This document describes the current stable version of Celery (5.2). For development docs, go here.

Extensions and Bootsteps

Custom Message Consumers

You may want to embed custom Kombu consumers to manually process your messages.

For that purpose a special ConsumerStep bootstep class exists, where you only need to define the get_consumers method, that must return a list of kombu.Consumer objects to start whenever the connection is established:

from celery import Celery
from celery import bootsteps
from kombu import Consumer, Exchange, Queue

my_queue = Queue('custom', Exchange('custom'), 'routing_key')

app = Celery(broker='amqp://')


class MyConsumerStep(bootsteps.ConsumerStep):

    def get_consumers(self, channel):
        return [Consumer(channel,
                         queues=[my_queue],
                         callbacks=[self.handle_message],
                         accept=['json'])]

    def handle_message(self, body, message):
        print('Received message: {0!r}'.format(body))
        message.ack()
app.steps['consumer'].add(MyConsumerStep)

def send_me_a_message(who, producer=None):
    with app.producer_or_acquire(producer) as producer:
        producer.publish(
            {'hello': who},
            serializer='json',
            exchange=my_queue.exchange,
            routing_key='routing_key',
            declare=[my_queue],
            retry=True,
        )

if __name__ == '__main__':
    send_me_a_message('world!')

Note

Kombu Consumers can take use of two different message callback dispatching mechanisms. The first one is the callbacks argument that accepts a list of callbacks with a (body, message) signature, the second one is the on_message argument that takes a single callback with a (message,) signature. The latter won’t automatically decode and deserialize the payload.

def get_consumers(self, channel):
    return [Consumer(channel, queues=[my_queue],
                     on_message=self.on_message)]


def on_message(self, message):
    payload = message.decode()
    print(
        'Received message: {0!r} {props!r} rawlen={s}'.format(
        payload, props=message.properties, s=len(message.body),
    ))
    message.ack()

Blueprints

Bootsteps is a technique to add functionality to the workers. A bootstep is a custom class that defines hooks to do custom actions at different stages in the worker. Every bootstep belongs to a blueprint, and the worker currently defines two blueprints: Worker, and Consumer

---

Figure A: Bootsteps in the Worker and Consumer blueprints. Starting

from the bottom up the first step in the worker blueprint is the Timer, and the last step is to start the Consumer blueprint, that then establishes the broker connection and starts consuming messages.

---

Worker

The Worker is the first blueprint to start, and with it starts major components like the event loop, processing pool, and the timer used for ETA tasks and other timed events.

When the worker is fully started it continues with the Consumer blueprint, that sets up how tasks are executed, connects to the broker and starts the message consumers.

The WorkController is the core worker implementation, and contains several methods and attributes that you can use in your bootstep.

Attributes

app

The current app instance.

hostname

The workers node name (e.g., worker1@example.com)

blueprint

This is the worker Blueprint.

hub

Event loop object (Hub). You can use this to register callbacks in the event loop.

This is only supported by async I/O enabled transports (amqp, redis), in which case the worker.use_eventloop attribute should be set.

Your worker bootstep must require the Hub bootstep to use this:

class WorkerStep(bootsteps.StartStopStep):
    requires = {'celery.worker.components:Hub'}

pool

The current process/eventlet/gevent/thread pool. See celery.concurrency.base.BasePool.

Your worker bootstep must require the Pool bootstep to use this:

class WorkerStep(bootsteps.StartStopStep):
    requires = {'celery.worker.components:Pool'}

timer

Timer used to schedule functions.

Your worker bootstep must require the Timer bootstep to use this:

class WorkerStep(bootsteps.StartStopStep):
    requires = {'celery.worker.components:Timer'}

statedb

Database <celery.worker.state.Persistent>` to persist state between worker restarts.

This is only defined if the statedb argument is enabled.

Your worker bootstep must require the Statedb bootstep to use this:

class WorkerStep(bootsteps.StartStopStep):
    requires = {'celery.worker.components:Statedb'}

autoscaler

Autoscaler used to automatically grow and shrink the number of processes in the pool.

This is only defined if the autoscale argument is enabled.

Your worker bootstep must require the Autoscaler bootstep to use this:

class WorkerStep(bootsteps.StartStopStep):
    requires = ('celery.worker.autoscaler:Autoscaler',)

autoreloader

Autoreloader used to automatically reload use code when the file-system changes.

This is only defined if the autoreload argument is enabled. Your worker bootstep must require the Autoreloader bootstep to use this;

class WorkerStep(bootsteps.StartStopStep):
    requires = ('celery.worker.autoreloader:Autoreloader',)

Example worker bootstep

An example Worker bootstep could be:

from celery import bootsteps

class ExampleWorkerStep(bootsteps.StartStopStep):
    requires = {'celery.worker.components:Pool'}

    def __init__(self, worker, **kwargs):
        print('Called when the WorkController instance is constructed')
        print('Arguments to WorkController: {0!r}'.format(kwargs))

    def create(self, worker):
        # this method can be used to delegate the action methods
        # to another object that implements ``start`` and ``stop``.
        return self

    def start(self, worker):
        print('Called when the worker is started.')

    def stop(self, worker):
        print('Called when the worker shuts down.')

    def terminate(self, worker):
        print('Called when the worker terminates')

Every method is passed the current WorkController instance as the first argument.

Another example could use the timer to wake up at regular intervals:

from celery import bootsteps


class DeadlockDetection(bootsteps.StartStopStep):
    requires = {'celery.worker.components:Timer'}

    def __init__(self, worker, deadlock_timeout=3600):
        self.timeout = deadlock_timeout
        self.requests = []
        self.tref = None

    def start(self, worker):
        # run every 30 seconds.
        self.tref = worker.timer.call_repeatedly(
            30.0, self.detect, (worker,), priority=10,
        )

    def stop(self, worker):
        if self.tref:
            self.tref.cancel()
            self.tref = None

    def detect(self, worker):
        # update active requests
        for req in worker.active_requests:
            if req.time_start and time() - req.time_start > self.timeout:
                raise SystemExit()

Customizing Task Handling Logs

The Celery worker emits messages to the Python logging subsystem for various events throughout the lifecycle of a task. These messages can be customized by overriding the LOG_<TYPE> format strings which are defined in celery/app/trace.py. For example:

import celery.app.trace

celery.app.trace.LOG_SUCCESS = "This is a custom message"

The various format strings are all provided with the task name and ID for % formatting, and some of them receive extra fields like the return value or the exception which caused a task to fail. These fields can be used in custom format strings like so:

import celery.app.trace

celery.app.trace.LOG_REJECTED = "%(name)r is cursed and I won't run it: %(exc)s"

Consumer

The Consumer blueprint establishes a connection to the broker, and is restarted every time this connection is lost. Consumer bootsteps include the worker heartbeat, the remote control command consumer, and importantly, the task consumer.

When you create consumer bootsteps you must take into account that it must be possible to restart your blueprint. An additional ‘shutdown’ method is defined for consumer bootsteps, this method is called when the worker is shutdown.

Attributes

app

The current app instance.

controller

The parent WorkController object that created this consumer.

hostname

The workers node name (e.g., worker1@example.com)

blueprint

This is the worker Blueprint.

hub

Event loop object (Hub). You can use this to register callbacks in the event loop.

This is only supported by async I/O enabled transports (amqp, redis), in which case the worker.use_eventloop attribute should be set.

Your worker bootstep must require the Hub bootstep to use this:

class WorkerStep(bootsteps.StartStopStep):
    requires = {'celery.worker.components:Hub'}

connection

The current broker connection (kombu.Connection).

A consumer bootstep must require the ‘Connection’ bootstep to use this:

class Step(bootsteps.StartStopStep):
    requires = {'celery.worker.consumer.connection:Connection'}

event_dispatcher

A app.events.Dispatcher object that can be used to send events.

A consumer bootstep must require the Events bootstep to use this.

class Step(bootsteps.StartStopStep):
    requires = {'celery.worker.consumer.events:Events'}

gossip

Worker to worker broadcast communication (Gossip).

A consumer bootstep must require the Gossip bootstep to use this.

class RatelimitStep(bootsteps.StartStopStep):
    """Rate limit tasks based on the number of workers in the
    cluster."""
    requires = {'celery.worker.consumer.gossip:Gossip'}

    def start(self, c):
        self.c = c
        self.c.gossip.on.node_join.add(self.on_cluster_size_change)
        self.c.gossip.on.node_leave.add(self.on_cluster_size_change)
        self.c.gossip.on.node_lost.add(self.on_node_lost)
        self.tasks = [
            self.app.tasks['proj.tasks.add']
            self.app.tasks['proj.tasks.mul']
        ]
        self.last_size = None

    def on_cluster_size_change(self, worker):
        cluster_size = len(list(self.c.gossip.state.alive_workers()))
        if cluster_size != self.last_size:
            for task in self.tasks:
                task.rate_limit = 1.0 / cluster_size
            self.c.reset_rate_limits()
            self.last_size = cluster_size

    def on_node_lost(self, worker):
        # may have processed heartbeat too late, so wake up soon
        # in order to see if the worker recovered.
        self.c.timer.call_after(10.0, self.on_cluster_size_change)

Callbacks

• <set> gossip.on.node_join

  Called whenever a new node joins the cluster, providing a Worker instance.

• <set> gossip.on.node_leave

  Called whenever a new node leaves the cluster (shuts down), providing a Worker instance.

• <set> gossip.on.node_lost

  Called whenever heartbeat was missed for a worker instance in the cluster (heartbeat not received or processed in time), providing a Worker instance.

  This doesn’t necessarily mean the worker is actually offline, so use a time out mechanism if the default heartbeat timeout isn’t sufficient.

pool

The current process/eventlet/gevent/thread pool. See celery.concurrency.base.BasePool.

timer

Timer <celery.utils.timer2.Schedule used to schedule functions.

heart

Responsible for sending worker event heartbeats (Heart).

Your consumer bootstep must require the Heart bootstep to use this:

class Step(bootsteps.StartStopStep):
    requires = {'celery.worker.consumer.heart:Heart'}

task_consumer

The kombu.Consumer object used to consume task messages.

Your consumer bootstep must require the Tasks bootstep to use this:

class Step(bootsteps.StartStopStep):
    requires = {'celery.worker.consumer.tasks:Tasks'}

strategies

Every registered task type has an entry in this mapping, where the value is used to execute an incoming message of this task type (the task execution strategy). This mapping is generated by the Tasks bootstep when the consumer starts:

for name, task in app.tasks.items():
    strategies[name] = task.start_strategy(app, consumer)
    task.__trace__ = celery.app.trace.build_tracer(
        name, task, loader, hostname
    )

Your consumer bootstep must require the Tasks bootstep to use this:

class Step(bootsteps.StartStopStep):
    requires = {'celery.worker.consumer.tasks:Tasks'}

task_buckets

A defaultdict used to look-up the rate limit for a task by type. Entries in this dict may be None (for no limit) or a TokenBucket instance implementing consume(tokens) and expected_time(tokens).

TokenBucket implements the token bucket algorithm, but any algorithm may be used as long as it conforms to the same interface and defines the two methods above.

qos

The QoS object can be used to change the task channels current prefetch_count value:

# increment at next cycle
consumer.qos.increment_eventually(1)
# decrement at next cycle
consumer.qos.decrement_eventually(1)
consumer.qos.set(10)

Methods

consumer.reset_rate_limits()

Updates the task_buckets mapping for all registered task types.

consumer.bucket_for_task(type, Bucket=TokenBucket)

Creates rate limit bucket for a task using its task.rate_limit attribute.

consumer.add_task_queue(name, exchange=None, exchange_type=None,

routing_key=None, \*\*options):

Adds new queue to consume from. This will persist on connection restart.

consumer.cancel_task_queue(name)

Stop consuming from queue by name. This will persist on connection restart.

apply_eta_task(request)

Schedule ETA task to execute based on the request.eta attribute. (Request)

Installing Bootsteps

app.steps['worker'] and app.steps['consumer'] can be modified to add new bootsteps:

>>> app = Celery()
>>> app.steps['worker'].add(MyWorkerStep)  # < add class, don't instantiate
>>> app.steps['consumer'].add(MyConsumerStep)

>>> app.steps['consumer'].update([StepA, StepB])

>>> app.steps['consumer']
{step:proj.StepB{()}, step:proj.MyConsumerStep{()}, step:proj.StepA{()}

The order of steps isn’t important here as the order is decided by the resulting dependency graph (Step.requires).

To illustrate how you can install bootsteps and how they work, this is an example step that prints some useless debugging information. It can be added both as a worker and consumer bootstep:

from celery import Celery
from celery import bootsteps

class InfoStep(bootsteps.Step):

    def __init__(self, parent, **kwargs):
        # here we can prepare the Worker/Consumer object
        # in any way we want, set attribute defaults, and so on.
        print('{0!r} is in init'.format(parent))

    def start(self, parent):
        # our step is started together with all other Worker/Consumer
        # bootsteps.
        print('{0!r} is starting'.format(parent))

    def stop(self, parent):
        # the Consumer calls stop every time the consumer is
        # restarted (i.e., connection is lost) and also at shutdown.
        # The Worker will call stop at shutdown only.
        print('{0!r} is stopping'.format(parent))

    def shutdown(self, parent):
        # shutdown is called by the Consumer at shutdown, it's not
        # called by Worker.
        print('{0!r} is shutting down'.format(parent))

    app = Celery(broker='amqp://')
    app.steps['worker'].add(InfoStep)
    app.steps['consumer'].add(InfoStep)

Starting the worker with this step installed will give us the following logs:

<Worker: w@example.com (initializing)> is in init
<Consumer: w@example.com (initializing)> is in init
[2013-05-29 16:18:20,544: WARNING/MainProcess]
    <Worker: w@example.com (running)> is starting
[2013-05-29 16:18:21,577: WARNING/MainProcess]
    <Consumer: w@example.com (running)> is starting
<Consumer: w@example.com (closing)> is stopping
<Worker: w@example.com (closing)> is stopping
<Consumer: w@example.com (terminating)> is shutting down

The print statements will be redirected to the logging subsystem after the worker has been initialized, so the “is starting” lines are time-stamped. You may notice that this does no longer happen at shutdown, this is because the stop and shutdown methods are called inside a signal handler, and it’s not safe to use logging inside such a handler. Logging with the Python logging module isn’t reentrant: meaning you cannot interrupt the function then call it again later. It’s important that the stop and shutdown methods you write is also reentrant.

Starting the worker with --loglevel=debug will show us more information about the boot process:

[2013-05-29 16:18:20,509: DEBUG/MainProcess] | Worker: Preparing bootsteps.
[2013-05-29 16:18:20,511: DEBUG/MainProcess] | Worker: Building graph...
<celery.apps.worker.Worker object at 0x101ad8410> is in init
[2013-05-29 16:18:20,511: DEBUG/MainProcess] | Worker: New boot order:
    {Hub, Pool, Timer, StateDB, Autoscaler, InfoStep, Beat, Consumer}
[2013-05-29 16:18:20,514: DEBUG/MainProcess] | Consumer: Preparing bootsteps.
[2013-05-29 16:18:20,514: DEBUG/MainProcess] | Consumer: Building graph...
<celery.worker.consumer.Consumer object at 0x101c2d8d0> is in init
[2013-05-29 16:18:20,515: DEBUG/MainProcess] | Consumer: New boot order:
    {Connection, Mingle, Events, Gossip, InfoStep, Agent,
     Heart, Control, Tasks, event loop}
[2013-05-29 16:18:20,522: DEBUG/MainProcess] | Worker: Starting Hub
[2013-05-29 16:18:20,522: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:20,522: DEBUG/MainProcess] | Worker: Starting Pool
[2013-05-29 16:18:20,542: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:20,543: DEBUG/MainProcess] | Worker: Starting InfoStep
[2013-05-29 16:18:20,544: WARNING/MainProcess]
    <celery.apps.worker.Worker object at 0x101ad8410> is starting
[2013-05-29 16:18:20,544: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:20,544: DEBUG/MainProcess] | Worker: Starting Consumer
[2013-05-29 16:18:20,544: DEBUG/MainProcess] | Consumer: Starting Connection
[2013-05-29 16:18:20,559: INFO/MainProcess] Connected to amqp://guest@127.0.0.1:5672//
[2013-05-29 16:18:20,560: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:20,560: DEBUG/MainProcess] | Consumer: Starting Mingle
[2013-05-29 16:18:20,560: INFO/MainProcess] mingle: searching for neighbors
[2013-05-29 16:18:21,570: INFO/MainProcess] mingle: no one here
[2013-05-29 16:18:21,570: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:21,571: DEBUG/MainProcess] | Consumer: Starting Events
[2013-05-29 16:18:21,572: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:21,572: DEBUG/MainProcess] | Consumer: Starting Gossip
[2013-05-29 16:18:21,577: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:21,577: DEBUG/MainProcess] | Consumer: Starting InfoStep
[2013-05-29 16:18:21,577: WARNING/MainProcess]
    <celery.worker.consumer.Consumer object at 0x101c2d8d0> is starting
[2013-05-29 16:18:21,578: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:21,578: DEBUG/MainProcess] | Consumer: Starting Heart
[2013-05-29 16:18:21,579: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:21,579: DEBUG/MainProcess] | Consumer: Starting Control
[2013-05-29 16:18:21,583: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:21,583: DEBUG/MainProcess] | Consumer: Starting Tasks
[2013-05-29 16:18:21,606: DEBUG/MainProcess] basic.qos: prefetch_count->80
[2013-05-29 16:18:21,606: DEBUG/MainProcess] ^-- substep ok
[2013-05-29 16:18:21,606: DEBUG/MainProcess] | Consumer: Starting event loop
[2013-05-29 16:18:21,608: WARNING/MainProcess] celery@example.com ready.

Command-line programs

Adding new command-line options

Command-specific options

You can add additional command-line options to the worker, beat, and events commands by modifying the user_options attribute of the application instance.

Celery commands uses the click module to parse command-line arguments, and so to add custom arguments you need to add click.Option instances to the relevant set.

Example adding a custom option to the celery worker command:

from celery import Celery
from click import Option

app = Celery(broker='amqp://')

app.user_options['worker'].add(Option(('--enable-my-option',),
                                      is_flag=True,
                                      help='Enable custom option.'))

All bootsteps will now receive this argument as a keyword argument to Bootstep.__init__:

from celery import bootsteps

class MyBootstep(bootsteps.Step):

    def __init__(self, parent, enable_my_option=False, **options):
        super().__init__(parent, **options)
        if enable_my_option:
            party()

app.steps['worker'].add(MyBootstep)

Preload options

The celery umbrella command supports the concept of ‘preload options’. These are special options passed to all sub-commands.

You can add new preload options, for example to specify a configuration template:

from celery import Celery
from celery import signals
from click import Option

app = Celery()

app.user_options['preload'].add(Option(('-Z', '--template'),
                                       default='default',
                                       help='Configuration template to use.'))

@signals.user_preload_options.connect
def on_preload_parsed(options, **kwargs):
    use_template(options['template'])

Adding new celery sub-commands

New commands can be added to the celery umbrella command by using setuptools entry-points.

Entry-points is special meta-data that can be added to your packages setup.py program, and then after installation, read from the system using the importlib module.

Celery recognizes celery.commands entry-points to install additional sub-commands, where the value of the entry-point must point to a valid click command.

This is how the Flower monitoring extension may add the celery flower command, by adding an entry-point in setup.py:

setup(
    name='flower',
    entry_points={
        'celery.commands': [
           'flower = flower.command:flower',
        ],
    }
)

The command definition is in two parts separated by the equal sign, where the first part is the name of the sub-command (flower), then the second part is the fully qualified symbol path to the function that implements the command:

flower.command:flower

The module path and the name of the attribute should be separated by colon as above.

In the module flower/command.py, the command function may be defined as the following:

import click

@click.command()
@click.option('--port', default=8888, type=int, help='Webserver port')
@click.option('--debug', is_flag=True)
def flower(port, debug):
    print('Running our command')

Worker API

Hub - The workers async event loop

supported transports:

amqp, redis

New in version 3.0.

The worker uses asynchronous I/O when the amqp or redis broker transports are used. The eventual goal is for all transports to use the event-loop, but that will take some time so other transports still use a threading-based solution.

hub.add(fd, callback, flags)

hub.add_reader(fd, callback, \*args)

Add callback to be called when fd is readable.

The callback will stay registered until explicitly removed using hub.remove(fd), or the file descriptor is automatically discarded because it’s no longer valid.

Note that only one callback can be registered for any given file descriptor at a time, so calling add a second time will remove any callback that was previously registered for that file descriptor.

A file descriptor is any file-like object that supports the fileno method, or it can be the file descriptor number (int).

hub.add_writer(fd, callback, \*args)

Add callback to be called when fd is writable. See also notes for hub.add_reader() above.

hub.remove(fd)

Remove all callbacks for file descriptor fd from the loop.

Timer - Scheduling events

timer.call_after(secs, callback, args=(), kwargs=(),

priority=0)

timer.call_repeatedly(secs, callback, args=(), kwargs=(),

priority=0)

timer.call_at(eta, callback, args=(), kwargs=(),

priority=0)