Creating background tasks in Django generally involves setting up a Python task queue and worker system to handle running tasks outside the scope of a HTTP request. One of the popular ways to achieve this is by using a tool called Celery, along with a message broker like RabbitMQ or Redis.
Firstly, install the necessary packages in your virtual environment:
```
pip install celery redis
```
Then, in your Django project directory, make a new file named `celery.py` and add the following:
```
import os
from celery import Celery
os.environ.setdefault(‘DJANGO_SETTINGS_MODULE’, ‘projectname.settings’)
app = Celery(‘projectname’)
app.config_from_object(‘django.conf:settings’, namespace=‘CELERY’)
app.autodiscover_tasks()
```
In this code, `projectname` should be replaced with the actual name of your Django project. This code also assumes that the settings of the project are located in `projectname/settings.py`.
Next, you’ll need to update your Django settings file with the configuration for the broker you’re using (Redis in this example):
```
CELERY_BROKER_URL = ‘redis://localhost:6379‘
CELERY_RESULT_BACKEND = ‘redis://localhost:6379‘
```
These values indicate that Redis is running on your localhost on port 6379.
Then, add a `__init__.py` file in the same directory as your settings file, if it does not already exist. Inside this file, import your Celery app:
```
from .celery import app as celery_app
all = (‘celery_app’,)
```
At this point, the basic setup for Celery is complete. Now you can start creating tasks. In any of your Django apps, create a new file called `tasks.py`. Within this file, you define your long-running functions:
```
from projectname import app as celery_app
celery_app.task
def long_running_task():
# Your long running task code goes here.
```
You can simply add `celery_app.task` before the function that you want to run in the background.
Lastly, to run the tasks, you need to start the Celery worker server from your command line in your project directory:
```
celery -A projectname worker —loglevel=info
```
You can execute your tasks using the function `long_running_task.delay()`
Please note that Celery requires a message broker to handle requests. In this example, Redis was chosen, but other options, such as RabbitMQ, are also viable.
Additional settings and more complex configurations can also be implemented, depending on your requirements.
This response is based on validated information sources for developing Django applications\1\23.
References:
