This document describes the current stable version of Celery (5.4). For development docs, go here.

Workers Guide

Starting the worker

You can start the worker in the foreground by executing the command:

$ celery -A proj worker -l INFO

For a full list of available command-line options see worker, or simply do:

$ celery worker --help

You can start multiple workers on the same machine, but be sure to name each individual worker by specifying a node name with the --hostname argument:

$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker1@%h
$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker2@%h
$ celery -A proj worker --loglevel=INFO --concurrency=10 -n worker3@%h

The hostname argument can expand the following variables:

  • %h: Hostname, including domain name.

  • %n: Hostname only.

  • %d: Domain name only.

If the current hostname is, these will expand to:











Note for users

The % sign must be escaped by adding a second one: %%h.

Stopping the worker

Shutdown should be accomplished using the TERM signal.

When shutdown is initiated the worker will finish all currently executing tasks before it actually terminates. If these tasks are important, you should wait for it to finish before doing anything drastic, like sending the KILL signal.

If the worker won’t shutdown after considerate time, for being stuck in an infinite-loop or similar, you can use the KILL signal to force terminate the worker: but be aware that currently executing tasks will be lost (i.e., unless the tasks have the acks_late option set).

Also as processes can’t override the KILL signal, the worker will not be able to reap its children; make sure to do so manually. This command usually does the trick:

$ pkill -9 -f 'celery worker'

If you don’t have the pkill command on your system, you can use the slightly longer version:

$ ps auxww | awk '/celery worker/ {print $2}' | xargs kill -9

Changed in version 5.2: On Linux systems, Celery now supports sending KILL signal to all child processes after worker termination. This is done via PR_SET_PDEATHSIG option of prctl(2).

Restarting the worker

To restart the worker you should send the TERM signal and start a new instance. The easiest way to manage workers for development is by using celery multi:

$ celery multi start 1 -A proj -l INFO -c4 --pidfile=/var/run/celery/
$ celery multi restart 1 --pidfile=/var/run/celery/

For production deployments you should be using init-scripts or a process supervision system (see Daemonization).

Other than stopping, then starting the worker to restart, you can also restart the worker using the HUP signal. Note that the worker will be responsible for restarting itself so this is prone to problems and isn’t recommended in production:

$ kill -HUP $pid


Restarting by HUP only works if the worker is running in the background as a daemon (it doesn’t have a controlling terminal).

HUP is disabled on macOS because of a limitation on that platform.

Automatic re-connection on connection loss to broker

Added in version 5.3.

Unless broker_connection_retry_on_startup is set to False, Celery will automatically retry reconnecting to the broker after the first connection loss. broker_connection_retry controls whether to automatically retry reconnecting to the broker for subsequent reconnects.

Added in version 5.1.

If worker_cancel_long_running_tasks_on_connection_loss is set to True, Celery will also cancel any long running task that is currently running.

Added in version 5.3.

Since the message broker does not track how many tasks were already fetched before the connection was lost, Celery will reduce the prefetch count by the number of tasks that are currently running multiplied by worker_prefetch_multiplier. The prefetch count will be gradually restored to the maximum allowed after each time a task that was running before the connection was lost is complete.

This feature is enabled by default, but can be disabled by setting False to worker_enable_prefetch_count_reduction.

Process Signals

The worker’s main process overrides the following signals:


Warm shutdown, wait for tasks to complete.


Cold shutdown, terminate ASAP


Dump traceback for all active threads.


Remote debug, see celery.contrib.rdb.

Variables in file paths

The file path arguments for --logfile, --pidfile, and --statedb can contain variables that the worker will expand:

Node name replacements

  • %p: Full node name.

  • %h: Hostname, including domain name.

  • %n: Hostname only.

  • %d: Domain name only.

  • %i: Prefork pool process index or 0 if MainProcess.

  • %I: Prefork pool process index with separator.

For example, if the current hostname is then these will expand to:

  • --logfile=%p.log ->

  • --logfile=%h.log ->

  • --logfile=%n.log -> george.log

  • --logfile=%d.log ->

Prefork pool process index

The prefork pool process index specifiers will expand into a different filename depending on the process that’ll eventually need to open the file.

This can be used to specify one log file per child process.

Note that the numbers will stay within the process limit even if processes exit or if autoscale/maxtasksperchild/time limits are used. That is, the number is the process index not the process count or pid.

  • %i - Pool process index or 0 if MainProcess.

    Where -n -c2 -f %n-%i.log will result in three log files:

    • worker1-0.log (main process)

    • worker1-1.log (pool process 1)

    • worker1-2.log (pool process 2)

  • %I - Pool process index with separator.

    Where -n -c2 -f %n%I.log will result in three log files:

    • worker1.log (main process)

    • worker1-1.log (pool process 1)

    • worker1-2.log (pool process 2)


By default multiprocessing is used to perform concurrent execution of tasks, but you can also use Eventlet. The number of worker processes/threads can be changed using the --concurrency argument and defaults to the number of CPUs available on the machine.

Number of processes (multiprocessing/prefork pool)

More pool processes are usually better, but there’s a cut-off point where adding more pool processes affects performance in negative ways. There’s even some evidence to support that having multiple worker instances running, may perform better than having a single worker. For example 3 workers with 10 pool processes each. You need to experiment to find the numbers that works best for you, as this varies based on application, work load, task run times and other factors.

Remote control

Added in version 2.0.

pool support:

prefork, eventlet, gevent, thread, blocking:solo (see note)

broker support:

amqp, redis

Workers have the ability to be remote controlled using a high-priority broadcast message queue. The commands can be directed to all, or a specific list of workers.

Commands can also have replies. The client can then wait for and collect those replies. Since there’s no central authority to know how many workers are available in the cluster, there’s also no way to estimate how many workers may send a reply, so the client has a configurable timeout — the deadline in seconds for replies to arrive in. This timeout defaults to one second. If the worker doesn’t reply within the deadline it doesn’t necessarily mean the worker didn’t reply, or worse is dead, but may simply be caused by network latency or the worker being slow at processing commands, so adjust the timeout accordingly.

In addition to timeouts, the client can specify the maximum number of replies to wait for. If a destination is specified, this limit is set to the number of destination hosts.


The solo pool supports remote control commands, but any task executing will block any waiting control command, so it is of limited use if the worker is very busy. In that case you must increase the timeout waiting for replies in the client.

The broadcast() function

This is the client function used to send commands to the workers. Some remote control commands also have higher-level interfaces using broadcast() in the background, like rate_limit(), and ping().

Sending the rate_limit command and keyword arguments:

>>> app.control.broadcast('rate_limit',
...                          arguments={'task_name': 'myapp.mytask',
...                                     'rate_limit': '200/m'})

This will send the command asynchronously, without waiting for a reply. To request a reply you have to use the reply argument:

>>> app.control.broadcast('rate_limit', {
...     'task_name': 'myapp.mytask', 'rate_limit': '200/m'}, reply=True)
[{'': 'New rate limit set successfully'},
 {'': 'New rate limit set successfully'},
 {'': 'New rate limit set successfully'}]

Using the destination argument you can specify a list of workers to receive the command:

>>> app.control.broadcast('rate_limit', {
...     'task_name': 'myapp.mytask',
...     'rate_limit': '200/m'}, reply=True,
...                             destination=[''])
[{'': 'New rate limit set successfully'}]

Of course, using the higher-level interface to set rate limits is much more convenient, but there are commands that can only be requested using broadcast().


revoke: Revoking tasks

pool support:

all, terminate only supported by prefork and eventlet

broker support:

amqp, redis


celery -A proj control revoke <task_id>

All worker nodes keeps a memory of revoked task ids, either in-memory or persistent on disk (see Persistent revokes).


The maximum number of revoked tasks to keep in memory can be specified using the CELERY_WORKER_REVOKES_MAX environment variable, which defaults to 50000. When the limit has been exceeded, the revokes will be active for 10800 seconds (3 hours) before being expired. This value can be changed using the CELERY_WORKER_REVOKE_EXPIRES environment variable.

Memory limits can also be set for successful tasks through the CELERY_WORKER_SUCCESSFUL_MAX and CELERY_WORKER_SUCCESSFUL_EXPIRES environment variables, and default to 1000 and 10800 respectively.

When a worker receives a revoke request it will skip executing the task, but it won’t terminate an already executing task unless the terminate option is set.


The terminate option is a last resort for administrators when a task is stuck. It’s not for terminating the task, it’s for terminating the process that’s executing the task, and that process may have already started processing another task at the point when the signal is sent, so for this reason you must never call this programmatically.

If terminate is set the worker child process processing the task will be terminated. The default signal sent is TERM, but you can specify this using the signal argument. Signal can be the uppercase name of any signal defined in the signal module in the Python Standard Library.

Terminating a task also revokes it.


>>> result.revoke()

>>> AsyncResult(id).revoke()

>>> app.control.revoke('d9078da5-9915-40a0-bfa1-392c7bde42ed')

>>> app.control.revoke('d9078da5-9915-40a0-bfa1-392c7bde42ed',
...                    terminate=True)

>>> app.control.revoke('d9078da5-9915-40a0-bfa1-392c7bde42ed',
...                    terminate=True, signal='SIGKILL')

Revoking multiple tasks

Added in version 3.1.

The revoke method also accepts a list argument, where it will revoke several tasks at once.


>>> app.control.revoke([
...    '7993b0aa-1f0b-4780-9af0-c47c0858b3f2',
...    'f565793e-b041-4b2b-9ca4-dca22762a55d',
...    'd9d35e03-2997-42d0-a13e-64a66b88a618',

The GroupResult.revoke method takes advantage of this since version 3.1.

Persistent revokes

Revoking tasks works by sending a broadcast message to all the workers, the workers then keep a list of revoked tasks in memory. When a worker starts up it will synchronize revoked tasks with other workers in the cluster.

The list of revoked tasks is in-memory so if all workers restart the list of revoked ids will also vanish. If you want to preserve this list between restarts you need to specify a file for these to be stored in by using the –statedb argument to celery worker:

$ celery -A proj worker -l INFO --statedb=/var/run/celery/worker.state

or if you use celery multi you want to create one file per worker instance so use the %n format to expand the current node name:

celery multi start 2 -l INFO --statedb=/var/run/celery/%n.state

See also Variables in file paths

Note that remote control commands must be working for revokes to work. Remote control commands are only supported by the RabbitMQ (amqp) and Redis at this point.

revoke_by_stamped_header: Revoking tasks by their stamped headers

pool support:

all, terminate only supported by prefork and eventlet

broker support:

amqp, redis


celery -A proj control revoke_by_stamped_header <header=value>

This command is similar to revoke(), but instead of specifying the task id(s), you specify the stamped header(s) as key-value pair(s), and each task that has a stamped header matching the key-value pair(s) will be revoked.


The revoked headers mapping is not persistent across restarts, so if you restart the workers, the revoked headers will be lost and need to be mapped again.


This command may perform poorly if your worker pool concurrency is high and terminate is enabled, since it will have to iterate over all the running tasks to find the ones with the specified stamped header.


>>> app.control.revoke_by_stamped_header({'header': 'value'})

>>> app.control.revoke_by_stamped_header({'header': 'value'}, terminate=True)

>>> app.control.revoke_by_stamped_header({'header': 'value'}, terminate=True, signal='SIGKILL')

Revoking multiple tasks by stamped headers

Added in version 5.3.

The revoke_by_stamped_header method also accepts a list argument, where it will revoke by several headers or several values.


>> app.control.revoke_by_stamped_header({
...    'header_A': 'value_1',
...    'header_B': ['value_2', 'value_3'],

This will revoke all of the tasks that have a stamped header header_A with value value_1, and all of the tasks that have a stamped header header_B with values value_2 or value_3.

CLI Example

$ celery -A proj control revoke_by_stamped_header stamped_header_key_A=stamped_header_value_1 stamped_header_key_B=stamped_header_value_2

$ celery -A proj control revoke_by_stamped_header stamped_header_key_A=stamped_header_value_1 stamped_header_key_B=stamped_header_value_2 --terminate

$ celery -A proj control revoke_by_stamped_header stamped_header_key_A=stamped_header_value_1 stamped_header_key_B=stamped_header_value_2 --terminate --signal=SIGKILL

Time Limits

Added in version 2.0.

pool support:

prefork/gevent (see note below)

A single task can potentially run forever, if you have lots of tasks waiting for some event that’ll never happen you’ll block the worker from processing new tasks indefinitely. The best way to defend against this scenario happening is enabling time limits.

The time limit (–time-limit) is the maximum number of seconds a task may run before the process executing it is terminated and replaced by a new process. You can also enable a soft time limit (–soft-time-limit), this raises an exception the task can catch to clean up before the hard time limit kills it:

from myapp import app
from celery.exceptions import SoftTimeLimitExceeded

def mytask():
    except SoftTimeLimitExceeded:

Time limits can also be set using the task_time_limit / task_soft_time_limit settings. You can also specify time limits for client side operation using timeout argument of AsyncResult.get() function.


Time limits don’t currently work on platforms that don’t support the SIGUSR1 signal.


The gevent pool does not implement soft time limits. Additionally, it will not enforce the hard time limit if the task is blocking.

Changing time limits at run-time

Added in version 2.3.

broker support:

amqp, redis

There’s a remote control command that enables you to change both soft and hard time limits for a task — named time_limit.

Example changing the time limit for the tasks.crawl_the_web task to have a soft time limit of one minute, and a hard time limit of two minutes:

>>> app.control.time_limit('tasks.crawl_the_web',
                           soft=60, hard=120, reply=True)
[{'': {'ok': 'time limits set successfully'}}]

Only tasks that starts executing after the time limit change will be affected.

Rate Limits

Changing rate-limits at run-time

Example changing the rate limit for the myapp.mytask task to execute at most 200 tasks of that type every minute:

>>> app.control.rate_limit('myapp.mytask', '200/m')

The above doesn’t specify a destination, so the change request will affect all worker instances in the cluster. If you only want to affect a specific list of workers you can include the destination argument:

>>> app.control.rate_limit('myapp.mytask', '200/m',
...            destination=[''])


This won’t affect workers with the worker_disable_rate_limits setting enabled.

Max tasks per child setting

Added in version 2.0.

pool support:


With this option you can configure the maximum number of tasks a worker can execute before it’s replaced by a new process.

This is useful if you have memory leaks you have no control over for example from closed source C extensions.

The option can be set using the workers --max-tasks-per-child argument or using the worker_max_tasks_per_child setting.

Max memory per child setting

Added in version 4.0.

pool support:


With this option you can configure the maximum amount of resident memory a worker can execute before it’s replaced by a new process.

This is useful if you have memory leaks you have no control over for example from closed source C extensions.

The option can be set using the workers --max-memory-per-child argument or using the worker_max_memory_per_child setting.


Added in version 2.2.

pool support:

prefork, gevent

The autoscaler component is used to dynamically resize the pool based on load:

  • The autoscaler adds more pool processes when there is work to do,
    • and starts removing processes when the workload is low.

It’s enabled by the --autoscale option, which needs two numbers: the maximum and minimum number of pool processes:

     Enable autoscaling by providing
     max_concurrency,min_concurrency.  Example:
       --autoscale=10,3 (always keep 3 processes, but grow to
      10 if necessary).

You can also define your own rules for the autoscaler by subclassing Autoscaler. Some ideas for metrics include load average or the amount of memory available. You can specify a custom autoscaler with the worker_autoscaler setting.


A worker instance can consume from any number of queues. By default it will consume from all queues defined in the task_queues setting (that if not specified falls back to the default queue named celery).

You can specify what queues to consume from at start-up, by giving a comma separated list of queues to the -Q option:

$ celery -A proj worker -l INFO -Q foo,bar,baz

If the queue name is defined in task_queues it will use that configuration, but if it’s not defined in the list of queues Celery will automatically generate a new queue for you (depending on the task_create_missing_queues option).

You can also tell the worker to start and stop consuming from a queue at run-time using the remote control commands add_consumer and cancel_consumer.

Queues: Adding consumers

The add_consumer control command will tell one or more workers to start consuming from a queue. This operation is idempotent.

To tell all workers in the cluster to start consuming from a queue named “foo” you can use the celery control program:

$ celery -A proj control add_consumer foo
-> worker1.local: OK
    started consuming from u'foo'

If you want to specify a specific worker you can use the --destination argument:

$ celery -A proj control add_consumer foo -d celery@worker1.local

The same can be accomplished dynamically using the app.control.add_consumer() method:

>>> app.control.add_consumer('foo', reply=True)
[{u'worker1.local': {u'ok': u"already consuming from u'foo'"}}]

>>> app.control.add_consumer('foo', reply=True,
...                          destination=[''])
[{u'worker1.local': {u'ok': u"already consuming from u'foo'"}}]

By now we’ve only shown examples using automatic queues, If you need more control you can also specify the exchange, routing_key and even other options:

>>> app.control.add_consumer(
...     queue='baz',
...     exchange='ex',
...     exchange_type='topic',
...     routing_key='media.*',
...     options={
...         'queue_durable': False,
...         'exchange_durable': False,
...     },
...     reply=True,
...     destination=['', ''])

Queues: Canceling consumers

You can cancel a consumer by queue name using the cancel_consumer control command.

To force all workers in the cluster to cancel consuming from a queue you can use the celery control program:

$ celery -A proj control cancel_consumer foo

The --destination argument can be used to specify a worker, or a list of workers, to act on the command:

$ celery -A proj control cancel_consumer foo -d celery@worker1.local

You can also cancel consumers programmatically using the app.control.cancel_consumer() method:

>>> app.control.cancel_consumer('foo', reply=True)
[{u'worker1.local': {u'ok': u"no longer consuming from u'foo'"}}]

Queues: List of active queues

You can get a list of queues that a worker consumes from by using the active_queues control command:

$ celery -A proj inspect active_queues

Like all other remote control commands this also supports the --destination argument used to specify the workers that should reply to the request:

$ celery -A proj inspect active_queues -d celery@worker1.local

This can also be done programmatically by using the active_queues() method:

>>> app.control.inspect().active_queues()

>>> app.control.inspect(['worker1.local']).active_queues()

Inspecting workers

app.control.inspect lets you inspect running workers. It uses remote control commands under the hood.

You can also use the celery command to inspect workers, and it supports the same commands as the app.control interface.

>>> # Inspect all nodes.
>>> i = app.control.inspect()

>>> # Specify multiple nodes to inspect.
>>> i = app.control.inspect(['',

>>> # Specify a single node to inspect.
>>> i = app.control.inspect('')

Dump of registered tasks

You can get a list of tasks registered in the worker using the registered():

>>> i.registered()
[{'': ['tasks.add',

Dump of currently executing tasks

You can get a list of active tasks using active():

    [{'name': 'tasks.sleeptask',
      'id': '32666e9b-809c-41fa-8e93-5ae0c80afbbf',
      'args': '(8,)',
      'kwargs': '{}'}]}]

Dump of scheduled (ETA) tasks

You can get a list of tasks waiting to be scheduled by using scheduled():

>>> i.scheduled()
    [{'eta': '2010-06-07 09:07:52', 'priority': 0,
      'request': {
        'name': 'tasks.sleeptask',
        'id': '1a7980ea-8b19-413e-91d2-0b74f3844c4d',
        'args': '[1]',
        'kwargs': '{}'}},
     {'eta': '2010-06-07 09:07:53', 'priority': 0,
      'request': {
        'name': 'tasks.sleeptask',
        'id': '49661b9a-aa22-4120-94b7-9ee8031d219d',
        'args': '[2]',
        'kwargs': '{}'}}]}]


These are tasks with an ETA/countdown argument, not periodic tasks.

Dump of reserved tasks

Reserved tasks are tasks that have been received, but are still waiting to be executed.

You can get a list of these using reserved():

>>> i.reserved()
    [{'name': 'tasks.sleeptask',
      'id': '32666e9b-809c-41fa-8e93-5ae0c80afbbf',
      'args': '(8,)',
      'kwargs': '{}'}]}]


The remote control command inspect stats (or stats()) will give you a long list of useful (or not so useful) statistics about the worker:

$ celery -A proj inspect stats

For the output details, consult the reference documentation of stats().

Additional Commands

Remote shutdown

This command will gracefully shut down the worker remotely:

>>> app.control.broadcast('shutdown') # shutdown all workers
>>> app.control.broadcast('shutdown', destination='')


This command requests a ping from alive workers. The workers reply with the string ‘pong’, and that’s just about it. It will use the default one second timeout for replies unless you specify a custom timeout:

[{'': 'pong'},
 {'': 'pong'},
 {'': 'pong'}]

ping() also supports the destination argument, so you can specify the workers to ping:

>>> ping(['', ''])
[{'': 'pong'},
 {'': 'pong'}]

Enable/disable events

You can enable/disable events by using the enable_events, disable_events commands. This is useful to temporarily monitor a worker using celery events/celerymon.

>>> app.control.enable_events()
>>> app.control.disable_events()

Writing your own remote control commands

There are two types of remote control commands:

  • Inspect command

    Does not have side effects, will usually just return some value found in the worker, like the list of currently registered tasks, the list of active tasks, etc.

  • Control command

    Performs side effects, like adding a new queue to consume from.

Remote control commands are registered in the control panel and they take a single argument: the current celery.worker.control.ControlDispatch instance. From there you have access to the active Consumer if needed.

Here’s an example control command that increments the task prefetch count:

from celery.worker.control import control_command

    args=[('n', int)],
    signature='[N=1]',  # <- used for help on the command-line.
def increase_prefetch_count(state, n=1):
    return {'ok': 'prefetch count incremented'}

Make sure you add this code to a module that is imported by the worker: this could be the same module as where your Celery app is defined, or you can add the module to the imports setting.

Restart the worker so that the control command is registered, and now you can call your command using the celery control utility:

$ celery -A proj control increase_prefetch_count 3

You can also add actions to the celery inspect program, for example one that reads the current prefetch count:

from celery.worker.control import inspect_command

def current_prefetch_count(state):
    return {'prefetch_count': state.consumer.qos.value}

After restarting the worker you can now query this value using the celery inspect program:

$ celery -A proj inspect current_prefetch_count