This document describes the current stable version of Celery (3.1). For development docs, go here.
Workers Guide¶
- Starting the worker
- Stopping the worker
- Restarting the worker
- Process Signals
- Variables in file paths
- Concurrency
- Remote control
- Commands
- Time Limits
- Rate Limits
- Max tasks per child setting
- Autoscaling
- Queues
- Autoreloading
- Inspecting workers
- Additional Commands
- Writing your own remote control commands
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 reason 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: 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 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 (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 thepyinotify
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 have been received, but are 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
orredis
)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'}