Metadata-Version: 2.5
Name: aws_lambda_env_modeler
Version: 4.0.0
Summary: AWS-Lambda-Env-Modeler is a Python library designed to simplify the process of managing and validating environment variables in your AWS Lambda functions.
Project-URL: Repository, https://github.com/ran-isenberg/aws-lambda-env-modeler
Project-URL: Issue tracker, https://github.com/ran-isenberg/aws-lambda-env-modeler/issues
Project-URL: Releases, https://github.com/ran-isenberg/aws-lambda-env-modeler/releases
Author: Ran Isenberg
License-Expression: MIT-0
License-File: LICENSE
Keywords: aws lambda,aws serverless,environment variables parser,lambda environment variables,serverless best practices
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Programming Language :: Python :: 3.15
Requires-Python: <4,>=3.11.0
Requires-Dist: pydantic<3,>=2.12.0
Description-Content-Type: text/markdown


# AWS Lambda Environment Variables Modeler (Python)

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AWS-Lambda-Env-Modeler is a Python library designed to simplify the process of managing and validating environment variables in your AWS Lambda functions.

It leverages the power of [Pydantic](https://docs.pydantic.dev/latest/) models to define the expected structure and types of the environment variables.

This library is especially handy for serverless applications where managing configuration via environment variables is a common practice.

**[📜Documentation](https://ran-isenberg.github.io/aws-lambda-env-modeler/)** | **[Website](https://www.ranthebuilder.cloud)**
> **Contact details | ran.isenberg@ranthebuilder.cloud**


## **The Problem**

Environment variables are often viewed as an essential utility. They serve as static AWS Lambda function configuration.

Their values are set during the Lambda deployment, and the only way to change them is to redeploy the Lambda function with updated values.

However, many engineers use them unsafely despite being such an integral and fundamental part of any AWS Lambda function deployment.

This usage may cause nasty bugs or even crashes in production.


This library allows you to correctly parse, validate, and use your environment variables in your Python AWS Lambda code.

Read more about it in the [AWS Lambda environment variables article on the Ran The Builder website](https://www.ranthebuilder.cloud/post/aws-lambda-cookbook-environment-variables).

### **Features**

- Validates the environment variables against a Pydantic model: define both semantic and syntactic validation.
- Parses the string environment variables into complex classes and types.
- Provides means to access the environment variables safely with a global getter function in every part of the function.
- Provides a decorator to initialize the environment variables before executing a function.
- Caches the parsed model for performance improvement for multiple 'get' calls.


## Installation

Supported Python versions: 3.11, 3.12, 3.13, 3.14 and 3.15. Python 3.10 is no longer supported, as it is deprecated. See the [AWS Lambda runtimes](https://docs.aws.amazon.com/lambda/latest/dg/lambda-runtimes.html) page for further information.

You can install it using pip:

```bash
pip install aws-lambda-env-modeler
```

or uv:

```bash
uv add aws-lambda-env-modeler
```

## Getting started
Head over to the complete documentation at [https://ran-isenberg.github.io/aws-lambda-env-modeler](https://ran-isenberg.github.io/aws-lambda-env-modeler/)


## Usage
First, define a Pydantic model for your environment variables:

```python
from pydantic import BaseModel, HttpUrl


class MyEnvVariables(BaseModel):
    DB_HOST: str
    DB_PORT: int
    DB_USER: str
    DB_PASS: str
    FLAG_X: bool
    API_URL: HttpUrl
```

Before executing a function, you must use the `@init_environment_variables` decorator to validate and initialize the environment variables automatically.

The decorator guarantees that the function will run with the correct variable configuration.

```python
from aws_lambda_env_modeler import init_environment_variables


@init_environment_variables(model=MyEnvVariables)
def my_handler_entry_function(event, context):
    # At this point, environment variables are already validated and initialized
    pass
```

Then, you can fetch the environment variables using the global getter function, 'get_environment_variables,' and use them just like a data class. At this point, they are parsed and validated.

```python
from aws_lambda_env_modeler import get_environment_variables

env_vars = get_environment_variables(model=MyEnvVariables)
print(env_vars.DB_HOST)
```

## Disabling Cache for Testing

By default, the modeler uses cache - the parsed model is cached for performance improvement for multiple 'get' calls.

In some cases, such as during testing, you may want to turn off the cache. You can do this by setting the `LAMBDA_ENV_MODELER_DISABLE_CACHE` environment variable to `true`.

This is especially useful in tests where each test sets a different set of environment variables for the same model.

Here's an example of how you can use this in a pytest test:

```python
import json
from http import HTTPStatus
from typing import Any, Dict, Literal
from unittest.mock import patch

from pydantic import BaseModel

from aws_lambda_env_modeler import LAMBDA_ENV_MODELER_DISABLE_CACHE, get_environment_variables, init_environment_variables


class MyHandlerEnvVars(BaseModel):
    LOG_LEVEL: Literal['DEBUG', 'INFO', 'ERROR', 'CRITICAL', 'WARNING', 'EXCEPTION']


@init_environment_variables(model=MyHandlerEnvVars)
def my_handler(event: Dict[str, Any], context) -> Dict[str, Any]:
    env_vars = get_environment_variables(model=MyHandlerEnvVars)  # noqa: F841
    # can access directly env_vars.LOG_LEVEL as dataclass
    return {
        'statusCode': HTTPStatus.OK,
        'headers': {'Content-Type': 'application/json'},
        'body': json.dumps({'message': 'success'}),
    }


@patch.dict('os.environ', {LAMBDA_ENV_MODELER_DISABLE_CACHE: 'true', 'LOG_LEVEL': 'DEBUG'})
def test_my_handler():
    response = my_handler({}, None)
    assert response['statusCode'] == HTTPStatus.OK
    assert response['headers'] == {'Content-Type': 'application/json'}
    assert json.loads(response['body']) == {'message': 'success'}
```

## Code Contributions
Code contributions are welcomed. Read this [guide.](https://github.com/ran-isenberg/aws-lambda-env-modeler/blob/main/CONTRIBUTING.md)

## Code of Conduct
Read our code of conduct [here.](https://github.com/ran-isenberg/aws-lambda-env-modeler/blob/main/CODE_OF_CONDUCT.md)

## Connect
* Email: [ran.isenberg@ranthebuilder.cloud](mailto:ran.isenberg@ranthebuilder.cloud)
* Website [RanTheBuilder](https://www.ranthebuilder.cloud)
* LinkedIn: [ranisenberg](https://www.linkedin.com/in/ranisenberg/)
* Twitter: [RanBuilder](https://twitter.com/RanBuilder)
* Bluesky: [@ranthebuilder.cloud](https://bsky.app/profile/ranthebuilder.cloud)


## License
This library is licensed under the MIT License. See the [LICENSE](https://github.com/ran-isenberg/aws-lambda-env-modeler/blob/main/LICENSE) file.
