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In this issue, Xiaobian will bring you about how to use django-celery to complete asynchronous tasks in Django. The article is rich in content and analyzed and described from a professional perspective. After reading this article, I hope you can gain something.

How to use django-celery for asynchronous tasks in Django

Install Celery

We can use pip to install in vietualenv:

pip install django-celery celery

django settings

import djcelery

djcelery.setup_loader()

BROKER_URL = 'redis://127.0.0.1:6379/2' ---Use redis when message queuing

registered

INSTALLED_APPS = [

'django.contrib.admin',

'django.contrib.auth',

'django.contrib.contenttypes',

'django.contrib.sessions',

'django.contrib.messages',

'django.contrib.staticfiles',

'devops',

'apps',

'common',

'djcelery',

]

Create a task.py file under apps

from celery import task

@task

def add(x, y):

return x + y

@task def pp(): return 'ffffffffffffffffffffff'

Go back to settings and import them.

import djcelery

djcelery.setup_loader()

BROKER_URL = 'redis://127.0.0.1:6379/2'

CELERY_IMPORTS = ('apps.task')

Choose a method call in views and change it to task test.

#Project List

def project_list(request):

admin = Admin.objects.get(id=get_current_admin_id(request))

pt=admin.projects.all().order_by('-id')

from apps import task

tt=task.add.delay(2,2)

print 'vv',tt ----Call the add method of task when accessing this feature

page_objects = pages(pt, request, 5) ##pagination

return render_to_response('project/project_list.html',locals())

Install redis: slightly

Start runserver and celery

python manage.py runserver

python manage.py celery worker --loglevel=info

Visit the corresponding page to see the log

[2017-11-10 14:55:17,016: INFO/MainProcess] Task apps.task.add[9bafe6d2-8411-4f5f-8eed-10444da0ae3a] succeeded in 0.00280212797225s: 4 --You can see worker logs, return results

Manual test task

Open a new terminal, activate virtualenv, and switch to the django project directory:

$ python manage.py shell

>>> from apps.task import add

>>> add.delay(2, 2)

At this point, you can see the worker executing the task in the worker window:

[2014-10-07 08:47:08,076: INFO/MainProcess] Got task from broker: myapp.tasks.add[e080e047-b2a2-43a7-af74-d7d9d98b02fc]

[2014-10-07 08:47:08,299: INFO/MainProcess] Task myapp.tasks.add[e080e047-b2a2-43a7-af74-d7d9d98b02fc] succeeded in 0.183349132538s: 4

Eager model

Eager model

If you set in settings.py:

CELERY_ALWAYS_EAGER = True

Then Celery runs in eager mode, and task does not need to run with delay:

#If eager mode is enabled, the following two lines of code are the same

add.delay(2, 2)

add(2, 2)

dj-celery timed task

settings Configuration Add

If you need to pass on references, you can write like this

CELERYBEAT_SCHEDULE = {

'add-every-3-minutes': {

'task': 'apps.task.add',

'schedule': timedelta(seconds=3),

'args': (16, 16)

},

}

CELERYBEAT_SCHEDULER = 'djcelery.schedulers.DatabaseScheduler' #scheduled tasks

CELERYBEAT_SCHEDULE = {

'add-every-3-seconds': {

'task': 'apps.task.pp',

'schedule': timedelta(seconds=3) ---executes pp function under task every 3 seconds

},

}

Start Celery Beat

In fact, there is a simple way to start the worker and beat together:

python manage.py celery worker --loglevel=info --beat

Start Beat

Celery performs timed tasks through the celerybeat process. Celerybeat keeps running and adds a timed task to the queue when it needs to be performed. Unlike worker processes, Celerybeat requires only one.

Start:

python manage.py celery beat --loglevel=info

View worker logs

[2017-11-10 16:17:25,853: INFO/MainProcess] Received task: apps.task.pp[8a3af6fb-5189-4647-91f2-8aa07489dd1e]

[2017-11-10 16:17:25,858: INFO/MainProcess] Task apps.task.pp[8a3af6fb-5189-4647-91f2-8aa07489dd1e] succeeded in 0.00379144400358s: 'ffffffffffffffffffffff'

[2017-11-10 16:17:28,858: INFO/MainProcess] Received task: apps.task.pp[d87e4ea0-8881-449a-b993-e7657f50ef25]

[2017-11-10 16:17:28,864: INFO/MainProcess] Task apps.task.pp[d87e4ea0-8881-449a-b993-e7657f50ef25] succeeded in 0.0049942266196s: 'ffffffffffffffffffffff'

[2017-11-10 16:17:31,859: INFO/MainProcess] Received task: apps.task.pp[4d05b4f3-92ff-4922-a8f4-7e047749239a]

[2017-11-10 16:17:31,865: INFO/MainProcess] Task apps.task.pp[4d05b4f3-92ff-4922-a8f4-7e047749239a] succeeded in 0.00537821277976s: 'ffffffffffffffffffffff'

[2017-11-10 16:17:34,859: INFO/MainProcess] Received task: apps.task.pp[5b21afc1-ebf1-4858-be68-20b9bf318452]

[2017-11-10 16:17:34,865: INFO/MainProcess] Task apps.task.pp[5b21afc1-ebf1-4858-be68-20b9bf318452] succeeded in 0.00530335493386s: 'ffffffffffffffffffffff'

There is another way to supplement that is celery. The above is how to use django-celery to complete asynchronous tasks in Django shared by everyone. If you happen to have similar doubts, you may wish to refer to the above analysis for understanding. If you want to know more about it, please pay attention to the industry information channel.

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