celery

I'm updating some LEDs using python. I've been doing this like so: from LEDs import * myLEDs = LEDs() done = False while not done: myLEDs.iterate() I wanted to use Flask to act as a bridge between some nice-looking ReactJS front-end I can run in my browser (to change the current pattern, etc) and the LED-controlling code in Python. I have Flask working fine, can handle HTTP requests, etc. I'm wondering how I can set myLEDs.iterate() to continuously run (or run on a rapid schedule) concurrently with my flask app, while still being able to communicate with one another, like so: myLEDs = LEDs()

Celery介绍和基本使用 Celery是一个基于python开发的分布式异步消息任务队列，通过它可以轻松的实现任务的异步处理，如果你的业务场景中需要用到异步任务，就可以考虑使用celery,举几个实例场景中可用的例子: 你想对100台机器执行一条批量命令，可能会花很长时间，但你不想让你的程序等着结果返回，而是给你返回一个任务ID，你过段时间只需要拿着这个任务ID就可以拿到任务执行结果，在任务执行ing进行时，你可以继续做其他的事情。 2.你想做一个定时任务，比如每天检测一下你们所有客户到资料，如果发现今天是客户生日，就给它发个短信祝福. Celery在执行任务时需要通过一个消息中间件来接收和发送消息，以及存储任务结果，一般会使用rabbitMQ or Redis, Celery有以下优点: 简单：一单熟悉了celery的工作流程后，配置和使用还是比较简单的。 高可用：当任务执行失败或者执行过程中发生连接中断，celery会自动尝试重新执行任务 快速：一个单进程的celery每分钟可处理上百万个任务 灵活：几乎celery的各个组件都可以被扩展及自定制 Celery基本工作流程图 https://www.cnblogs.com/alex3714/articles/6351797.html 来源： https://www.cnblogs.com/venvive/p/11802483

I have setup celery + rabbitmq for on a 3 cluster machine. I have also created a task which generates a regular expression based on data from the file and uses the information to parse text. from celery import Celery celery = Celery('tasks', broker='amqp://localhost//') import re @celery.task def add(x, y): return x + y def get_regular_expression(): with open("text") as fp: data = fp.readlines() str_re = "|".join([x.split()[2] for x in data ]) return str_re @celery.task def analyse_json(tw): str_re = get_regular_expression() re.match(str_re,tw.text) I can make the call to this task very easily

I have celery beat and celery (four workers) to do some processing steps in bulk. One of those tasks is roughly along the lines of, "for each X that hasn't had a Y created, create a Y." The task is run periodically at a semi-rapid rate (10sec). The task completes very quickly. There are other tasks going on as well. I've run into the issue multiple times in which the beat tasks apparently become backlogged, and so the same task (from different beat times) are executed simultaneously, causing incorrectly duplicated work. It also appears that the tasks are executed out-of-order. Is it possible

I'm trying to shutdown the main celery process by raisin SystemExit() in the task_postrun signal. The signal gets fired just fine, and the exception gets raised, but the worker never completely exits and just hangs there. HOW DO I MAKE THIS WORK? Am I forgetting some setting somewhere? Below is the code that I'm using for the worker (worker.py): from celery import Celery from celery import signals app = Celery('tasks', set_as_current = True, broker='amqp://guest@localhost//', backend="mongodb://localhost//", ) app.config_from_object({ "CELERYD_MAX_TASKS_PER_CHILD": 1, "CELERYD_POOL": "solo",

I recently upgraded to Celery 3.0.1 from 2.3.0 and all the tasks run fine. Unfortunately. I'm getting a "Framing Error" exception pretty frequently. I'm also running supervisor to restart the threads but since these are never really killed supervisor has no way of knowing that celery needs to be restarted. Has anyone seen this before? 2012-07-13 18:53:59,004: ERROR/MainProcess] Unrecoverable error: Exception('Framing Error, received 0x00 while expecting 0xce',) Traceback (most recent call last): File "/usr/local/lib/python2.7/dist-packages/celery/worker/__init__.py", line 350, in start