cfn-python-lint VS pre-commit

Now the first 3 options are pretty straight forward. The template itself is a bit more complicated. In my example I used an inline template, I did this for the sake of this blog. But you can also reference an existing object on S3. This way you can use linting tools like cfn-lint on your conformance pack. This will reduce errors during deployment as you can catch them before you commit and push your code.

Automate testing and validation: Before deploying your templates, it's important to test and validate them to ensure that they will work as expected. Use tools like AWS CloudFormation Linter and Azure Resource Manager Template Tester to automate this process.

Honestly I've had good luck writing clean Cloud Formation. It's AWS only. But Nested Stacks can help keep things pretty clean and tools like cfn-lint do a pretty good job of preventing you from going too crazy with spaghetti code. Additionally, as it's all json/yaml, you can parse it to look for common problems your organization wants to enforce. So you can ensure things like specific tags your roles/vpc etc..., or usage of an "approved" set of AMI, requiring an EKS/RDS cluster to be split across availability zones; they're all just a test in your CI pipeline away.

CodeBuild will run a linting check against the CloudFormation Template using cfn-lint and will then run cfn-nag to check for patterns that indicate insecure resources within the CloudFormation template.

A linter for our AWSCloudformation stack called cfn-lint

cfn-lint and cfn_nag or other tools of that nature to check as you write so you don't need to continually try to deploy only to find that you've done something dumb.

https://github.com/aws-cloudformation/cfn-lint as mentioned will do what you've explicitly called-out.

cfn-lint can do basic validation and rule-based linting. Highly recommend using it even if it doesn't solve your problem.

To help validate your AWS CloudFormation templates you can use a tool called cfn-lint.

Today we are going to look at how to setup Black (a python code formatter) and pre-commit (a package for handling git hooks in python) to automatically format you code on commit.

# See https://pre-commit.com for more information # See https://pre-commit.com/hooks.html for more hooks repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v3.2.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-toml - id: check-added-large-files - repo: local hooks: - id: tox lint name: tox-validation entry: pdm run tox -e test,lint language: system files: ^src\/.+py$|pyproject.toml|^tests\/.+py$ types_or: [python, toml] pass_filenames: false - id: tox docs name: tox-docs language: system entry: pdm run tox -e docs types_or: [python, rst, toml] files: ^src\/.+py$|pyproject.toml|^docs\/ pass_filenames: false - repo: https://github.com/pdm-project/pdm rev: 2.10.4 # a PDM release exposing the hook hooks: - id: pdm-lock-check - repo: https://github.com/jumanjihouse/pre-commit-hooks rev: 3.0.0 hooks: - id: markdownlint

Pre-commit hooks act as the first line of defense in maintaining code quality, seamlessly integrating with linters and code formatters. They automatically execute these tools each time a developer tries to commit code to the repository, ensuring the code adheres to the project's standards. If the hooks detect issues, the commit is paused until the issues are resolved, guaranteeing that only code meeting quality standards makes it into the repository.

Pre-commit Hooks: Pre-commit is a tool that can be set up to enforce coding rules and standards before you commit your changes to your code repository. This ensures that you can't even check in (commit) code that doesn't meet your standards. This allows a code reviewer to focus on the architecture of a change while not wasting time with trivial style nitpicks.

Ah, fair enough!

On my team we use pre-commit[0] a lot. I guess I would define the history to be something like "has this commit ever been run through our pre-commit hooks?". If you rewrite history, you'll (usually) produce commits that have not been through pre-commit (and they've therefore dodged a lot of static checks that might catch code that wasn't working, at that point in time). That gives some manner of objectivity to the "history", although it does depend on each user having their pre-commit hooks activated in their local workspace.

[0]: https://pre-commit.com/

Pre-commit is a framework for managing and maintaining multi-language pre-commit hooks. It supports hooks for various programming languages. Using this framework, you only have to specify a list of hooks you want to run before every commit, and pre-commit handles the installation and execution of those hooks despite your project’s primary language.

You can learn more here: pre-commit.com

Note this includes support for pre-commit.

As a nice next step, you could also add mypy to check your type hints are consistent, and automate running all this via pre-commit hooks set up with… pre-commit.