Celery Python
Celery Python:
Celery is a powerful distributed task queue library for Python that allows you to run asynchronous tasks in a distributed manner. It is commonly used for handling tasks that are time-consuming, computationally intensive, or need to be executed in the background.
Here’s a brief overview of how Celery works:
Installation: You can install Celery using pip by running
pip install celery
. Make sure you have a compatible version of Python installed.Task Creation: In Celery, you define tasks as Python functions. These tasks can be executed asynchronously in the background. You can create a task by using the
@celery.task
decorator.pythonfrom celery import Celery app = Celery('myapp', broker='amqp://guest@localhost//') @app.task def add(x, y): return x + y
Task Invocation: To execute a task, you need to use the
delay()
orapply_async()
method on the task object. Thedelay()
method schedules the task for execution, whileapply_async()
provides more advanced options.pythonresult = add.delay(4, 6)
Task Execution: Celery requires a message broker, such as RabbitMQ or Redis, to pass messages between the application and the workers. The broker holds the tasks until a worker is available to execute them.
Worker Setup: You need to start Celery worker processes to process the tasks. Workers are responsible for executing the tasks and reporting the results back to the broker.
shellcelery -A myapp worker --loglevel=info
Monitoring: Celery provides tools to monitor and manage the tasks and workers. You can use utilities like Flower or Celery’s built-in command-line tools to inspect the status of tasks, track worker activity, and monitor the overall health of your distributed system.
This is just a basic overview of Celery’s functionality. Celery supports various advanced features such as task result storage, task retries, task scheduling, and more. You can refer to the official Celery documentation for detailed information on these features and how to configure and use them in your projects.
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