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Terraform

Terraform Directory Watching: Does It Exist?

By Filip on 10/07/2024

Learn how Terraform detects changes and if it can automatically monitor a directory for modifications to your infrastructure code.

Terraform Directory Watching: Does It Exist?

Table of Contents

Introduction

Terraform, a powerful infrastructure-as-code tool, doesn't natively support automatic directory watching and triggering actions like "terraform apply" upon changes. However, this can be achieved using external scripting and tools. A common approach involves using a scripting language like Python to monitor a directory for modifications and execute Terraform commands when changes are detected.

Step-by-Step Guide

Terraform itself doesn't have a built-in feature to automatically watch a directory for changes and trigger actions like "terraform apply". However, you can achieve this behavior using external scripting and tools. Here's a common approach using a scripting language like Python:

  1. Calculate Directory Checksum:

    • Use a scripting language like Python to calculate a checksum (e.g., MD5) of the directory you want to monitor. This checksum will change whenever a file within the directory is modified.
    • Store this initial checksum.

  2. Watch for Changes:

    • Utilize a library like watchdog in Python to monitor the directory for any changes (file modifications, additions, deletions).

  3. Compare Checksums:

    • When the watchdog library detects a change:
      • Recalculate the directory's checksum.
      • Compare the new checksum with the stored initial checksum.
      • If the checksums differ, it means changes have occurred within the directory.

  4. Trigger Terraform Actions:

    • If changes are detected (checksums don't match):
      • Execute Terraform commands like terraform plan and terraform apply using Python's subprocess module or similar.

Example (Conceptual Python Code):

import time
import hashlib
import subprocess
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler

class Watcher:
    def __init__(self, directory_to_watch):
        self.directory_to_watch = directory_to_watch
        self.initial_checksum = self.calculate_checksum()

    def calculate_checksum(self):
        # Logic to calculate directory checksum (e.g., using hashlib.md5())

    def run(self):
        event_handler = Handler()
        observer = Observer()
        observer.schedule(event_handler, self.directory_to_watch, recursive=True)
        observer.start()
        try:
            while True:
                time.sleep(5)
        except:
            observer.stop()
        observer.join()

class Handler(FileSystemEventHandler):
    @staticmethod
    def on_any_event(event):
        if not event.is_directory:
            # Directory modified, recalculate checksum and compare
            new_checksum = Watcher.calculate_checksum()
            if new_checksum != Watcher.initial_checksum:
                # Trigger Terraform actions
                subprocess.run(["terraform", "plan"])
                subprocess.run(["terraform", "apply", "-auto-approve"])  # Be cautious with auto-approve

if __name__ == "__main__":
    w = Watcher("/path/to/your/terraform/directory")
    w.run()

Important Considerations:

  • Error Handling: Implement robust error handling in your script to prevent unexpected interruptions.
  • Idempotency: Ensure your Terraform code is idempotent to avoid unintended consequences when applied multiple times.
  • Security: Be mindful of security implications, especially if using "auto-approve" in your script.
  • Alternative Tools: Explore tools like entr or fswatch for directory watching, and integrate them with Terraform execution.

Remember to adapt this approach to your specific use case and environment.

Code Example

This Python script uses the 'watchdog' library to monitor a specified directory for changes. When a change is detected, it calculates the directory's checksum and compares it to the initial checksum. If the checksums differ, indicating modifications, the script executes 'terraform plan' and 'terraform apply' commands within the specified directory to automatically update the infrastructure based on the changes made to the Terraform files. The script is designed to continuously monitor the directory and automatically apply Terraform changes, simplifying the process of keeping infrastructure configurations up-to-date.

import time
import hashlib
import subprocess
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler

class Watcher:
    def __init__(self, directory_to_watch):
        self.directory_to_watch = directory_to_watch
        self.initial_checksum = self.calculate_checksum()

    def calculate_checksum(self):
        # Logic to calculate directory checksum using hashlib.md5()
        hash_md5 = hashlib.md5()
        for root, _, files in os.walk(self.directory_to_watch):
            for file in files:
                filepath = os.path.join(root, file)
                with open(filepath, "rb") as f:
                    for chunk in iter(lambda: f.read(4096), b""):
                        hash_md5.update(chunk)
        return hash_md5.hexdigest()

    def run(self):
        event_handler = Handler(self)
        observer = Observer()
        observer.schedule(event_handler, self.directory_to_watch, recursive=True)
        observer.start()
        try:
            print(f"Watching directory: {self.directory_to_watch}")
            while True:
                time.sleep(5)
        except KeyboardInterrupt:
            observer.stop()
        observer.join()

class Handler(FileSystemEventHandler):
    def __init__(self, watcher):
        self.watcher = watcher

    def on_any_event(self, event):
        if not event.is_directory:
            new_checksum = self.watcher.calculate_checksum()
            if new_checksum != self.watcher.initial_checksum:
                print("Changes detected. Running Terraform...")
                try:
                    subprocess.run(["terraform", "plan"], cwd=self.watcher.directory_to_watch, check=True)
                    subprocess.run(["terraform", "apply", "-auto-approve"], cwd=self.watcher.directory_to_watch, check=True)
                    print("Terraform apply successful!")
                    self.watcher.initial_checksum = new_checksum  # Update checksum after successful apply
                except subprocess.CalledProcessError as e:
                    print(f"Error executing Terraform: {e}")

if __name__ == "__main__":
    import os
    terraform_dir = os.path.join(os.getcwd(), "path/to/your/terraform/directory")  # Replace with your Terraform directory
    w = Watcher(terraform_dir)
    w.run()

Explanation:

  1. Import Necessary Modules:

    • time: For pausing execution.
    • hashlib: For calculating MD5 checksums.
    • subprocess: For running Terraform commands.
    • watchdog: For monitoring directory changes.

  2. Watcher Class:

    • __init__: Initializes the watcher with the target directory and calculates the initial checksum.
    • calculate_checksum: Calculates the MD5 checksum of the directory.
    • run:
      • Creates a Handler object to handle file system events.
      • Creates an Observer object and schedules the handler to watch the directory recursively.
      • Starts the observer and enters an infinite loop (until interrupted) to keep the script running.

  3. Handler Class (Inherits from FileSystemEventHandler):

    • on_any_event: This method is triggered on any file system event (modification, creation, deletion).
      • It checks if the event involves a file (not a directory).
      • Recalculates the directory checksum.
      • Compares the new checksum with the initial checksum.
      • If checksums differ, it means changes occurred:
        • Prints a message indicating changes and Terraform execution.
        • Uses subprocess.run to execute terraform plan and terraform apply (with -auto-approve - use caution!).
        • Important: The cwd argument in subprocess.run is crucial to set the working directory for Terraform commands to the correct path.
        • Updates the initial_checksum after a successful terraform apply to avoid redundant executions.

  4. Main Execution Block (if __name__ == "__main__":)

    • Sets the terraform_dir variable to the path of your Terraform project directory.
    • Creates a Watcher instance, passing in the terraform_dir.
    • Calls the run() method of the watcher to start the monitoring process.

How to Use:

  1. Install watchdog:

    pip install watchdog

  2. Save the Code: Save the code as a Python file (e.g., terraform_watcher.py).

  3. Modify terraform_dir: Replace "path/to/your/terraform/directory" with the actual path to your Terraform project directory.

  4. Run the Script:

    python terraform_watcher.py

Now, the script will continuously monitor your Terraform directory and automatically apply changes whenever it detects modifications to the files within.

Additional Notes

Functionality:

  • Checksum Implementation: The provided calculate_checksum function demonstrates a robust way to calculate a directory's checksum using hashlib.md5(). It iterates through all files within the directory and its subdirectories, ensuring any change is detected.
  • Working Directory: The cwd argument in subprocess.run is essential for setting the correct working directory when executing Terraform commands. This ensures Terraform operates on the intended directory structure.
  • Error Handling: The code includes basic error handling using a try-except block to catch subprocess.CalledProcessError. This helps identify issues during Terraform command execution.

Improvements:

  • Configuration File: Instead of hardcoding the directory path and potentially the "auto-approve" flag, consider using a configuration file (like YAML or INI) to make the script more flexible and reusable.
  • Logging: Implement a logging mechanism to record events, errors, and Terraform output. This can be invaluable for debugging and auditing purposes.
  • User Input: For scenarios where "auto-approve" isn't desirable, prompt the user for confirmation before applying Terraform changes.
  • Debouncing: For highly dynamic directories, consider adding a debouncing mechanism to prevent rapid consecutive executions of Terraform. This can be achieved by introducing a small delay or aggregating changes within a short timeframe.
  • Git Integration: If your workflow involves Git, you could enhance the script to automatically commit and push changes after successful Terraform applies.

Alternatives:

  • Terraform Cloud/Enterprise: For more advanced features like remote runs, collaboration, and approval workflows, consider using Terraform Cloud or Terraform Enterprise.
  • CI/CD Pipelines: Integrate the directory watching and Terraform execution into your existing CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions) for a more robust and automated workflow.

Security:

  • Auto-Approve Caution: The use of -auto-approve in the script should be carefully considered. While convenient, it bypasses any manual review or approval steps, potentially leading to unintended consequences.
  • Sensitive Information: Never store sensitive information (API keys, secrets) directly in your scripts or configuration files. Utilize environment variables or secret management solutions.

Summary

While Terraform lacks built-in directory watching, you can achieve this using external scripts and tools. Here's a common approach:

1. Directory Checksumming:

  • Calculate and store a checksum (e.g., MD5) of your Terraform directory. This checksum acts as a fingerprint, changing whenever files within the directory are modified.

2. Change Detection:

  • Employ a library like Python's watchdog to monitor the directory for any file changes (modifications, additions, deletions).

3. Checksum Comparison:

  • Upon detecting a change, recalculate the directory's checksum.
  • Compare the new checksum with the stored initial checksum. A mismatch indicates changes within the directory.

4. Triggering Terraform:

  • If checksums differ, execute Terraform commands (terraform plan, terraform apply) using Python's subprocess module or similar.

Key Considerations:

  • Error Handling: Implement robust error handling to prevent script failures.
  • Idempotency: Ensure your Terraform code is idempotent to avoid unintended consequences from repeated applications.
  • Security: Be cautious with "auto-approve" in scripts, considering potential security implications.
  • Alternative Tools: Explore tools like entr or fswatch for directory watching and integrate them with Terraform execution.

This approach provides a basic framework. Adapt and customize it based on your specific needs and environment.

Conclusion

This approach, while effective, necessitates careful consideration of error handling, idempotency of Terraform code, and security implications, especially when using "auto-approve." Alternative tools like entr or fswatch can be explored for directory watching. Remember to tailor this approach to your specific use case and environment for optimal results. By implementing such a solution, you can leverage the power of automation to streamline your infrastructure management workflow, ensuring that your infrastructure remains consistent with your Terraform codebase.

References

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