Celery 1.0.6 (stable) documentation

This Page

Frequently Asked Questions


What kinds of things should I use celery for?

Answer: Queue everything and delight everyone is a good article describing why you would use a queue in a web context.

These are some common use cases:

  • Running something in the background. For example, to finish the web request as soon as possible, then update the users page incrementally. This gives the user the impression of good performane and “snappiness”, even though the real work might actually take some time.
  • Running something after the web request has finished.
  • Making sure something is done, by executing it asynchronously and using retries.
  • Scheduling periodic work.

And to some degree:

  • Distributed computing.
  • Parallel execution.


Is celery dependent on pickle?

Answer: No.

Celery can support any serialization scheme and has support for JSON/YAML and Pickle by default. You can even send one task using pickle, and another one with JSON seamlessly, this is because every task is associated with a content-type. The default serialization scheme is pickle because it’s the most used, and it has support for sending complex objects as task arguments.

You can set a global default serializer, the default serializer for a particular Task, or even what serializer to use when sending a single task instance.

Is celery for Django only?

Answer: No.

You can use all of the features without using Django.

Why is Django a dependency?

Celery uses the Django ORM for database access when using the database result backend, the Django cache framework when using the cache result backend, and the Django signal dispatch mechanisms for signaling.

This doesn’t mean you need to have a Django project to use celery, it just means that sometimes we use internal Django components.

The long term plan is to replace these with other solutions, (e.g. SQLAlchemy as the ORM, and louie, for signaling). The celery distribution will be split into two:

  • celery

    The core. Using SQLAlchemy for the database backend.

  • django-celery

    Celery integration for Django, using the Django ORM for the database backend.

We’re currently seeking people with SQLAlchemy experience, so please contact the project if you want this done sooner.

The reason for the split is for purity only. It shouldn’t affect you much as a user, so please don’t worry about the Django dependency, just have a good time using celery.

Do I have to use AMQP/RabbitMQ?

Answer: No.

You can also use Redis or an SQL database, see Using other queues.

Redis or a database won’t perform as well as an AMQP broker. If you have strict reliability requirements you are encouraged to use RabbitMQ or another AMQP broker. Redis/database also use polling, so they are likely to consume more resources. However, if you for some reason are not able to use AMQP, feel free to use these alternatives. They will probably work fine for most use cases, and note that the above points are not specific to celery; If using Redis/database as a queue worked fine for you before, it probably will now. You can always upgrade later if you need to.

Is celery multi-lingual?

Answer: Yes.

celeryd is an implementation of celery in python. If the language has an AMQP client, there shouldn’t be much work to create a worker in your language. A celery worker is just a program connecting to the broker to consume messages. There’s no other communication involved.

Also, there’s another way to be language indepedent, and that is to use REST tasks, instead of your tasks being functions, they’re URLs. With this information you can even create simple web servers that enable preloading of code. See: User Guide: Remote Tasks.


MySQL is throwing deadlock errors, what can I do?

Answer: MySQL has default isolation level set to REPEATABLE-READ, if you don’t really need that, set it to READ-COMMITTED. You can do that by adding the following to your my.cnf:

transaction-isolation = READ-COMMITTED

For more information about InnoDBs transaction model see MySQL - The InnoDB Transaction Model and Locking in the MySQL user manual.

(Thanks to Honza Kral and Anton Tsigularov for this solution)

celeryd is not doing anything, just hanging

Answer: See MySQL is throwing deadlock errors, what can I do?.
or Why is Task.delay/apply* just hanging?.

Why is Task.delay/apply*/celeryd just hanging?

Answer: There is a bug in some AMQP clients that will make it hang if it’s not able to authenticate the current user, the password doesn’t match or the user does not have access to the virtual host specified. Be sure to check your broker logs (for RabbitMQ that is /var/log/rabbitmq/rabbit.log on most systems), it usually contains a message describing the reason.

Why won’t celeryd run on FreeBSD?

Answer: multiprocessing.Pool requires a working POSIX semaphore implementation which isn’t enabled in FreeBSD by default. You have to enable POSIX semaphores in the kernel and manually recompile multiprocessing.

Luckily, Viktor Petersson has written a tutorial to get you started with Celery on FreeBSD here: http://www.playingwithwire.com/2009/10/how-to-get-celeryd-to-work-on-freebsd/

I’m having IntegrityError: Duplicate Key errors. Why?

Answer: See MySQL is throwing deadlock errors, what can I do?. Thanks to howsthedotcom.

Why aren’t my tasks processed?

Answer: With RabbitMQ you can see how many consumers are currently receiving tasks by running the following command:

$ rabbitmqctl list_queues -p <myvhost> name messages consumers
Listing queues ...
celery     2891    2

This shows that there’s 2891 messages waiting to be processed in the task queue, and there are two consumers processing them.

One reason that the queue is never emptied could be that you have a stale celery process taking the messages hostage. This could happen if celeryd wasn’t properly shut down.

When a message is recieved by a worker the broker waits for it to be acknowledged before marking the message as processed. The broker will not re-send that message to another consumer until the consumer is shut down properly.

If you hit this problem you have to kill all workers manually and restart them:

ps auxww | grep celeryd | awk '{print $2}' | xargs kill

You might have to wait a while until all workers have finished the work they’re doing. If it’s still hanging after a long time you can kill them by force with:

ps auxww | grep celeryd | awk '{print $2}' | xargs kill -9

Why won’t my Task run?

Answer: Did you register the task in the applications tasks.py module? (or in some other module Django loads by default, like models.py?). Also there might be syntax errors preventing the tasks module being imported.

You can find out if celery is able to run the task by executing the task manually:

>>> from myapp.tasks import MyPeriodicTask
>>> MyPeriodicTask.delay()

Watch celeryds logfile to see if it’s able to find the task, or if some other error is happening.

Why won’t my Periodic Task run?

Answer: See Why won’t my Task run?.

How do I discard all waiting tasks?

Answer: Use celery.task.discard_all(), like this:

>>> from celery.task import discard_all
>>> discard_all()

The number 1753 is the number of messages deleted.

You can also start celeryd with the --discard argument which will accomplish the same thing.

I’ve discarded messages, but there are still messages left in the queue?

Answer: Tasks are acknowledged (removed from the queue) as soon as they are actually executed. After the worker has received a task, it will take some time until it is actually executed, especially if there are a lot of tasks already waiting for execution. Messages that are not acknowledged are hold on to by the worker until it closes the connection to the broker (AMQP server). When that connection is closed (e.g because the worker was stopped) the tasks will be re-sent by the broker to the next available worker (or the same worker when it has been restarted), so to properly purge the queue of waiting tasks you have to stop all the workers, and then discard the tasks using discard_all.

Windows: The -B / --beat option to celeryd doesn’t work?

Answer: That’s right. Run celerybeat and celeryd as separate services instead.


How can I reuse the same connection when applying tasks?

Answer: See Executing Tasks.

Can I execute a task by name?

Answer: Yes. Use celery.execute.send_task(). You can also execute a task by name from any language that has an AMQP client.

>>> from celery.execute import send_task
>>> send_task("tasks.add", args=[2, 2], kwargs={})
<AsyncResult: 373550e8-b9a0-4666-bc61-ace01fa4f91d>


How dow I get the result of a task if I have the ID that points there?

Answer: Use Task.AsyncResult:

>>> result = MyTask.AsyncResult(task_id)
>>> result.get()

This will give you a celery.result.BaseAsyncResult instance using the tasks current result backend.

If you need to specify a custom result backend you should use celery.result.BaseAsyncResult directly:

>>> from celery.result import BaseAsyncResult
>>> result = BaseAsyncResult(task_id, backend=...)
>>> result.get()


Why is RabbitMQ crashing?

RabbitMQ will crash if it runs out of memory. This will be fixed in a future release of RabbitMQ. please refer to the RabbitMQ FAQ: http://www.rabbitmq.com/faq.html#node-runs-out-of-memory

Some common Celery misconfigurations can crash RabbitMQ:

  • Events.

Running celeryd with the -E/--events option will send messages for events happening inside of the worker. If these event messages are not consumed, you will eventually run out of memory.

Events should only be enabled if you have an active monitor consuming them.

  • AMQP backend results.

When running with the AMQP result backend, every task result will be sent as a message. If you don’t collect these results, they will build up and RabbitMQ will eventually run out of memory.

If you don’t use the results for a task, make sure you set the ignore_result option:

Results can also be disabled globally using the CELERY_IGNORE_RESULT setting.

Can I use celery with ActiveMQ/STOMP?

Answer: Yes, but this is somewhat experimental for now. It is working ok in a test configuration, but it has not been tested in production like RabbitMQ has. If you have any problems with using STOMP and celery, please report the bugs to the issue tracker:

First you have to use the master branch of celery:

$ git clone git://github.com/ask/celery.git
$ cd celery
$ sudo python setup.py install
$ cd ..

Then you need to install the stompbackend branch of carrot:

$ git clone git://github.com/ask/carrot.git
$ cd carrot
$ git checkout stompbackend
$ sudo python setup.py install
$ cd ..

And my fork of python-stomp which adds non-blocking support:

$ hg clone http://bitbucket.org/asksol/python-stomp/
$ cd python-stomp
$ sudo python setup.py install
$ cd ..

In this example we will use a queue called celery which we created in the ActiveMQ web admin interface.

Note: For ActiveMQ the queue name has to have "/queue/" prepended to it. i.e. the queue celery becomes /queue/celery.

Since a STOMP queue is a single named entity and it doesn’t have the routing capabilities of AMQP you need to set both the queue, and exchange settings to your queue name. This is a minor inconvenience since carrot needs to maintain the same interface for both AMQP and STOMP (obviously the one with the most capabilities won).

Use the following specific settings in your settings.py:

# Makes python-stomp the default backend for carrot.

# STOMP hostname and port settings.
BROKER_HOST = "localhost"

# The queue name to use (both queue and exchange must be set to the
# same queue name when using STOMP)
CELERY_DEFAULT_QUEUE = "/queue/celery"

    "/queue/celery": {"exchange": "/queue/celery"}

Now you can go on reading the tutorial in the README, ignoring any AMQP specific options.

What features are not supported when using STOMP?

This is a (possible incomplete) list of features not available when using the STOMP backend:

  • routing keys
  • exchange types (direct, topic, headers, etc)
  • immediate
  • mandatory


How can I run a task once another task has finished?

Answer: You can safely launch a task inside a task. Also, a common pattern is to use callback tasks:

def add(x, y, callback=None):
    result = x + y
    if callback:
    return result

def log_result(result, **kwargs):
    logger = log_result.get_logger(**kwargs)
    logger.info("log_result got: %s" % (result, ))

>>> add.delay(2, 2, callback=log_result)

Can I cancel the execution of a task?

Answer: Yes. Use result.revoke:

>>> result = add.apply_async(args=[2, 2], countdown=120)
>>> result.revoke()

or if you only have the task id:

>>> from celery.task.control import revoke
>>> revoke(task_id)

Why aren’t my remote control commands received by all workers?

Answer: To receive broadcast remote control commands, every celeryd uses its hostname to create a unique queue name to listen to, so if you have more than one worker with the same hostname, the control commands will be recieved in round-robin between them.

To work around this you can explicitly set the hostname for every worker using the --hostname argument to celeryd:

$ celeryd --hostname=$(hostname).1
$ celeryd --hostname=$(hostname).2

etc, etc.

Can I send some tasks to only some servers?

Answer: Yes. You can route tasks to an arbitrary server using AMQP, and a worker can bind to as many queues as it wants.

Say you have two servers, x, and y that handles regular tasks, and one server z, that only handles feed related tasks, you can use this configuration:

  • Servers x and y: settings.py:
CELERY_DEFAULT_QUEUE = "regular_tasks"
    "regular_tasks": {
        "binding_key": "task.#",
  • Server z: settings.py:
    "feed_tasks": {
        "binding_key": "feed.#",

CELERY_QUEUES is a map of queue names and their exchange/type/binding_key, if you don’t set exchange or exchange type, they will be taken from the CELERY_DEFAULT_EXCHANGE/CELERY_DEFAULT_EXCHANGE_TYPE settings.

Now to make a Task run on the z server you need to set its routing_key attribute so it starts with the words "task.feed.":

from feedaggregator.models import Feed
from celery.decorators import task

def import_feed(feed_url):

or if subclassing the Task class directly:

class FeedImportTask(Task):
    routing_key = "feed.importer"

    def run(self, feed_url):

You can also override this using the routing_key argument to celery.task.apply_async():

>>> from myapp.tasks import RefreshFeedTask
>>> RefreshFeedTask.apply_async(args=["http://cnn.com/rss"],
...                             routing_key="feed.importer")

If you want, you can even have your feed processing worker handle regular tasks as well, maybe in times when there’s a lot of work to do. Just add a new queue to server z‘s CELERY_QUEUES:

    "feed_tasks": {
        "binding_key": "feed.#",
    "regular_tasks": {
        "binding_key": "task.#",

Since the default exchange is tasks, they will both use the same exchange.

If you have another queue but on another exchange you want to add, just specify a custom exchange and exchange type:

        "feed_tasks": {
            "binding_key": "feed.#",
        "regular_tasks": {
            "binding_key": "task.#",
        "image_tasks": {
            "binding_key": "image.compress",
            "exchange": "mediatasks",
            "exchange_type": "direct",

If you’re confused about these terms, you should read up on AMQP and RabbitMQ. Rabbits and Warrens is an excellent blog post describing queues and exchanges. There’s also AMQP in 10 minutes*: Flexible Routing Model, and Standard Exchange Types. For users of RabbitMQ the RabbitMQ FAQ could also be useful as a source of information.

Can I use celery without Django?

Answer: Yes.

Celery uses something called loaders to read/setup configuration, import modules that register tasks and to decide what happens when a task is executed. Currently there are two loaders, the default loader and the Django loader. If you want to use celery without a Django project, you either have to use the default loader, or write a loader of your own.

The rest of this answer describes how to use the default loader.

While it is possible to use Celery from outside of Django, we still need Django itself to run, this is to use the ORM and cache-framework. Duplicating these features would be time consuming and mostly pointless, so while me might rewrite these in the future, this is a good solution in the mean time. Install Django using your favorite install tool, easy_install, pip, or whatever:

# easy_install django # as root

You need a configuration file named celeryconfig.py, either in the directory you run celeryd in, or in a Python library path where it is able to find it. The configuration file can contain any of the settings described in celery.conf. In addition; if you’re using the database backend you have to configure the database. Here is an example configuration using the database backend with MySQL:

# Broker configuration
BROKER_HOST = "localhost"
BROKER_PORT = "5672"
BROKER_VHOST = "celery"
BROKER_USER = "celery"
BROKER_PASSWORD = "celerysecret"

# Using the database backend.
DATABASE_ENGINE = "mysql" # see Django docs for a description of these.
DATABASE_HOST = "mydb.example.org"
DATABASE_USER = "myuser"

# Number of processes that processes tasks simultaneously.

# Modules to import when celeryd starts.
# This must import every module where you register tasks so celeryd
# is able to find and run them.
CELERY_IMPORTS = ("mytaskmodule1", "mytaskmodule2")

With this configuration file in the current directory you have to run celeryinit to create the database tables:

$ celeryinit

At this point you should be able to successfully run celeryd:

$ celeryd --loglevel=INFO

and send a task from a python shell (note that it must be able to import celeryconfig.py):

>>> from celery.task.builtins import PingTask
>>> result = PingTask.apply_async()
>>> result.get()

The celery test-suite is failing

Answer: If you’re running tests from your Django project, and the celery test suite is failing in that context, then follow the steps below. If the celery tests are failing in another context, please report an issue to our issue tracker at GitHub:

That Django is running tests for all applications in INSTALLED_APPS by default is a pet peeve for many. You should use a test runner that either

  1. Explicitly lists the apps you want to run tests for, or
  2. Make a test runner that skips tests for apps you don’t want to run.

For example the test runner that celery is using:

To use this test runner, add the following to your settings.py:

TEST_RUNNER = "celery.tests.runners.run_tests"

Or, if you just want to skip the celery tests:

TEST_RUNNER = "celery.tests.runners.run_tests"
TEST_APPS = filter(lambda k: k != "celery", INSTALLED_APPS)

Can I change the interval of a periodic task at runtime?

Answer: Yes. You can override PeriodicTask.is_due or turn PeriodicTask.run_every into a property:

class MyPeriodic(PeriodicTask):

    def run(self):
        # ...

    def run_every(self):
        return get_interval_from_database(...)

Does celery support task priorities?

Answer: No. In theory, yes, as AMQP supports priorities. However RabbitMQ doesn’t implement them yet.

The usual way to prioritize work in celery, is to route high priority tasks to different servers. In the real world this may actually work better than per message priorities. You can use this in combination with rate limiting to achieve a highly performant system.

Should I use retry or acks_late?

Answer: Depends. It’s not necessarily one or the other, you may want to use both.

Task.retry is used to retry tasks, notably for expected errors that is catchable with the try: block. The AMQP transaction is not used for these errors: if the task raises an exception it is still acked!.

The acks_late setting would be used when you need the task to be executed again if the worker (for some reason) crashes mid-execution. It’s important to note that the worker is not known to crash, and if it does it is usually an unrecoverable error that requires human intervention (bug in the worker, or task code).

In an ideal world you could safely retry any task that has failed, but this is rarely the case. Imagine the following task:

def process_upload(filename, tmpfile):
    # Increment a file count stored in a database
    add_file_metadata_to_db(filename, tmpfile)
    copy_file_to_destination(filename, tmpfile)

If this crashed in the middle of copying the file to its destination the world would contain incomplete state. This is not a critical scenario of course, but you can probably imagine something far more sinister. So for ease of programming we have less reliability; It’s a good default, users who require it and know what they are doing can still enable acks_late (and in the future hopefully use manual acknowledgement)

In addition Task.retry has features not available in AMQP transactions: delay between retries, max retries, etc.

So use retry for Python errors, and if your task is reentrant combine that with acks_late if that level of reliability is required.

Can I schedule tasks to execute at a specific time?

Answer: Yes. You can use the eta argument of Task.apply_async().

Or to schedule a periodic task at a specific time, use the celery.task.schedules.crontab schedule behavior:

from celery.task.schedules import crontab
from celery.decorators import periodic_task

@periodic_task(run_every=crontab(hours=7, minute=30, day_of_week="mon"))
def every_monday_morning():
    print("This is run every monday morning at 7:30")

How do I shut down celeryd safely?

Answer: Use the TERM signal, and celery will finish all currently executing jobs and shut down as soon as possible. No tasks should be lost.

You should never stop celeryd with the KILL signal (-9), unless you’ve tried TERM a few times and waited a few minutes to let it get a chance to shut down. As if you do tasks may be terminated mid-execution, and they will not be re-run unless you have the acks_late option set. (Task.acks_late / CELERY_ACKS_LATE).

How do I run celeryd in the background on [platform]?

Answer: Please see Running celeryd as a daemon.


Generating a template in a task doesn’t seem to respect my i18n settings?

Answer: To enable the Django translation machinery you need to activate it with a language. Note: Be sure to reset to the previous language when done.

>>> from django.utils import translation
>>> prev_language = translation.get_language()
>>> translation.activate(language)
>>> try:
...     render_template()
... finally:

The common pattern here would be for the task to take a language argument:

from celery.decorators import task

from django.utils import translation
from django.template.loader import render_to_string

def generate_report(template="report.html", language=None):
    prev_language = translation.get_language()
    language and translation.activate(language)
        report = render_to_string(template)