introductory
Have you ever encountered a situation where you have some time-consuming operations in your application, such as sending emails, processing images, communicating with external APIs, and so on, which make users wait for a long time? And when the number of users increases, the load on the server increases dramatically.
To solve this problem, you can use Celery, a powerful Python library. It can help you put these time-consuming operations into the background for processing so that users don't have to wait, and it can also improve server throughput and responsiveness.
What is Celery?
Celery is a Python library for building asynchronous task/job queues. It uses messaging middleware as a transport and supports RabbitMQ, Redis, Amazon SQS, etc. Celery is designed to be simple, flexible, and extensible, and can be used to handle almost any type of task.
mounting
To use Celery, you first need to install it into your project. You can use the pip command to install it:
pip install celery
Creating a Celery Application
Before using Celery, we need to create a Celery application. A Celery application is represented by a Celery instance, and we can configure some parameters in the application, such as message middleware, concurrency number, etc. Here is a sample code to create a Celery application:
from celery import Celery
# Create a Celery instance
app = Celery('myapp', broker='redis://localhost:6379/0')
# Register the task module with the Celery application
app.autodiscover_tasks()In the above sample code, we have created a Celery application named 'myapp' and specified Redis as the message middleware. You can choose other message middleware as per your requirement.
Defining tasks
In Celery, tasks are blocks of code that perform specific actions. You can define as many tasks as you want and then give them to the Celery application to manage. Here is an example of a defined task:
from myapp import app
@
def add(x, y):
return x + y
In the sample code above, we define a file namedadd task for calculating the sum of two numbers.
Start the Celery worker
Before using Celery, we need to start a Celery worker to handle the task. The Celery worker can be started with the following command:
celery -A myapp worker --loglevel=info
In the above command, the-A parameter specifies our Celery application.--loglevel=info parameter specifies the logging level as info.
invoke a task
After the task is defined, we can invoke the task through the Celery application. Below is an example of calling a task:
from import add result = (2, 3) print(())
In the sample code above, we call the taskadd, and passed in two parameters.delay method is used to place a task into a queue to wait for execution.get method is used to get the result of the task.
Extended Description
Celery provides a rich set of features and extensions that can be used to handle a variety of task types. Below is a description of some of the commonly used extensions:
timed task: Celery supports timed tasks, you can specify the execution time of the task through the configuration, such as executing the task at 3:00 am every day.
Results storage: Celery supports storing the result of task execution to different backends, such as database, Redis, etc. You can specify the result storage by configuration. You can specify the backend for storing the results through the configuration.
Task Status Monitoring: Celery provides the ability to monitor the status of a task. You can monitor the execution of a task in real time, such as whether the task has been completed or not, whether there is an error or not, and so on.
summarize
In this tutorial, we introduced the Celery library and how to build asynchronous task queues using Celery. We learned how to create a Celery application, define tasks, start a Celery worker, and invoke tasks.
By using Celery, we can easily put time-consuming operations into the background, improving application performance and user experience.
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