Celery 1.0.6 (stable) documentation

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A task is a class that encapsulates a function and its execution options. Given a function create_user, that takes two arguments: username and password, you can create a task like this:

from celery.task import Task

class CreateUserTask(Task):
    def run(self, username, password):
        create_user(username, password)

For convenience there is a shortcut decorator that turns any function into a task, celery.decorators.task:

from celery.decorators import task
from django.contrib.auth import User

def create_user(username, password):
    User.objects.create(username=username, password=password)

The task decorator takes the same execution options the Task class does:

def create_user(username, password):
    User.objects.create(username=username, password=password)

An alternative way to use the decorator is to give the function as an argument instead, but if you do this be sure to set the resulting tasks __name__ attribute, so pickle is able to find it in reverse:

create_user_task = task()(create_user)
create_user_task.__name__ = "create_user_task"

Default keyword arguments

Celery supports a set of default arguments that can be forwarded to any task. Tasks can choose not to take these, or list the ones they want. The worker will do the right thing.

The current default keyword arguments are:

  • logfile

    The log file, can be passed on to self.get_logger to gain access to the workers log file. See Logging.

  • loglevel

    The loglevel used.

  • task_id

    The unique id of the executing task.

  • task_name

    Name of the executing task.

  • task_retries

    How many times the current task has been retried. An integer starting at 0.

  • task_is_eager

    Set to True if the task is executed locally in the client, and not by a worker.

  • delivery_info

    Additional message delivery information. This is a mapping containing the exchange and routing key used to deliver this task. It’s used by e.g. retry() to resend the task to the same destination queue.

    NOTE As some messaging backends doesn’t have advanced routing capabilities, you can’t trust the availability of keys in this mapping.


You can use the workers logger to add diagnostic output to the worker log:

class AddTask(Task):
    def run(self, x, y, **kwargs):
        logger = self.get_logger(**kwargs)
        logger.info("Adding %s + %s" % (x, y))
        return x + y

or using the decorator syntax:

def add(x, y, **kwargs):
    logger = add.get_logger(**kwargs)
    logger.info("Adding %s + %s" % (x, y))
    return x + y

There are several logging levels available, and the workers loglevel setting decides whether or not they will be written to the log file.

Retrying a task if something fails

Simply use Task.retry() to re-send the task. It will do the right thing, and respect the Task.max_retries attribute:

def send_twitter_status(oauth, tweet, **kwargs):
        twitter = Twitter(oauth)
    except (Twitter.FailWhaleError, Twitter.LoginError), exc:
        send_twitter_status.retry(args=[oauth, tweet], kwargs=kwargs, exc=exc)

Here we used the exc argument to pass the current exception to Task.retry(). At each step of the retry this exception is available as the tombstone (result) of the task. When Task.max_retries has been exceeded this is the exception raised. However, if an exc argument is not provided the RetryTaskError exception is raised instead.

Important note: The task has to take the magic keyword arguments in order for max retries to work properly, this is because it keeps track of the current number of retries using the task_retries keyword argument passed on to the task. In addition, it also uses the task_id keyword argument to use the same task id, and delivery_info to route the retried task to the same destination.

Using a custom retry delay

When a task is to be retried, it will wait for a given amount of time before doing so. The default delay is in the Task.default_retry_delay attribute on the task. By default this is set to 3 minutes. Note that the unit for setting the delay is in seconds (int or float).

You can also provide the countdown argument to Task.retry() to override this default.

class MyTask(Task):
    default_retry_delay = 30 * 60 # retry in 30 minutes

    def run(self, x, y, **kwargs):
        except Exception, exc:
            self.retry([x, y], kwargs, exc=exc,
                       countdown=60) # override the default and
                                     # - retry in 1 minute

Task options

  • name

    The name the task is registered as. You can set this name manually, or just use the default which is automatically generated using the module and class name.

  • abstract

    Abstract classes are not registered, but are used as the superclass when making new task types by subclassing.

  • max_retries

    The maximum number of attempted retries before giving up. If this is exceeded the :exc`celery.execptions.MaxRetriesExceeded` exception will be raised. Note that you have to retry manually, it’s not something that happens automatically.

  • default_retry_delay

    Default time in seconds before a retry of the task should be executed. Can be either an int or a float. Default is a 1 minute delay (60 seconds).

  • rate_limit

    Set the rate limit for this task type, that is, how many times in a given period of time is the task allowed to run.

    If this is None no rate limit is in effect. If it is an integer, it is interpreted as “tasks per second”.

    The rate limits can be specified in seconds, minutes or hours by appending "/s", "/m" or “/h"” to the value. Example: "100/m" (hundred tasks a minute). Default is the ``CELERY_DEFAULT_RATE_LIMIT setting, which if not specified means rate limiting for tasks is turned off by default.

  • ignore_result

    Don’t store the status and return value. This means you can’t

    use the celery.result.AsyncResult to check if the task is done, or get its return value. Only use if you need the performance and is able live without these features. Any exceptions raised will store the return value/status as usual.

  • disable_error_emails

    Disable error e-mails for this task. Default is False. Note: You can also turn off error e-mails globally using the CELERY_SEND_TASK_ERROR_EMAILS setting.

  • serializer

    A string identifying the default serialization method to use. Defaults to the CELERY_TASK_SERIALIZER setting. Can be pickle json, yaml, or any custom serialization methods that have been registered with carrot.serialization.registry.

    Please see Executing Tasks for more information.

Message and routing options

  • routing_key

    Override the global default routing_key for this task.

  • exchange

    Override the global default exchange for this task.

  • mandatory

    If set, the task message has mandatory routing. By default the task is silently dropped by the broker if it can’t be routed to a queue. However - If the task is mandatory, an exception will be raised instead.

  • immediate

    Request immediate delivery. If the task cannot be routed to a task worker immediately, an exception will be raised. This is instead of the default behavior, where the broker will accept and queue the task, but with no guarantee that the task will ever be executed.

  • priority

    The message priority. A number from 0 to 9, where 0 is the highest. Note: RabbitMQ does not support priorities yet.

See Executing Tasks for more information about the messaging options available.


Let’s take a real wold example; A blog where comments posted needs to be filtered for spam. When the comment is created, the spam filter runs in the background, so the user doesn’t have to wait for it to finish.

We have a Django blog application allowing comments on blog posts. We’ll describe parts of the models/views and tasks for this application.


The comment model looks like this:

from django.db import models
from django.utils.translation import ugettext_lazy as _

class Comment(models.Model):
    name = models.CharField(_("name"), max_length=64)
    email_address = models.EmailField(_("e-mail address"))
    homepage = models.URLField(_("home page"),
                               blank=True, verify_exists=False)
    comment = models.TextField(_("comment"))
    pub_date = models.DateTimeField(_("Published date"),
                                    editable=False, auto_add_now=True)
    is_spam = models.BooleanField(_("spam?"),
                                  default=False, editable=False)

    class Meta:
        verbose_name = _("comment")
        verbose_name_plural = _("comments")

In the view where the comment is posted, we first write the comment to the database, then we launch the spam filter task in the background.


from django import forms
frmo django.http import HttpResponseRedirect
from django.template.context import RequestContext
from django.shortcuts import get_object_or_404, render_to_response

from blog import tasks
from blog.models import Comment

class CommentForm(forms.ModelForm):

    class Meta:
        model = Comment

def add_comment(request, slug, template_name="comments/create.html"):
    post = get_object_or_404(Entry, slug=slug)
    remote_addr = request.META.get("REMOTE_ADDR")

    if request.method == "post":
        form = CommentForm(request.POST, request.FILES)
        if form.is_valid():
            comment = form.save()
            # Check spam asynchronously.
            return HttpResponseRedirect(post.get_absolute_url())
        form = CommentForm()

    context = RequestContext(request, {"form": form})
    return render_to_response(template_name, context_instance=context)

To filter spam in comments we use Akismet, the service used to filter spam in comments posted to the free weblog platform Wordpress. Akismet is free for personal use, but for commercial use you need to pay. You have to sign up to their service to get an API key.

To make API calls to Akismet we use the akismet.py library written by Michael Foord.


from akismet import Akismet
from celery.decorators import task

from django.core.exceptions import ImproperlyConfigured
from django.contrib.sites.models import Site

from blog.models import Comment

def spam_filter(comment_id, remote_addr=None, **kwargs):
        logger = spam_filter.get_logger(**kwargs)
        logger.info("Running spam filter for comment %s" % comment_id)

        comment = Comment.objects.get(pk=comment_id)
        current_domain = Site.objects.get_current().domain
        akismet = Akismet(settings.AKISMET_KEY, "http://%s" % domain)
        if not akismet.verify_key():
            raise ImproperlyConfigured("Invalid AKISMET_KEY")

        is_spam = akismet.comment_check(user_ip=remote_addr,
        if is_spam:
            comment.is_spam = True

        return is_spam

How it works

Here comes the technical details, this part isn’t something you need to know, but you may be interested.

All defined tasks are listed in a registry. The registry contains a list of task names and their task classes. You can investigate this registry yourself:

>>> from celery import registry
>>> from celery import task
>>> registry.tasks
  <celery.task.builtins.DeleteExpiredTaskMetaTask object at 0x101d1f510>,
  <celery.task.base.ExecuteRemoteTask object at 0x101d17890>,
  <celery.task.rest.RESTProxyTask object at 0x101d1f410>,
'celery.task.rest.Task': <celery.task.rest.Task object at 0x101d1f4d0>,
  <celery.task.base.AsynchronousMapTask object at 0x101d17910>,
'celery.ping': <celery.task.builtins.PingTask object at 0x101d1f550>}

This is the list of tasks built-in to celery. Note that we had to import celery.task first for these to show up. This is because the tasks will only be registered when the module they are defined in is imported.

The default loader imports any modules listed in the CELERY_IMPORTS setting. If using Django it loads all tasks.py modules for the applications listed in INSTALLED_APPS. If you want to do something special you can create your own loader to do what you want.

The entity responsible for registering your task in the registry is a meta class, TaskType. This is the default meta class for Task. If you want to register your task manually you can set the abstract attribute:

class MyTask(Task):
    abstract = True

This way the task won’t be registered, but any task subclassing it will.

When tasks are sent, we don’t send the function code, just the name of the task. When the worker receives the message it can just look it up in the task registry to find the execution code.

This means that your workers should always be updated with the same software as the client. This is a drawback, but the alternative is a technical challenge that has yet to be solved.

Tips and Best Practices

Ignore results you don’t want

If you don’t care about the results of a task, be sure to set the ignore_result option, as storing results wastes time and resources.

def mytask(...)

Results can even be disabled globally using the CELERY_IGNORE_RESULT setting.

Disable rate limits if they’re not used

Disabling rate limits altogether is recommended if you don’t have any tasks using them. This is because the rate limit subsystem introduces quite a lot of complexity.

Set the CELERY_DISABLE_RATE_LIMITS setting to globally disable rate limits:


Avoid launching synchronous subtasks

Having a task wait for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted.

Make your design asynchronous instead, for example by using callbacks.


def update_page_info(url):
    page = fetch_page.delay(url).get()
    info = parse_page.delay(url, page).get()
    store_page_info.delay(url, info)

def fetch_page(url):
    return myhttplib.get(url)

def parse_page(url, page):
    return myparser.parse_document(page)

def store_page_info(url, info):
    return PageInfo.objects.create(url, info)


from functools import curry

def update_page_info(url):
    # fetch_page -> parse_page -> store_page
    callback = curry(parse_page.delay, callback=store_page_info)
    fetch_page.delay(url, callback=callback)

def fetch_page(url, callback=None):
    page = myparser.parse_document(page)
    if callback:

def parse_page(url, page, callback=None):
    info = myparser.parse_document(page)
    if callback:
        callback(url, info)

def store_page_info(url, info):
    PageInfo.objects.create(url, info)

Performance and Strategies


The task’s granularity is the degree of parallelization your task have. It’s better to have many small tasks, than a few long running ones.

With smaller tasks, you can process more tasks in parallel and the tasks won’t run long enough to block the worker from processing other waiting tasks.

However, there’s a limit. Sending messages takes processing power and bandwidth. If your tasks are so short the overhead of passing them around is worse than just executing them in-line, you should reconsider your strategy. There is no universal answer here.

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 being a full transfer from another continent.

If the data is far away, you could try to run another worker at location, or if that’s not possible, cache often used data, or preload data you know is going to be used.

The easiest way to share data between workers is to use a distributed caching system, like memcached.

For more information about data-locality, please read http://research.microsoft.com/pubs/70001/tr-2003-24.pdf


Since celery is a distributed system, you can’t know in which process, or even on what machine the task will run. Indeed you can’t even know if the task will run in a timely manner, so please be wary of the state you pass on to tasks.

One gotcha is Django model objects. They shouldn’t be passed on as arguments to task classes, it’s almost always better to re-fetch the object from the database instead, as there are possible race conditions involved.

Imagine the following scenario where you have an article and a task that automatically expands some abbreviations in it.

class Article(models.Model):
    title = models.CharField()
    body = models.TextField()

def expand_abbreviations(article):
    article.body.replace("MyCorp", "My Corporation")

First, an author creates an article and saves it, then the author clicks on a button that initiates the abbreviation task.

>>> article = Article.objects.get(id=102)
>>> expand_abbreviations.delay(model_object)

Now, the queue is very busy, so the task won’t be run for another 2 minutes, in the meantime another author makes some changes to the article, when the task is finally run, the body of the article is reverted to the old version, because the task had the old body in its argument.

Fixing the race condition is easy, just use the article id instead, and re-fetch the article in the task body:

def expand_abbreviations(article_id)
    article = Article.objects.get(id=article_id)
    article.body.replace("MyCorp", "My Corporation")

>>> expand_abbreviations(article_id)

There might even be performance benefits to this approach, as sending large messages may be expensive.