Celery Note2 Tasks

hi, there ~:

in this note, i talk about the Task. looking forward to it :-)”

Task Concept

as i talked in note1, task in nothing other than a block code formed function. but you should also acknowledge that tasks are the building blocks of Celery applications.

task performs dual roles: it defines both what happens when a task is called(sends a message), and what happens when a worker receives that message.

the same as Rabbitmq no_ack attribute, a task message does not disappear untill the message acknowledged by a worker. Ideally task functions should be idempotent, so that i can set acks_late option to have the the worker acknowledge the message after the task returns INSTEAD the default behavior, which is to acknowledge in advance, before it’s executed, so that a task that has been started is never executed again..


i can easily create a task from any callable by using the task() decorator:


def add(x, y):

return x+y


every task must have a unique name.

if not specified, the function name will be used.


def add(x, y):

return x+y

but this is not the best practice, we can use the module name as a namespace to avoid confilction


def add(x, y):

return x + y

Automatic naming and Relative imports

Relative imports and automatic name generation does not go well together, so if you’re using relative imports you should set the name explicitly just like above

for example, if the client imports the module ‘myapp.tasks’ as ‘.tasks’, and the worker imports the module as ‘myapp.tasks’, then generated names won’t match and an NotRegistered error will be raised by the worker

from project.myapp.tasks import mytask



from myapp.tasks import mytask



so for this reason you must be consistent in how you import modules, which is also a python best practice

similary, you should not use old-style relative imports:

from module import foo # very bad!!

from proj.module import foo # good practice

new-style relative imports are fine and can be used:(but not the best practice)

from .module import foo # Good

so if you don’t have time to refactor old code, then you have to specifying the names explicitly instead of the automatic naming.

List of Options

the @task decorator can take a number of options that change the way the task behaves. Any keyword argument passed to the task decorator will actually be set as an attribute of the resulting task class.

RabbiMQ Result Backend

this backed is special as it does not actually store the states, but rather sends them as message. This is an important difference as it means that a result can only be retrieved once; if you have two processes waiting for same result, one of the process will never receive the result!

this may also affect Rabbitmq performs in negative ways as every task will creates a new queue on the server, with thousands of tasks the broker may be overloaded with queues.

Even with that limitation, it’s excellent choice if you need to receive state changes in real-time. Using messagi ng means the client does not have to poll for new states.

Custom task classes

All tasks inherit from the app.Task class. the run() method becomes the task body.


def add(x, y):

return x+ y

celery will do this behind the scense:

class AddTask(app.Task):

def run(self, x, y):
    return x+y

add = app.tasks(AddTask.name)


A task is not instantiated for every request, but is registered in the task registry as a global intance. this is very useful to cache resources, e.g. a base Task that caches a database connection:

from celery import Task

class DatabaseTask(Task):

abstract = True

db = None


def db(self):

    if self._db is None:
        self._db = Database.connect()
        return self._db


  • after_return(self, status, retval, task_id, args, kwargs, einfo)

  • on_failure(self, exc, task_id, args, kwargs, einfo)

  • on_retry(self, exc, task_id, args, kwargs, einfo)

  • on_success(self, retval, task_id, args, kwargs)

Tips and Best practice

  • Ignore results you don’t want as storing results wastes time and resources.



Results can even be disabled globally using the CELERY_IGNORE_RESULT setting.

  • Disabling rate limits altogether is recommanded if you don’t have any tasks using them.

    this is because the rate limit subsystem introduces quite a lot of comlexity.

set the __ CELERY_DISABLE_RATE_LIMITS=True to disable rate limits globaly.

  • Avoid launching synchronous subtasks

subtask can be seen as a callback function chain after the task. or say **it is an object used to pass around the signature of task invocation,(for example to send it over the network) and they also support the Calling api. task.s(arg1,arg2,kwarg1=’x’,kwarg2=’y’).apply__async()

having a task wait for the result of another task is really ineffcient, and may cause a deadlock. make your design asynchronous instead, for example by using callbacks. In celery you can create a chain of tasks by linking together differnt subtasks.

Performance and Strategies

  • Granularity

    in general, it’s better to split the problem up into many small tasks, than have a few long running tasks.

  • Data locality

    the worker processing the task should be as close to the data as possible. the best would be to have a copy in memory, the worst would be a full transfer from another continent.

  • State

Since celery is a distributed system, you can’t know in which process, or on what machine the task will be executed. You can’t even know if the task wil run in a timely manner.i

reference celery user guide