Automate the automation of IaC with Python – How Python Integrates with IaC Concepts

Automate the automation of IaC with Python
IaC has grown in popularity and in ways that people have never imagined. The most popular IaC framework currently is arguably Terraform. Terraform doesn’t just work on servers and more solid resources of the like, it works on APIs and looser infrastructure as well. Basically, anything that you can use in any major cloud, there is a Terraform API to use it. Terraform is the ultimate automation tool in many ways, and it can be further automated with the help of Python.
Terraform is, of course, written in Go, which is great because Go is a very good complement to Python. One is good where the other lacks and vice versa. While Go is good for singular implementations, Python is great at multiplying the effectiveness of that implementation. Basically, Go is the bullet and Python is the gun, and that is an effective combination. We will use this combination to great effect in this section.
HashiCorp, the creator of Terraform, has created APIs that allow Python to interact with Terraform. These APIs are packaged in the cdktf library published by HashiCorp in a number of languages. Let’s take a look at the steps we need to perform to install and run the cdktf library:

1. You need to first have NPM, NodeJS, and the Terraform CLI installed to install cdktf-cli:
   npm install -g cdktf-cli
2. Next, you can use the cdktf to create a Python environment:
   cdktf init –template=python
3. This will create a new Python template, which can be used then to run a Terraform template. In the template, you can find the main.py file in the stacks folder. In the folder, you can add this script:
   from constructs import Construct
   from cdktf import App, TerraformStack
   from imports.aws import AwsProvider, S3Bucket
   class MyStack(TerraformStack):
   def init(self, scope: Construct, ns: str):
   super().init(scope, ns)
   AwsProvider(self, ‘Aws’, region=’us-east-1′)
   S3Bucket(
   self,
   ”,
   bucket=<‘Bucket_Name_Here>’,
   acl=’private’
   )
   app = App()
   MyStack(app, “python-cdktf”)
   app.synth()
4. This code is simple enough to understand; it works exactly like an IaC module would, creating an S3 bucket with the name given at the time of your execution of the program. To execute the program, run the following command:
   cdktf deploy
5. If you want to revert the program and simply delete the resource that you created, you can just run the following command:
   cdktf destroy
   That is basically all you need to do. The AWS, Google Cloud, and Azure modules for Terraform are usually included with the installation, as are the equivalent Python libraries when they are installed using cdktf. You can find the documentation for all of these libraries and what they do on HashiCorp’s GitHub and on its website. HashiCorp is very clear about the things that its products can do, even if it is sometimes difficult to collate all of that information in one place. So, using these references, you can practically create any resource you want; it is all up to your imagination.
   Summary
   So, that is the chapter, one that was a little more serious than the ones you might have been used to, but this is serious business. IaC is a concept to be taken seriously; it is very powerful and can be the solution to a lot of problems that require the application of DevOps principles.
   We initially looked at a very basic application of IaC using Python in SaltStack. It was quite rudimentary but very effective for simple projects. We closed it out by diving into the guts of SaltStack and understanding the logic behind its Python modules.
   After that, we looked at the slightly more flexible Ansible and discovered all of the conveniences that it provides as well as the customization possibilities.
   Lastly, we looked at Terraform and cdktf, which is used with Python Terraform and resource modules in order to perform Terraform’s resource creation.
   All of this has hopefully helped you gain a new perspective on IaC and allowed you to understand its importance in DevOps and its integration with Python.
   In conclusion, IaC is powerful, vast, and useful to learn. In the next chapter, we will take all that you have learned here and in the previous chapters, and raise it all to a higher level.