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Optimizing Terraform `plan` and `apply` times

Learn about Optimizing Terraform `plan` and `apply` times as part of Terraform Infrastructure as Code Mastery

Table of Contents

Optimizing Terraform Plan and Apply Times

As infrastructure scales, the time it takes for Terraform's

code
plan
and
code
apply
commands to execute can become a significant bottleneck. Understanding and implementing optimization strategies is crucial for efficient Infrastructure as Code (IaC) management. This module explores key techniques to reduce execution times and improve developer productivity.

Understanding the Bottlenecks

Terraform's execution time is influenced by several factors:

  • State File Size and Complexity: Larger, more complex state files take longer to read and parse.
  • Provider Operations: The number and type of resources being managed, and how quickly providers respond to API calls.
  • Resource Dependencies: Complex dependency graphs can lead to sequential operations, slowing down parallel execution.
  • Network Latency: Communication with cloud provider APIs.
  • Local Machine Performance: CPU, memory, and disk I/O on the machine running Terraform.
What are the primary factors that contribute to slow Terraform plan and apply times?

State file size/complexity, provider operations, resource dependencies, network latency, and local machine performance.

Strategies for Optimization

Several strategies can be employed to speed up Terraform operations. These range from architectural changes in your IaC to leveraging specific Terraform features and tools.

1. Modularization and Refactoring

Breaking down your infrastructure into smaller, reusable modules can significantly improve performance. Smaller modules mean smaller state files and more targeted

code
plan
and
code
apply
operations. Refactoring monolithic configurations into modules allows you to manage and update components independently, reducing the scope of changes.

2. Parallelism and Workspaces

Terraform automatically parallelizes operations where possible. However, you can influence this with the

code
-parallelism=
flag. For managing distinct environments (e.g., dev, staging, prod) without duplicating code, Terraform Workspaces are invaluable. Each workspace has its own state file, allowing for isolated operations.

3. Targeted Operations with `-target`

The

code
-target
flag allows you to focus
code
plan
and
code
apply
operations on specific resources or modules. While useful for debugging or targeted updates, overuse can lead to state drift and is generally discouraged for regular workflows. It's best used for isolated troubleshooting.

Use -target judiciously. It can bypass intended dependencies and lead to an inconsistent infrastructure state if not managed carefully.

4. State Management and Remote Backends

Using a remote backend (like S3, Azure Blob Storage, or HashiCorp Consul) for your Terraform state is essential for collaboration and performance. Remote backends often offer better performance for state locking and retrieval compared to local state files. Ensure your backend configuration is optimized for your cloud provider.

5. Provider Configuration and Versioning

Keep your Terraform providers updated. Newer versions often include performance improvements and bug fixes. Additionally, some providers allow for specific configuration options that can influence API call efficiency. Review provider documentation for tuning parameters.

6. Minimizing Resource Drift

Resource drift occurs when infrastructure resources are changed outside of Terraform. Frequent

code
terraform plan
runs can help detect and correct drift. Minimizing manual changes to your infrastructure reduces the likelihood of drift and the complexity of Terraform's reconciliation process.

7. Leveraging `terraform plan -refresh=false`

By default,

code
terraform plan
refreshes the state of all resources before generating the plan. For situations where you are confident in the state and only need to see planned changes based on your configuration, using
code
terraform plan -refresh=false
can significantly speed up the planning phase. This bypasses the need to query all resources from the cloud provider.

The terraform plan command works by first refreshing the state of your infrastructure. This involves querying the cloud provider for the current status of all managed resources. Once the state is up-to-date, Terraform compares this refreshed state with your desired configuration to determine the necessary changes. Skipping the refresh (-refresh=false) means Terraform uses the existing state file directly, which is faster but assumes the state accurately reflects reality. This is analogous to checking a pre-existing inventory list instead of recounting all items in a warehouse.

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8. Using `terraform apply -auto-approve` with Caution

While not directly speeding up the plan phase,

code
terraform apply -auto-approve
can streamline the apply process by removing the interactive confirmation step. This is best used in automated pipelines where the
code
plan
has already been reviewed and approved. It's crucial to ensure your CI/CD pipeline includes robust validation steps before auto-approving.

Advanced Techniques and Tools

Beyond core Terraform features, external tools and practices can further enhance performance.

1. Terraform Cloud/Enterprise

Terraform Cloud and Enterprise offer features like remote execution, state management, and parallel runs, which can significantly improve performance and collaboration, especially for larger teams and complex infrastructures.

2. Custom Providers and Provisioners

For highly specific or performance-critical operations, consider developing custom Terraform providers or leveraging provisioners (like

code
remote-exec
or
code
user-data
) judiciously. However, be mindful that complex provisioners can sometimes add overhead.

Summary of Best Practices

StrategyImpact on PerformanceConsiderations
ModularizationHigh (Smaller state, targeted ops)Requires refactoring effort
WorkspacesMedium (State isolation)Best for environment separation
-targetHigh (for specific resources)Risk of state drift, use cautiously
Remote BackendHigh (Faster state access)Essential for collaboration
-refresh=falseHigh (for plan phase)Assumes state accuracy
Provider UpdatesMedium (Bug fixes, optimizations)Always recommended
Which strategy can significantly speed up the plan phase by bypassing state refreshing?

terraform plan -refresh=false

Learning Resources

Terraform Documentation: State(documentation)

Understand how Terraform manages state, which is fundamental to optimizing plan and apply times.

Terraform Documentation: Workspaces(documentation)

Learn about using workspaces to manage multiple environments and states efficiently.

Terraform Documentation: Modules(documentation)

Explore best practices for structuring your infrastructure as code using modules to improve manageability and performance.

Terraform CLI: `-target` Flag(documentation)

Details on using the `-target` flag for focused operations, with important warnings about its usage.

Terraform CLI: `-refresh` Flag(documentation)

Understand the `-refresh` flag and how disabling it can speed up `terraform plan`.

Terraform Cloud Documentation(documentation)

Discover how Terraform Cloud can enhance performance through remote execution and state management.

HashiCorp Blog: Optimizing Terraform Performance(blog)

A blog post from HashiCorp offering practical tips and strategies for improving Terraform execution speed.

Terraform Provider Best Practices(tutorial)

Guidance on how to effectively use and configure Terraform providers for better performance and reliability.

Understanding Terraform's Execution Plan(video)

A video explaining how Terraform generates its execution plan, which is key to understanding performance factors.

Terraform Best Practices for Large-Scale Deployments(video)

A video discussing advanced strategies for managing Terraform in large and complex environments, including performance considerations.