Django QSerializer started as an internal Buser project to improve our serialization process.
Database queries and serialization are two separated steps, but really ORM coupled. Relationships must be fetched from database before serialization, but Django don't have an easy way to setup that.
Start with a custom manager SerializableManager
.
from django.db import models
from django_qserializer import SerializableManager
class Company(models.Model):
name = models.CharField(max_length=64)
class Bus(models.Model):
objects = SerializableManager()
company = models.ForeignKey(Company, on_delete=models.SET_NULL)
A basic serializer implementation would be:
from django_qserializer import BaseSerializer
class BusSerializer(BaseSerializer):
select_related = ['company']
def serialize_object(self, obj):
return {
'id': obj.id,
'company': {
'name': obj.company.name,
}
}
Add the serializer to your queryset as:
buses = Bus.objects.to_serialize(BusSerializer).all()
for bus in buses:
# The serialize method is bound to BusSerializer.serialize_object.
print(bus.serialize())
List of model fields to add to queryset with a select_related
call.
List of model fields to add to queryset with a prefetch_related
call.
class BusSerializer(BaseSerializer):
prefetch_related = ['company']
def serialize_object(self, obj):
return {
'id': obj.id,
'company': {
'name': obj.company.name,
}
}
Callable to change the queryset. It is possible to implement select_related
and prefetch_related
attributes with it, but they work together with
prepare_queryset
.
class BusSerializer(BaseSerializer):
select_related = ['company']
def prepare_queryset(self, qs):
return qs.annotate(state=Value('broken'))
Prepare objects after they are loaded to memory. Add data in bulk to them, like fetching information from cache and attaching to loaded objects.
Required implementation. It converts the Django model to a serializable dict. Avoid slow calls here because it will cause N+1 issues.
Execute serialize_object
for each model object.
To run the project, it is necessary the following tools:
- Python 3.9 or higher
To create a virtual environment, run
python3 -m venv .venv
To activate the virtual environment, run
source .venv/bin/activate
To install the project's requirements in the virtual environment, run
pip install -e .\[test\]
To deactivate the virtual environment, run deactivate
.
Run the commands of the following sections with the virtual environment active.
The quality metrics of the project are reproduced by the continuos integration (CI) pipeline of the project. CI configuration in .github/workflows/ci.yml
file.
To run tests, coverage report and linter, run
pytest
To see the html report, check htmlcov/index.html
.
Tests and coverage configuration in setup.cfg
file, at [tool:pytest]
section.
Linter configuration in setup.cfg
file, at [flake8]
section.
This repository is licensed under the terms of MIT License.