feat(parser): Allow primitive data types to be parsed using TypeAdapter

[sthulb]

Issue number: #2734

Summary

This PR allows customers to use union types as per #2734.

Changes

Please provide a summary of what's being changed

This change adopts the TypeAdapter from Pydantic v2 to allow the parser and base envelope to validate between the potentially different types.

User experience

Please share what the user experience looks like before and after this change

from aws_lambda_powertools.utilities.parser import event_parser
from pydantic import BaseModel, Field
from typing import Annotated, Any, Literal, Union


class SuccessfulCallback(BaseModel):
    status: Literal["succeeded"]
    name: str
    breed: Literal["Husky", "Labrador"]


class FailedCallback(BaseModel):
    status: Literal["failed"]
    error: str


DogCallback = Annotated[
    Union[SuccessfulCallback, FailedCallback], Field(discriminator="status")
]

@event_parser(model=DogCallback)
def lambda_handler(event: DogCallback, _: Any) -> str:
    if isinstance(event, FailedCallback): # alternatively `if event.status == "failed"` (if your type checker is on top of things)
        return f"Uh oh. Had a problem: {event.error}"

Checklist

If your change doesn't seem to apply, please leave them unchecked.

• Meet tenets criteria
• I have performed a self-review of this change
• Changes have been tested
• Changes are documented
• PR title follows conventional commit semantics

Is this a breaking change?

RFC issue number:

Checklist:

• Migration process documented
• Implement warnings (if it can live side by side)

Acknowledgment

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[github-actions]

⚠️Large PR detected⚠️

Please consider breaking into smaller PRs to avoid significant review delays. Ignore if this PR has naturally grown to this size after reviews.

[github-actions]

⚠️Large PR detected⚠️

Please consider breaking into smaller PRs to avoid significant review delays. Ignore if this PR has naturally grown to this size after reviews.

[github-actions]

⚠️Large PR detected⚠️

Please consider breaking into smaller PRs to avoid significant review delays. Ignore if this PR has naturally grown to this size after reviews.

[leandrodamascena]

Before I start reviewing this PR, I'm curious as to why CI wasn't run here. Any thoughts @sthulb?

[sthulb]

I'd like to say I know why, but I don't 😂

[leandrodamascena]

Hey @sthulb, I was reviewing this PR and exploring more use cases where TypeAdapter can be beneficial. I believe that if we go in this direction, we can make the Parser feature much more flexible without introducing a significant breaking change, I really like this.

When it comes to Pydantic v2, there are no significant performance changes when using model_validate or model_validate_json and TypeAdapter with validate_json and validate_python. Instead, I noticed that when using a dataclass with TypeAdapter, the performance is better than using a Pydantic model.

In my point of view I think we can move forward with this solution and fix some small things in this PR. I'll schedule a discussion with you and @heitorlessa to talk about this.

Thanks

Things missing in this PR

• Update PR body with use case and example
• Fix tests
• Update messages when raising InvalidModelTypeError error - we now support many more types than just BaseModel
• Update documentation - We need to improve as a whole in another PR
• Update docstrings

[leandrodamascena]

Hi @sthulb! Thank you for addressing all the feedback! I've made a few minor changes, such as creating a dedicated function to handle the cache. Since we need to use this in two places, I believe this approach makes more sense. I've also removed the pydanticv1 compatibility, as we no longer require it.

Additionally, I've created end-to-end (e2e) tests and a performance test.

@heitorlessa we need a final review here before merge.

[heitorlessa]

SHIIIIPPPPP IT!!

Superb work you two!

[sonarqubecloud]

Quality Gate passed

Issues
1 New issue
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarCloud