celery

1.什么是Clelery Celery是一个简单、灵活且可靠的，处理大量消息的分布式系统 专注于实时处理的异步任务队列 同时也支持任务调度 Celery架构： Celery的架构由三部分组成，消息中间件（message broker），任务执行单元（worker）和任务执行结果存储（task result store）组成。 消息中间件： Celery本身不提供消息服务，但是可以方便的和第三方提供的消息中间件集成。包括，RabbitMQ, Redis等等 任务执行单元： Worker是Celery提供的任务执行的单元，worker并发的运行在分布式的系统节点中。 任务结果存储： Task result store用来存储Worker执行的任务的结果，Celery支持以不同方式存储任务的结果，包括AMQP, redis等 版本支持情况： Celery version 4.0 runs on Python ❨2.7, 3.4, 3.5❩ PyPy ❨5.4, 5.5❩ This is the last version to support Python 2.7, and from the next version (Celery 5.x) Python 3.5 or newer is required. If you’re running an older version of

I am working on a small but computationally-intensive Python app. The computationally-intensive work can be broken into several pieces that can be executed concurrently. I am trying to identify a suitable stack to accomplish this. Currently I am planning to use a Flask app on Apache2+WSGI with Celery for the task queue. In the following, will a_long_process() , another_long_process() and yet_another_long_process() execute concurrently if there are 3 or more workers available? Will the Flask app be blocked while the processes are executing? from the Flask app: @myapp.route('/foo') def bar():

i am working on celery and using rabbitmq server and created a project in django project in a server(where message queue,database exists) and it is working fine, i have created multiple workers also from kombu import Exchange, Queue CELERY_CONCURRENCY = 8 CELERY_ACCEPT_CONTENT = ['pickle', 'json', 'msgpack', 'yaml'] CELERY_RESULT_BACKEND = 'amqp' CELERYD_HIJACK_ROOT_LOGGER = True CELERY_HIJACK_ROOT_LOGGER = True BROKER_URL = 'amqp://guest:guest@localhost:5672//' CELERY_QUEUES = ( Queue('default', Exchange('default'), routing_key='default'), Queue('q1', Exchange('A'), routing_key='routingKey1')

I am using Celery with RabbitMQ. Lately, I have noticed that a large number of temporary queues are getting made. So, I experimented and found that when a task fails (that is a tasks raises an Exception), then a temporary queue with a random name (like c76861943b0a4f3aaa6a99a6db06952c) is formed and the queue remains. Some properties of the temporary queue as found in rabbitmqadmin are as follows - auto_delete : True consumers : 0 durable : False messages : 1 messages_ready : 1 And one such temporary queue is made everytime a task fails (that is, raises an Exception). How to avoid this

示例一：用户发起request，并等待response返回。在这些views中，可能需要执行一段耗时的程序，那么用户就会等待很长时间，造成不好的用户体验。 示例二：网站每小时需要同步一次天气预报信息，但是http是请求触发的，难道要一小时请求一次吗？ 使用celery后，情况就不一样了 示例一的解决：将耗时的程序放到celery中执行 示例二的解决：使用celery定时执行 名词 任务task：就是一个Python函数 队列queue：将需要执行的任务加入到队列中 工人worker：在一个新进程中，负责执行队列中的任务 代理人broker：负责调度，在布置环境中使用redis 使用 安装包 celery == 3.1 .25 celery - with - redis == 3.0 django - celery == 3.1 .17 配置settings INSTALLED_APPS = ( . . . 'djcelery' , } . . . import djcelery djcelery . setup_loader ( ) BROKER_URL = 'redis://127.0.0.1:6379/0' CELERY_IMPORTS = ( '应用名称.task' ) 在应用目录下创建task.py文件 import time from celery import task

环境版本： windows7 x64 django 1.11.6 django-celery 3.2.2 工程结构说明： 待补充 1、新建django项目DjangoCelery 2、在...\DjangoCelery\DjangoCelery\DjangoCelery下新建celery配置文件celeryconfig.py： 待补充 3、在项目路径...\DjangoCelery\下新建app，命名为course 4、在...\DjangoCelery\course下新建tasks.py文件 待补充 来源： https://www.cnblogs.com/apple2016/p/11425307.html