Celery 分佈式任務隊列快速入門

本節內容

Celery介紹和基本使用

在項目中如何使用celery

啓用多個workers

Celery 定時任務

與django結合

通過django配置celery periodic task

 

 

一、Celery介紹和基本使用 

Celery 是一個 基於python開發的分佈式異步消息任務隊列，通過它可以輕鬆的實現任務的異步處理， 如果你的業務場景中需要用到異步任務，就可以考慮使用celery， 舉幾個實例場景中可用的例子:

1. 你想對100臺機器執行一條批量命令，可能會花很長時間 ，但你不想讓你的程序等着結果返回，而是給你返回 一個任務ID,你過一段時間只需要拿着這個任務id就可以拿到任務執行結果， 在任務執行ing進行時，你可以繼續做其它的事情。 
2. 你想做一個定時任務，比如每天檢測一下你們所有客戶的資料，如果發現今天 是客戶的生日，就給他發個短信祝福

 

Celery 在執行任務時需要通過一個消息中間件來接收和發送任務消息，以及存儲任務結果， 一般使用rabbitMQ or Redis,後面會講

1.1 Celery有以下優點：

1. 簡單：一單熟悉了celery的工作流程後，配置和使用還是比較簡單的
2. 高可用：當任務執行失敗或執行過程中發生連接中斷，celery 會自動嘗試重新執行任務
3. 快速：一個單進程的celery每分鐘可處理上百萬個任務
4. 靈活： 幾乎celery的各個組件都可以被擴展及自定製

Celery基本工作流程圖

 

 

1.2 Celery安裝使用

Celery的默認broker是RabbitMQ, 僅需配置一行就可以

1	broker_url = 'amqp://guest:guest@localhost:5672//'

rabbitMQ 沒裝的話請裝一下，安裝看這裏  http://docs.celeryproject.org/en/latest/getting-started/brokers/rabbitmq.html#id3

 

使用Redis做broker也可以

安裝redis組件

1	$ pip install -U "celery[redis]"

配置

Configuration is easy, just configure the location of your Redis database:

app.conf.broker_url = 'redis://localhost:6379/0'

Where the URL is in the format of:

redis://:password@hostname:port/db_number

all fields after the scheme are optional, and will default to localhost on port 6379, using database 0.

 

如果想獲取每個任務的執行結果，還需要配置一下把任務結果存在哪

If you also want to store the state and return values of tasks in Redis, you should configure these settings:

app.conf.result_backend = 'redis://localhost:6379/0'

 

1. 3 開始使用Celery啦　　

安裝celery模塊

1	$ pip install celery

 

創建一個celery application 用來定義你的任務列表

創建一個任務文件就叫tasks.py吧

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from celery import Celery   app = Celery('tasks',              broker='redis://localhost',              backend='redis://localhost')   @app.task def add(x,y):     print("running...",x,y)     return x+y

 

啓動Celery Worker來開始監聽並執行任務

1	$ celery -A tasks worker --loglevel=info

　　

調用任務

再打開一個終端， 進行命令行模式，調用任務　　

1 2	>>> from tasks import add >>> add.delay(4, 4)

看你的worker終端會顯示收到 一個任務，此時你想看任務結果的話，需要在調用 任務時　賦值個變量

1	>>> result = add.delay(4, 4)

The ready() method returns whether the task has finished processing or not:

>>> result.ready()
False

You can wait for the result to complete, but this is rarely used since it turns the asynchronous call into a synchronous one:

>>> result.get(timeout=1)
8

In case the task raised an exception, get() will re-raise the exception, but you can override this by specifying the propagate argument:

>>> result.get(propagate=False)

If the task raised an exception you can also gain access to the original traceback:

>>> result.traceback
…

　　

二、在項目中如何使用celery　

可以把celery配置成一個應用

目錄格式如下

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proj/__init__.py     /celery.py     /tasks.py

proj/celery.py內容

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from __future__ import absolute_import, unicode_literals from celery import Celery   app = Celery('proj',              broker='amqp://',              backend='amqp://',              include=['proj.tasks'])   # Optional configuration, see the application user guide. app.conf.update(     result_expires=3600, )   if __name__ == '__main__':     app.start()

proj/tasks.py中的內容

from __future__ import absolute_import, unicode_literals
from .celery import app


@app.task
def add(x, y):
    return x + y


@app.task
def mul(x, y):
    return x * y


@app.task
def xsum(numbers):
    return sum(numbers)

啓動worker 

1	$ celery -A proj worker -l info

輸出

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-------------- [e-mail] v4.0.2 (latentcall) ---- **** ----- --- * ***  * -- Darwin-15.6.0-x86_64-i386-64bit 2017-01-26 21:50:24 -- * - **** --- - ** ---------- [config] - ** ---------- .> app:         proj:0x103a020f0 - ** ---------- .> transport:   redis://localhost:6379// - ** ---------- .> results:     redis://localhost/ - *** --- * --- .> concurrency: 8 (prefork) -- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker) --- ***** -----  -------------- [queues]                 .> celery           exchange=celery(direct) key=celery

　　

後臺啓動worker

In the background

In production you’ll want to run the worker in the background, this is described in detail in the daemonization tutorial.

The daemonization scripts uses the celery multi command to start one or more workers in the background:

$ celery multi start w1 -A proj -l info
celery multi v4.0.0 (latentcall)
> Starting nodes...
    > w1.halcyon.local: OK

You can restart it too:

$ celery  multi restart w1 -A proj -l info
celery multi v4.0.0 (latentcall)
> Stopping nodes...
    > w1.halcyon.local: TERM -> 64024
> Waiting for 1 node.....
    > w1.halcyon.local: OK
> Restarting node w1.halcyon.local: OK
celery multi v4.0.0 (latentcall)
> Stopping nodes...
    > w1.halcyon.local: TERM -> 64052

or stop it:

$ celery multi stop w1 -A proj -l info

The stop command is asynchronous so it won’t wait for the worker to shutdown. You’ll probably want to use the stopwait command instead, this ensures all currently executing tasks is completed before exiting:

$ celery multi stopwait w1 -A proj -l info

　　

 

三、Celery 定時任務

 celery支持定時任務，設定好任務的執行時間，celery就會定時自動幫你執行， 這個定時任務模塊叫celery beat

 

寫一個腳本 叫periodic_task.py

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from celery import Celery from celery.schedules import crontab   app = Celery()   @app.on_after_configure.connect def setup_periodic_tasks(sender, **kwargs):     # Calls test('hello') every 10 seconds.     sender.add_periodic_task(10.0, test.s('hello'), name='add every 10')       # Calls test('world') every 30 seconds     sender.add_periodic_task(30.0, test.s('world'), expires=10)       # Executes every Monday morning at 7:30 a.m.     sender.add_periodic_task(         crontab(hour=7, minute=30, day_of_week=1),         test.s('Happy Mondays!'),     )   @app.task def test(arg):     print(arg)

add_periodic_task 會添加一條定時任務

 

上面是通過調用函數添加定時任務，也可以像寫配置文件 一樣的形式添加， 下面是每30s執行的任務

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app.conf.beat_schedule = {     'add-every-30-seconds': {         'task': 'tasks.add',         'schedule': 30.0,         'args': (16, 16)     }, } app.conf.timezone = 'UTC'

　　

任務添加好了，需要讓celery單獨啓動一個進程來定時發起這些任務， 注意， 這裏是發起任務，不是執行，這個進程只會不斷的去檢查你的任務計劃， 每發現有任務需要執行了，就發起一個任務調用消息，交給celery worker去執行

啓動任務調度器 celery beat

1	$ celery -A periodic_task beat

輸出like below

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celery beat v4.0.2 (latentcall) is starting. __    -    ... __   -        _ LocalTime -> 2017-02-08 18:39:31 Configuration ->     . broker -> redis://localhost:6379//     . loader -> celery.loaders.app.AppLoader     . scheduler -> celery.beat.PersistentScheduler     . db -> celerybeat-schedule     . logfile -> [stderr]@%WARNING     . maxinterval -> 5.00 minutes (300s)

此時還差一步，就是還需要啓動一個worker,負責執行celery beat發起的任務

啓動celery worker來執行任務

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$ celery -A periodic_task worker     -------------- [e-mail] v4.0.2 (latentcall) ---- **** ----- --- * ***  * -- Darwin-15.6.0-x86_64-i386-64bit 2017-02-08 18:42:08 -- * - **** --- - ** ---------- [config] - ** ---------- .> app:         tasks:0x104d420b8 - ** ---------- .> transport:   redis://localhost:6379// - ** ---------- .> results:     redis://localhost/ - *** --- * --- .> concurrency: 8 (prefork) -- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker) --- ***** -----  -------------- [queues]                 .> celery           exchange=celery(direct) key=celery

　　

好啦，此時觀察worker的輸出，是不是每隔一小會，就會執行一次定時任務呢！

注意：Beat needs to store the last run times of the tasks in a local database file (named celerybeat-schedule by default), so it needs access to write in the current directory, or alternatively you can specify a custom location for this file:

1	$ celery -A periodic_task beat -s /home/celery/var/run/celerybeat-schedule

 

更復雜的定時配置　　

上面的定時任務比較簡單，只是每多少s執行一個任務，但如果你想要每週一三五的早上8點給你發郵件怎麼辦呢？哈，其實也簡單，用crontab功能，跟linux自帶的crontab功能是一樣的，可以個性化定製任務執行時間

linux crontab http://www.cnblogs.com/peida/archive/2013/01/08/2850483.html 

 

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from celery.schedules import crontab   app.conf.beat_schedule = {     # Executes every Monday morning at 7:30 a.m.     'add-every-monday-morning': {         'task': 'tasks.add',         'schedule': crontab(hour=7, minute=30, day_of_week=1),         'args': (16, 16),     }, }

上面的這條意思是每週1的早上7.30執行tasks.add任務

還有更多定時配置方式如下：

Example	Meaning
crontab()	Execute every minute.
crontab(minute=0, hour=0)	Execute daily at midnight.
crontab(minute=0, hour='*/3')	Execute every three hours: midnight, 3am, 6am, 9am, noon, 3pm, 6pm, 9pm.
crontab(minute=0, hour='0,3,6,9,12,15,18,21')	Same as previous.
crontab(minute='*/15')	Execute every 15 minutes.
crontab(day_of_week='sunday')	Execute every minute (!) at Sundays.
crontab(minute='*', hour='*',day_of_week='sun')	Same as previous.
crontab(minute='*/10', hour='3,17,22',day_of_week='thu,fri')	Execute every ten minutes, but only between 3-4 am, 5-6 pm, and 10-11 pm on Thursdays or Fridays.
crontab(minute=0,hour='*/2,*/3')	Execute every even hour, and every hour divisible by three. This means: at every hour except: 1am, 5am, 7am, 11am, 1pm, 5pm, 7pm, 11pm
crontab(minute=0, hour='*/5')	Execute hour divisible by 5. This means that it is triggered at 3pm, not 5pm (since 3pm equals the 24-hour clock value of “15”, which is divisible by 5).
crontab(minute=0, hour='*/3,8-17')	Execute every hour divisible by 3, and every hour during office hours (8am-5pm).
crontab(0, 0,day_of_month='2')	Execute on the second day of every month.
crontab(0, 0, day_of_month='2-30/3')	Execute on every even numbered day.
crontab(0, 0, day_of_month='1-7,15-21')	Execute on the first and third weeks of the month.
crontab(0, 0,day_of_month='11', month_of_year='5')	Execute on the eleventh of May every year.
crontab(0, 0, month_of_year='*/3')	Execute on the first month of every quarter.

 

上面能滿足你絕大多數定時任務需求了，甚至還能根據潮起潮落來配置定時任務， 具體看 http://docs.celeryproject.org/en/latest/userguide/periodic-tasks.html#solar-schedules 　　

　　　

　　

四、最佳實踐之與django結合 

django 可以輕鬆跟celery結合實現異步任務，只需簡單配置即可

If you have a modern Django project layout like:

- proj/
  - proj/__init__.py
  - proj/settings.py
  - proj/urls.py
- manage.py

then the recommended way is to create a new proj/proj/celery.py module that defines the Celery instance:

file: proj/proj/celery.py　　

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from __future__ import absolute_import, unicode_literals import os from celery import Celery   # set the default Django settings module for the 'celery' program. os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'proj.settings')   app = Celery('proj')   # Using a string here means the worker don't have to serialize # the configuration object to child processes. # - namespace='CELERY' means all celery-related configuration keys #   should have a `CELERY_` prefix. app.config_from_object('django.conf:settings', namespace='CELERY')   # Load task modules from all registered Django app configs. app.autodiscover_tasks()     @app.task(bind=True) def debug_task(self):     print('Request: {0!r}'.format(self.request))

Then you need to import this app in your proj/proj/__init__.py module. This ensures that the app is loaded when Django starts so that the @shared_task decorator (mentioned later) will use it:　　

proj/proj/__init__.py:

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from __future__ import absolute_import, unicode_literals   # This will make sure the app is always imported when # Django starts so that shared_task will use this app. from .celery import app as celery_app   __all__ = ['celery_app']

Note that this example project layout is suitable for larger projects, for simple projects you may use a single contained module that defines both the app and tasks, like in the First Steps with Celery tutorial.　　

Let’s break down what happens in the first module, first we import absolute imports from the future, so that our celery.py module won’t clash with the library:

1	from __future__ import absolute_import

Then we set the default DJANGO_SETTINGS_MODULE environment variable for the celery command-line program:

1	os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'proj.settings')

You don’t need this line, but it saves you from always passing in the settings module to the celery program. It must always come before creating the app instances, as is what we do next:

1	app = Celery('proj')

This is our instance of the library.

 

We also add the Django settings module as a configuration source for Celery. This means that you don’t have to use multiple configuration files, and instead configure Celery directly from the Django settings; but you can also separate them if wanted.

The uppercase name-space means that all Celery configuration options must be specified in uppercase instead of lowercase, and start with CELERY_, so for example the task_always_eager` setting becomes CELERY_TASK_ALWAYS_EAGER, and the broker_url setting becomes CELERY_BROKER_URL.

You can pass the object directly here, but using a string is better since then the worker doesn’t have to serialize the object.

1	app.config_from_object('django.conf:settings', namespace='CELERY')

Next, a common practice for reusable apps is to define all tasks in a separate tasks.pymodule, and Celery does have a way to  auto-discover these modules:

1	app.autodiscover_tasks()

With the line above Celery will automatically discover tasks from all of your installed apps, following the tasks.py convention:

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- app1/     - tasks.py     - models.py - app2/     - tasks.py     - models.py

Finally, the debug_task example is a task that dumps its own request information. This is using the new bind=True task option introduced in Celery 3.1 to easily refer to the current task instance.

然後在具體的app裏的tasks.py裏寫你的任務

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# Create your tasks here from __future__ import absolute_import, unicode_literals from celery import shared_task     @shared_task def add(x, y):     return x + y     @shared_task def mul(x, y):     return x * y     @shared_task def xsum(numbers):     return sum(numbers)

 

 

在你的django views裏調用celery task

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from django.shortcuts import render,HttpResponse   # Create your views here.   from  bernard import tasks   def task_test(request):       res = tasks.add.delay(228,24)     print("start running task")     print("async task res",res.get() )       return HttpResponse('res %s'%res.get())

　　

　　

　　

五、在django中使用計劃任務功能　　

There’s  the django-celery-beat extension that stores the schedule in the Django database, and presents a convenient admin interface to manage periodic tasks at runtime.

To install and use this extension:

1. Use pip to install the package:

   $ pip install django-celery-beat
   

2. Add the django_celery_beat module to INSTALLED_APPS in your Django project’ settings.py:

       INSTALLED_APPS = (
           ...,
           'django_celery_beat',
       )
   
   Note that there is no dash in the module name, only underscores.
   

3. Apply Django database migrations so that the necessary tables are created:

   $ python manage.py migrate
   

4. Start the celery beat service using the django scheduler:

   $ celery -A proj beat -l info -S django
   

5. Visit the Django-Admin interface to set up some periodic tasks.

　　

在admin頁面裏，有3張表

配置完長這樣

 

 

此時啓動你的celery beat 和worker，會發現每隔2分鐘，beat會發起一個任務消息讓worker執行scp_task任務

 

注意，經測試，每添加或修改一個任務，celery beat都需要重啓一次，要不然新的配置不會被celery beat進程讀到