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Interfaces and Annotations in Python3

TL;DR: Annotations in Python3 are very useful when declaring interfaces using abc metaclass.

If you want to stop reading here, I’m not going to stop you:)

If not, allow me to take you to on a small journey where I explain what all of this is about …

A Java example #

Let’s assume you have a PostgreSQL database containing employee records. You are building a web site that will be able to display information about an employee given its ID number.

You have a class called SQLRepository that allows you to fetch employees by ID, and a EmployeeController class with a viewEmployee method will be called when the user goes through the correct URL.

Lastly, you have a Renderer class that knows how to generate the HTML description of your employees.

First attempt #

The most obvious way to implement the controller looks like this:

package mypackage.controller;

import mypackage.Employee;
import mypackage.repository.SQLRepository;
import mypackage.renderer.Renderer;

public class Controller {
    private SQLRepository repository;
    private Renderer renderer;

    public Controller() {
        repository = new SQLRepository();
        renderer = new Renderer();

    public String viewEmployee(int id) {
        Employee employee = repository.getEmployeeByID(id);
        String res = renderer.renderEmployee(employee);
        return res;

But there are a few problems with this implementation.

Testing the controller #

It’s going to be difficult, right?

As soon as you want to instantiate a new controller, the constructor of SQLRepository() will get called too, which means you’ll have to set up a PostgreSQL database just so you can run the tests.

We say that the “flow of control” goes from the Controller to the SQLRepository class, or that there is a runtime dependency between the Controller and the SQLRepository.

And since contains an import of the SQLRepository class, we say there is a source code dependency between the Controller and the SQLRepository classes.

This is where interfaces come in.

Introducing an interface #

We are going to “decouple” the Controller and the SQLRepository by introducing an interface:

// In
public interface Repository {
    public Employee getEmployeeByID(int id);

Then, we tell SQLRepository to implement the interface:

+import mypackage.repository.Repository;

-public class SQLRepository {
+public class SQLRepository implements Repository {

And finally we pass the repository as an argument to the Controller constructor:

-import mypackage.repository.SQLRepository;
+import mypackage.repository.Repository;
 import mypackage.renderer.Renderer;

 public class Controller {
-    private SQLRepository repository;
+    private Repository repository;
     private Renderer renderer;

-    public Controller() {
-        repository = new SQLRepository();
+    public Controller(Repository repository) {
+        this.repository = repository;
         renderer = new Renderer();

Note the flow of control still goes from the Controller to the Repository, but now the file no longer depends on the source file.

Instead, both source files now depend on the interface.

This is called the “Dependency Inversion Principle”.

An other way to describe the change is that we now have a boundary between the controller and the repository, and that the interface is what allows code to cross the boundary.

Using a different implementation of the Repository interface, it’s now possible to test the Controller without ever touching the database:

public class FakeRepository implements Repository {
    private ArrayList<Employee> employees;

    public FakeRepository() {
        employees = new ArrayList<Employee>();

    public void addEmployee(Employee employee) {

    public Employee getEmployeeByID(int id) {
        return employees.get(id);

public class ControllerTest extends TestCase {

    // ...

    public void testViewEmployee() {
        Employee smith = new Employee("John Smith");

        FakeRepository fakeRepository = new FakeRepository();

        Controller controller = new Controller(fakeRepository);

        String html = controller.viewEmployee(0);
        assertEquals(html, "<span class=\"employe\">John Smith</p>");

Interfaces in Java have other use than just dependency inversion, the example is mostly here for the sake of what’s coming next.

Same thing, with Python #

Let’s rewrite our first version of the Java code in Python:

# First version, hard to test

from renderer import Renderer
from repository import SQLRepository

class Controller:
    def __init__(self):
        self.repository = SQLRepository()
        self.renderer = Renderer()

    def view_employee(self, id):
        employee = self.repository.get_employee_by_id(id)
        res = self.renderer.render_employee(employee)
        return res

Same thing, we have a source code dependency between and

Now, let’s invert the dependency:

-from repository import SQLRepository

 class Controller:
-    def __init__(self):
-        self.repository = SQLRepository()
+    def __init__(self, repository):
+        self.repository = repository
         self.renderer = Renderer()

Yup, that’s all we have to change!

Testing with a FakeRepository is as easy as:

from controller import Controller
from employee import Employee

class FakeRepository:
    def __init__(self):
        self._employees = list()

    def add_employee(self, employee):

    def get_employee_by_id(self, id):
        return self._employees[id]

def test_view_employee():
    smith = Employee("John Smith")

    fake_repository = FakeRepository()

    controller = Controller(fake_repository)

    assert "John Smith" in controller.view_employee(0)

So what just happened here?

We completely removed the source code dependency between the repository and the controller.

We did introduce an interface but it’s hidden behind the implementation: the interface between the controller and the repository is simply the list of all methods sent from the controller to its repository instance.

This is also known as “Duck Typing”: we can pass a walrus instead of a duck as long as it’s a quacking walrus.

You may have heard an other definition of duck typing. I find this one much more accurate. It was coined by Avdi Grim, from Ruby Tapas fame.

The problem with Duck Typing #

Let’s assume the database schema is changed. We now have several companies in the same database, and the ID are only unique for a given company.

This means that we will need to provide both a company name and an employee id when querying the repository.

getEmployeeByID has to be renamed to just getEmployee and now takes a string (the company name) and an integer (the employee id).

In the Java world, as soon as we rename the method in the SQLRepository class, we’ll a compilation error.[6,8]
mypackage.repository.SQLRepository is not abstract and does not override
abstract method getEmployeeByID(int) in mypackage.repository.Repository

In fact, we’ll get all kind of errors until we also change the Repository interface, the FakeRepository class, the production code and the test code.

But it Python, nothing like this will happen. Our test will continue to pass, and the production code will just crash.

AttributeError: 'SQLRepository' object has no attribute 'get_employee_by_id'

(This, by the way, is the difference between “static” and “dynamic” typing)

There are many ways to tackle this problem:

  • Write integration tests that will check what happens when boundaries are crossed.
  • Use mock.MagicMock(SQLRepository) in order to make sure FakeRepository has the same methods as SQLRepository
  • Use static analysis provided by a linter
  • Take inspiration from statically typed languages and write an explicit Repository interface.

In this article we’ll only talk about the last one, but please bear in mind that the other ways can work really well too :)

Anyway, let’s change the code so that both SQLRepository and FakeRepository inherit from a Repository class.

How do we make sure all the methods from the interface are implemented ?

The dark ages #

A long time ago, you had to write something like this:

class Repository:

    def get_employee(self, company_name, id):
        raise NotImplementedError()

That way, if you forget to override the get_employee method, you get a NotImplementedError exception when the method is called.

But sometimes you do have some code that is not implemented yet, and you want to use NotImplementedError to signal this to the callers of your function.

How do you make the difference between a real “not implemented” error and a typo in the name of the method you defined in the daughter class?

Metaclass to the rescue! #

Nowadays, it’s better to write your interface like this:

import abc

class Repository(metaclass=abc.ABCMeta):

    def get_employee(self, company_name, id):

That way, if you don’t implement all the abstract methods, you’ll get an error when the daughter class is instantiated:

TypeError: Can't instantiate abstract class FakeRepository with
abstract methods get_employee

Documenting #

There’s still an issue though. What if you don’t call the method with the right types, like using a string instead of an integer for the id parameter ?

Until not long ago, one solution was to use the docstring for this, and then use a tool like sphinx.autodoc to generate nice HTML documentation:

def get_employee(self, company_name, id):
    """ Fetch an employee from the database

    :param company_name: name of the company
    :param id: ID of the employee in the company
    :type company_name: string
    :type id: int
    :returns: an `Employee` instance


I don’t know about you, but I don’t find this really readable.

In fact, there are at least two other ways to document the parameters names and types:

# Numpy style
def get_employee(self, company_name, id):
    """ Fetch an employee from the database

    company_name : str
      Name of the company
    id : integer
      ID of the employee

    An Employee instance

# Google style:
def get_employee(self, company_name, id):
    """ Fetch an employee from the database

      company_name : name of the company as a string
      id : id of the employee as an integer
      An employee instance

But in every case, you have to spell the name of the arguments twice: once in the definition of the function, and once in the docstring.

Annotations to the rescue! #

Annotations are a Python feature that was added in Python3.

Basically, you can use a colon after the parameter names to specify their types, and add an arrow at the end of the definition to specify the returned type:

import abc

class Repository(metaclass=abc.ABCMeta):

    def get_employee(self, company_name: str, id: int) -> Employee:

I really like this solution. I find it concise, readable, and it makes it possible to get rid of the entire docstring when the name of the functions and parameters are obvious.

So if you ever need to introduce an interface in Python3, I highly suggest you use annotations to describe the types of your arguments. Future you and users of your interface will thank you for this.

One last word #

They are tools like mypy that go even further and use annotations to provide gradual static typing for Python.

I never used any of them (yet), because for now I’m happy catching type errors through linters and automatic tests, but if you have, please let me know!

Update: One year later, I finally gave mypy a go. You can read more about it in the aptly named giving mypy a go blog post.

Thanks for reading this far :)

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