Python环境下安装使用异步任务队列包Celery的基础教程
1.简介
celery(芹菜)是一个异步任务队列/基于分布式消息传递的作业队列。它侧重于实时操作，但对调度支持也很好。
celery用于生产系统每天处理数以百万计的任务。
celery是用Python编写的，但该协议可以在任何语言实现。它也可以与其他语言通过webhooks实现。
建议的消息代理RabbitMQ的，但提供有限支持Redis, Beanstalk, MongoDB, CouchDB, ，和数据库（使用SQLAlchemy的或Django的 ORM） 。
celery是易于集成Django, Pylons and Flask，使用 django-celery, celery-pylons and Flask-Celery 附加包即可。
2. 安装
有了上面的概念，需要安装这么几个东西：RabbitMQ、SQLAlchemy、Celery
安装方式也都很简单： RabbitMQ:
mac下：
brew install rabbitmq
linux:
sudo apt-get install rabbitmq-server
剩下两个都是Python的东西了，直接pip安装就好了，对于从来没有安装过MySQL驱动的同学可能需要安装MySQL-python。
安装完成之后，启动服务:
$ rabbitmq-server[回车]
启动后不要关闭窗口, 下面操作新建窗口(Tab)
3. 简单案例
确保你之前的RabbitMQ已经启动。
还是官网的那个例子，在任意目录新建一个tasks.py的文件，内容如下：
from celery import Celery
app = Celery('tasks', broker='amqp://guest@localhost//')
@app.task
def add(x, y):
return x + y
在同级目录执行:
$ celery -A tasks worker --loglevel=info
该命令的意思是启动一个worker，把tasks中的任务（add(x,y)）把任务放到队列中。
保持窗口打开，新开一个窗口进入交互模式，python或者ipython:
>>> from tasks import add >>> add.delay(4, 4)
到此为止，你已经可以使用celery执行任务了，上面的python交互模式下简单的调用了add任务，并传递4，4参数。
但此时有一个问题，你突然想知道这个任务的执行结果和状态，到底完了没有。因此就需要设置backend了。
修改之前的tasks.py中的代码为：
# coding:utf-8
import subprocess
from time import sleep
from celery import Celery
backend = 'db+mysql://root:@192.168.0.102/celery'
broker = 'amqp://guest@192.168.0.102:5672'
app = Celery('tasks', backend=backend, broker=broker)
@app.task
def add(x, y):
sleep(10)
return x + y
@app.task
def hostname():
return subprocess.check_output(['hostname'])
除了添加backend之外，上面还添加了一个who的方法用来测试多服务器操作。修改完成之后，还是按照之前的方式启动。
同样进入python的交互模型:
>>> from tasks import add, hostname >>> r = add.delay(4, 4) >>> r.ready() # 10s内执行，会输出False，因为add中sleep了10s >>> >>> r = hostname.delay() >>> r.result # 输出你的hostname
4. 测试多服务器
做完上面的测试之后，产生了一个疑惑，Celery叫做分布式任务管理，那它的分布式体现在哪？它的任务都是怎么执行的？在哪个机器上执行的？
在当前服务器上的celery服务不关闭的情况下，按照同样的方式在另外一台服务器上安装Celery，并启动:
$ celery -A tasks worker --loglevel=info
发现前一个服务器的Celery服务中输出你刚启动的服务器的hostname，前提是那台服务器连上了你的rabbitmq。
然后再进入python交互模式:
>>> from tasks import hostname >>> >>> for i in range(10): ... r = hostname.delay() ... print r.result # 输出你的hostname >>>
看你输入的内容已经观察两台服务器上你启动celery服务的输出。
5. RabbitMQ远程连接的问题
一开始测试时远程服务器无法连接本地的RabbitMQ服务，后来发现需要设置权限，在/usr/local/etc/rabbitmq/rabbitmq-env.conf这个文件中，修改NODE_IP_ADDRESS=127.0.0.1中的ip为0.0.0.0。
6. 总结的说
这篇文章简单的介绍了Celery的使用，重点还是在分布式的使用。觉得不太爽的地方是，在扩展时，需要重新把代码（tasks.py）部署一遍，而不是可以直接把tasks进行共享，可能Celery是通过task来进行不同的worker的匹配的？目前还不太了解，等深入使用之后再说。
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