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 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.
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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 here
- Validates the environment variables against a Pydantic model: define both semantic and syntactic validation.
- Serializes 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.
You can install it using pip:
pip install aws-lambda-env-modelerHead over to the complete project documentation pages at GitHub pages at https://ran-isenberg.github.io/aws-lambda-env-modeler
First, define a Pydantic model for your environment variables:
frompydanticimportBaseModel, HttpUrlclassMyEnvVariables(BaseModel):
DB_HOST: strDB_PORT: intDB_USER: strDB_PASS: strFLAG_X: boolAPI_URL: HttpUrlBefore 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.
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.
fromaws_lambda_env_modelerimportinit_environment_variables@init_environment_variables(MyEnvVariables)defmy_handler_entry_function(event, context):
# At this point, environment variables are already validated and initializedpassThen, you can fetch and validate the environment variables with your model:
fromaws_lambda_env_modelerimportget_environment_variablesenv_vars=get_environment_variables(MyEnvVariables)
print(env_vars.DB_HOST)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 you want to run multiple tests concurrently, each with a different set of environment variables.
Here's an example of how you can use this in a pytest test:
importjsonfromhttpimportHTTPStatusfromtypingimportAny, Dictfromunittest.mockimportpatchfrompydanticimportBaseModelfromtyping_extensionsimportLiteralfromaws_lambda_env_modelerimportLAMBDA_ENV_MODELER_DISABLE_CACHE, get_environment_variables, init_environment_variablesclassMyHandlerEnvVars(BaseModel):
LOG_LEVEL: Literal['DEBUG', 'INFO', 'ERROR', 'CRITICAL', 'WARNING', 'EXCEPTION']
@init_environment_variables(model=MyHandlerEnvVars)defmy_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 dataclassreturn {
'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'})deftest_my_handler():
response=my_handler({}, None)
assertresponse['statusCode'] ==HTTPStatus.OKassertresponse['headers'] == {'Content-Type': 'application/json'}
assertjson.loads(response['body']) == {'message': 'success'}Code contributions are welcomed. Read this guide.
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This library is licensed under the MIT License. See the LICENSE file.