Elephant in the Room
6. The "Terragrunt Tax": Weighing Complexity Against Benefit
   • The Learning Curve Steepens
   • Is It Overkill for Your Use Case?
7. Recognizing Anti-Patterns
8. Conclusion: Charting a Course for Efficient IaC Management
9. Summary: Terragrunt Challenges and Platform Perspectives

1. Introduction: The Promise and Pitfalls of Terragrunt

Terragrunt. For many DevOps and platform engineering teams, the name is synonymous with bringing order to the potential chaos of large-scale Terraform deployments. Its goals are laudable: keep configurations DRY (Don't Repeat Yourself), manage multiple modules and environments with grace, and simplify remote state. And for many, it delivers, offering better organization and dependency management.

But let's be frank. The journey with Terragrunt isn't always smooth. As infrastructure complexity grows, so too can the intricacies of managing Terragrunt itself. What starts as a solution to tame Terraform can sometimes feel like another layer of complexity to wrestle with. This isn't to say Terragrunt isn't powerful; it is. However, understanding its common pain points is crucial for any team committing to it, or for those evaluating if it's the right long-term fit compared to, say, a more integrated platform approach.

2. Configuration Conundrums: Keeping It DRY, or Drowning in HCL?

At its heart, Terragrunt aims to reduce repetition. But the mechanisms to achieve this can themselves become sources of confusion.

The include and find_in_parent_folders Maze

The include block, often paired with find_in_parent_folders(), is Terragrunt's workhorse for sharing configurations. The idea is simple: define common settings in a parent file, and child configurations inherit them.

// child/terragrunt.hcl
include "root" {
  path = find_in_parent_folders()
}

inputs = {
  environment = "staging"
  instance_count = 5
}

This works, but find_in_parent_folders() can be finicky. Non-standard root file names or complex directory trees can lead to Terragrunt picking up the wrong parent, or none at all. The community-driven best practice has shifted towards renaming the root configuration from terragrunt.hcl to something like env.hcl or root.hcl to create a clearer distinction.

// child/terragrunt.hcl
include "env_config" {
  path = find_in_parent_folders("env.hcl") // Explicitly look for env.hcl
}
// ...

While this clarifies intent, it also means every child include needs updating. And if you're managing dozens or hundreds of modules, that's a significant refactor. Moreover, deep include chains, while DRY, can obscure where a setting originates, making debugging a treasure hunt. Each include also adds to HCL parsing time. It makes one wonder if a system with a more visual or structured way of managing configuration inheritance, perhaps like those found in dedicated IaC platforms, might simplify this.

HCL Parsing: Speed Bumps at Scale

Terragrunt parses HCL. No surprise there. But issues can crop up. For instance, terragrunt.hcl.json files with embedded Terragrunt functions reportedly broke after v0.32.6 due to changes in the underlying HCL manipulation library. More universally, in large repositories with many modules and deep include hierarchies, the cumulative HCL parsing time can become a real drag on plan and apply operations. While caching mechanisms have been introduced in later Terragrunt versions (e.g., v0.38.9), the sheer volume of parsing in extensive setups remains a concern.

Remote Backend Blues: Automation vs. Control

Terragrunt's ability to automatically generate backend configurations and even create S3 buckets and DynamoDB tables for AWS is a major convenience, especially for new projects.

// root/env.hcl (example for AWS)
remote_state {
  backend = "s3"
  generate = {
    path      = "backend.tf"
    if_exists = "overwrite_terragrunt"
  }
  config = {
    bucket         = "my-company-tfstate-${get_aws_account_id()}-${get_aws_region()}"
    key            = "${path_relative_to_include()}/terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "my-company-tfstate-lock-${get_aws_account_id()}-${get_aws_region()}"

    s3_bucket_tags = {
      owner = "infra-team"
      name  = "Terraform State Storage"
    }
    dynamodb_table_tags = {
      owner = "infra-team"
      name  = "Terraform State Lock"
    }
  }
}

However, this automation comes with trade-offs, particularly for production environments demanding tight security and control. Auto-created S3 log buckets might lack stringent security defaults. The disable_init attribute, intended to prevent auto-creation, has had issues where it also disables backend initialization altogether. Fine-grained IAM controls for state access (like session tags) or customizing all backend resource fields (e.g., specific KMS keys for S3 encryption beyond a simple boolean) are not fully supported through this auto-generation. This often leads to a dilemma: use Terragrunt's convenience and accept limitations, or manually manage these critical resources, somewhat diluting Terragrunt's value proposition for backend management. Platforms specializing in IaC often provide robust, secure, and pre-configured state backends as a core service, abstracting these concerns.

3. The Performance Puzzle: When run-all Becomes Run...eventually

Performance is a recurring theme in Terragrunt discussions, especially at scale.

Provider Proliferation and Cache Considerations

By default, each Terragrunt unit downloads its own provider binaries. With many units, this means repeated, time-consuming downloads. Terragrunt's --provider-cache flag helps by caching providers. But the cache server itself has startup/shutdown overhead, potentially making single runs slower. Measurement is key.

The Cost of Fetching Outputs

dependency blocks often fetch outputs from one module as inputs for another. The standard terraform output -json command invoked by Terragrunt can be slow as it loads providers. For AWS S3 backends, --dependency-fetch-output-from-state offers a speedup by reading the state file directly.

// app/terragrunt.hcl
dependency "vpc" {
  config_path = "../vpc"
  // Terragrunt will fetch outputs from the vpc module
}

inputs = {
  vpc_id = dependency.vpc.outputs.vpc_id
}

// To potentially speed this up if using S3 backend:
// terragrunt run-all plan --dependency-fetch-output-from-state

This is a welcome optimization, but it's S3-specific and relies on the Terraform state file schema remaining compatible.

run-all Under Pressure

Commands like terragrunt run-all plan are indispensable for managing multiple modules. But their performance can degrade significantly in large repositories. All the aforementioned bottlenecks—HCL parsing, provider downloads, output fetching—are amplified. Limiting parallelism (e.g., --terragrunt-parallelism 1) to work around other issues can make run-all operations painfully slow. A 145-module init taking 40 minutes is a real-world example of this pain. This is where orchestrated execution environments, like those provided by Scalr, can offer significant advantages by optimizing job distribution, caching, and state interactions at a platform level.

4. Dependency Dramas: Untangling the Web

Managing inter-module dependencies is a core Terragrunt strength, but it's not without its own set of challenges, especially with run-all.

run-all plan: What You See Isn't Always What You Get

A common pitfall: if a dependency (Module A) has unapplied changes, run-all plan for a dependent module (Module B) will be based on Module A's old state. The plan for Module B might not accurately reflect the final state until Module A is applied. This can lead to surprises.

The run-all destroy Dilemma

terragrunt run-all destroy often confuses users. The expectation is that destroying a module might also destroy modules that depend on it. Instead, it typically targets only the specified module(s). Using --terragrunt-ignore-dependency-errors can even lead to attempts to destroy the module's own dependencies (e.g., destroying the VPC when trying to destroy an RDS instance within it), which is rarely the desired outcome.

Making Sense of run-all show -json

For automation, terragrunt run-all show -json <planfile> is key. However, the output can be problematic: concatenated JSON (invalid as a single document) rather than a merged object, and missing module origin identifiers. This makes programmatic parsing and integration with policy tools or cost estimators harder than it should be. A platform that provides structured, easily consumable plan outputs is invaluable here.

5. Operational Obstacles: From Local Setups to CI/CD Nightmares

Beyond configuration and performance, day-to-day operational hurdles can frustrate Terragrunt users.

The Usual Suspects: Path Errors and File System Foibles

The classic "terraform": executable file not found in $PATH is a common entry point into Terragrunt troubleshooting. More subtly, issues with symbolic links, path resolution for module sources (especially in CI or with run-all), or failures in generating backend.tf due to misconfigured include or generate blocks can stop workflows in their tracks.

CI/CD Integration: The Terraform Cloud Elephant in the Room

This is a big one. Terragrunt doesn't work natively with Terraform Cloud (TFC) or Terraform Enterprise (TFE). For organizations invested in the HashiCorp ecosystem, this is a significant drawback. Workarounds involve custom runners or wrapper scripts, adding complexity.

# Example of a CI script snippet for Terragrunt
# (Illustrative - actual scripts will be more complex)

echo "Planning changes for ${MODULE_PATH}"
cd ${MODULE_PATH}

# Ensure Terragrunt and Terraform are available and configured
# Handle authentication to cloud providers

terragrunt init -reconfigure
terragrunt plan -out=tfplan

# Further steps to store/review plan, then apply
# Error handling, notifications, etc.

This manual scripting for CI/CD pipelines, while standard for CLI tools, highlights the operational overhead. Platforms like Scalr, with built-in GitOps workflows, environment management, RBAC, and OPA integration, can offer a much more streamlined and governed CI/CD experience for IaC. Managing SSH keys for private Git modules in CI or ensuring environment consistency (e.g., Python versions for data sources) are other common CI friction points.

6. The "Terragrunt Tax": Weighing Complexity Against Benefit

Terragrunt introduces an abstraction layer. This layer has a "tax" associated with it.

The Learning Curve Steepens

Teams must learn Terraform and Terragrunt's own HCL extensions, functions, blocks, and CLI. This dual learning curve can slow down onboarding.

Is It Overkill for Your Use Case?

Applying Terragrunt to small projects or simple environments can feel like over-engineering. If you're not facing the large-scale problems Terragrunt solves (many environments, hundreds of modules, complex dependencies), its added complexity might not be justified. The "Terragrunt Tax" is more palatable when the pain of managing vanilla Terraform at scale becomes acute.

7. Recognizing Anti-Patterns

Several anti-patterns can emerge:

• Premature Adoption: Using Terragrunt before its complexity is warranted.
• Copy-Pasting Configurations: Ironically, despite Terragrunt's DRY principles, users can fall into copy-pasting terragrunt.hcl files or large input blocks if include and locals aren't used effectively. This negates Terragrunt's benefits. Platforms often offer templating or environment cloning features that can mitigate this.
• Over-reliance on run-all without Guardrails: Using run-all apply indiscriminately without understanding the blast radius or having robust review gates can be risky.

8. Conclusion: Charting a Course for Efficient IaC Management

Terragrunt is undeniably a powerful tool that has helped many organizations manage complex Terraform estates. It brings valuable patterns for code reuse and environment separation. However, as we've seen, it's not a silver bullet. Configuration complexity, performance bottlenecks, dependency orchestration nuances, operational hurdles in CI/CD, and an inherent learning curve are all part of the Terragrunt landscape.

For teams hitting these friction points, or for those architecting their IaC strategy from the ground up, it's worth considering if a dedicated IaC management platform might offer a more streamlined, governed, and efficient experience. Platforms like Scalr are designed to address many of these challenges head-on, providing integrated solutions for state management, environment promotion, CI/CD automation, policy enforcement (e.g., OPA), and collaborative workflows, often with a more intuitive user experience for managing complexity than CLI-centric tools alone.

The choice isn't necessarily Terragrunt or a platform; for some, they might coexist. But understanding the trade-offs is key to building a sustainable and scalable IaC practice.

9. Summary: Terragrunt Challenges and Platform Perspectives

Terragrunt Challenge Area	Common Issue	How a Platform (e.g., Scalr) Might Address It
Configuration	Complex include hierarchies, find_in_parent_folders confusion, HCL parsing at scale.	Structured UI for configuration, managed inheritance, optimized backend parsing, visual environment definition.