How to use Celery to implement distributed task scheduling
How to use Celery to implement distributed task scheduling
Overview:
Celery is one of the most commonly used distributed task queue libraries in Python, which can be used to implement asynchronous task scheduling. This article will introduce how to use Celery to implement distributed task scheduling, and attach code examples.
- Installation and Configuration of Celery
First, we need to install the Celery library. Celery can be installed through the following command:
pip install celery
After the installation is complete, we need to create a Celery configuration file. Create a file called celeryconfig.py
and add the following content:
broker_url = 'amqp://guest@localhost//' # RabbitMQ服务器地址 result_backend = 'db+sqlite:///results.sqlite' # 结果存储方式(使用SQLite数据库) task_serializer = 'json' # 任务序列化方式 result_serializer = 'json' # 结果序列化方式 accept_content = ['json'] # 接受的内容类型 timezone = 'Asia/Shanghai' # 时区设置
- Create Celery App
In the code we need to import Celery library and create a Celery application. Here is an example:
from celery import Celery app = Celery('mytasks', include=['mytasks.tasks']) app.config_from_object('celeryconfig')
In the above code, we create a Celery application named mytasks
and apply the configuration in celeryconfig.py
into the Celery application.
- Create a task
Next, we need to create a task. A task is an independent function that can perform individual operations. Here is an example:
# tasks.py from mytasks import app @app.task def add(x, y): return x + y
In the above code, we have defined a task named add
to calculate the sum of two numbers.
- Start Celery Worker
To enable distributed execution of tasks, we need to start one or more Celery Workers to process tasks. Celery Worker can be started through the following command:
celery -A mytasks worker --loglevel=info
After the startup is completed, Celery Worker will listen and process tasks in the queue.
- Submitting tasks
In other code, we can submit tasks to the Celery queue. Here is an example:
# main.py from mytasks.tasks import add result = add.delay(4, 6) print(result.get())
In the above code, we import the add
task defined previously and then submit a task using the delay
method. The delay
method will return an AsyncResult
object, and we can get the result of the task by calling the get
method.
- Monitoring task completion status
We can use the AsyncResult
object to monitor the execution status of the task. The following is an example:
# main.py from mytasks.tasks import add result = add.delay(4, 6) while not result.ready(): print("Task is still running...") time.sleep(1) print(result.get())
In the above code, we monitor the execution status of the task through a loop. ready
The method will return a Boolean value indicating whether the task has been completed.
Summary:
This article briefly introduces how to use Celery to implement distributed task scheduling. By installing and configuring Celery, creating a Celery application, defining tasks, starting Celery Workers, and submitting tasks to the queue, we can implement distributed task scheduling. Using Celery can improve task execution efficiency and is suitable for situations where parallel computing or asynchronous processing is required.
The above is the detailed content of How to use Celery to implement distributed task scheduling. For more information, please follow other related articles on the PHP Chinese website!

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