By Sebastian Stadil —

Terraform Checks: Reliability and Compliance

Infrastructure as Code (IaC) has become the standard for managing modern IT environments, offering speed and consistency. However, ensuring your codified infrastructure is correct, secure, and compliant is paramount. This is where robust validation practices, including Terraform's native check blocks, come into play.

1. The Evolution of IaC Validation

Validating IaC is crucial to prevent misconfigurations, security vulnerabilities, and compliance breaches. The journey of Terraform validation tools reflects a growing maturity:

  • Initial Stages: Focused on syntax validation (terraform validate).
  • Growing Sophistication: Emergence of linters for provider-specific checks.
  • Security & Compliance Focus: Development of dedicated scanning tools and policy-as-code frameworks.
  • Native Advancements: Introduction of Terraform check blocks for in-configuration assertions.

This evolution highlights a "shift left" approach, embedding validation earlier and continuously throughout the infrastructure lifecycle.

2. Introducing Terraform check Blocks

Terraform check blocks are a native feature for defining custom assertions to validate your infrastructure continuously. They verify assumptions about infrastructure components not just at provisioning, but on an ongoing basis.

Core Purpose and Benefits

  • Decoupled from Resource Lifecycles: check blocks can assert conditions across multiple resources or external systems, unlike resource-specific custom conditions. This allows for holistic system-level validation.
  • Leveraging the Terraform Language (HCL): Define assertions using familiar HCL syntax, lowering the learning curve.
  • Enabling Ongoing Infrastructure Verification: Executed with every terraform plan and terraform apply, providing continuous feedback. This is further enhanced by features like HCP Terraform's health assessments for scheduled checks.
  • Non-Blocking Operations: Failed assertions issue warnings, not errors that halt operations. This provides valuable feedback without being overly disruptive, allowing operators to make informed decisions. While this flexibility is good, for stricter enforcement across complex environments, organizations often look to overarching management platforms that can layer more sophisticated policy enforcement.

Anatomy of a check Block

A check block has a local name and contains one or more assert blocks.

check "descriptive_local_name" {
  # One or more assert blocks
  assert {
    condition     = # boolean expression
    error_message = # string displayed if condition is false
  }
}
  • condition: A boolean expression. If true, the assertion passes. If false, it fails.
  • error_message: A clear message explaining the failure, supporting interpolation for dynamic values.

Practical Examples

Example 1: Validating TLS Certificate Status

Ensure an AWS ACM certificate is in the ISSUED state.

resource "aws_acm_certificate" "cert" {
  domain_name       = "myapp.example.com"
  validation_method = "DNS"
  # ... other configurations
}

check "certificate_is_issued" {
  assert {
    condition     = aws_acm_certificate.cert.status == "ISSUED"
    error_message = "The certificate for ${aws_acm_certificate.cert.domain_name} is not ISSUED. Current status: ${aws_acm_certificate.cert.status}."
  }
}

Example 2: Verifying Service Endpoint Responsiveness

Use a scoped http data source to check if a service endpoint returns a 200 OK status.

resource "aws_lb" "app_lb" {
  # ... load balancer configuration ...
  name = "my-app-lb"
}

check "service_endpoint_health" {
  # Scoped data source, only visible within this check block
  data "http" "health_check" {
    url = "https://${aws_lb.app_lb.dns_name}/health"
    # In production, ensure you have valid certificates and set insecure = false
    # For this example, we might use insecure if it's an internal or test endpoint
    insecure = true 
    
    # Ensure this data source is queried only after the LB is available
    depends_on = [aws_lb.app_lb] 
  }

  assert {
    condition     = data.http.health_check.status_code == 200
    error_message = "Service endpoint at ${data.http.health_check.url} did not return HTTP 200. Status: ${data.http.health_check.status_code}."
  }
}

Note on Scoped data Sources: Data sources defined inside a check block are "scoped" to that block. If a provider error occurs during data retrieval (e.g., network issue), it's masked as a warning, aligning with the non-blocking nature of check blocks. The depends_on meta-argument is crucial for ensuring data sources are queried only after dependent resources are created/updated.

Execution Flow and Behavior

  • Evaluation Point: check blocks are evaluated at the end of terraform plan and terraform apply.
  • "Known After Apply": If a check's condition depends on attributes unknown until after apply (e.g., a new VM's IP), Terraform issues a warning that the result is "known after apply." The actual outcome is reported post-apply.
  • Outcomes: Passing checks are recorded in the state file. Failing checks produce warnings in the CLI output with the error_message.

HCP Terraform Plus Edition enhances this with "health assessments," automating check block evaluations and providing notifications, which is a step towards proactive governance.

3. Terraform check Blocks vs. Other Native Validation

Terraform offers several native validation mechanisms. Here’s how check blocks compare:

Feature

Scope of Validation

Execution Point

Behavior on Failure

Typical Use Cases

Provider Interaction

terraform validate

Static config files (syntax, schema)

CLI command (before plan/apply)

Error & Halt

Syntax checking, basic type validation

No

Input Variable Validation

Individual input variable values

During variable processing (before plan)

Error & Halt

Ensuring variable format/constraints (regex, length)

No

Resource precondition

State/attributes for a single resource

Before resource operation (create/update/delete)

Error & Halt

Gatekeeping resource changes

Yes (resource data)

Resource postcondition

Outcome of a single resource operation

After resource operation (create/update/delete)

Error & Halt

Verifying immediate result of a resource change

Yes (resource data)

check Block

Entire config, inter-resource relations, external systems

End of plan/apply; HCP Health Assessments

Warning & Continue

Ongoing health checks, operational states, integrations, compliance assertions

Via scoped data sources

This layered approach allows for selecting the right tool for the job. While check blocks offer flexibility with warnings, stricter, policy-driven enforcement often requires more comprehensive solutions.

4. The Broader Terraform Validation Ecosystem

Beyond native features, several tools enhance Terraform validation:

  • terraform validate: The foundational static analysis check.
  • TFLint: A linter for best practices, potential bugs, and provider-specific issues (e.g., invalid instance types) that validate might miss.
  • Checkov / TFSec: Static analysis tools for security misconfigurations and compliance violations against standards like CIS Benchmarks, HIPAA, PCI-DSS.
  • Open Policy Agent (OPA) / HashiCorp Sentinel: Policy-as-code frameworks for advanced, customizable governance. OPA uses Rego, while Sentinel is integrated into HCP Terraform and Terraform Enterprise, offering fine-grained policy enforcement.

While these tools are powerful, managing their configurations, integrating them into pipelines, and ensuring consistent application across numerous projects and teams can become complex. This is an area where specialized Infrastructure Automation and Management platforms, such as Scalr, can provide significant value by offering a centralized control plane for policy definition, enforcement, and visibility, streamlining the integration of these diverse validation mechanisms.

5. Strategic Implementation: Best Practices

  • Ideal Use Cases for check Blocks:
    • Verifying operational states (e.g., database accessible, web server responding).
    • Checking integrations (e.g., load balancer targets are healthy).
    • Validating external dependencies via data sources.
    • Confirming runtime assumptions.
  • Develop a Layered Validation Strategy:
    1. Local Development: terraform fmt, validate, IDE integration, pre-commit hooks with linters/scanners.
    2. CI/CD Pipeline: Static analysis (TFLint, Checkov), policy enforcement (OPA/Sentinel), plan review (including check warnings), apply review.
    3. Continuous Monitoring: HCP Terraform health assessments or custom solutions for ongoing check block evaluation.
  • Craft Effective and Maintainable Assertions:
    • Write clear, actionable error_messages.
    • Keep conditions focused.
    • Test check blocks thoroughly.
    • Document and version control check blocks with your IaC.
  • Integrate into CI/CD Pipelines: Automate all validation steps for consistent assurance. Pipeline logic can act on check block warnings (e.g., pause for review, trigger alerts). Platforms that offer robust CI/CD integration or built-in execution environments can simplify this significantly, ensuring that checks and policies are consistently applied without manual overhead.

6. Conclusion: Towards Proactive and Reliable Infrastructure

Terraform check blocks are a valuable addition to the IaC validation toolkit, enabling native, continuous verification of your infrastructure's state and behavior. They offer a flexible, HCL-native way to define assertions that run with every plan and apply.

However, check blocks are one piece of a larger puzzle. A holistic validation strategy combines native Terraform features with specialized linters, security scanners, and policy-as-code engines. Adopting a proactive validation culture—where checks and policies are treated as living code, continuously refined based on operational learnings—is key.

For organizations managing infrastructure at scale, the challenge extends beyond just implementing these tools. It involves establishing consistent governance, ensuring visibility across all environments, and simplifying the operational burden of managing complex validation pipelines. This is where comprehensive IaC management platforms can play a crucial role, providing the framework and automation necessary to effectively deploy and manage a multi-layered validation strategy, ultimately leading to more reliable, secure, and compliant infrastructure.