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This document describes Celery 2.5. For development docs, go here.

Frequently Asked Questions

General

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 performance 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.

Misconceptions

Is Celery dependent on pickle?

Answer: No.

Celery can support any serialization scheme and has built-in support for JSON, YAML, Pickle and msgpack. Also, as every task is associated with a content type, you can even send one task using pickle, and another using JSON.

The default serialization format is pickle simply because it is convenient (it supports sending complex Python objects as task arguments).

If you need to communicate with other languages you should change to a serialization format that is suitable for that.

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.

Celery does not depend on Django anymore. To use Celery with Django you have to use the django-celery package.

Do I have to use AMQP/RabbitMQ?

Answer: No.

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

These “virtual transports” may have limited broadcast and event functionality. For example remote control commands only works with AMQP and Redis.

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 multilingual?

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 process messages.

Also, there’s another way to be language independent, 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.

Troubleshooting

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:

[mysqld]
transaction-isolation = READ-COMMITTED

For more information about InnoDB`s 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)

Task results aren’t reliably returning

Answer: If you’re using the database backend for results, and in particular using MySQL, see MySQL is throwing deadlock errors, what can I do?.

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.

Does it work on FreeBSD?

Answer: The 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/

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 worker process taking the messages hostage. This could happen if celeryd wasn’t properly shut down.

When a message is received 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: 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 celeryd`s log file to see if it’s able to find the task, or if some other error is happening.

How do I discard all waiting tasks?

Answer: You can use celeryctl to purge all configured task queues:

$ celeryctl purge

or programatically:

>>> from celery.task.control import discard_all
>>> discard_all()
1753

If you only want to purge messages from a specific queue you have to use the AMQP API or the camqadm utility:

$ camqadm queue.purge <queue name>

The number 1753 is the number of messages deleted.

You can also start celeryd with the --purge argument, to purge messages when the worker starts.

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 held 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().

Results

How do 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 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()

Security

Isn’t using pickle a security concern?

Answer: Yes, indeed it is.

You are right to have a security concern, as this can indeed be a real issue. It is essential that you protect against unauthorized access to your broker, databases and other services transmitting pickled data.

For the task messages you can set the CELERY_TASK_SERIALIZER setting to “json” or “yaml” instead of pickle. There is currently no alternative solution for task results (but writing a custom result backend using JSON is a simple task)

Note that this is not just something you should be aware of with Celery, for example also Django uses pickle for its cache client.

Can messages be encrypted?

Answer: Some AMQP brokers supports using SSL (including RabbitMQ). You can enable this using the BROKER_USE_SSL setting.

It is also possible to add additional encryption and security to messages, if you have a need for this then you should contact the Mailing list.

Is it safe to run celeryd as root?

Answer: No!

We’re not currently aware of any security issues, but it would be incredibly naive to assume that they don’t exist, so running the Celery services (celeryd, celerybeat, celeryev, etc) as an unprivileged user is recommended.

Brokers

Why is RabbitMQ crashing?

Answer: 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

Note

This is no longer the case, RabbitMQ versions 2.0 and above includes a new persister, that is tolerant to out of memory errors. RabbitMQ 2.1 or higher is recommended for Celery.

If you’re still running an older version of RabbitMQ and experience crashes, then please upgrade!

Misconfiguration of Celery can eventually lead to a crash on older version of RabbitMQ. Even if it doesn’t crash, this can still consume a lot of resources, so it is very important that you are aware of the common pitfalls.

  • Events.

Running celeryd with the -E/--events option will send messages for events happening inside of the worker.

Events should only be enabled if you have an active monitor consuming them, or if you purge the event queue periodically.

  • 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.

Results expire after 1 day by default. It may be a good idea to lower this value by configuring the CELERY_TASK_RESULT_EXPIRES setting.

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

Can I use Celery with ActiveMQ/STOMP?

Answer: No. It used to be supported by Carrot, but is not currently supported in Kombu.

What features are not supported when not using an AMQP broker?

This is an incomplete list of features not available when using the virtual transports:

  • Remote control commands (supported only by Redis).

  • Monitoring with events may not work in all virtual transports.

  • The header and fanout exchange types

    (fanout is supported by Redis).

Tasks

How can I reuse the same connection when applying tasks?

Answer: See the BROKER_POOL_LIMIT setting. The connection pool is enabled by default since version 2.5.

Sudo in a subprocess returns None

There is a sudo configuration option that makes it illegal for process without a tty to run sudo:

Defaults requiretty

If you have this configuration in your /etc/sudoers file then tasks will not be able to call sudo when celeryd is running as a daemon. If you want to enable that, then you need to remove the line from sudoers.

See: http://timelordz.com/wiki/Apache_Sudo_Commands

Why do workers delete tasks from the queue if they are unable to process them?

Answer:

The worker discards unknown tasks, messages with encoding errors and messages that doesn’t contain the proper fields (as per the task message protocol).

If it did not ack (delete) them, they would be redelivered again and again causing a loop.

There has been talk about moving these messages to a dead-letter queue, but that has not yet been implemented.

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 can I get the task id of the current task?

Answer: The current id and more is available in the task request:

@task
def mytask():
    cache.set(mytask.request.id, "Running")

For more information see Context.

Can I specify a custom task_id?

Answer: Yes. Use the task_id argument to Task.apply_async():

>>> task.apply_async(args, kwargs, task_id="...")

Can I use decorators with tasks?

Answer: Yes. But please see note at Decorating tasks.

Can I use natural task ids?

Answer: Yes, but make sure it is unique, as the behavior for two tasks existing with the same id is undefined.

The world will probably not explode, but at the worst they can overwrite each others results.

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:

@task()
def add(x, y, callback=None):
    result = x + y
    if callback:
        subtask(callback).delay(result)
    return result


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

Invocation:

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

See Sets of tasks, Subtasks and Callbacks for more information.

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 worker node uses its host name to create a unique queue name to listen to, so if you have more than one worker with the same host name, the control commands will be received in round-robin between them.

To work around this you can explicitly set the host name 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.

See Routing Tasks for more information.

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

Answer: Yes. You can use the Django database scheduler, or you can override PeriodicTask.is_due or turn PeriodicTask.run_every into a property:

class MyPeriodic(PeriodicTask):

    def run(self):
        # ...

    @property
    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 responsive 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 acknowledged!.

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:

@task()
def process_upload(filename, tmpfile):
    # Increment a file count stored in a database
    increment_file_counter()
    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 idempotent 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.schedules.crontab schedule behavior:

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

@periodic_task(run_every=crontab(hour=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 the worker 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).

See also

Stopping the worker

Django

What purpose does the database tables created by django-celery have?

Several database tables are created by default, these relate to

  • Monitoring

    When you use the django-admin monitor, the cluster state is written to the TaskState and WorkerState models.

  • Periodic tasks

    When the database-backed schedule is used the periodic task schedule is taken from the PeriodicTask model, there are also several other helper tables (IntervalSchedule, CrontabSchedule, PeriodicTasks).

  • Task results

    The database result backend is enabled by default when using django-celery (this is for historical reasons, and thus for backward compatibility).

    The results are stored in the TaskMeta and TaskSetMeta models. these tables are not created if another result backend is configured.

Windows

celeryd keeps spawning processes at startup

Answer: This is a known issue on Windows. You have to start celeryd with the command:

$ python -m celeryd.bin.celeryd

Any additional arguments can be appended to this command.

See http://bit.ly/bo9RSw

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

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

django-celery can’t find settings?

Answer: You need to specify the --settings argument to manage.py:

$ python manage.py celeryd start --settings=settings

See http://bit.ly/bo9RSw

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