I'm working on a project using django and celery(django-celery). Our team decided to wrap all data access code within (app-name)/manager.py
(NOT wrap into Managers like the django
way), and let code in (app-name)/task.py only dealing with assemble and perform tasks with celery(so we don't have django ORM dependency in this layer).
In my manager.py
, I have something like this:
def get_tag(tag_name):
ctype = ContentType.objects.get_for_model(Photo)
try:
tag = Tag.objects.get(name=tag_name)
except ObjectDoesNotExist:
return Tag.objects.none()
return tag
def get_tagged_photos(tag):
ctype = ContentType.objects.get_for_model(Photo)
return TaggedItem.objects.filter(content_type__pk=ctype.pk, tag__pk=tag.pk)
def get_tagged_photos_count(tag):
return get_tagged_photos(tag).count()
In my task.py, I like to wrap them into tasks (then maybe use these tasks to do more complicated tasks), so I write this decorator:
import manager #the module within same app containing data access functions
class mfunc_to_task(object):
def __init__(mfunc_type='get'):
self.mfunc_type = mfunc_type
def __call__(self, f):
def wrapper_f(*args, **kwargs):
callback = kwargs.pop('callback', None)
mfunc = getattr(manager, f.__name__)
result = mfunc(*args, **kwargs)
if callback:
if self.mfunc_type == 'get':
subtask(callback).delay(result)
elif self.mfunc_type == 'get_or_create':
subtask(callback).delay(result[0])
else:
subtask(callback).delay()
return result
return wrapper_f
then (still in task.py
):
#@task
@mfunc_to_task()
def get_tag():
pass
#@task
@mfunc_to_task()
def get_tagged_photos():
pass
#@task
@mfunc_to_task()
def get_tagged_photos_count():
pass
Things work fine without @task
.
But, after applying that @task
decorator(to the top as celery documentation instructed), things just start to fall apart. Apparently, every time the mfunc_to_task.__call__
gets called, the same task.get_tag
function gets passed as f
. So I ended up with the same wrapper_f
every time, and now the only thing I cat do is to get a single tag.
I'm new to decorators. Any one can help me understand what went wrong here, or point out other ways to achieve the task? I really hate to write the same task wrap code for every of my data access functions.
Not quite sure why passing arguments won't work?
if you use this example:
@task()
def add(x, y):
return x + y
lets add some logging to the MyCoolTask:
from celery import task
from celery.registry import tasks
import logging
import celery
logger = logging.getLogger(__name__)
class MyCoolTask(celery.Task):
def __call__(self, *args, **kwargs):
"""In celery task this function call the run method, here you can
set some environment variable before the run of the task"""
logger.info("Starting to run")
return self.run(*args, **kwargs)
def after_return(self, status, retval, task_id, args, kwargs, einfo):
#exit point of the task whatever is the state
logger.info("Ending run")
pass
and create an extended class (extending MyCoolTask, but now with arguments):
class AddTask(MyCoolTask):
def run(self,x,y):
if x and y:
result=add(x,y)
logger.info('result = %d' % result)
return result
else:
logger.error('No x or y in arguments')
tasks.register(AddTask)
and make sure you pass the kwargs as json data:
{"x":8,"y":9}
I get the result:
[2013-03-05 17:30:25,853: INFO/MainProcess] Starting to run
[2013-03-05 17:30:25,855: INFO/MainProcess] result = 17
[2013-03-05 17:30:26,739: INFO/MainProcess] Ending run
[2013-03-05 17:30:26,741: INFO/MainProcess] Task iamscheduler.tasks.AddTask[6a62641d-16a6-44b6-a1cf-7d4bdc8ea9e0] succeeded in 0.888684988022s: 17