celery task and customize decorator

Question

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.

Answer 1

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

Answer 2

Instead of use decorator why you don't create a base class that extend celery.Task ?

In this way all your tasks can extend your customized task class, where you can implement your personal behavior by using methods __call__ and after_return . You can also define common methods and object for all your task.

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"""
        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
        pass