Django fixtures are a way of loading data into the database from files. A fixture is a collection of data that Django knows how to import into a database. The primary characteristic of fixtures is that they provide a stable baseline upon which tests can reliably be run.
They are extremely useful in testing scenarios where you need your database to be populated with particular data sets before tests are run. They also assist in migrating data from one database setup to another. You might refer to a fixture as a database dump.
Essentially, fixtures are described as a serialized data dump that loads data into your application interactively. Django supports various formats for fixtures: XML, YAML (Yet Another Markup Language), and JSON (JavaScript Object Notation), with the latter being the most commonly used.
For example, suppose you have a Django model ‘Car’ with some attributes (model, color, and manufacture date). A fixture file in a JSON format for this model would look something like this:
\`\`\`json
[
{
“model”: “Car”,
“pk”: 1,
“fields”: {
“model”: “Tesla Model 3”,
“color”: “red”,
“manufacture\_date”: “2018-07-30“
}
},
{
“model”: “Car”,
“pk”: 2,
“fields”: {
“model”: “BMW i3”,
“color”: “white”,
“manufacture\_date”: “2015-02-20“
}
}
]
\`\`\`
To use this fixture, you can use Django’s built-in `loaddata` command: `python manage.py loaddata fixture.json`. Django will then load the data into the respective table in the database.
Creating fixtures manually could be time-consuming especially when dealing with big data. Hence, Django provides a built-in `dumpdata` command which allows you to create fixture data from the existing data in your database.
For example, to create a fixture file from the ‘Car’ model, you would use the command: `python manage.py dumpdata myapp.Car —indent 4 > car_fixture.json`.
The concept of Django fixtures is fully outlined in the Django documentation and further practical examples can be found in Django for APIs 3.0 by William S. Vincent.
It’s important to mention, however, that while fixtures are useful, they also come with their own drawbacks. As reported by Bennett, Django fixtures can notably become problematic when dealing with large data sets since they consume lots of memory, they also become hard to manage when they become outdated, and they can make tests slower.
Sources:
1. Django Documentation: Fixtures – https://docs.djangoproject.com/en/3.2/howto/initial-data/
2. Django for APIs 3.0 by William S. Vincent – https://djangoforapis.com/
3. Bennett, Daniel Roy Greenfeld Audrey Roy. Two Scoops of Django: Best Practices for Django 1.8. Two Scoops Press, 2015.
