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![Django Styleguide](logo.png)
---
Django styleguide used in [HackSoft](https://hacksoft.io) projects.
Expect often updates as we discuss & decide upon different things.
If you want to check an existing project showing most of the styleguide, [check the Styleguide-Example](https://github.com/HackSoftware/Styleguide-Example)
**Table of contents:**
<!-- toc -->
- [Overview](#overview)
- [Cookie Cutter](#cookie-cutter)
- [Models](#models)
* [Custom validation](#custom-validation)
* [Properties](#properties)
* [Methods](#methods)
* [Testing](#testing)
- [Services](#services)
* [Naming convention](#naming-convention)
- [Selectors](#selectors)
* [Naming convention](#naming-convention-1)
- [APIs & Serializers](#apis--serializers)
* [Naming convention](#naming-convention-2)
* [An example list API](#an-example-list-api)
* [An example detail API](#an-example-detail-api)
* [An example create API](#an-example-create-api)
* [An example update API](#an-example-update-api)
* [Nested serializers](#nested-serializers)
- [Urls](#urls)
- [Exception Handling](#exception-handling)
* [Raising Exceptions in Services / Selectors](#raising-exceptions-in-services--selectors)
* [Handle Exceptions in APIs](#handle-exceptions-in-apis)
* [Error formatting](#error-formatting)
- [Testing](#testing-1)
* [Naming conventions](#naming-conventions)
* [Example](#example)
+ [Example models](#example-models)
+ [Example selectors](#example-selectors)
+ [Example services](#example-services)
* [Testing services](#testing-services)
* [Testing selectors](#testing-selectors)
- [Celery](#celery)
* [Structure](#structure)
+ [Configuration](#configuration)
+ [Tasks](#tasks)
* [Periodic Tasks](#periodic-tasks)
* [Configuration](#configuration-1)
- [Misc](#misc)
* [mypy / type annotations](#mypy--type-annotations)
- [Inspiration](#inspiration)
<!-- tocstop -->
## Overview
**In Django, business logic should live in:**
* Model properties (with some exceptions).
* Model `clean` method for additional validations (with some exceptions).
* Services - functions, that take care of writing to the database.
* Selectors - functions, that take care of fetching from the database.
**In Django, business logic should not live in:**
* APIs and Views.
* Serializers and Forms.
* Form tags.
* Model `save` method.
**Model properties vs selectors:**
* If the model property spans multiple relations, it should better be a selector.
* If a model property, added to some list API, will cause `N + 1` problem that cannot be easily solved with `select_related`, it should better be a selector.
## Cookie Cutter
We recommend starting every new project with some kind of cookiecutter. Having the proper structure from the start pays off.
For example, you can use [`cookiecutter-django`](https://github.com/pydanny/cookiecutter-django)
## Models
Lets take a look at an example model:
```python
class Course(models.Model):
name = models.CharField(unique=True, max_length=255)
start_date = models.DateField()
end_date = models.DateField()
attendable = models.BooleanField(default=True)
students = models.ManyToManyField(
Student,
through='CourseAssignment',
through_fields=('course', 'student')
)
teachers = models.ManyToManyField(
Teacher,
through='CourseAssignment',
through_fields=('course', 'teacher')
)
slug_url = models.SlugField(unique=True)
repository = models.URLField(blank=True)
video_channel = models.URLField(blank=True, null=True)
facebook_group = models.URLField(blank=True, null=True)
logo = models.ImageField(blank=True, null=True)
public = models.BooleanField(default=True)
generate_certificates_delta = models.DurationField(default=timedelta(days=15))
objects = CourseManager()
def clean(self):
if self.start_date > self.end_date:
raise ValidationError("End date cannot be before start date!")
def save(self, *args, **kwargs):
self.full_clean()
return super().save(*args, **kwargs)
@property
def visible_teachers(self):
return self.teachers.filter(course_assignments__hidden=False).select_related('profile')
@property
def duration_in_weeks(self):
weeks = rrule.rrule(
rrule.WEEKLY,
dtstart=self.start_date,
until=self.end_date
)
return weeks.count()
@property
def has_started(self):
now = get_now()
return self.start_date <= now.date()
@property
def has_finished(self):
now = get_now()
return self.end_date <= now.date()
@property
def can_generate_certificates(self):
now = get_now()
return now.date() <= self.end_date + self.generate_certificates_delta
def __str__(self) -> str:
return self.name
```
Few things to spot here.
**Custom validation:**
* There's a custom model validation, defined in `clean()`. This validation uses only model fields and spans no relations.
* This requires someone to call `full_clean()` on the model instance. The best place to do that is in the `save()` method of the model. Otherwise people can forget to call `full_clean()` in the respective service.
**Properties:**
* All properties, except `visible_teachers`, work directly on model fields.
* `visible_teachers` is a great candidate for a **selector**.
We have few general rules for custom validations & model properties / methods:
### Custom validation
* If the custom validation depends only on the **non-relational model fields**, define it in `clean` and call `full_clean` in `save`.
* If the custom validation is more complex & **spans relationships**, do it in the service that creates the model.
* It's OK to combine both `clean` and additional validation in the `service`.
* As proposed in [this issue](https://github.com/HackSoftware/Django-Styleguide/issues/22), if you can do a validation using [Django's constraints](https://docs.djangoproject.com/en/2.2/ref/models/constraints/), then you should aim for that. Less code to write.
### Properties
* If your model properties use only **non-relational model fields**, they are OK to stay as properties.
* If a property, such as `visible_teachers` starts **spanning relationships**, it's better to define a selector for that.
### Methods
* If you need a method that updates several fields at once (for example - `created_at` and `created_by` when something happens), you can create a model method that does the job.
* Every model method should be wrapped in a service. There should be no model method calling outside a service.
### Testing
Models need to be tested only if there's something additional to them - like custom validation or properties.
If we are strict & don't do custom validation / properties, then we can test the models without actually writing anything to the database => we are going to get quicker tests.
For example, if we want to test the custom validation, here's how a test could look like:
```python
from datetime import timedelta
from django.test import TestCase
from django.core.exceptions import ValidationError
from project.common.utils import get_now
from project.education.factories import CourseFactory
from project.education.models import Course
class CourseTests(TestCase):
def test_course_end_date_cannot_be_before_start_date(self):
start_date = get_now()
end_date = get_now() - timedelta(days=1)
course_data = CourseFactory.build()
course_data['start_date'] = start_date
course_data['end_date'] = end_date
course = Course(**course_data)
with self.assertRaises(ValidationError):
course.full_clean()
```
There's a lot going on in this test:
* `get_now()` returns a timezone aware datetime.
* `CourseFactory.build()` will return a dictionary with all required fields for a course to exist.
* We replace the values for `start_date` and `end_date`.
* We assert that a validation error is going to be raised if we call `full_clean`.
* We are not hitting the database at all, since there's no need for that.
Here's how `CourseFactory` looks like:
```python
class CourseFactory(factory.DjangoModelFactory):
name = factory.Sequence(lambda n: f'{n}{faker.word()}')
start_date = factory.LazyAttribute(
lambda _: get_now()
)
end_date = factory.LazyAttribute(
lambda _: get_now() + timedelta(days=30)
)
slug_url = factory.Sequence(lambda n: f'{n}{faker.slug()}')
repository = factory.LazyAttribute(lambda _: faker.url())
video_channel = factory.LazyAttribute(lambda _: faker.url())
facebook_group = factory.LazyAttribute(lambda _: faker.url())
class Meta:
model = Course
@classmethod
def _build(cls, model_class, *args, **kwargs):
return kwargs
@classmethod
def _create(cls, model_class, *args, **kwargs):
return create_course(**kwargs)
```
## Services
A service is a simple function that:
* Lives in `your_app/services.py` module
* Takes keyword-only arguments
* Is type-annotated (even if you are not using [`mypy`](https://github.com/python/mypy) at the moment)
* Works mostly with models & other services and selectors
* Does business logic - from simple model creation to complex cross-cutting concerns, to calling external services & tasks.
An example service that creates an user:
```python
def create_user(
*,
email: str,
name: str
) -> User:
user = User(email=email)
user.full_clean()
user.save()
create_profile(user=user, name=name)
send_confirmation_email(user=user)
return user
```
As you can see, this service calls 2 other services - `create_profile` and `send_confirmation_email`
### Naming convention
Naming conventions depend on your taste. It pays off to have a consistent naming convention through out a project.
If we take the example above, our service is named `create_user`. The pattern is - `<action>_<entity>`.
What we usually prefer in our projects, again, depending on taste, is `<entity>_<action>` or with the example above: `user_create`. This seems odd at first, but it has few nice features:
* Namespacing. It's easy to spot all services starting with `user_` and it's a good idea to put them in a `users.py` module.
* Greppability. Or in other words, if you want to see all actions for a specific entity, just grep for `user_`.
A full example would look like this:
```python
def user_create(
*,
email: str,
name: str
) -> User:
user = User(email=email)
user.full_clean()
user.save()
profile_create(user=user, name=name)
confirmation_email_send(user=user)
return user
```
## Selectors
A selector is a simple function that:
* Lives in `your_app/selectors.py` module
* Takes keyword-only arguments
* Is type-annotated (even if you are not using [`mypy`](https://github.com/python/mypy) at the moment)
* Works mostly with models & other services and selectors
* Does business logic around fetching data from your database
An example selector that lists users from the database:
```python
def get_users(*, fetched_by: User) -> Iterable[User]:
user_ids = get_visible_users_for(user=fetched_by)
query = Q(id__in=user_ids)
return User.objects.filter(query)
```
As you can see, `get_visible_users_for` is another selector.
### Naming convention
Read the section in services. Same rules apply here.
## APIs & Serializers
When using services & selectors, all of your APIs should look simple & identical.
General rules for an API is:
* Do 1 API per operation. For CRUD on a model, this means 4 APIs.
* Use the most simple `APIView` or `GenericAPIView`
* Use services / selectors & don't do business logic in your API.
* Use serializers for fetching objects from params - passed either via `GET` or `POST`
* Serializer should be nested in the API and be named either `InputSerializer` or `OutputSerializer`
* `OutputSerializer` can subclass `ModelSerializer`, if needed.
* `InputSerializer` should always be a plain `Serializer`
* Reuse serializers as little as possible
* If you need a nested serializer, use the `inline_serializer` util
### Naming convention
For our APIs we use the following naming convention: `<Entity><Action>Api`.
Here are few examples: `UserCreateApi`, `UserSendResetPasswordApi`, `UserDeactivateApi`, etc.
### An example list API
```python
class CourseListApi(SomeAuthenticationMixin, APIView):
class OutputSerializer(serializers.ModelSerializer):
class Meta:
model = Course
fields = ('id', 'name', 'start_date', 'end_date')
def get(self, request):
courses = get_courses()
serializer = self.OutputSerializer(courses, many=True)
return Response(serializer.data)
```
### An example detail API
```python
class CourseDetailApi(SomeAuthenticationMixin, APIView):
class OutputSerializer(serializers.ModelSerializer):
class Meta:
model = Course
fields = ('id', 'name', 'start_date', 'end_date')
def get(self, request, course_id):
course = get_course(id=course_id)
serializer = self.OutputSerializer(course)
return Response(serializer.data)
```
### An example create API
```python
class CourseCreateApi(SomeAuthenticationMixin, APIView):
class InputSerializer(serializers.Serializer):
name = serializers.CharField()
start_date = serializers.DateField()
end_date = serializers.DateField()
def post(self, request):
serializer = self.InputSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
create_course(**serializer.validated_data)
return Response(status=status.HTTP_201_CREATED)
```
### An example update API
```python
class CourseUpdateApi(SomeAuthenticationMixin, APIView):
class InputSerializer(serializers.Serializer):
name = serializers.CharField(required=False)
start_date = serializers.DateField(required=False)
end_date = serializers.DateField(required=False)
def post(self, request, course_id):
serializer = self.InputSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
update_course(course_id=course_id, **serializer.validated_data)
return Response(status=status.HTTP_200_OK)
```
### Nested serializers
In case you need to use a nested serializer, you can do the following thing:
```python
class Serializer(serializers.Serializer):
weeks = inline_serializer(many=True, fields={
'id': serializers.IntegerField(),
'number': serializers.IntegerField(),
})
```
The implementation of `inline_serializer` can be found in `utils.py` in this repo.
## Urls
We usually organize our urls the same way we organize our APIs - 1 url per API, meaning 1 url per action.
A general rule of thumb is to split urls from different domains in their own `domain_patterns` list & include from `urlpatterns`.
Here's an example with the APIs from above:
```python
from django.urls import path, include
from project.education.apis import (
CourseCreateApi,
CourseUpdateApi,
CourseListApi,
CourseDetailApi,
CourseSpecificActionApi,
)
course_patterns = [
path('', CourseListApi.as_view(), name='list'),
path('<int:course_id>/', CourseDetailApi.as_view(), name='detail'),
path('create/', CourseCreateApi.as_view(), name='create'),
path('<int:course_id>/update/', CourseUpdateApi.as_view(), name='update'),
path(
'<int:course_id>/specific-action/',
CourseSpecificActionApi.as_view(),
name='specific-action'
),
]
urlpatterns = [
path('courses/', include((course_patterns, 'courses'))),
]
```
**Splitting urls like that can give you the extra flexibility to move separate domain patterns to separate modules**, especially for really big projects, where you'll often have merge conflicts in `urls.py`.
## Exception Handling
### Raising Exceptions in Services / Selectors
Now we have separation between our HTTP interface & the core logic of our application.
In order to keep this separation of concerns, our services and selectors must not use the `rest_framework.exception` classes because they are bounded with HTTP status codes.
Our services and selectors must use one of:
* [Python built-in exceptions](https://docs.python.org/3/library/exceptions.html)
* Exceptions from `django.core.exceptions`
* Custom exceptions, inheriting from the ones above.
Here is a good example of service that performs some validation and raises `django.core.exceptions.ValidationError`:
```python
from django.core.exceptions import ValidationError
def create_topic(*, name: str, course: Course) -> Topic:
if course.end_date < timezone.now():
raise ValidationError('You can not create topics for course that has ended.')
topic = Topic.objects.create(name=name, course=course)
return topic
```
### Handle Exceptions in APIs
In order to transform the exceptions raised in the services or selectors, to a standard HTTP response, you need to catch the exception and raise something that the rest framework understands.
The best place to do this is in the `handle_exception` method of the `APIView`. There you can map your Python/Django exception to a DRF exception.
By default, the [`handle_exception` method implementation in DRF](https://www.django-rest-framework.org/api-guide/exceptions/#exception-handling-in-rest-framework-views) handles the Django's built-in `Http404` and `PermissionDenied` exceptions, thus there is no need for you to handle it by hand.
Here is an example:
```python
from rest_framework import exceptions as rest_exceptions
from django.core.exceptions import ValidationError
class CourseCreateApi(SomeAuthenticationMixin, APIView):
expected_exceptions = {
ValidationError: rest_exceptions.ValidationError
}
class InputSerializer(serializers.Serializer):
...
def post(self, request):
serializer = self.InputSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
create_course(**serializer.validated_data)
return Response(status=status.HTTP_201_CREATED)
def handle_exception(self, exc):
if isinstance(exc, tuple(self.expected_exceptions.keys())):
drf_exception_class = self.expected_exceptions[exc.__class__]
drf_exception = drf_exception_class(get_error_message(exc))
return super().handle_exception(drf_exception)
return super().handle_exception(exc)
```
Here's the implementation of `get_error_message`:
```python
def get_first_matching_attr(obj, *attrs, default=None):
for attr in attrs:
if hasattr(obj, attr):
return getattr(obj, attr)
return default
def get_error_message(exc):
if hasattr(exc, 'message_dict'):
return exc.message_dict
error_msg = get_first_matching_attr(exc, 'message', 'messages')
if isinstance(error_msg, list):
error_msg = ', '.join(error_msg)
if error_msg is None:
error_msg = str(exc)
return error_msg
```
You can move this code to a mixin and use it in every API to prevent code duplication.
We call this `ApiErrorsMixin`. Here's a sample implementation from one of our projects:
```python
from rest_framework import exceptions as rest_exceptions
from django.core.exceptions import ValidationError
from project.common.utils import get_error_message
class ApiErrorsMixin:
"""
Mixin that transforms Django and Python exceptions into rest_framework ones.
Without the mixin, they return 500 status code which is not desired.
"""
expected_exceptions = {
ValueError: rest_exceptions.ValidationError,
ValidationError: rest_exceptions.ValidationError,
PermissionError: rest_exceptions.PermissionDenied
}
def handle_exception(self, exc):
if isinstance(exc, tuple(self.expected_exceptions.keys())):
drf_exception_class = self.expected_exceptions[exc.__class__]
drf_exception = drf_exception_class(get_error_message(exc))
return super().handle_exception(drf_exception)
return super().handle_exception(exc)
```
Having this mixin in mind, our API can be written like that:
```python
class CourseCreateApi(
SomeAuthenticationMixin,
ApiErrorsMixin,
APIView
):
class InputSerializer(serializers.Serializer):
...
def post(self, request):
serializer = self.InputSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
create_course(**serializer.validated_data)
return Response(status=status.HTTP_201_CREATED)
```
All of code above can be found in `utils.py` in this repository.
### Error formatting
Next step is to generalize the format of the errors we get from our APIs. This will ease the process of displaying errors to the end user, via JavaScript.
If we have a standard serializer and there is an error with one of the fields, the message we get by default looks like this:
```python
{
"url": [
"This field is required."
]
}
```
If we have a validation error with just a message - `raise ValidationError('Something is wrong.')` - it will look like this:
```python
[
"some error"
]
```
Another error format may look like this:
```python
{
"detail": "Method \"GET\" not allowed."
}
```
**Those are 3 different ways of formatting for our errors.** What we want to have is a single format, for all errors.
Luckily, DRF provides a way for us to give our own custom exception handler, where we can implement the desired formatting: <https://www.django-rest-framework.org/api-guide/exceptions/#custom-exception-handling>
In our projects, we format the errors like that:
```python
{
"errors": [
{
"message": "Error message",
"code": "Some code",
"field": "field_name"
},
{
"message": "Error message",
"code": "Some code",
"field": "nested.field_name"
},
]
}
```
If we raise a `ValidationError`, then field is optional.
In order to achieve that, we implement a custom exception handler:
```python
from rest_framework.views import exception_handler
def exception_errors_format_handler(exc, context):
response = exception_handler(exc, context)
# If unexpected error occurs (server error, etc.)
if response is None:
return response
formatter = ErrorsFormatter(exc)
response.data = formatter()
return response
```
which needs to be added to the `REST_FRAMEWORK` project settings:
```python
REST_FRAMEWORK = {
'EXCEPTION_HANDLER': 'project.app.handlers.exception_errors_format_handler',
...
}
```
**The magic happens in the `ErrorsFormatter` class.** The implementation of that class can be found in the `utils.py` file, located in that repo.
Combining `ApiErrorsMixin`, the custom exception handler & the errors formatter class, we can have predictable behavior in our APIs, when it comes to errors.
## Testing
In our Django projects, we split our tests depending on the type of code they represent.
Meaning, we generally have tests for models, services, selectors & APIs / views.
The file structure usually looks like this:
```
project_name
├── app_name
│   ├── __init__.py
│   └── tests
│   ├── __init__.py
│   ├── models
│   │   └── __init__.py
│   │   └── test_some_model_name.py
│   ├── selectors
│   │   └── __init__.py
│   │   └── test_some_selector_name.py
│   └── services
│   ├── __init__.py
│   └── test_some_service_name.py
└── __init__.py
```
### Naming conventions
We follow 2 general naming conventions:
* The test file names should be `test_the_name_of_the_thing_that_is_tested.py`
* The test case shoud be `class TheNameOfTheThingThatIsTestedTests(TestCase):`
For example if we have:
```python
def a_very_neat_service(*args, **kwargs):
pass
```
We are going to have the following for file name:
```
project_name/app_name/tests/services/test_a_very_neat_service.py
```
And the following for test case:
```python
class AVeryNeatServiceTests(TestCase):
pass
```
For tests of utility functions, we follow a similiar pattern.
For example, if we have `project_name/common/utils.py`, then we are going to have `project_name/common/tests/test_utils.py` and place different test cases in that file.
If we are to split the `utils.py` module into submodules, the same will happen for the tests:
* `project_name/common/utils/files.py`
* `project_name/common/tests/utils/test_files.py`
We try to match the stucture of our modules with the structure of their respective tests.
### Example
We have a demo `django_styleguide` project.
#### Example models
```python
import uuid
from django.db import models
from django.contrib.auth.models import User
from django.utils import timezone
from djmoney.models.fields import MoneyField
class Item(models.Model):
id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)
name = models.CharField(max_length=255)
description = models.TextField()
price = MoneyField(
max_digits=14,
decimal_places=2,
default_currency='EUR'
)
def __str__(self):
return f'Item {self.id} / {self.name} / {self.price}'
class Payment(models.Model):
item = models.ForeignKey(
Item,
on_delete=models.CASCADE,
related_name='payments'
)
user = models.ForeignKey(
User,
on_delete=models.CASCADE,
related_name='payments'
)
successful = models.BooleanField(default=False)
created_at = models.DateTimeField(default=timezone.now)
def __str__(self):
return f'Payment for {self.item} / {self.user}'
```
#### Example selectors
For implementation of `QuerySetType`, check `queryset_type.py`.
```python
from django.contrib.auth.models import User
from django_styleguide.common.types import QuerySetType
from django_styleguide.payments.models import Item
def get_items_for_user(
*,
user: User
) -> QuerySetType[Item]:
return Item.objects.filter(payments__user=user)
```
#### Example services
```python
from django.contrib.auth.models import User
from django.core.exceptions import ValidationError
from django_styleguide.payments.selectors import get_items_for_user
from django_styleguide.payments.models import Item, Payment
from django_styleguide.payments.tasks import charge_payment
def buy_item(
*,
item: Item,
user: User,
) -> Payment:
if item in get_items_for_user(user=user):
raise ValidationError(f'Item {item} already in {user} items.')
payment = Payment.objects.create(
item=item,
user=user,
successful=False
)
charge_payment.delay(payment_id=payment.id)
return payment
```
### Testing services
Service tests are the most important tests in the project. Usually, those are the heavier tests with most lines of code.
General rule of thumb for service tests:
* The tests should cover the business logic behind the services in an exhaustive manner.
* The tests should hit the database - creating & reading from it.
* The tests should mock async task calls & everything that goes outside the project.
When creating the required state for a given test, one can use a combination of:
* Fakes (We recommend using [`faker`](https://github.com/joke2k/faker))
* Other services, to create the required objects.
* Special test utility & helper methods.
* Factories (We recommend using [`factory_boy`](https://factoryboy.readthedocs.io/en/latest/orms.html))
* Plain `Model.objects.create()` calls, if factories are not yet introduced in the project.
**Lets take a look at our service from the example:**
```python
from django.contrib.auth.models import User
from django.core.exceptions import ValidationError
from django_styleguide.payments.selectors import get_items_for_user
from django_styleguide.payments.models import Item, Payment
from django_styleguide.payments.tasks import charge_payment
def buy_item(
*,
item: Item,
user: User,
) -> Payment:
if item in get_items_for_user(user=user):
raise ValidationError(f'Item {item} already in {user} items.')
payment = Payment.objects.create(
item=item,
user=user,
successful=False
)
charge_payment.delay(payment_id=payment.id)
return payment
```
The service:
* Calls a selector for validation
* Creates ORM object
* Calls a task
**Those are our tests:**
```python
from unittest.mock import patch
from django.test import TestCase
from django.contrib.auth.models import User
from django.core.exceptions import ValidationError
from django_styleguide.payments.services import buy_item
from django_styleguide.payments.models import Payment, Item
class BuyItemTests(TestCase):
def setUp(self):
self.user = User.objects.create_user(username='Test User')
self.item = Item.objects.create(
name='Test Item',
description='Test Item description',
price=10.15
)
self.service = buy_item
@patch('django_styleguide.payments.services.get_items_for_user')
def test_buying_item_that_is_already_bought_fails(self, get_items_for_user_mock):
"""
Since we already have tests for `get_items_for_user`,
we can safely mock it here and give it a proper return value.
"""
get_items_for_user_mock.return_value = [self.item]
with self.assertRaises(ValidationError):
self.service(user=self.user, item=self.item)
@patch('django_styleguide.payments.services.charge_payment.delay')
def test_buying_item_creates_a_payment_and_calls_charge_task(
self,
charge_payment_mock
):
self.assertEqual(0, Payment.objects.count())
payment = self.service(user=self.user, item=self.item)
self.assertEqual(1, Payment.objects.count())
self.assertEqual(payment, Payment.objects.first())
self.assertFalse(payment.successful)
charge_payment_mock.assert_called()
```
### Testing selectors
Testing selectors is also an important part of every project.
Sometimes, the selectors can be really straightforward, and if we have to "cut corners", we can omit those tests. But it the end, it's important to cover our selectors too.
Lets take another look at our example selector:
```python
from django.contrib.auth.models import User
from django_styleguide.common.types import QuerySetType
from django_styleguide.payments.models import Item
def get_items_for_user(
*,
user: User
) -> QuerySetType[Item]:
return Item.objects.filter(payments__user=user)
```
As you can see, this is a very straightforward & simple selector. We can easily cover that with 2 to 3 tests.
**Here are the tests:**
```python
from django.test import TestCase
from django.contrib.auth.models import User
from django_styleguide.payments.selectors import get_items_for_user
from django_styleguide.payments.models import Item, Payment
class GetItemsForUserTests(TestCase):
def test_selector_returns_nothing_for_user_without_items(self):
"""
This is a "corner case" test.
We should get nothing if the user has no items.
"""
user = User.objects.create_user(username='Test User')
expected = []
result = list(get_items_for_user(user=user))
self.assertEqual(expected, result)
def test_selector_returns_item_for_user_with_that_item(self):
"""
This test will fail in case we change the model structure.
"""
user = User.objects.create_user(username='Test User')
item = Item.objects.create(
name='Test Item',
description='Test Item description',
price=10.15
)
Payment.objects.create(
item=item,
user=user
)
expected = [item]
result = list(get_items_for_user(user=user))
self.assertEqual(expected, result)
```
## Celery
We use [Celery](http://www.celeryproject.org/) for the following general cases:
* Communicating with 3rd party services (sending emails, notifications, etc.)
* Offloading heavier computational tasks outside the HTTP cycle.
* Periodic tasks (using Celery beat)
We try to treat Celery as if it's just another interface to our core logic - meaning - **don't put business logic there.**
An example task might look like this:
```python
from celery import shared_task
from project.app.services import some_service_name as service
@shared_task
def some_service_name(*args, **kwargs):
service(*args, **kwargs)
```
This is a task, having the same name as a service, which holds the actual business logic.
**Of course, we can have more complex situations**, like a chain or chord of tasks, each of them doing different domain related logic. In that case, it's hard to isolate everything in a service, because we now have dependencies between the tasks.
If that happens, we try to expose an interface to our domain & let the tasks work with that interface.
One can argue that having an ORM object is an interface by itself, and that's true. Sometimes, you can just update your object from a task & that's OK.
But there are times where you need to be strict and don't let tasks do database calls straight from the ORM, but rather, via an exposed interface for that.
**More complex scenarios depend on their context. Make sure you are aware of the architecture & the decisions you are making.**
### Structure
#### Configuration
We put Celery configuration in a Django app called `tasks`. The [Celery config](https://docs.celeryproject.org/en/latest/django/first-steps-with-django.html) itself is located in `apps.py`, in `TasksConfig.ready` method.
This Django app also holds any additional utilities, related to Celery.
Here's an example `project/tasks/apps.py` file:
```python
import os
from celery import Celery
from django.apps import apps, AppConfig
from django.conf import settings
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings.local')
app = Celery('project')
class TasksConfig(AppConfig):
name = 'project.tasks'
verbose_name = 'Celery Config'
def ready(self):
app.config_from_object('django.conf:settings', namespace="CELERY")
app.autodiscover_tasks()
@app.task(bind=True)
def debug_task(self):
from celery.utils.log import base_logger
base_logger = base_logger
base_logger.debug('debug message')
base_logger.info('info message')
base_logger.warning('warning message')
base_logger.error('error message')
base_logger.critical('critical message')
print('Request: {0!r}'.format(self.request))
return 42
```
#### Tasks
Tasks are located in in `tasks.py` modules in different apps.
We follow the same rules as with everything else (APIs, services, selectors): **if the tasks for a given app grow too big, split them by domain.**
Meaning, you can end up with `tasks/domain_a.py` and `tasks/domain_b.py`. All you need to do is import them in `tasks/__init__.py` for Celery to autodiscover them.
The general rule of thumb is - split your tasks in a way that'll make sense to you.
### Periodic Tasks
Managing periodic tasks is quite important, especially when you have tens, or hundreds of them.
We use [Celery Beat](https://docs.celeryproject.org/en/latest/userguide/periodic-tasks.html) + `django_celery_beat.schedulers:DatabaseScheduler` + [`django-celery-beat`](https://github.com/celery/django-celery-beat) for our periodic tasks.
The extra thing that we do is to have a management command, called `setup_periodic_tasks`, which holds the definition of all periodic tasks within the system. This command is located in the `tasks` app, discussed above.
Here's how `project.tasks.management.commands.setup_periodic_tasks.py` looks like:
```python
from django.core.management.base import BaseCommand
from django.db import transaction
from django_celery_beat.models import IntervalSchedule, CrontabSchedule, PeriodicTask
from project.app.tasks import some_periodic_task
class Command(BaseCommand):
help = f"""
Setup celery beat periodic tasks.
Following tasks will be created:
- {some_periodic_task.name}
"""
@transaction.atomic
def handle(self, *args, **kwargs):
print('Deleting all periodic tasks and schedules...\n')
IntervalSchedule.objects.all().delete()
CrontabSchedule.objects.all().delete()
PeriodicTask.objects.all().delete()
periodic_tasks_data = [
{
'task': some_periodic_task
'name': 'Do some peridoic stuff',
# https://crontab.guru/#15_*_*_*_*
'cron': {
'minute': '15',
'hour': '*',
'day_of_week': '*',
'day_of_month': '*',
'month_of_year': '*',
},
'enabled': True
},
]
for periodic_task in periodic_tasks_data:
print(f'Setting up {periodic_task["task"].name}')
cron = CrontabSchedule.objects.create(
**periodic_task['cron']
)
PeriodicTask.objects.create(
name=periodic_task['name'],
task=periodic_task['task'].name,
crontab=cron,
enabled=periodic_task['enabled']
)
```
Few key things:
* We use this task as part of a deploy procedure.
* We always put a link to [`crontab.guru`](https://crontab.guru) to explain the cron. Otherwhise it's unreadable.
* Everything is in one place.
### Configuration
Celery is a complex topic, so it's a good idea to invest time reading the documentation & understanding the different configuration options.
We constantly do that & find new things or find better approaches to our problems.
## Misc
### mypy / type annotations
About type annotations & using `mypy`, [this tweet](https://twitter.com/queroumavodka/status/1294789817071542272) resonates a lot with our philosophy.
We have projects where we enforce `mypy` on CI and are very strict with types.
We have projects where types are more loose.
Context is king here.
## Inspiration
The way we do Django is inspired by the following things:
* The general idea for **separation of concerns**
* [Boundaries by Gary Bernhardt](https://www.youtube.com/watch?v=yTkzNHF6rMs)
* Rails service objects
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