Celery and Redis are powerful tools that can be used to run background tasks in a Django project. Background tasks are tasks that don’t need to be done immediately, but can be run later. Celery and Redis make it easy to run these tasks in the background, so that they don’t block the main application and slow it down.
To use Celery and Redis with Django, you’ll first need to install them. You can do this using the pip package manager by running the following command:
pip install celery redis
Download the Redis ZIP archive from the official Redis website (https://github.com/microsoftarchive/redis/releases)
Redis is a database that is used to store information about the tasks that Celery will run. To set it up, you’ll need to start a Redis server and make sure it’s running. You can start the Redis server by running the following command:
redis-server
To make Celery and Redis work with Django, you’ll need to add some code to your Django project. You’ll need to create a Celery instance, configure it to use Redis as its backend, and then define the tasks that Celery will run.
Here’s an example of how you might set up Celery and Redis in your Django project:
from celery import Celery
app = Celery('your_django_project', backend='redis://localhost:6379/0', broker='redis://localhost:6379/0')
@app.task
def send_email(email, message):
# Your code to send an email
pass
A task is a piece of code that you want Celery to run in the background. You can define tasks in Django by creating a Python function and using the @task decorator from the Celery instance you created. For example, you might define a task to send an email, process an image, or generate a report.
Here’s an example of how you might define a task in your Django project:
@app.task
def send_email(email, message):
# Your code to send an email
pass
To run Celery, you’ll need to start a Celery worker. A Celery worker is a process that runs in the background and listens for tasks from the Redis database. You can start a Celery worker by running the following command:
celery -A your_django_project worker -l info
To trigger a task, you’ll need to call it from your Django code. When you call a task, Celery will add it to the Redis database, and the Celery worker will pick it up and run it.
Here’s an example of how you might trigger a task in your Django code:
send_email.delay('example@example.com', 'Hello!')
After a task has been run, you can check the results by accessing the Redis database. The results will be stored in the database and can be retrieved by your Django code.
Here’s an example of how you might check the results of a task in your Django code:
result = send_email.AsyncResult('task_id')
if result.ready():
print(result.result)
In this example, you are using the AsyncResult method to retrieve the result of the task with the ID ‘task_id’. You can use the ready() method to check if the task has completed and the results are available. If the task has completed, you can use the result attribute to retrieve the results.
You can also check the status of a task to see if it’s running, has completed, or if an error occurred. For example:
result = send_email.AsyncResult('task_id')
if result.status == 'SUCCESS':
print(result.result)
elif result.status == 'FAILURE':
print(result.traceback)
In this example, you are using the status attribute to check the status of the task. If the status is ‘SUCCESS’, the task has completed and you can retrieve the results using the result attribute. If the status is ‘FAILURE’, the task has encountered an error and you can retrieve the traceback using the traceback attribute.
When a task is triggered, Celery adds it to the Redis database. The Celery worker, which is constantly running in the background, will pick up the task from the database and run it.
The worker communicates with the Redis database to check for new tasks, and to report the results of the tasks it has run.
When the worker runs a task, it performs the work defined in the task’s code. The worker can run multiple tasks at the same time, which is useful for improving performance and taking advantage of multiple processors or cores. The worker can also run tasks in parallel, which is useful for tasks that take a long time to complete.
When the worker finishes running a task, it stores the results in the Redis database. The results can then be retrieved by your Django code. The worker continues to run in the background, listening for new tasks from the Redis database and running them when they become available.
Celery and Redis work together to provide a powerful system for running background tasks in a Django project.
The combination of Celery and Redis provides a simple and efficient way to run tasks in the background, which is useful for improving performance, reducing latency, and providing a better user experience for your users.