How to use Celery Redis Django to improve the asynchronous task processing efficiency of the website

How to use Celery Redis Django to improve the asynchronous task processing efficiency of the website

With the rapid development of the Internet, the complexity of website functions is also increasing. In order to provide a better user experience, we often need to handle various time-consuming tasks, such as sending emails, generating reports, crawler data processing, etc. In the traditional synchronous processing method, all tasks will block the main thread, causing users to wait for too long or even the website crashes. In order to solve this problem, we can use Celery Redis Django combination to implement asynchronous task processing to improve the efficiency and performance of the website.

Celery is a distributed task queue based on Python, which implements asynchronous execution of tasks through message middleware (such as Redis). Django is a powerful Python web framework that can be easily integrated with Celery. Below, we will introduce how to use Celery Redis Django to implement asynchronous task processing.

Step One: Install and Configure Celery and Redis

First, we need to install Celery and Redis. You can use the pip command to install Celery and Redis-py:

pip install celery
pip install redis

Next, we need to configure the connection information of Celery and Redis in the configuration file settings.py of the Django project:

# 配置Celery Broker和Backend
CELERY_BROKER_URL = 'redis://localhost:6379/0'
CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'

# 配置Celery Worker数量
CELERYD_CONCURRENCY = 4

Chapter Step 2: Create a Celery task

In an App of the Django project, create the tasks.py file and write the code for the asynchronous task. For example, we create a task to send emails:

from celery import shared_task
from django.core.mail import send_mail

@shared_task
def send_email_task(subject, message, from_email, recipient_list):
    send_mail(subject, message, from_email, recipient_list)

In this example, we use the @shared_task decorator to convert the function into a Celery task. Note that this task is independent of any Django request and can be called elsewhere.

Step 3: Start Celery Worker

In the root directory of the project, create a celery.py file and configure the Celery application:

from __future__ import absolute_import
import os
from celery import Celery

# 设置Django默认的配置模块
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'your_project.settings')

app = Celery('your_project')

# 从Django项目的配置文件中加载Celery配置
app.config_from_object('django.conf:settings')

# 自动加载tasks.py中的任务
app.autodiscover_tasks()

After completing the above configuration, We can run Celery Worker through the following command:

celery -A your_project worker --loglevel=info

Step 4: Call asynchronous tasks in Django views

In Django's view functions or classes, you can call asynchronous in the following ways Task:

from your_app.tasks import send_email_task

def send_email_view(request):
    subject = 'Hello'
    message = 'This is a test email'
    from_email = 'noreply@example.com'
    recipient_list = ['user1@example.com', 'user2@example.com']

    # 异步调用发送邮件的任务
    send_email_task.delay(subject, message, from_email, recipient_list)

    return HttpResponse('Email sent successfully!')

In this example, we use the delay() method to asynchronously call the task of sending emails. Note that the delay() method is non-blocking, it returns immediately and performs tasks asynchronously in the background.

Through the above steps, we successfully implemented asynchronous task processing using Celery Redis Django. Celery will put tasks into the message queue, and Celery Worker will process these tasks asynchronously, improving the processing efficiency and performance of the website.

Summary:

Using Celery Redis Django can effectively improve the asynchronous task processing efficiency of the website. By executing time-consuming tasks asynchronously, we can avoid blocking the main thread and speed up the response speed of the website. When configuring and writing tasks, you need to pay attention to Celery's related configuration and calling methods. At the same time, in order to improve performance, the number of concurrent Celery Workers can be appropriately adjusted.

For code examples, please refer to the following official documents:

• Celery official documentation: http://docs.celeryproject.org/en/latest/
• Django official documentation : https://docs.djangoproject.com/
• Redis-py official documentation: https://redis-py.readthedocs.io/

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