cfn_nag VS pydantic

.pre-commit-config.yaml – contains the cfn-lint and cfn_nag pre-commit hooks.

For generic CloudFormation templates, check CFN-NAG.

If you use CDK, you should implement CDK nag; otherwise, use cfn-nag.

cfn_nag

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.

Security checks for the Cloudformation stack using cfn-nag

cdk-nag contains several Aspects to check your applications for best practices. It is especially useful if you need to be HIPAA-compliant or have other compliance requirements. It is inspired by cfn_nag which is a a tool checking for patterns in your CloudFormation templates.

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.

There is another tool called cfn_nag that can check your code for potentially any insecure infrastructure. When you read the documentation around this tool, the author says it can check for things such as:

cfn-nag: Verify that there is no code that poses a security risk.

utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses

The easiest implementation I found was to use a PyDantic class for my target schema — and use that as a parameter for the method call to “ChatCompletion.create()”. Here’s a fragment of the GinAI Python classes used.

Also, FastStream uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your applications, so you can serialize your input messages just using type annotations.

Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages

Well the performance increase is so huge because pydantic1 is really really slow. And for using rust, I'd have expected more tbh…

I've been benchmarking pydantic v2 against typedload (which I write) and despite the rust, it still manages to be slower than pure python in some benchmarks.

The ones on the website are still about comparing to v1 because v2 was not out yet at the time of the last release.

pydantic's author will refuse to benchmark any library that is faster (https://github.com/pydantic/pydantic/pull/3264 https://github.com/pydantic/pydantic/pull/1525 https://github.com/pydantic/pydantic/pull/1810) and keep boasting about amazing performances.

On pypy, v2 beta was really really really slow.

As for the most utilized and still loved library, that would probably be pydantic, it helps declaring types so convenient - be it dto's, models or just complex arguments - and plays nice with bunch of other libraries from it's own ecosystem.

I did read this ... Pydantic Docs.

Schema validations logic — I use Pydantic for input validation and schema validation (boto responses, API responses, input validation, etc.) use cases. The Pydantic schema can contain type and value constraint checks or even more complicated logic with the custom validator code.