This document describes the current stable version of Celery (3.1). 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 also start multiple workers on the same machine. If you do so be sure to give a unique name to each individual worker by specifying a host name with the --hostname|-n 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.

E.g. if the current hostname is george.example.com then these will expand to:

  • worker1.%h -> worker1.george.example.com
  • worker1.%n -> worker1.george
  • worker1.%d -> worker1.example.com

Note for supervisord 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, so 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 example because of tasks stuck in an infinite-loop, you can use the KILL signal to force terminate the worker, but be aware that currently executing tasks will be lost (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, so make sure to do so manually. This command usually does the trick:

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

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/%n.pid
$ celery multi restart 1 --pidfile=/var/run/celery/%n.pid

For production deployments you should be using init scripts or other process supervision systems (see Running the worker as a daemon).

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

$ kill -HUP $pid

Note

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

HUP is disabled on OS X because of a limitation on that platform.

Process Signals

The worker’s main process overrides the following signals:

TERM Warm shutdown, wait for tasks to complete.
QUIT Cold shutdown, terminate ASAP
USR1 Dump traceback for all active threads.
USR2 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

  • %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.

E.g. if the current hostname is george.example.com then these will expand to:

  • --logfile=%h.log -> george.example.com.log
  • --logfile=%n.log -> george.log
  • --logfile=%d -> example.com.log

Prefork pool process index

The prefork pool process index specifiers will expand into a different filename depending on the process that will 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. I.e. the number is the process index not the process count or pid.

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

    Where -n worker1@example.com -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 worker1@example.com -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)

Concurrency

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 is 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

New in version 2.0.

pool support: prefork, eventlet, gevent, blocking:threads/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 is 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.

Note

The solo and threads 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)
[{'worker1.example.com': 'New rate limit set successfully'},
 {'worker2.example.com': 'New rate limit set successfully'},
 {'worker3.example.com': '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=['worker1@example.com'])
[{'worker1.example.com': '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().

Commands

revoke: Revoking tasks

pool support:all
broker support:amqp, redis
command: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).

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.

Note

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 is executing the task, and that process may have already started processing another task at the point when the signal is sent, so for this rason you must never call this programatically.

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.

Example

>>> 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

New in version 3.1.

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

Example

>>> 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 will want to create one file per worker instance so then you can 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.

Time Limits

New in version 2.0.

pool support: prefork/gevent

A single task can potentially run forever, if you have lots of tasks waiting for some event that will never happen you will 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

@app.task
def mytask():
    try:
        do_work()
    except SoftTimeLimitExceeded:
        clean_up_in_a_hurry()

Time limits can also be set using the CELERYD_TASK_TIME_LIMIT / CELERYD_TASK_SOFT_TIME_LIMIT settings.

Note

Time limits do not currently work on Windows and other platforms that do not support the SIGUSR1 signal.

Changing time limits at runtime

New in version 2.3.

broker support: amqp, redis

There is 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)
[{'worker1.example.com': {'ok': 'time limits set successfully'}}]

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

Rate Limits

Changing rate-limits at runtime

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 does not 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=['celery@worker1.example.com'])

Warning

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

Max tasks per child setting

New in version 2.0.

pool support: prefork

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 –maxtasksperchild argument or using the CELERYD_MAX_TASKS_PER_CHILD setting.

Autoscaling

New 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:

--autoscale=AUTOSCALE
     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 CELERYD_AUTOSCALER setting.

Queues

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

You can specify what queues to consume from at startup, 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 CELERY_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 CELERY_CREATE_MISSING_QUEUES option).

You can also tell the worker to start and stop consuming from a queue at runtime 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 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=['worker1@example.com'])
[{u'worker1.local': {u'ok': u"already consuming from u'foo'"}}]

By now I have 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=['w1@example.com', 'w2@example.com'])

Queues: Cancelling 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 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 which workers should reply to the request:

$ celery -A proj inspect active_queues -d worker1.local
[...]

This can also be done programmatically by using the app.control.inspect.active_queues() method:

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

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

Autoreloading

New in version 2.5.

pool support: prefork, eventlet, gevent, threads, solo

Starting celery worker with the --autoreload option will enable the worker to watch for file system changes to all imported task modules imported (and also any non-task modules added to the CELERY_IMPORTS setting or the -I|--include option).

This is an experimental feature intended for use in development only, using auto-reload in production is discouraged as the behavior of reloading a module in Python is undefined, and may cause hard to diagnose bugs and crashes. Celery uses the same approach as the auto-reloader found in e.g. the Django runserver command.

When auto-reload is enabled the worker starts an additional thread that watches for changes in the file system. New modules are imported, and already imported modules are reloaded whenever a change is detected, and if the prefork pool is used the child processes will finish the work they are doing and exit, so that they can be replaced by fresh processes effectively reloading the code.

File system notification backends are pluggable, and it comes with three implementations:

  • inotify (Linux)

    Used if the pyinotify library is installed. If you are running on Linux this is the recommended implementation, to install the pyinotify library you have to run the following command:

    $ pip install pyinotify
    
  • kqueue (OS X/BSD)

  • stat

    The fallback implementation simply polls the files using stat and is very expensive.

You can force an implementation by setting the CELERYD_FSNOTIFY environment variable:

$ env CELERYD_FSNOTIFY=stat celery worker -l info --autoreload

Pool Restart Command

New in version 2.5.

Requires the CELERYD_POOL_RESTARTS setting to be enabled.

The remote control command pool_restart sends restart requests to the workers child processes. It is particularly useful for forcing the worker to import new modules, or for reloading already imported modules. This command does not interrupt executing tasks.

Example

Running the following command will result in the foo and bar modules being imported by the worker processes:

>>> app.control.broadcast('pool_restart',
...                       arguments={'modules': ['foo', 'bar']})

Use the reload argument to reload modules it has already imported:

>>> app.control.broadcast('pool_restart',
...                       arguments={'modules': ['foo'],
...                                  'reload': True})

If you don’t specify any modules then all known tasks modules will be imported/reloaded:

>>> app.control.broadcast('pool_restart', arguments={'reload': True})

The modules argument is a list of modules to modify. reload specifies whether to reload modules if they have previously been imported. By default reload is disabled. The pool_restart command uses the Python reload() function to reload modules, or you can provide your own custom reloader by passing the reloader argument.

Note

Module reloading comes with caveats that are documented in reload(). Please read this documentation and make sure your modules are suitable for reloading.

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(['worker1.example.com',
                            'worker2.example.com'])

# Specify a single node to inspect.
>>> i = app.control.inspect('worker1.example.com')

Dump of registered tasks

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

>>> i.registered()
[{'worker1.example.com': ['tasks.add',
                          'tasks.sleeptask']}]

Dump of currently executing tasks

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

>>> i.active()
[{'worker1.example.com':
    [{'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()
[{'worker1.example.com':
    [{'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': '{}'}}]}]

Note

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

Dump of reserved tasks

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

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

>>> i.reserved()
[{'worker1.example.com':
    [{'name': 'tasks.sleeptask',
      'id': '32666e9b-809c-41fa-8e93-5ae0c80afbbf',
      'args': '(8,)',
      'kwargs': '{}'}]}]

Statistics

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

The output will include the following fields:

  • broker

    Section for broker information.

    • connect_timeout

      Timeout in seconds (int/float) for establishing a new connection.

    • heartbeat

      Current heartbeat value (set by client).

    • hostname

      Hostname of the remote broker.

    • insist

      No longer used.

    • login_method

      Login method used to connect to the broker.

    • port

      Port of the remote broker.

    • ssl

      SSL enabled/disabled.

    • transport

      Name of transport used (e.g. amqp or redis)

    • transport_options

      Options passed to transport.

    • uri_prefix

      Some transports expects the host name to be an URL, this applies to for example SQLAlchemy where the host name part is the connection URI:

      redis+socket:///tmp/redis.sock

      In this example the uri prefix will be redis.

    • userid

      User id used to connect to the broker with.

    • virtual_host

      Virtual host used.

  • clock

    Value of the workers logical clock. This is a positive integer and should be increasing every time you receive statistics.

  • pid

    Process id of the worker instance (Main process).

  • pool

    Pool-specific section.

    • max-concurrency

      Max number of processes/threads/green threads.

    • max-tasks-per-child

      Max number of tasks a thread may execute before being recycled.

    • processes

      List of pids (or thread-id’s).

    • put-guarded-by-semaphore

      Internal

    • timeouts

      Default values for time limits.

    • writes

      Specific to the prefork pool, this shows the distribution of writes to each process in the pool when using async I/O.

  • prefetch_count

    Current prefetch count value for the task consumer.

  • rusage

    System usage statistics. The fields available may be different on your platform.

    From getrusage(2):

    • stime

      Time spent in operating system code on behalf of this process.

    • utime

      Time spent executing user instructions.

    • maxrss

      The maximum resident size used by this process (in kilobytes).

    • idrss

      Amount of unshared memory used for data (in kilobytes times ticks of execution)

    • isrss

      Amount of unshared memory used for stack space (in kilobytes times ticks of execution)

    • ixrss

      Amount of memory shared with other processes (in kilobytes times ticks of execution).

    • inblock

      Number of times the file system had to read from the disk on behalf of this process.

    • oublock

      Number of times the file system has to write to disk on behalf of this process.

    • majflt

      Number of page faults which were serviced by doing I/O.

    • minflt

      Number of page faults which were serviced without doing I/O.

    • msgrcv

      Number of IPC messages received.

    • msgsnd

      Number of IPC messages sent.

    • nvcsw

      Number of times this process voluntarily invoked a context switch.

    • nivcsw

      Number of times an involuntary context switch took place.

    • nsignals

      Number of signals received.

    • nswap

      The number of times this process was swapped entirely out of memory.

  • total

    List of task names and a total number of times that task have been executed since worker start.

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="worker1@example.com")

Ping

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:

>>> app.control.ping(timeout=0.5)
[{'worker1.example.com': 'pong'},
 {'worker2.example.com': 'pong'},
 {'worker3.example.com': 'pong'}]

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

>>> ping(['worker2.example.com', 'worker3.example.com'])
[{'worker2.example.com': 'pong'},
 {'worker3.example.com': '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

Remote control commands are registered in the control panel and they take a single argument: the current 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 Panel

@Panel.register
def increase_prefetch_count(state, n=1):
    state.consumer.qos.increment_eventually(n)
    return {'ok': 'prefetch count incremented'}

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