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

Calling Tasks

Basics

This document describes Celery’s uniform “Calling API” used by task instances and the canvas.

The API defines a standard set of execution options, as well as three methods:

  • apply_async(args[, kwargs[, …]])

    Sends a task message.

  • delay(*args, **kwargs)

    Shortcut to send a task message, but doesn’t support execution options.

  • calling (__call__)

    Applying an object supporting the calling API (e.g., add(2, 2)) means that the task will not be executed by a worker, but in the current process instead (a message won’t be sent).

Quick Cheat Sheet

  • T.delay(arg, kwarg=value)

    Star arguments shortcut to .apply_async. (.delay(*args, **kwargs) calls .apply_async(args, kwargs)).

  • T.apply_async((arg,), {'kwarg': value})

  • T.apply_async(countdown=10)

    executes in 10 seconds from now.

  • T.apply_async(eta=now + timedelta(seconds=10))

    executes in 10 seconds from now, specified using eta

  • T.apply_async(countdown=60, expires=120)

    executes in one minute from now, but expires after 2 minutes.

  • T.apply_async(expires=now + timedelta(days=2))

    expires in 2 days, set using datetime.

Example

The delay() method is convenient as it looks like calling a regular function:

task.delay(arg1, arg2, kwarg1='x', kwarg2='y')

Using apply_async() instead you have to write:

task.apply_async(args=[arg1, arg2], kwargs={'kwarg1': 'x', 'kwarg2': 'y'})

So delay is clearly convenient, but if you want to set additional execution options you have to use apply_async.

The rest of this document will go into the task execution options in detail. All examples use a task called add, returning the sum of two arguments:

@app.task
def add(x, y):
    return x + y

There’s another way…

You’ll learn more about this later while reading about the Canvas, but signature’s are objects used to pass around the signature of a task invocation, (for example to send it over the network), and they also support the Calling API:

task.s(arg1, arg2, kwarg1='x', kwargs2='y').apply_async()

Linking (callbacks/errbacks)

Celery supports linking tasks together so that one task follows another. The callback task will be applied with the result of the parent task as a partial argument:

add.apply_async((2, 2), link=add.s(16))

Here the result of the first task (4) will be sent to a new task that adds 16 to the previous result, forming the expression (2 + 2) + 16 = 20

You can also cause a callback to be applied if task raises an exception (errback). The worker won’t actually call the errback as a task, but will instead call the errback function directly so that the raw request, exception and traceback objects can be passed to it.

This is an example error callback:

@app.task
def error_handler(request, exc, traceback):
    print('Task {0} raised exception: {1!r}\n{2!r}'.format(
          request.id, exc, traceback))

it can be added to the task using the link_error execution option:

add.apply_async((2, 2), link_error=error_handler.s())

In addition, both the link and link_error options can be expressed as a list:

add.apply_async((2, 2), link=[add.s(16), other_task.s()])

The callbacks/errbacks will then be called in order, and all callbacks will be called with the return value of the parent task as a partial argument.

On message

Celery supports catching all states changes by setting on_message callback.

For example for long-running tasks to send task progress you can do something like this:

@app.task(bind=True)
def hello(self, a, b):
    time.sleep(1)
    self.update_state(state="PROGRESS", meta={'progress': 50})
    time.sleep(1)
    self.update_state(state="PROGRESS", meta={'progress': 90})
    time.sleep(1)
    return 'hello world: %i' % (a+b)
def on_raw_message(body):
    print(body)

a, b = 1, 1
r = hello.apply_async(args=(a, b))
print(r.get(on_message=on_raw_message, propagate=False))

Will generate output like this:

{'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7',
 'result': {'progress': 50},
 'children': [],
 'status': 'PROGRESS',
 'traceback': None}
{'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7',
 'result': {'progress': 90},
 'children': [],
 'status': 'PROGRESS',
 'traceback': None}
{'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7',
 'result': 'hello world: 10',
 'children': [],
 'status': 'SUCCESS',
 'traceback': None}
hello world: 10

ETA and Countdown

The ETA (estimated time of arrival) lets you set a specific date and time that is the earliest time at which your task will be executed. countdown is a shortcut to set ETA by seconds into the future.

>>> result = add.apply_async((2, 2), countdown=3)
>>> result.get()    # this takes at least 3 seconds to return
20

The task is guaranteed to be executed at some time after the specified date and time, but not necessarily at that exact time. Possible reasons for broken deadlines may include many items waiting in the queue, or heavy network latency. To make sure your tasks are executed in a timely manner you should monitor the queue for congestion. Use Munin, or similar tools, to receive alerts, so appropriate action can be taken to ease the workload. See Munin.

While countdown is an integer, eta must be a datetime object, specifying an exact date and time (including millisecond precision, and timezone information):

>>> from datetime import datetime, timedelta

>>> tomorrow = datetime.utcnow() + timedelta(days=1)
>>> add.apply_async((2, 2), eta=tomorrow)

Expiration

The expires argument defines an optional expiry time, either as seconds after task publish, or a specific date and time using datetime:

>>> # Task expires after one minute from now.
>>> add.apply_async((10, 10), expires=60)

>>> # Also supports datetime
>>> from datetime import datetime, timedelta
>>> add.apply_async((10, 10), kwargs,
...                 expires=datetime.now() + timedelta(days=1)

When a worker receives an expired task it will mark the task as REVOKED (TaskRevokedError).

Message Sending Retry

Celery will automatically retry sending messages in the event of connection failure, and retry behavior can be configured – like how often to retry, or a maximum number of retries – or disabled all together.

To disable retry you can set the retry execution option to False:

add.apply_async((2, 2), retry=False)

Retry Policy

A retry policy is a mapping that controls how retries behave, and can contain the following keys:

  • max_retries

    Maximum number of retries before giving up, in this case the exception that caused the retry to fail will be raised.

    A value of None means it will retry forever.

    The default is to retry 3 times.

  • interval_start

    Defines the number of seconds (float or integer) to wait between retries. Default is 0 (the first retry will be instantaneous).

  • interval_step

    On each consecutive retry this number will be added to the retry delay (float or integer). Default is 0.2.

  • interval_max

    Maximum number of seconds (float or integer) to wait between retries. Default is 0.2.

For example, the default policy correlates to:

add.apply_async((2, 2), retry=True, retry_policy={
    'max_retries': 3,
    'interval_start': 0,
    'interval_step': 0.2,
    'interval_max': 0.2,
})

the maximum time spent retrying will be 0.4 seconds. It’s set relatively short by default because a connection failure could lead to a retry pile effect if the broker connection is down – For example, many web server processes waiting to retry, blocking other incoming requests.

Connection Error Handling

When you send a task and the message transport connection is lost, or the connection cannot be initiated, an OperationalError error will be raised:

>>> from proj.tasks import add
>>> add.delay(2, 2)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "celery/app/task.py", line 388, in delay
        return self.apply_async(args, kwargs)
  File "celery/app/task.py", line 503, in apply_async
    **options
  File "celery/app/base.py", line 662, in send_task
    amqp.send_task_message(P, name, message, **options)
  File "celery/backends/rpc.py", line 275, in on_task_call
    maybe_declare(self.binding(producer.channel), retry=True)
  File "/opt/celery/kombu/kombu/messaging.py", line 204, in _get_channel
    channel = self._channel = channel()
  File "/opt/celery/py-amqp/amqp/connection.py", line 272, in connect
    self.transport.connect()
  File "/opt/celery/py-amqp/amqp/transport.py", line 100, in connect
    self._connect(self.host, self.port, self.connect_timeout)
  File "/opt/celery/py-amqp/amqp/transport.py", line 141, in _connect
    self.sock.connect(sa)
  kombu.exceptions.OperationalError: [Errno 61] Connection refused

If you have retries enabled this will only happen after retries are exhausted, or when disabled immediately.

You can handle this error too:

>>> from celery.utils.log import get_logger
>>> logger = get_logger(__name__)

>>> try:
...     add.delay(2, 2)
... except add.OperationalError as exc:
...     logger.exception('Sending task raised: %r', exc)

Serializers

Data transferred between clients and workers needs to be serialized, so every message in Celery has a content_type header that describes the serialization method used to encode it.

The default serializer is JSON, but you can change this using the task_serializer setting, or for each individual task, or even per message.

There’s built-in support for JSON, pickle, YAML and msgpack, and you can also add your own custom serializers by registering them into the Kombu serializer registry

See also

Message Serialization in the Kombu user guide.

Each option has its advantages and disadvantages.

json – JSON is supported in many programming languages, is now

a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries, such as simplejson.

The primary disadvantage to JSON is that it limits you to the following data types: strings, Unicode, floats, Boolean, dictionaries, and lists. Decimals and dates are notably missing.

Binary data will be transferred using Base64 encoding, increasing the size of the transferred data by 34% compared to an encoding format where native binary types are supported.

However, if your data fits inside the above constraints and you need cross-language support, the default setting of JSON is probably your best choice.

See http://json.org for more information.

Note

(From Python official docs https://docs.python.org/3.6/library/json.html) Keys in key/value pairs of JSON are always of the type str. When a dictionary is converted into JSON, all the keys of the dictionary are coerced to strings. As a result of this, if a dictionary is converted into JSON and then back into a dictionary, the dictionary may not equal the original one. That is, loads(dumps(x)) != x if x has non-string keys.

pickle – If you have no desire to support any language other than

Python, then using the pickle encoding will gain you the support of all built-in Python data types (except class instances), smaller messages when sending binary files, and a slight speedup over JSON processing.

See pickle for more information.

yaml – YAML has many of the same characteristics as json,

except that it natively supports more data types (including dates, recursive references, etc.).

However, the Python libraries for YAML are a good bit slower than the libraries for JSON.

If you need a more expressive set of data types and need to maintain cross-language compatibility, then YAML may be a better fit than the above.

See http://yaml.org/ for more information.

msgpack – msgpack is a binary serialization format that’s closer to JSON

in features. It’s very young however, and support should be considered experimental at this point.

See http://msgpack.org/ for more information.

The encoding used is available as a message header, so the worker knows how to deserialize any task. If you use a custom serializer, this serializer must be available for the worker.

The following order is used to decide the serializer used when sending a task:

  1. The serializer execution option.

  2. The Task.serializer attribute

  3. The task_serializer setting.

Example setting a custom serializer for a single task invocation:

>>> add.apply_async((10, 10), serializer='json')

Compression

Celery can compress messages using the following builtin schemes:

  • brotli

    brotli is optimized for the web, in particular small text documents. It is most effective for serving static content such as fonts and html pages.

    To use it, install Celery with:

    $ pip install celery[brotli]
    
  • bzip2

    bzip2 creates smaller files than gzip, but compression and decompression speeds are noticeably slower than those of gzip.

    To use it, please ensure your Python executable was compiled with bzip2 support.

    If you get the following ImportError:

    >>> import bz2
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ImportError: No module named 'bz2'
    

    it means that you should recompile your Python version with bzip2 support.

  • gzip

    gzip is suitable for systems that require a small memory footprint, making it ideal for systems with limited memory. It is often used to generate files with the “.tar.gz” extension.

    To use it, please ensure your Python executable was compiled with gzip support.

    If you get the following ImportError:

    >>> import gzip
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ImportError: No module named 'gzip'
    

    it means that you should recompile your Python version with gzip support.

  • lzma

    lzma provides a good compression ratio and executes with fast compression and decompression speeds at the expense of higher memory usage.

    To use it, please ensure your Python executable was compiled with lzma support and that your Python version is 3.3 and above.

    If you get the following ImportError:

    >>> import lzma
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ImportError: No module named 'lzma'
    

    it means that you should recompile your Python version with lzma support.

    Alternatively, you can also install a backport using:

    $ pip install celery[lzma]
    
  • zlib

    zlib is an abstraction of the Deflate algorithm in library form which includes support both for the gzip file format and a lightweight stream format in its API. It is a crucial component of many software systems - Linux kernel and Git VCS just to name a few.

    To use it, please ensure your Python executable was compiled with zlib support.

    If you get the following ImportError:

    >>> import zlib
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ImportError: No module named 'zlib'
    

    it means that you should recompile your Python version with zlib support.

  • zstd

    zstd targets real-time compression scenarios at zlib-level and better compression ratios. It’s backed by a very fast entropy stage, provided by Huff0 and FSE library.

    To use it, install Celery with:

    $ pip install celery[zstd]
    

You can also create your own compression schemes and register them in the kombu compression registry.

The following order is used to decide the compression scheme used when sending a task:

  1. The compression execution option.

  2. The Task.compression attribute.

  3. The task_compression attribute.

Example specifying the compression used when calling a task:

>>> add.apply_async((2, 2), compression='zlib')

Connections

You can handle the connection manually by creating a publisher:

results = []
with add.app.pool.acquire(block=True) as connection:
    with add.get_publisher(connection) as publisher:
        try:
            for args in numbers:
                res = add.apply_async((2, 2), publisher=publisher)
                results.append(res)
print([res.get() for res in results])

Though this particular example is much better expressed as a group:

>>> from celery import group

>>> numbers = [(2, 2), (4, 4), (8, 8), (16, 16)]
>>> res = group(add.s(i, j) for i, j in numbers).apply_async()

>>> res.get()
[4, 8, 16, 32]

Routing options

Celery can route tasks to different queues.

Simple routing (name <-> name) is accomplished using the queue option:

add.apply_async(queue='priority.high')

You can then assign workers to the priority.high queue by using the workers -Q argument:

$ celery -A proj worker -l INFO -Q celery,priority.high

See also

Hard-coding queue names in code isn’t recommended, the best practice is to use configuration routers (task_routes).

To find out more about routing, please see Routing Tasks.

Results options

You can enable or disable result storage using the task_ignore_result setting or by using the ignore_result option:

>>> result = add.apply_async((1, 2), ignore_result=True)
>>> result.get()
None

>>> # Do not ignore result (default)
...
>>> result = add.apply_async((1, 2), ignore_result=False)
>>> result.get()
3

If you’d like to store additional metadata about the task in the result backend set the result_extended setting to True.

See also

For more information on tasks, please see Tasks.

Advanced Options

These options are for advanced users who want to take use of AMQP’s full routing capabilities. Interested parties may read the routing guide.

  • exchange

    Name of exchange (or a kombu.entity.Exchange) to send the message to.

  • routing_key

    Routing key used to determine.

  • priority

    A number between 0 and 255, where 255 is the highest priority.

    Supported by: RabbitMQ, Redis (priority reversed, 0 is highest).