aws-lambda-decorators 0.53

Description:

awslambdadecorators 0.53

aws-lambda-decorators
A set of Python decorators to ease the development of AWS lambda functions.
Installation
The easiest way to use these AWS Lambda Decorators is to install them through Pip:
pip install aws-lambda-decorators
Logging
The Logging level of the decorators can be controlled by setting a LOG_LEVEL environment variable. In python:
os.environ["LOG_LEVEL"] = "INFO"
The default value is "INFO"
Package Contents
Decorators
The current list of AWS Lambda Python Decorators includes:

extract: a decorator to extract and validate specific keys of a dictionary parameter passed to a AWS Lambda function.
extract_from_event: a facade of extract to extract and validate keys from an AWS API Gateway lambda function event parameter.
extract_from_context: a facade of extract to extract and validate keys from an AWS API Gateway lambda function context parameter.
extract_from_ssm: a decorator to extract from AWS SSM the values of a set of parameter keys.
validate: a decorator to validate a list of function parameters.
log: a decorator to log the parameters passed to the lambda function and/or the response of the lambda function.
handle_exceptions: a decorator to handle any type of declared exception generated by the lambda function.
response_body_as_json: a decorator to transform a response dictionary body to a json string.
handle_all_exceptions: a decorator to handle all exceptions thrown by the lambda function.
cors: a decorator to add cors headers to a lambda function.
push_ws_errors: a decorator to push unsuccessful responses back to the calling user via websockets with api gateway.
push_ws_responses: a decorator to push all responses back to the calling user via websockets with api gateway.

Validators
Currently, the package offers 12 validators:

Mandatory: Checks if a parameter has a value.
RegexValidator: Checks a parameter against a regular expression.
SchemaValidator: Checks if an object adheres to the schema. Uses schema library.
Minimum: Checks if an optional numerical value is greater than a minimum value.
Maximum: Checks if an optional numerical value is less than a maximum value.
MinLength: Checks if an optional string value is longer than a minimum length.
MaxLength: Checks if an optional string value is shorter than a maximum length.
Type: Checks if an optional object value is of a given python type.
EnumValidator: Checks if an optional object value is in a list of valid values.
NonEmpty: Checks if an optional object value is not an empty value.
DateValidator: Checks if a given string is a valid date according to a passed in date format.
CurrencyCodeValidator: Checks if a given string is a valid currency code (ISO 4217).

Decoders
The package offers functions to decode from JSON and JWT.

decode_json: decodes/converts a json string to a python dictionary
decode_jwt: decodes/converts a JWT string to a python dictionary

Examples
You can see some basic examples in the examples folder.
extract
This decorator extracts and validates values from dictionary parameters passed to a Lambda Function.

The decorator takes a list of Parameter objects.
Each Parameter object requires a non-empty path to the parameter in the dictionary, and the name of the dictionary (func_param_name)
The parameter value is extracted and added as a kwarg to the lambda handler (or any other decorated function/method).
You can add the parameter to the handler signature, or access it in the handler through kwargs.
The name of the extracted parameter is defaulted to the last element of the path name, but can be changed by passing a (valid pythonic variable name) var_name
You can define a default value for the parameter in the Parameter or in the lambda handler itself.
A 400 exception is raised when the parameter cannot be extracted or when it does not validate.
A variable path (e.g. '/headers/Authorization[jwt]/sub') can be annotated to specify a decoding. In the example, Authorization might contain a JWT, which needs to be decoded before accessing the "sub" element.

Example:
@extract(parameters=[
Parameter(path='/parent/my_param', func_param_name='a_dictionary'), # extracts a non mandatory my_param from a_dictionary
Parameter(path='/parent/missing_non_mandatory', func_param_name='a_dictionary', default='I am missing'), # extracts a non mandatory missing_non_mandatory from a_dictionary
Parameter(path='/parent/missing_mandatory', func_param_name='a_dictionary'), # does not fail as the parameter is not validated as mandatory
Parameter(path='/parent/child/id', validators=[Mandatory], var_name='user_id', func_param_name='another_dictionary') # extracts a mandatory id as "user_id" from another_dictionary
])
def extract_example(a_dictionary, another_dictionary, my_param='aDefaultValue', missing_non_mandatory='I am missing', missing_mandatory=None, user_id=None):
"""
Given these two dictionaries:

a_dictionary = {
'parent': {
'my_param': 'Hello!'
},
'other': 'other value'
}

another_dictionary = {
'parent': {
'child': {
'id': '123'
}
}
}

you can now access the extracted parameters directly:
"""
return my_param, missing_non_mandatory, missing_mandatory, user_id

Or you can use kwargs instead of specific parameter names:
Example:
@extract(parameters=[
Parameter(path='/parent/my_param', func_param_name='a_dictionary') # extracts a non mandatory my_param from a_dictionary
])
def extract_to_kwargs_example(a_dictionary, **kwargs):
"""
a_dictionary = {
'parent': {
'my_param': 'Hello!'
},
'other': 'other value'
}
"""
return kwargs['my_param'] # returns 'Hello!'

A missing mandatory parameter, or a parameter that fails validation, will raise an exception:
Example:
@extract(parameters=[
Parameter(path='/parent/mandatory_param', func_param_name='a_dictionary', validators=[Mandatory]) # extracts a mandatory mandatory_param from a_dictionary
])
def extract_mandatory_param_example(a_dictionary, mandatory_param=None):
return 'Here!' # this part will never be reached, if the mandatory_param is missing

response = extract_mandatory_param_example({'parent': {'my_param': 'Hello!'}, 'other': 'other value'} )

print(response) # prints { 'statusCode': 400, 'body': '{"message": [{"mandatory_param": ["Missing mandatory value"]}]}' } and logs a more detailed error

You can add custom error messages to all validators, and incorporate to those error messages the validated value and the validation condition:
Example:
@extract(parameters=[
Parameter(path='/parent/an_int', func_param_name='a_dictionary', validators=[Minimum(100, 'Bad value {value}: should be at least {condition}')]) # extracts a mandatory mandatory_param from a_dictionary
])
def extract_minimum_param_with_custom_error_example(a_dictionary, mandatory_param=None):
return 'Here!' # this part will never be reached, if the an_int param is less than 100

response = extract_minimum_param_with_custom_error_example({'parent': {'an_int': 10}})

print(response) # prints { 'statusCode': 400, 'body': '{"message": [{"an_int": ["Bad value 10: should be at least 100"]}]}' } and logs a more detailed error

You can group the validation errors together (instead of exiting on first error).
Example:
@extract(parameters=[
Parameter(path='/parent/mandatory_param', func_param_name='a_dictionary', validators=[Mandatory]), # extracts two mandatory parameters from a_dictionary
Parameter(path='/parent/another_mandatory_param', func_param_name='a_dictionary', validators=[Mandatory]),
Parameter(path='/parent/an_int', func_param_name='a_dictionary', validators=[Maximum(10), Minimum(5)])
], group_errors=True) # groups both errors together
def extract_multiple_param_example(a_dictionary, mandatory_param=None, another_mandatory_param=None, an_int=0):
return 'Here!' # this part will never be reached, if the mandatory_param is missing

response = extract_multiple_param_example({'parent': {'my_param': 'Hello!', 'an_int': 20}, 'other': 'other value'})

print(response) # prints {'statusCode': 400, 'body': '{"message": [{"mandatory_param": ["Missing mandatory value"]}, {"another_mandatory_param": ["Missing mandatory value"]}, {"an_int": ["\'20\' is greater than maximum value \'10\'"]}]}'}

You can decode any part of the parameter path from json or any other existing annotation.
Example:
@extract(parameters=[
Parameter(path='/parent[json]/my_param', func_param_name='a_dictionary') # extracts a non mandatory my_param from a_dictionary
])
def extract_from_json_example(a_dictionary, my_param=None):
"""
a_dictionary = {
'parent': '{"my_param": "Hello!" }',
'other': 'other value'
}
"""
return my_param # returns 'Hello!'

You can also use an integer annotation to access an specific list element by index.
Example:
@extract(parameters=[
Parameter(path='/parent[1]/my_param', func_param_name='a_dictionary') # extracts a non mandatory my_param from a_dictionary
])
def extract_from_list_example(a_dictionary, my_param=None):
"""
a_dictionary = {
'parent': [
{'my_param': 'Hello!'},
{'my_param': 'Bye!'}
]
}
"""
return my_param # returns 'Bye!'

You can extract all parameters into a dictionary
Example:
@extract(parameters=[
Parameter(path='/params/my_param_1', func_param_name='a_dictionary'), # extracts a non mandatory my_param_1 from a_dictionary
Parameter(path='/params/my_param_2', func_param_name='a_dictionary') # extracts a non mandatory my_param_2 from a_dictionary
])
def extract_dictionary_example(a_dictionary, **kwargs):
"""
a_dictionary = {
'params': {
'my_param_1': 'Hello!',
'my_param_2': 'Bye!'
}
}
"""
return kwargs # returns {'my_param_1': 'Hello!', 'my_param_2': 'Bye!'}

You can apply a transformation to an extracted value. The transformation will happen before validation.
Example:
@extract(parameters=[
Parameter(path='/params/my_param', func_param_name='a_dictionary', transform=int) # extracts a non mandatory my_param from a_dictionary
])
def extract_with_transform_example(a_dictionary, my_param=None):
"""
a_dictionary = {
'params': {
'my_param': '2' # the original value is the string '2'
}
}
"""
return my_param # returns the int value 2

The transform function can be any function, with its own error handling.
Example:
def to_int(arg):
try:
return int(arg)
except Exception:
raise Exception("My custom error message")

@extract(parameters=[
Parameter(path='/params/my_param', func_param_name='a_dictionary', transform=to_int) # extracts a non mandatory my_param from a_dictionary
])
def extract_with_custom_transform_example(a_dictionary, my_param=None):
return {}

response = extract_with_custom_transform_example({'params': {'my_param': 'abc'}})

print(response) # prints {'statusCode': 400, 'body': '{"message": "Error extracting parameters"}'}, and the logs will contain the "My custom error message" message.

extract_from_event
This decorator is just a facade to the extract method to be used in AWS Api Gateway Lambdas. It automatically extracts from the event lambda parameter.
Example:
@extract_from_event(parameters=[
Parameter(path='/body[json]/my_param', validators=[Mandatory]), # extracts a mandatory my_param from the json body of the event
Parameter(path='/headers/Authorization[jwt]/sub', validators=[Mandatory], var_name='user_id') # extract the mandatory sub value as user_id from the authorization JWT
])
def extract_from_event_example(event, context, my_param=None, user_id=None):
"""
event = {
'body': '{"my_param": "Hello!"}',
'headers': {
'Authorization': 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c'
}
}
"""
return my_param, user_id # returns ('Hello!', '1234567890')

extract_from_context
This decorator is just a facade to the extract method to be used in AWS Api Gateway Lambdas. It automatically extracts from the context lambda parameter.
Example:
@extract_from_context(parameters=[
Parameter(path='/parent/my_param', validators=[Mandatory]) # extracts a mandatory my_param from the parent element in context
])
def extract_from_context_example(event, context, my_param=None):
"""
context = {
'parent': {
'my_param': 'Hello!'
}
}
"""
return my_param # returns 'Hello!'

extract_from_ssm
This decorator extracts a parameter from AWS SSM and passes the parameter down to your function as a kwarg.

The decorator takes a list of SSMParameter objects.
Each SSMParameter object requires the name of the SSM parameter (ssm_name)
If no var_name is passed in, the extracted value is passed to the function with the ssm_name name

Example:
@extract_from_ssm(ssm_parameters=[
SSMParameter(ssm_name='one_key'), # extracts the value of one_key from SSM as a kwarg named "one_key"
SSMParameter(ssm_name='another_key', var_name="another") # extracts another_key as a kwarg named "another"
])
def extract_from_ssm_example(your_func_params, one_key=None, another=None):
return your_func_params, one_key, another

validate
This decorator validates a list of non dictionary parameters from your lambda function.

The decorator takes a list of ValidatedParameter objects.
Each parameter object needs the name of the lambda function parameter that it is going to be validated, and the list of rules to validate.
A 400 exception is raised when the parameter does not validate.

Example:
@validate(parameters=[
ValidatedParameter(func_param_name='a_param', validators=[Mandatory]), # validates a_param as mandatory
ValidatedParameter(func_param_name='another_param', validators=[Mandatory, RegexValidator(r'\d+')]) # validates another_param as mandatory and containing only digits
ValidatedParameter(func_param_name='param_with_schema', validators=[SchemaValidator(Schema({'a': Or(str, dict)}))]) # validates param_with_schema as an object with specified schema
])
def validate_example(a_param, another_param, param_with_schema):
return a_param, another_param, param_with_schema # returns 'Hello!', '123456', {'a': {'b': 'c'}}

validate_example('Hello!', '123456', {'a': {'b': 'c'}})

Given the same function validate_example, a 400 exception is returned if at least one parameter does not validate (as per the extract decorator, you can group errors with the group_errors flag):
validate_example('Hello!', 'ABCD') # returns a 400 status code and an error message

log
This decorator allows for logging the function arguments and/or the response.
Example:
@log(parameters=True, response=True)
def log_example(parameters):
return 'Done!'

log_example('Hello!') # logs 'Hello!' and 'Done!'

handle_exceptions
This decorator handles a list of exceptions, returning a 400 response containing the specified friendly message to the caller.

The decorator takes a list of ExceptionHandler objects.
Each ExceptionHandler requires the type of exception to check, and an optional friendly message to return to the caller.

Example:
@handle_exceptions(handlers=[
ExceptionHandler(ClientError, "Your message when a client error happens.")
])
def handle_exceptions_example():
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('non_existing_table')
table.query(KeyConditionExpression=Key('user_id').eq(user_id))
# ...

handle_exceptions_example() # returns {'body': '{"message": "Your message when a client error happens."}', 'statusCode': 400}

handle_all_exceptions
This decorator handles all exceptions thrown by a lambda, returning a 400 response and the exception's message.
Example:
@handle_all_exceptions()
def handle_exceptions_example():
test_list = [1, 2, 3]
invalid_value = test_list[5]
# ...

handle_all_exceptions_example() # returns {'body': '{"message": "list index out of range"}, 'statusCode': 400}

response_body_as_json
This decorator ensures that, if the response contains a body, the body is dumped as json.

Returns a 500 error if the response body cannot be dumped as json.

Example:
@response_body_as_json
def response_body_as_json_example():
return {'statusCode': 400, 'body': {'param': 'hello!'}}

response_body_as_json_example() # returns { 'statusCode': 400, 'body': "{'param': 'hello!'}" }

cors
This decorator adds your defined CORS headers to the decorated function response.

Returns a 500 error if one or more of the CORS headers have an invalid type

Example:
@cors(allow_origin='*', allow_methods='POST', allow_headers='Content-Type', max_age=86400)
def cors_example():
return {'statusCode': 200}

cors_example() # returns {'statusCode': 200, 'headers': {'access-control-allow-origin': '*', 'access-control-allow-methods': 'POST', 'access-control-allow-headers': 'Content-Type', 'access-control-max-age': 86400}}

hsts
This decorator adds HSTS header to the decorated function response. Uses 2 years max-age (recommended default from https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Strict-Transport-Security) unless custom value provided as parameter.
Example:
@hsts()
def hsts_example():
return {'statusCode': 200}

hsts_example() # returns {'statusCode': 200, 'headers': {'Strict-Transport-Security': 'max-age=63072000'}}

push_ws_errors
This decorator pushes unsuccessful responses back to the calling client over websockets built on api gateway
This decorator requires the client is connected to the websocket api gateway instance, and will therefore have a connection id
Example:
@push_ws_errors('https://api_id.execute_id.region.amazonaws.com/Prod')
@handle_all_exceptions()
def handler(event, context):
return {
'statusCode': 400,
'body': {
'message': 'Bad request'
}
}

# will push {'type': 'error', 'statusCode': 400, 'message': 'Bad request'} back to the client via websockets

push_ws_response
This decorator pushes all responses back to the calling client over websockets built on api gateway
This decorator requires the client is connected to the websocket api gateway instance, and will therefore have a connection id
Example:
@push_ws_response('https://api_id.execute_id.region.amazonaws.com/Prod')
def handler(event, context):
return {
'statusCode': 200,
'body': 'Hello, world!'
}

# will push {'statusCode': 200, 'body': 'Hello, world!'} back to the client via websockets

Writing your own validators
You can create your own validators by inheriting from the Validator class.
Fix length validator example:
class FixLength(Validator):
ERROR_MESSAGE = "'{value}' length should be '{condition}'"

def __init__(self, fix_length: int, error_message=None):
super().__init__(error_message=error_message, condition=fix_length)

def validate(self, value=None):
if value is None:
return True

return len(str(value)) == self._condition

Documentation
You can get the docstring help by running:
>>> from aws_lambda_decorators.decorators import extract
>>> help(extract)

Links

PyPi
Test PyPi
Github