Articles on programming in Python and Angular.
Do you find yourself having to repeat literal values (like strings and integers) in parametrised tests? I often find myself in this situation and have been looking for ways of reducing this duplication. To show an example, consider this trivial function:
def get_second_word(text: str) -> str | None:
"""Get the second word from text."""
words = text.split()
return words[1] if len(words) >= 2 else None
In PEP 589 Python has introduced type hints for dictionaries. This helps with defining the structure of dictionaries where the keys and the types of the values are well known. TypedDict also supports keys that may not be present using the concept of totality. Using inheritance a dictionary where some keys are not required can be built. The default syntax for defining TypedDict is a class based syntax (example below). Whilst this is easy to understand, it does limit the names of the keys of the dictionary to valid python variable names. If a dictionary needs to include keys with, for example, a dash, the class based syntax no longer is appropriate for defining the TypedDict. There is an alternative syntax (similar to named tuples) that allows for arbitrary key names (example below). However, this syntax does not support inheritance which means that a dictionary with mixed totality cannot be constructed using this syntax alone.
Continue reading “Python TypedDict Arbitrary Key Names with Totality”In software engineering one of the key principles of object oriented software is the concept of inheritance. It can be used to increase code re-use which reduces the volume of tests and speeds up development. You can use inheritance in SQLAlchemy as described here. However, this inheritance is mainly used to describe relationships between tables and not for the purpose of re-using certain pieces of models defined elsewhere.
The openapi specification allows for inheritance using the allOf statement. This means that you could, for example, define a schema for id properties once and re-use that schema for any number of objects where you can customise things like the description that may differ object by object. You can also use allOf to combine objects, which is a powerful way of reducing duplication. You could, for example, define a base object with an id and name property that you then use repeatedly for other objects so that you don’t have to keep giving objects an id and a name.
If this feature could be brought to SQLAlchemy models, you would have a much shorter models.py files which is easier to maintain and understand. The plan for the openapi-SQLAlchemy package is to do just that. The first step has been completed with the addition of support for allOf for column definitions. If you aren’t familiar with the package, the Reducing API Code Duplication article describes the aims of the package.
To start using the column inheritance feature, read the documentation for the feature which describes it in detail and gives and example specification that makes use of it.
A new version of openapi-SQLAlchemy has been release which adds support for $ref for columns. The package now supports openapi schemas such as the following:
components:
schemas:
Id:
type: integer
description: Unique identifier for the employee.
example: 0
Employee:
description: Person that works for a company.
type: object
properties:
id:
$ref: "#/components/schemas/Id"
name:
type: string
description: The name of the employee.
example: David Andersson.
If you are interested in how this was accomplished with decorators and recursive programming, the following article describes the implementation: Python Recursive Decorators.
In Python decorators are a useful tool for changing the behaviour of functions without modifying the original function. For a recent release of openapi-SQLAlchemy, which added support for references among table columns, I used decorators to de-reference columns. I needed a way of supporting cases where a reference to a column was just a reference to another column. The solution was to essentially keep applying the decorator until the column was actually found.
If you are not familiar with openapi specifications, a simple schema for an object might be the following:
components:
schemas:
Employee:
description: Person that works for a company.
type: object
properties:
id:
type: integer
description: Unique identifier for the employee.
example: 0
name:
type: string
description: The name of the employee.
example: David Andersson.
To be able to re-use the definition of the id property for another schema, you can do the following:
components:
schemas:
Id:
type: integer
description: Unique identifier for the employee.
example: 0
Employee:
description: Person that works for a company.
type: object
properties:
id:
$ref: "#/components/schemas/Id"
name:
type: string
description: The name of the employee.
example: David Andersson.
In this case, the id property just references the Id schema. This could be done for other property definitions where the same schema applies to reduce duplicate schema definitions.
The openapi-SQLAlchemy package allows you to map openapi schemas to SQLAlchemy models where an object becomes a table and a property becomes a column. The architecture of the package is broken into a factory for tables which then calls a column factory for each property of an object. The problem I had to solve was that ,when a reference is encountered, the column factory gets called with the following dictionary as the schema for the column (for the id column in this case):
{"$ref": "#/components/schemas/Id"}
instead of the schema for the column. Apart from doing some basic checks, what needs to happen is that the schema of Id needs to be found and the column factory be called with that schema instead of the reference. A perfect case for a decorator! The following is the code (minus some input checks):
_REF_PATTER = re.compile(r"^#\/components\/schemas\/(\w+)$")
def resolve_ref(func):
"""Resolve $ref schemas."""
def inner(schema, schemas, **kwargs):
"""Replace function."""
# Checking for $ref
ref = schema.get("$ref")
if ref is None:
return func(schema=schema, **kwargs)
# Retrieving new schema
match = _REF_PATTER.match(ref)
schema_name = match.group(1)
ref_schema = schemas.get(schema_name)
return func(schema=ref_schema, **kwargs)
return inner
The first step is to check if the schema is a reference schema and call the column factory if not (lines 3-4). If it is a reference, then the referenced schema needs to be retrieved (lines 14-16) and the factory called with the referenced schema instead (line 18).
The problem with the simple decorator is that the following openapi specification is valid:
components:
schemas:
Id:
$ref: "#/components/schemas/RefId"
RefId:
type: integer
description: Unique identifier for the employee.
example: 0
Employee:
description: Person that works for a company.
type: object
x-tablename: employee
properties:
id:
$ref: "#/components/schemas/Id"
name:
type: string
description: The name of the employee.
example: David Andersson.
Noe that the Id schema just references the RefId schema. When this schema is used, the column factory would now be called with:
{"$ref": "#/components/schemas/RefId"}
That looks a lot like the original problem! The solution is that the decorator needs to be applied again. This also looks like a case for recursive programming. In recursive programming, you have the base case and the recursive case. The base case sounds a lot like the code that checks for whether the schema is a reference (lines 3-4 above). The recursive case needs to take a step towards the base case and apply the function again. In this case this is done by resolving one reference and then calling the decorator again. This means that on line 18 above we need to somehow apply the decorator again. The solution is actually quite simple, the code needs to be changed to the following:
_REF_PATTER = re.compile(r"^#\/components\/schemas\/(\w+)$")
def resolve_ref(func):
"""Resolve $ref schemas."""
def inner(schema, schemas, **kwargs):
"""Replace function."""
# Checking for $ref
ref = schema.get("$ref")
if ref is None:
return func(schema=schema, **kwargs)
# Retrieving new schema
match = _REF_PATTER.match(ref)
schema_name = match.group(1)
ref_schema = schemas.get(schema_name)
return inner(schema=ref_schema, schemas=schemas, **kwargs)
return inner
The change is that inner is called instead of the original function func. We then also need to pass in all the required arguments for inner, which in this case, means passing through the schemas which func does not need.
Recursive decorators combine the ideas behind decorators and recursive programming. If the problem a decorator solves can be broken into steps where, after each step, the decorator might need to be applied again, you might have a case for a recursive decorator. The decorator must then implement a base case where the decorator is not applied again and a recursive case where one step is taken towards the base case and the decorator is applied again.
The first alpha version of openapi-SQLAlchemy has been released! It is basically what I wrote about in the post on Reducing API Code Duplication. The project is available on PyPI here: openapi-SQLAlchemy. To read more documentation and about how to use the project you can visit the GitHub repository.
One of the basic principles of good software engineering is the DRY principle – Don’t Repeat Yourself. It means that information should only exist in one place and should not be repeated elsewhere. This leads to code that is easier to maintain since any change only has to be made once, among a range of other benefits.
Practicing the principle is harder than stating it. For example, in the case of an API that is supported by a database, chances are there are overlaps between the database and API schema.
In my experience, there usually is significant overlap between the schema that is defined in the database and the schema that is returned by the linked API. Changes to the database schema might accidently not be properly propagate to the API schema or the API interface documentation might not get updated.
A popular tool for exposing a database schema to a Python application is the SQLAlchemy library. This is usually achieved by defining a models.py file with classes that map to tables in the database. For one of the API endpoints you might retrieve some of these objects from the database, apply some business logic to them and then return them through the API interface.
To communicate to your users how to interact with your API, you might write an openapi specification. You could even go further and use tools like connexion to map endpoints to Python functions for fulfilment. One part of that openapi specification is to define the returned schema of each endpoint.
To get closer to fulfilling the DRY principle you might wish that there was some way to connect the SQLAlchemy models and openapi schema so that you only have to define the schema in one place. To fulfil that wish I started an open source package called openapi-SQLAlchemy.
The aim of the package is to accept an openapi specification and simplify creating the SQLAlchemy models file. The aim for the MVP is the following. Given an openapi specification, when it is read into a python dictionary and passed to the module, a model factory is returned. That model factory can be called with the name of a schema which returns a class that is a valid SQLAlchemy model. For example:
# example-spec.yml
openapi: "3.0.0"
info:
title: Test Schema
description: API to illustrate openapi-SQLALchemy MVP.
version: "0.1"
paths:
/employee:
get:
summary: Used to retrieve all employees.
responses:
200:
description: Return all employees from the database.
content:
application/json:
schema:
type: array
items:
"$ref": "#/components/schemas/Employee"
components:
schemas:
Employee:
description: Person that works for a company.
type: object
properties:
id:
type: integer
description: Unique identifier for the employee.
example: 0
name:
type: string
description: The name of the employee.
example: David Andersson.
division:
type: string
description: The part of the company the employee works in.
example: Engineering
salary:
type: number
description: The amount of money the employee is paid.
example: 1000000.00
required:
- id
- name
- division
Normally the following models.py file would be required.
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, Float
Base = declarative_base()
class Employee(Base):
"""
Person that works for a company.
Attrs:
id: Unique identifier for the employee.
name: The name of the employee.
division: The part of the company the employee works in.
salary: The amount of money the employee is paid.
"""
__tablename__ = "employee"
id = Column(Integer, primary_key=True, autoincrement=True)
name = Column(String, index=True, nullable=False)
division = Column(String, index=True, nullable=False)
salary = Column(Float, nullable=False)
As you can see there is a lot of duplicate information. The aim is to instead pass the specification to openapi-SQLAlchemy and reduce the models.py file to the following:
from yaml import loads
from sqlalchemy.ext.declarative import declarative_base
from openapi_sqlalchemy import ModelFactory
Base = declarative_base()
with open("example-spec.yml") as spec_file:
SPEC = yaml.load(spec_file)
model_factory = ModelFactory(base=Base, spec=SPEC)
Employee = model_factory(name="Employee")
There is significantly less duplicate information across the specification and models file. The name of the object (Employee) is repeated a few times, although this can be viewed as a reference which means that it is acceptable.
Whilst things like whether a column is nullable can be derived from the required property of the object, there are some additional pieces of information that have to be included in the specification file. For example, not every schema must be a table. Also, the primary key, auto increment and index column modifications are not currently recorded in the specification. The final Employee schema might look like the following.
Employee:
description: Person that works for a company.
type: object
x-tablename: employee
properties:
id:
type: integer
description: Unique identifier for the employee.
example: 0
x-primary-key: true
x-autoincrement: true
name:
type: string
description: The name of the employee.
example: David Andersson.
x-index: true
division:
type: string
description: The part of the company the employee works in.
example: Engineering
x-index: true
salary:
type: number
description: The amount of money the employee is paid.
example: 1000000.00
required:
- id
- name
- division
There are more column modifiers that would need to be supported, such as the unique constraint. There are also more column types, such as foreign keys, and there is also inheritance and references that will need to be supported. All of these need to be supported whilst also ensuring that the specification remains valid.
This problem does not seem to be difficult to achieve in Python. There are also some opportunities to use some Python tricks to reduce the amount of code that needs to be written. As I develop this package I expect to write updates on progress and also write articles on some of the Python tricks.
connexion is a great tool for building APIs in Python. It allows you to make the openAPI specification define input validation that is automatically enforced, maps API endpoints to a named function and handles JSON serialising and de-serialising for you.
One of the things to keep in mind when using connexion is that, if you have defined query parameters, only the query parameters that you have define can every get passed to the functions handling the endpoint. For example, consider the following API specification for retrieving employees from a database:
specification.yml
# specification.yml
openapi: "3.0.0"
info:
title: Sample API
description: API demonstrating how to check for optional parameters.
version: "0.1"
paths:
/employee:
get:
summary: Gets Employees that match query parameters.
operationId: library.employee.search
parameters:
- in: query
name: join_date
schema:
type: string
format: date
required: false
description: Filters employees for a particular date that they joined the company.
- in: query
name: business_unit
schema:
type: string
required: false
description: Filters employees for a particular business unit.
- in: query
name: city
schema:
type: string
required: false
description: Filters employees for which city they are working in.
responses:
200:
description: Returns all the Employees in the database that match the query parameters.
content:
application/json:
schema:
type: object
properties:
first_name:
description: The first name of the Employee.
type: string
last_name:
description: The last name of the Employee.
type: string
If you call /employee with some_parameter, you don’t receive an error because connexion will not call your function withsome_parameter as it is not defined as a query parameter in the specification and hence gets ignored. To help prove that, consider the following project setup.
The above specification.yml is included in the root directory. The library folder is also in the root directory. Inside the library folder is the following __init__.py file:
library/__init__.py
# library.__init__.py
from . import employee
The library folder contains the employee folder which has the following __init__.py and controller.py files:
library/employee/__init__.py
library/employee/controller.py
# library.employee.__init__.py
from .controller import search
# library.data.employee.controller.py
def search(join_date = None, business_unit = None, city = None):
return [{'first_name': 'David', 'last_name': 'Andersson'}]
pytest is used to test the API. To help with the API tests, pytest-flask is used. To show that calling the /employee endpoint with parameters that aren’t in the specification does not result in an error, the following test configuration is placed in the root directory:
conftest.py
# conftest.py
from unittest import mock
import pytest
import connexion
import library
@pytest.fixture(scope='session')
def setup_mocked_search():
"""Sets up spy fr search function."""
with mock.patch.object(library.employee, 'search', wraps=library.employee.search) as mock_search:
yield mock_search
@pytest.fixture(scope='function')
def mocked_search(setup_mocked_search):
"""Resets search spy."""
setup_mocked_search.reset_mock()
return setup_mocked_search
@pytest.fixture(scope='session')
def app(setup_mocked_search):
"""Flask app for testing."""
# Adding swagger file
test_app = connexion.FlaskApp(__name__, specification_dir='.')
test_app.add_api('specification.yml')
return test_app.app
For now, focus on the app fixture which uses the standard connexion API setup and then yields the flask application. Through the magic of purest-flask, we can then define the following test to demonstrate that the API can be called with parameters not named in the openAPI specification:
def test_some_parameter(client):
"""
GIVEN a value for some parameter
WHEN GET /employee is called with the some_parameter set to the some parameter value
THEN no exception is raised.
"""
client.get('/employee?some_parameter=value 1')
This means that making a mistake with the query parameter names in the openAPI specification and the arguments for the function that handles the endpoint can go undetected. To check this is not the case, you could come up with a number of tests that only pass if the query parameter was correctly passed to the search function. This can be tedious without mocking the search function as you would have to create tests with just the right setup so that the effect of a particular query parameter is noticed.
There is an easier way with mocks. Unfortunately, it is not as easy as monkey patching the search function in the body of a test function. The problem is that, after the app fixture has been called, the reference to the search function has been hard coded into the flask application and will not get affected by mocking the search function. It is possible to retrieve the reference to the search function in the flask application instance, but you will have to dig fairly deep and may have to use some private member variables, which is not advisable.
Instead, let’s take another look at the contest.py file above. The trick is to apply a spy to the search function before the app fixture is invoked. The reason a spy rather than a mock is used is because the spy will be in p0lace for all tests and we may want to write a test where the real search function is called! With a spy the underlying function still gets called, which it does not if a mock is used instead of the search function. The fixture on lines 8-12 adds a spy to the search function. To ensure that it gets called before the app fixture, the app fixture takes it as an argument, although it doesn’t make use of the fixture. Because the app fixture is a session level fixture, we also want the fixture that sets up the spy to be a session level fixture, otherwise all the tests will be slowed down. However, this means that the spy state will leak into other tests. This can be solved with a fixture similar to the fixture on lines 15-19 which resets the spy state before each test.
Now we can use the test client to define tests that check that each of the query parameters is passed to the search function. The tests that perform these checks may look like the following:
test.py
# test.py
"""Endpoint tests for /employee"""
def test_some_parameter(client, mocked_search):
"""
GIVEN library.data.employee.search has a spy and a value for some parameter
WHEN GET /employee is called with the some_parameter set to the some parameter value
THEN library.data.employee.search is called with no parameters.
"""
client.get('/employee?some_parameter=value 1')
# Checking search call
mocked_search.assert_called_once_with()
def test_join_date(client, mocked_search):
"""
GIVEN library.data.employee.search has a spy and a date
WHEN GET /employee is called with the join_date set to the date
THEN library.data.employee.search is called with the join date.
"""
client.get('/employee?join_date=2000-01-01')
# Checking search call
mocked_search.assert_called_once_with(join_date='2000-01-01')
def test_business_unit(client, mocked_search):
"""
GIVEN library.data.employee.search has a spy and a business unit
WHEN GET /employee is called with the business_unit set to the business unit
THEN library.data.employee.search is called with the business unit.
"""
client.get('/employee?business_unit=business unit 1')
# Checking search call
mocked_search.assert_called_once_with(business_unit='business unit 1')
def test_city(client, mocked_search):
"""
GIVEN library.data.employee.search has a spy and a city
WHEN GET /employee is called with the city set to the city
THEN library.data.employee.search is called with the city.
"""
client.get('/employee?city=city 1')
# Checking search call
mocked_search.assert_called_once_with(city='city 1')
You now have a starting point for how to test that the function arguments and query parameter names in the specification match. You may not want to create these tests for each query parameter for every endpoint, especially if a query parameter is required, as an exception will be raised because the underlying function will be called with a query parameter which is not listed in the function signature.
Testing functions in Python that have a decorator applied to them is not ideal as the tests have to be written to take into account the decorator. The testing guard decorator can help remove the problem by selectively running the function with or without the decorator by detecting the environment in which it is run. When you make the decorator a part of your project you might like to write tests for the testing guard decorator itself.
As a reminder, the following is the decorator code for the testing guard.
# testing_guard.py
"""Demonstrates a guard decorator."""
import os
def testing_guard(decorator_func):
"""
Decorator that only applies another decorator if the appropriate
environment variable is not set.
Args:
decorator_func: The function that applies the decorator.
Returns:
Function that dynamically decides whether to apply the decorator based
on the environment.
"""
def replacement(original_func):
"""
Function that is used as the decorator function instead of the
decorator function.
Args:
original_func: The function being decorated.
Returns:
Function that dynamically picks between the original and
decorated function depending on the environment.
"""
# Creating decorated function
decorated_func = decorator_func(original_func)
def apply_guard(*args, **kwargs):
"""
Dynamically picks between decorated and original function based on
environment.
"""
if os.getenv('TESTING') is not None:
# Use original function
return original_func(*args, **kwargs)
# Use decorated function
return decorated_func(*args, **kwargs)
return apply_guard
return replacement
The key pieces of functionality are on lines 39-43. Line 39 is the check for whether the function is being executed in the testing environment. If it is, the original function is called without the decorator on line 41. If it isn’t, then the decorated function is called on line 43. The key reason that the decorator works is that it gets access to both the decorator and original function and that it gets to run code every time the decorated function is run.
There are 2 scenarios to test. The first is that the decorator is executed in the test environment, and the second is when it is executed in the non-test environment. In both cases we care about both that the correct function is called with the correct arguments (and that the other function is not called) and that the return value from the correct function is returned.
Let’s first define 2 fixtures that will help during testing. We will repeatedly need some generic *args and **kwargs and the particular value we choose for either doesn’t add anything to help understand the tests themselves so it is better to hide that detail in a fixture.
@pytest.fixture(scope='session')
def args():
"""Generates function *args"""
return ('arg1', 'arg2')
@pytest.fixture(scope='session')
def kwargs():
"""Generates function **kwargs"""
return {'kwargs1': 'kwarg1', 'kwarg2': 'kwarg2'}
When the decorator is executed in the test environment, it should call the original function and return the return value of the original function. It should not call the decorator return value. To focus the tests, let’s split those into three separate tests.
The first test just checks that the decorator return value is not called:
def test_testing_guard_set_decorated_call(monkeypatch):
"""
GIVEN TESTING environment variable is set and mock decorator
WHEN decorator is applied to a function after decorating it with the
testing guard and calling the decorated function
THEN decorator return value is not called.
"""
# Setting TESTING environment variable
monkeypatch.setenv('TESTING', '')
# Defining mock decorator
mock_decorator = mock.MagicMock()
# Decorating with testing guard and calling
guarded_mock_decorator = testing_guard(mock_decorator)
# Applying decorator
mock_decorated_func = guarded_mock_decorator(mock.MagicMock())
# Calling function
mock_decorated_func()
# Checking decorator call
mock_decorator.return_value.assert_not_called()
To understand the test, some understanding of Python decorators is required. A decorator is another function whose return value replaces the function it is decorating. A decorator could, for example, return the print function and then the function being decorator would never be called. In most cases, however, the function being decorator is called in the body of the function the decorator returns.
On line 9 of the test the testing environment is setup. On line 11 a mock function that will serve as the decorator to which the testing guard is applied is defined. On line 14 the testing guard is applied to the mock decorator. Then a mock function is decorated with guarded mock decorator on line 16 and the decorated function is called on line 18. On line 21 it is checked that the return value of the decorator, which would usually be called instead of the original function, is not called since, in the testing environment, the original function should always be called.
The next test checks that the original function is called with the correct arguments. For this we will ned the args and kwargs fixtures.
def test_testing_guard_set_func_call(monkeypatch, args, kwargs):
"""
GIVEN TESTING environment variable is set, mock function and args and
kwargs
WHEN a decorator is applied to the function after decorating it with the
testing guard and calling the decorated function with args and kwargs
THEN function is called with args and kwargs.
"""
# Setting TESTING environment variable
monkeypatch.setenv('TESTING', '')
# Defining mock decorator
mock_func = mock.MagicMock()
# Decorating with testing guard and calling
mock_decorator = mock.MagicMock()
guarded_mock_decorator = testing_guard(mock_decorator)
# Applying decorator
mock_decorated_func = guarded_mock_decorator(mock_func)
# Calling function
mock_decorated_func(*args, **kwargs)
# Checking decorator call
mock_func.assert_called_once_with(*args, **kwargs)
This tests is very similar to the first test but, instead of keeping track of the decorator, the original function is defined on line 12. The procedure on lines 14-20 is very similar to the first test with the only difference being that the mock decorated function is called with the args and kwargs fixtures. On line 23 the original function call is checked.
The final test checks the return value of the mock decorated function call. It is much like the previous test, except that it doesn’t pass in any args nor kwargs.
def test_testing_guard_set_return(monkeypatch):
"""
GIVEN TESTING environment variable is set and mock function
WHEN a decorator is applied to the function after decorating it with the
testing guard and calling the decorated function
THEN the return value is the function return value.
"""
# Setting TESTING environment variable
monkeypatch.setenv('TESTING', '')
# Defining mock function
mock_func = mock.MagicMock()
# Decorating with testing guard and calling
mock_decorator = mock.MagicMock()
guarded_mock_decorator = testing_guard(mock_decorator)
# Applying decorator
mock_decorated_func = guarded_mock_decorator(mock_func)
# Calling function
return_value = mock_decorated_func()
# Checking decorator call
assert return_value == mock_func.return_value
The second series of tests are outside the test environment. Since the tests for the testing guard are likely running inside the test environment, it is usually best to deactivate the testing environment as a part of the test as you may want to have the testing environment active by default during your tests.
The tests are very similar to the tests in the test environment. The difference is that now the decorator return value should be called, the original function should not be called and the return value of the decorator return value should be returned. The tests are shown below.
def test_testing_guard_not_set_decorated_call(monkeypatch, args, kwargs):
"""
GIVEN TESTING environment variable is not set, mock decorator and args and
kwargs
WHEN decorator is applied to a function after decorating it with the
testing guard and calling the decorated function with args and kwargs
THEN decorator return value is called with args and kwargs.
"""
# Removing TESTING environment variable
monkeypatch.delenv('TESTING', raising=False)
# Defining mock decorator
mock_decorator = mock.MagicMock()
# Decorating with testing guard and calling
guarded_mock_decorator = testing_guard(mock_decorator)
# Applying decorator
mock_decorated_func = guarded_mock_decorator(mock.MagicMock())
# Calling function
mock_decorated_func(*args, **kwargs)
# Checking decorator call
mock_decorator.return_value.assert_called_once_with(*args, **kwargs)
def test_testing_guard_not_set_return(monkeypatch):
"""
GIVEN TESTING environment variable is not set and mock decorator
WHEN decorator is applied to a function after decorating it with the
testing guard and calling the decorated function
THEN the return value is the decorator's return value return value.
"""
# Removing TESTING environment variable
monkeypatch.delenv('TESTING', raising=False)
# Defining mock decorator
mock_decorator = mock.MagicMock()
# Decorating with testing guard and calling
guarded_mock_decorator = testing_guard(mock_decorator)
# Applying decorator
mock_decorated_func = guarded_mock_decorator(mock.MagicMock())
# Calling function
return_value = mock_decorated_func()
# Checking decorator call
assert return_value == mock_decorator.return_value.return_value
def test_testing_guard_not_set_func_call(monkeypatch):
"""
GIVEN TESTING environment variable is not set and mock function
WHEN a decorator is applied to the function after decorating it with the
testing guard and calling the decorated function
THEN function is not called.
"""
# Removing TESTING environment variable
monkeypatch.delenv('TESTING', raising=False)
# Defining mock decorator
mock_func = mock.MagicMock()
# Decorating with testing guard and calling
mock_decorator = mock.MagicMock()
guarded_mock_decorator = testing_guard(mock_decorator)
# Applying decorator
mock_decorated_func = guarded_mock_decorator(mock_func)
# Calling function
mock_decorated_func()
# Checking decorator call
mock_func.assert_not_called()
This demonstrates how to test the testing guard. The setup and teardown of your test environment might be more complicated in which case your tests would have to reflect that.
Decorators are a great way of adding functionality to a function with minimal impact on the function itself. On top of that, decorator logic can be re-used on other functions that also require the new functionality. For example the following decorator prints a message to standard output every time a function is called.
# plain_main.py
"""Demonstrates a simple decorator."""
def decorator(func):
"""
A simple decorator that adds printing a message on a function call.
Args:
func: The function to decorate.
Returns:
The decorated function.
"""
def inner(*args, **kwargs):
"""Function that is called instead of original function."""
print('The decorator was called.')
return func(*args, **kwargs)
return inner
@decorator
def main():
print('The main function was called.')
if __name__ == '__main__':
print('Calling the main function.')
main()
$ python3 plain_main.py
Calling the main function.
The decorator was called.
The main function was called.
The drawback of decorators is that the decorator is applied as soon as the interpreter reaches the function definition and it is hard to access the original function without the decorator applied. This might be desirable during testing where testing of the function and the decorator should be separated.
The solution is to optionally skip the decorator logic if a certain condition is met that is only true during testing. For example, skip the decorator logic if the TESTING environment variable is set.
# check_main.py
"""Demonstrates a decorator with a testing guard."""
import os
def decorator(func):
"""
A simple decorator that adds printing a message on a function call unless
the TESTING environment variable is set.
Args:
func: The function to decorate.
Returns:
The decorated function.
"""
def inner(*args, **kwargs):
"""Function that is called instead of original function."""
# Checking for TESTING environment variable
if os.getenv('TESTING') is not None:
# Skipping decortor logic
return func(*args, **kwargs)
# Running decorator logic
print('The decorator was called.')
return func(*args, **kwargs)
return inner
@decorator
def main():
print('The main function was called.')
if __name__ == '__main__':
print('Calling the main function without TESTING set.')
main()
print('Calling the main function with TESTING set.')
os.environ['TESTING'] = ''
main()
$ python3 check_main.py
Calling the main function without TESTING set.
The decorator was called.
The main function was called.
Calling the main function with TESTING set.
The main function was called.
As you can see, the decorator logic was executed under normal circumstances (main call on line 39) and was skipped when the TESTING environment variable was set (main call on line 43). The reason was because of the guard statement on line 21 that checks for the TESTING environment variable.
You might now say: “great, thank you David. Now I have to rewrite all of my decorator functions!” Ah, but you don’t. If you stay with me through a little more complex decorator code, you won’t have to! The idea is to write a decorator that modifies another decorator’s behaviour.
# guard_main.py
"""Demonstrates a guard decorator."""
import os
def testing_guard(decorator_func):
"""
Decorator that only applies another decorator if the TESTING environment
variable is not set.
Args:
decorator_func: The decorator function.
Returns:
Function that calls a function after applying the decorator if TESTING
environment variable is not set and calls the plain function if it is set.
"""
def replacement(original_func):
"""Function that is called instead of original function."""
def apply_guard(*args, **kwargs):
"""Decides whether to use decorator on function call."""
if os.getenv('TESTING') is not None:
return original_func(*args, **kwargs)
return decorator_func(original_func)(*args, **kwargs)
return apply_guard
return replacement
@testing_guard
def decorator(func):
"""
A simple decorator that adds printing a message on a function call.
Args:
func: The function to decorate.
Returns:
The decorated function.
"""
def replacement(*args, **kwargs):
"""Function that is called instead of original function."""
print('The decorator was called.')
return func(*args, **kwargs)
return replacement
@decorator
def main():
print('The main function was called.')
if __name__ == '__main__':
print('Calling the main function without TESTING set.')
main()
print('Calling the main function with TESTING set.')
os.environ['TESTING'] = ''
main()
$ python3 guard_main.py
Calling the main function without TESTING set.
The decorator was called.
The main function was called.
Calling the main function with TESTING set.
The main function was called.
As you can see, the behaviour of the code is exactly the same but the decorator is in its original form. The reason this works is because the guard decorator gets to intercept each function call and can then decide whether to first apply the decorator or call the plain function on lines 23-25.
On top of not having to re-write decorator functions which you don’t want to execute during testing, you also get to separate the logic that determines whether the decorator is applied from the decorator logic which will reduce the chances of accidentally executing decorator logic as you make changes to the decorator. It also helps you write clear unit tests for both the decorator and the guard decorator.
The last consideration is how do you apply this in practice. I would argue that, unless you are testing the decorator or guard decorator, you should always have the TESTING environment variable set. This ensures that you are only testing function logic and not decorator logic. You can achieve this by putting a fixture in your root conftest.py file with autouse set to True.
@pytest.fixture(scope='function', autouse=True)
def set_testing(monkeypatch):
"""Sets the TESTING environment variable."""
monkeypatch.setenv('TESTING', '')
When you are testing the decorator you would always ensure that the TESTING environment variable is not set. You can achieve that using a fixture that clears the TESTING environment variable that also has autouse set to True as a part of the file that tests the decorator.
@pytest.fixture(scope='function', autouse=True)
def delete_testing(monkeypatch):
"""Deletes the TESTING environment variable."""
monkeypatch.delenv('TESTING', raising=False)
Finally, for testing the guard decorator, make whether the TESTING environment variable is set part of the tests themselves. Don’t be shy about overriding the functionality of any autouse fixtures as a part of the test function to demonstrate what the intended state of the test is clearly.
def test_guard_decorator_testing_set(monkeypatch):
"""
GIVEN TESTING environment variable set and ...
WHEN ...
THEN ...
"""
# Setting TESTING environment variable
monkeypatch.setenv('TESTING', '')
# Other test code
def test_guard_decorator_testing_not_set(monkeypatch):
"""
GIVEN TESTING environment variable is not set and ...
WHEN ...
THEN ...
"""
# Setting TESTING environment variable
monkeypatch.delenv('TESTING', raising=False)
# Other test code
Thats it! Consider whether environment variables is the best way of indicating that decorator logic should be skipped during testing and also what the best name of the environment variable is for you. I hope this was useful to you!
David Andersson 