Tasks are the building blocks of Celery applications.
A task is a class that can be created out of any callable. It performs dual roles in that it defines both what happens when a task is called (sends a message), and what happens when a worker receives that message.
Every task class has a unique name, and this name is referenced in messages so the worker can find the right function to execute.
A task message is not removed from the queue until that message has been :term:`acknowledged` by a worker. A worker can reserve many messages in advance and even if the worker is killed -- by power failure or some other reason -- the message will be redelivered to another worker.
Ideally task functions should be :term:`idempotent`: meaning the function won't cause unintended effects even if called multiple times with the same arguments. Since the worker cannot detect if your tasks are idempotent, the default behavior is to acknowledge the message in advance, just before it's executed, so that a task invocation that already started is never executed again.
If your task is idempotent you can set the :attr:`~Task.acks_late` option to have the worker acknowledge the message after the task returns instead. See also the FAQ entry :ref:`faq-acks_late-vs-retry`.
Note that the worker will acknowledge the message if the child process executing the task is terminated (either by the task calling :func:`sys.exit`, or by signal) even when :attr:`~Task.acks_late` is enabled. This behavior is intentional as...
- We don't want to rerun tasks that forces the kernel to send a :sig:`SIGSEGV` (segmentation fault) or similar signals to the process.
- We assume that a system administrator deliberately killing the task does not want it to automatically restart.
- A task that allocates too much memory is in danger of triggering the kernel OOM killer, the same may happen again.
- A task that always fails when redelivered may cause a high-frequency message loop taking down the system.
If you really want a task to be redelivered in these scenarios you should consider enabling the :setting:`task_reject_on_worker_lost` setting.
Warning
A task that blocks indefinitely may eventually stop the worker instance from doing any other work.
If your task does I/O then make sure you add timeouts to these operations, like adding a timeout to a web request using the :pypi:`requests` library:
connect_timeout, read_timeout = 5.0, 30.0
response = requests.get(URL, timeout=(connect_timeout, read_timeout)):ref:`Time limits <worker-time-limits>` are convenient for making sure all tasks return in a timely manner, but a time limit event will actually kill the process by force so only use them to detect cases where you haven't used manual timeouts yet.
In previous versions, the default prefork pool scheduler was not friendly to long-running tasks, so if you had tasks that ran for minutes/hours, it was advised to enable the :option:`-Ofair <celery worker -O>` command-line argument to the :program:`celery worker`. However, as of version 4.0, -Ofair is now the default scheduling strategy. See :ref:`optimizing-prefetch-limit` for more information, and for the best performance route long-running and short-running tasks to dedicated workers (:ref:`routing-automatic`).
If your worker hangs then please investigate what tasks are running before submitting an issue, as most likely the hanging is caused by one or more tasks hanging on a network operation.
--
In this chapter you'll learn all about defining tasks, and this is the table of contents:
You can easily create a task from any callable by using the :meth:`@task` decorator:
from .models import User
@app.task
def create_user(username, password):
User.objects.create(username=username, password=password)There are also many :ref:`options <task-options>` that can be set for the task, these can be specified as arguments to the decorator:
@app.task(serializer='json')
def create_user(username, password):
User.objects.create(username=username, password=password)The task decorator is available on your :class:`@Celery` application instance, if you don't know what this is then please read :ref:`first-steps`.
If you're using Django (see :ref:`django-first-steps`), or you're the author of a library then you probably want to use the :func:`@shared_task` decorator:
from celery import shared_task @shared_task def add(x, y): return x + y
When using multiple decorators in combination with the task decorator you must make sure that the task decorator is applied last (oddly, in Python this means it must be first in the list):
@app.task @decorator2 @decorator1 def add(x, y): return x + y
A task being bound means the first argument to the task will always
be the task instance (self), just like Python bound methods:
logger = get_task_logger(__name__)
@app.task(bind=True)
def add(self, x, y):
logger.info(self.request.id)Bound tasks are needed for retries (using :meth:`Task.retry() <@Task.retry>`), for accessing information about the current task request, and for any additional functionality you add to custom task base classes.
The base argument to the task decorator specifies the base class of the task:
import celery
class MyTask(celery.Task):
def on_failure(self, exc, task_id, args, kwargs, einfo):
print('{0!r} failed: {1!r}'.format(task_id, exc))
@app.task(base=MyTask)
def add(x, y):
raise KeyError()Every task must have a unique name.
If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name of the task function.
Example setting explicit name:
>>> @app.task(name='sum-of-two-numbers')
>>> def add(x, y):
... return x + y
>>> add.name
'sum-of-two-numbers'A best practice is to use the module name as a name-space, this way names won't collide if there's already a task with that name defined in another module.
>>> @app.task(name='tasks.add')
>>> def add(x, y):
... return x + yYou can tell the name of the task by investigating its .name attribute:
>>> add.name
'tasks.add'The name we specified here (tasks.add) is exactly the name that would've
been automatically generated for us if the task was defined in a module
named :file:`tasks.py`:
@app.task
def add(x, y):
return x + y>>> from tasks import add
>>> add.name
'tasks.add'Note
You can use the inspect command in a worker to view the names of all registered tasks. See the inspect registered command in the :ref:`monitoring-control` section of the User Guide.
.. versionadded:: 4.0
There are some cases when the default automatic naming isn't suitable. Consider having many tasks within many different modules:
project/
/__init__.py
/celery.py
/moduleA/
/__init__.py
/tasks.py
/moduleB/
/__init__.py
/tasks.py
Using the default automatic naming, each task will have a generated name like moduleA.tasks.taskA, moduleA.tasks.taskB, moduleB.tasks.test, and so on. You may want to get rid of having tasks in all task names. As pointed above, you can explicitly give names for all tasks, or you can change the automatic naming behavior by overriding :meth:`@gen_task_name`. Continuing with the example, celery.py may contain:
from celery import Celery
class MyCelery(Celery):
def gen_task_name(self, name, module):
if module.endswith('.tasks'):
module = module[:-6]
return super().gen_task_name(name, module)
app = MyCelery('main')So each task will have a name like moduleA.taskA, moduleA.taskB and moduleB.test.
Warning
Make sure that your :meth:`@gen_task_name` is a pure function: meaning that for the same input it must always return the same output.
:attr:`Task.request <@Task.request>` contains information and state related to the currently executing task.
The request defines the following attributes:
| id: | The unique id of the executing task. |
|---|---|
| group: | The unique id of the task's :ref:`group <canvas-group>`, if this task is a member. |
| chord: | The unique id of the chord this task belongs to (if the task is part of the header). |
| correlation_id: | Custom ID used for things like de-duplication. |
| args: | Positional arguments. |
| kwargs: | Keyword arguments. |
| origin: | Name of host that sent this task. |
| retries: | How many times the current task has been retried. An integer starting at 0. |
| is_eager: | Set to :const:`True` if the task is executed locally in the client, not by a worker. |
| eta: | The original ETA of the task (if any). This is in UTC time (depending on the :setting:`enable_utc` setting). |
| expires: | The original expiry time of the task (if any). This is in UTC time (depending on the :setting:`enable_utc` setting). |
| hostname: | Node name of the worker instance executing the task. |
| delivery_info: | Additional message delivery information. This is a mapping containing the exchange and routing key used to deliver this task. Used by for example :meth:`Task.retry() <@Task.retry>` to resend the task to the same destination queue. Availability of keys in this dict depends on the message broker used. |
| reply-to: | Name of queue to send replies back to (used with RPC result backend for example). |
| called_directly: | This flag is set to true if the task wasn't executed by the worker. |
| timelimit: | A tuple of the current (soft, hard) time limits active for
this task (if any). |
| callbacks: | A list of signatures to be called if this task returns successfully. |
| errbacks: | A list of signatures to be called if this task fails. |
| utc: | Set to true the caller has UTC enabled (:setting:`enable_utc`). |
.. versionadded:: 3.1
| headers: | Mapping of message headers sent with this task message (may be :const:`None`). |
|---|---|
| reply_to: | Where to send reply to (queue name). |
| correlation_id: | Usually the same as the task id, often used in amqp to keep track of what a reply is for. |
.. versionadded:: 4.0
| root_id: | The unique id of the first task in the workflow this task is part of (if any). |
|---|---|
| parent_id: | The unique id of the task that called this task (if any). |
| chain: | Reversed list of tasks that form a chain (if any).
The last item in this list will be the next task to succeed the
current task. If using version one of the task protocol the chain
tasks will be in request.callbacks instead. |
.. versionadded:: 5.2
| properties: | Mapping of message properties received with this task message (may be :const:`None` or :const:`{}`) |
|---|---|
| replaced_task_nesting: | How many times the task was replaced, if at all. (may be :const:`0`) |
An example task accessing information in the context is:
@app.task(bind=True)
def dump_context(self, x, y):
print('Executing task id {0.id}, args: {0.args!r} kwargs: {0.kwargs!r}'.format(
self.request))The bind argument means that the function will be a "bound method" so
that you can access attributes and methods on the task type instance.
The worker will automatically set up logging for you, or you can configure logging manually.
A special logger is available named "celery.task", you can inherit from this logger to automatically get the task name and unique id as part of the logs.
The best practice is to create a common logger for all of your tasks at the top of your module:
from celery.utils.log import get_task_logger
logger = get_task_logger(__name__)
@app.task
def add(x, y):
logger.info('Adding {0} + {1}'.format(x, y))
return x + yCelery uses the standard Python logger library, and the documentation can be found :mod:`here <logging>`.
You can also use :func:`print`, as anything written to standard out/-err will be redirected to the logging system (you can disable this, see :setting:`worker_redirect_stdouts`).
Note
The worker won't update the redirection if you create a logger instance somewhere in your task or task module.
If you want to redirect sys.stdout and sys.stderr to a custom
logger you have to enable this manually, for example:
import sys
logger = get_task_logger(__name__)
@app.task(bind=True)
def add(self, x, y):
old_outs = sys.stdout, sys.stderr
rlevel = self.app.conf.worker_redirect_stdouts_level
try:
self.app.log.redirect_stdouts_to_logger(logger, rlevel)
print('Adding {0} + {1}'.format(x, y))
return x + y
finally:
sys.stdout, sys.stderr = old_outsNote
If a specific Celery logger you need is not emitting logs, you should check that the logger is propagating properly. In this example "celery.app.trace" is enabled so that "succeeded in" logs are emitted:
import celery
import logging
@celery.signals.after_setup_logger.connect
def on_after_setup_logger(**kwargs):
logger = logging.getLogger('celery')
logger.propagate = True
logger = logging.getLogger('celery.app.trace')
logger.propagate = TrueNote
If you want to completely disable Celery logging configuration, use the :signal:`setup_logging` signal:
import celery
@celery.signals.setup_logging.connect
def on_setup_logging(**kwargs):
pass.. versionadded:: 4.0
Celery will verify the arguments passed when you call the task, just like Python does when calling a normal function:
>>> @app.task
... def add(x, y):
... return x + y
# Calling the task with two arguments works:
>>> add.delay(8, 8)
<AsyncResult: f59d71ca-1549-43e0-be41-4e8821a83c0c>
# Calling the task with only one argument fails:
>>> add.delay(8)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "celery/app/task.py", line 376, in delay
return self.apply_async(args, kwargs)
File "celery/app/task.py", line 485, in apply_async
check_arguments(*(args or ()), **(kwargs or {}))
TypeError: add() takes exactly 2 arguments (1 given)You can disable the argument checking for any task by setting its :attr:`[email protected]` attribute to :const:`False`:
>>> @app.task(typing=False)
... def add(x, y):
... return x + y
# Works locally, but the worker receiving the task will raise an error.
>>> add.delay(8)
<AsyncResult: f59d71ca-1549-43e0-be41-4e8821a83c0c>.. versionadded:: 4.0
When using :setting:`task_protocol` 2 or higher (default since 4.0), you can
override how positional arguments and keyword arguments are represented in logs
and monitoring events using the argsrepr and kwargsrepr calling
arguments:
>>> add.apply_async((2, 3), argsrepr='(<secret-x>, <secret-y>)')
>>> charge.s(account, card='1234 5678 1234 5678').set(
... kwargsrepr=repr({'card': '**** **** **** 5678'})
... ).delay()Warning
Sensitive information will still be accessible to anyone able to read your task message from the broker, or otherwise able intercept it.
For this reason you should probably encrypt your message if it contains sensitive information, or in this example with a credit card number the actual number could be stored encrypted in a secure store that you retrieve and decrypt in the task itself.
:meth:`Task.retry() <@Task.retry>` can be used to re-execute the task, for example in the event of recoverable errors.
When you call retry it'll send a new message, using the same
task-id, and it'll take care to make sure the message is delivered
to the same queue as the originating task.
When a task is retried this is also recorded as a task state, so that you can track the progress of the task using the result instance (see :ref:`task-states`).
Here's an example using retry:
@app.task(bind=True)
def send_twitter_status(self, oauth, tweet):
try:
twitter = Twitter(oauth)
twitter.update_status(tweet)
except (Twitter.FailWhaleError, Twitter.LoginError) as exc:
raise self.retry(exc=exc)Note
The :meth:`Task.retry() <@Task.retry>` call will raise an exception so any code after the retry won't be reached. This is the :exc:`~@Retry` exception, it isn't handled as an error but rather as a semi-predicate to signify to the worker that the task is to be retried, so that it can store the correct state when a result backend is enabled.
This is normal operation and always happens unless the
throw argument to retry is set to :const:`False`.
The bind argument to the task decorator will give access to self (the
task type instance).
The exc argument is used to pass exception information that's
used in logs, and when storing task results.
Both the exception and the traceback will
be available in the task state (if a result backend is enabled).
If the task has a max_retries value the current exception
will be re-raised if the max number of retries has been exceeded,
but this won't happen if:
An
excargument wasn't given.In this case the :exc:`~@MaxRetriesExceededError` exception will be raised.
There's no current exception
If there's no original exception to re-raise the
excargument will be used instead, so:self.retry(exc=Twitter.LoginError())
will raise the
excargument given.
When a task is to be retried, it can wait for a given amount of time before doing so, and the default delay is defined by the :attr:`[email protected]_retry_delay` attribute. By default this is set to 3 minutes. Note that the unit for setting the delay is in seconds (int or float).
You can also provide the countdown argument to :meth:`[email protected]` to override this default.
@app.task(bind=True, default_retry_delay=30 * 60) # retry in 30 minutes.
def add(self, x, y):
try:
something_raising()
except Exception as exc:
# overrides the default delay to retry after 1 minute
raise self.retry(exc=exc, countdown=60).. versionadded:: 4.0
Sometimes you just want to retry a task whenever a particular exception is raised.
Fortunately, you can tell Celery to automatically retry a task using autoretry_for argument in the :meth:`@task` decorator:
from twitter.exceptions import FailWhaleError
@app.task(autoretry_for=(FailWhaleError,))
def refresh_timeline(user):
return twitter.refresh_timeline(user)If you want to specify custom arguments for an internal :meth:`[email protected]` call, pass retry_kwargs argument to :meth:`@task` decorator:
@app.task(autoretry_for=(FailWhaleError,),
retry_kwargs={'max_retries': 5})
def refresh_timeline(user):
return twitter.refresh_timeline(user)This is provided as an alternative to manually handling the exceptions, and the example above will do the same as wrapping the task body in a :keyword:`try` ... :keyword:`except` statement:
@app.task
def refresh_timeline(user):
try:
twitter.refresh_timeline(user)
except FailWhaleError as exc:
raise refresh_timeline.retry(exc=exc, max_retries=5)If you want to automatically retry on any error, simply use:
@app.task(autoretry_for=(Exception,))
def x():
..... versionadded:: 4.2
If your tasks depend on another service, like making a request to an API, then it's a good idea to use exponential backoff to avoid overwhelming the service with your requests. Fortunately, Celery's automatic retry support makes it easy. Just specify the :attr:`~Task.retry_backoff` argument, like this:
from requests.exceptions import RequestException
@app.task(autoretry_for=(RequestException,), retry_backoff=True)
def x():
...By default, this exponential backoff will also introduce random jitter to avoid having all the tasks run at the same moment. It will also cap the maximum backoff delay to 10 minutes. All these settings can be customized via options documented below.
.. versionadded:: 4.4
You can also set autoretry_for, max_retries, retry_backoff, retry_backoff_max and retry_jitter options in class-based tasks:
class BaseTaskWithRetry(Task):
autoretry_for = (TypeError,)
max_retries = 5
retry_backoff = True
retry_backoff_max = 700
retry_jitter = False.. attribute:: Task.autoretry_for
A list/tuple of exception classes. If any of these exceptions are raised
during the execution of the task, the task will automatically be retried.
By default, no exceptions will be autoretried.
.. attribute:: Task.max_retries
A number. Maximum number of retries before giving up. A value of ``None``
means task will retry forever. By default, this option is set to ``3``.
.. attribute:: Task.retry_backoff
A boolean, or a number. If this option is set to ``True``, autoretries
will be delayed following the rules of `exponential backoff`_. The first
retry will have a delay of 1 second, the second retry will have a delay
of 2 seconds, the third will delay 4 seconds, the fourth will delay 8
seconds, and so on. (However, this delay value is modified by
:attr:`~Task.retry_jitter`, if it is enabled.)
If this option is set to a number, it is used as a
delay factor. For example, if this option is set to ``3``, the first retry
will delay 3 seconds, the second will delay 6 seconds, the third will
delay 12 seconds, the fourth will delay 24 seconds, and so on. By default,
this option is set to ``False``, and autoretries will not be delayed.
.. attribute:: Task.retry_backoff_max
A number. If ``retry_backoff`` is enabled, this option will set a maximum
delay in seconds between task autoretries. By default, this option is set to ``600``,
which is 10 minutes.
.. attribute:: Task.retry_jitter
A boolean. `Jitter`_ is used to introduce randomness into
exponential backoff delays, to prevent all tasks in the queue from being
executed simultaneously. If this option is set to ``True``, the delay
value calculated by :attr:`~Task.retry_backoff` is treated as a maximum,
and the actual delay value will be a random number between zero and that
maximum. By default, this option is set to ``True``.
.. versionadded:: 5.3.0
.. attribute:: Task.dont_autoretry_for
A list/tuple of exception classes. These exceptions won't be autoretried.
This allows to exclude some exceptions that match `autoretry_for
<Task.autoretry_for>`:attr: but for which you don't want a retry.
The task decorator can take a number of options that change the way the task behaves, for example you can set the rate limit for a task using the :attr:`rate_limit` option.
Any keyword argument passed to the task decorator will actually be set as an attribute of the resulting task class, and this is a list of the built-in attributes.
.. attribute:: Task.name
The name the task is registered as.
You can set this name manually, or a name will be
automatically generated using the module and class name.
See also :ref:`task-names`.
.. attribute:: Task.request
If the task is being executed this will contain information
about the current request. Thread local storage is used.
See :ref:`task-request-info`.
.. attribute:: Task.max_retries
Only applies if the task calls ``self.retry`` or if the task is decorated
with the :ref:`autoretry_for <task-autoretry>` argument.
The maximum number of attempted retries before giving up.
If the number of retries exceeds this value a :exc:`~@MaxRetriesExceededError`
exception will be raised.
.. note::
You have to call :meth:`[email protected]`
manually, as it won't automatically retry on exception..
The default is ``3``.
A value of :const:`None` will disable the retry limit and the
task will retry forever until it succeeds.
.. attribute:: Task.throws
Optional tuple of expected error classes that shouldn't be regarded
as an actual error.
Errors in this list will be reported as a failure to the result backend,
but the worker won't log the event as an error, and no traceback will
be included.
Example:
.. code-block:: python
@task(throws=(KeyError, HttpNotFound)):
def get_foo():
something()
Error types:
- Expected errors (in ``Task.throws``)
Logged with severity ``INFO``, traceback excluded.
- Unexpected errors
Logged with severity ``ERROR``, with traceback included.
.. attribute:: Task.default_retry_delay
Default time in seconds before a retry of the task
should be executed. Can be either :class:`int` or :class:`float`.
Default is a three minute delay.
.. attribute:: Task.rate_limit
Set the rate limit for this task type (limits the number of tasks
that can be run in a given time frame). Tasks will still complete when
a rate limit is in effect, but it may take some time before it's allowed to
start.
If this is :const:`None` no rate limit is in effect.
If it is an integer or float, it is interpreted as "tasks per second".
The rate limits can be specified in seconds, minutes or hours
by appending `"/s"`, `"/m"` or `"/h"` to the value. Tasks will be evenly
distributed over the specified time frame.
Example: `"100/m"` (hundred tasks a minute). This will enforce a minimum
delay of 600ms between starting two tasks on the same worker instance.
Default is the :setting:`task_default_rate_limit` setting:
if not specified means rate limiting for tasks is disabled by default.
Note that this is a *per worker instance* rate limit, and not a global
rate limit. To enforce a global rate limit (e.g., for an API with a
maximum number of requests per second), you must restrict to a given
queue.
.. attribute:: Task.time_limit
The hard time limit, in seconds, for this task.
When not set the workers default is used.
.. attribute:: Task.soft_time_limit
The soft time limit for this task.
When not set the workers default is used.
.. attribute:: Task.ignore_result
Don't store task state. Note that this means you can't use
:class:`~celery.result.AsyncResult` to check if the task is ready,
or get its return value.
Note: Certain features will not work if task results are disabled.
For more details check the Canvas documentation.
.. attribute:: Task.store_errors_even_if_ignored
If :const:`True`, errors will be stored even if the task is configured
to ignore results.
.. attribute:: Task.serializer
A string identifying the default serialization
method to use. Defaults to the :setting:`task_serializer`
setting. Can be `pickle`, `json`, `yaml`, or any custom
serialization methods that have been registered with
:mod:`kombu.serialization.registry`.
Please see :ref:`calling-serializers` for more information.
.. attribute:: Task.compression
A string identifying the default compression scheme to use.
Defaults to the :setting:`task_compression` setting.
Can be `gzip`, or `bzip2`, or any custom compression schemes
that have been registered with the :mod:`kombu.compression` registry.
Please see :ref:`calling-compression` for more information.
.. attribute:: Task.backend
The result store backend to use for this task. An instance of one of the
backend classes in `celery.backends`. Defaults to `app.backend`,
defined by the :setting:`result_backend` setting.
.. attribute:: Task.acks_late
If set to :const:`True` messages for this task will be acknowledged
**after** the task has been executed, not *just before* (the default
behavior).
Note: This means the task may be executed multiple times should the worker
crash in the middle of execution. Make sure your tasks are
:term:`idempotent`.
The global default can be overridden by the :setting:`task_acks_late`
setting.
.. attribute:: Task.track_started
If :const:`True` the task will report its status as "started"
when the task is executed by a worker.
The default value is :const:`False` as the normal behavior is to not
report that level of granularity. Tasks are either pending, finished,
or waiting to be retried. Having a "started" status can be useful for
when there are long running tasks and there's a need to report what
task is currently running.
The host name and process id of the worker executing the task
will be available in the state meta-data (e.g., `result.info['pid']`)
The global default can be overridden by the
:setting:`task_track_started` setting.
.. seealso::
The API reference for :class:`~@Task`.
Celery can keep track of the tasks current state. The state also contains the result of a successful task, or the exception and traceback information of a failed task.
There are several result backends to choose from, and they all have different strengths and weaknesses (see :ref:`task-result-backends`).
During its lifetime a task will transition through several possible states, and each state may have arbitrary meta-data attached to it. When a task moves into a new state the previous state is forgotten about, but some transitions can be deduced, (e.g., a task now in the :state:`FAILED` state, is implied to have been in the :state:`STARTED` state at some point).
There are also sets of states, like the set of :state:`FAILURE_STATES`, and the set of :state:`READY_STATES`.
The client uses the membership of these sets to decide whether the exception should be re-raised (:state:`PROPAGATE_STATES`), or whether the state can be cached (it can if the task is ready).
You can also define :ref:`custom-states`.
If you want to keep track of tasks or need the return values, then Celery
must store or send the states somewhere so that they can be retrieved later.
There are several built-in result backends to choose from: SQLAlchemy/Django ORM,
Memcached, RabbitMQ/QPid (rpc), and Redis -- or you can define your own.
No backend works well for every use case. You should read about the strengths and weaknesses of each backend, and choose the most appropriate for your needs.
Warning
Backends use resources to store and transmit results. To ensure that resources are released, you must eventually call :meth:`[email protected]` or :meth:`[email protected]` on EVERY :class:`~@AsyncResult` instance returned after calling a task.
.. seealso::
:ref:`conf-result-backend`
The RPC result backend (rpc://) is special as it doesn't actually store the states, but rather sends them as messages. This is an important difference as it means that a result can only be retrieved once, and only by the client that initiated the task. Two different processes can't wait for the same result.
Even with that limitation, it is an excellent choice if you need to receive state changes in real-time. Using messaging means the client doesn't have to poll for new states.
The messages are transient (non-persistent) by default, so the results will disappear if the broker restarts. You can configure the result backend to send persistent messages using the :setting:`result_persistent` setting.
Keeping state in the database can be convenient for many, especially for web applications with a database already in place, but it also comes with limitations.
Polling the database for new states is expensive, and so you should increase the polling intervals of operations, such as result.get().
Some databases use a default transaction isolation level that isn't suitable for polling tables for changes.
In MySQL the default transaction isolation level is REPEATABLE-READ: meaning the transaction won't see changes made by other transactions until the current transaction is committed.
Changing that to the READ-COMMITTED isolation level is recommended.
.. state:: PENDING
Task is waiting for execution or unknown. Any task id that's not known is implied to be in the pending state.
.. state:: STARTED
Task has been started. Not reported by default, to enable please see :attr:`@Task.track_started`.
| meta-data: | pid and hostname of the worker process executing the task. |
|---|
.. state:: SUCCESS
Task has been successfully executed.
| meta-data: | result contains the return value of the task. |
|---|---|
| propagates: | Yes |
| ready: | Yes |
.. state:: FAILURE
Task execution resulted in failure.
| meta-data: | result contains the exception occurred, and traceback contains the backtrace of the stack at the point when the exception was raised. |
|---|---|
| propagates: | Yes |
.. state:: RETRY
Task is being retried.
| meta-data: | result contains the exception that caused the retry, and traceback contains the backtrace of the stack at the point when the exceptions was raised. |
|---|---|
| propagates: | No |
.. state:: REVOKED
Task has been revoked.
| propagates: | Yes |
|---|
You can easily define your own states, all you need is a unique name. The name of the state is usually an uppercase string. As an example you could have a look at the :mod:`abortable tasks <~celery.contrib.abortable>` which defines a custom :state:`ABORTED` state.
Use :meth:`[email protected]_state` to update a task's state:.
@app.task(bind=True)
def upload_files(self, filenames):
for i, file in enumerate(filenames):
if not self.request.called_directly:
self.update_state(state='PROGRESS',
meta={'current': i, 'total': len(filenames)})Here I created the state "PROGRESS", telling any application aware of this state that the task is currently in progress, and also where it is in the process by having current and total counts as part of the state meta-data. This can then be used to create progress bars for example.
A rarely known Python fact is that exceptions must conform to some simple rules to support being serialized by the pickle module.
Tasks that raise exceptions that aren't pickleable won't work properly when Pickle is used as the serializer.
To make sure that your exceptions are pickleable the exception
MUST provide the original arguments it was instantiated
with in its .args attribute. The simplest way
to ensure this is to have the exception call Exception.__init__.
Let's look at some examples that work, and one that doesn't:
# OK:
class HttpError(Exception):
pass
# BAD:
class HttpError(Exception):
def __init__(self, status_code):
self.status_code = status_code
# OK:
class HttpError(Exception):
def __init__(self, status_code):
self.status_code = status_code
Exception.__init__(self, status_code) # <-- REQUIREDSo the rule is:
For any exception that supports custom arguments *args,
Exception.__init__(self, *args) must be used.
There's no special support for keyword arguments, so if you want to preserve keyword arguments when the exception is unpickled you have to pass them as regular args:
class HttpError(Exception):
def __init__(self, status_code, headers=None, body=None):
self.status_code = status_code
self.headers = headers
self.body = body
super(HttpError, self).__init__(status_code, headers, body)The worker wraps the task in a tracing function that records the final state of the task. There are a number of exceptions that can be used to signal this function to change how it treats the return of the task.
The task may raise :exc:`~@Ignore` to force the worker to ignore the task. This means that no state will be recorded for the task, but the message is still acknowledged (removed from queue).
This can be used if you want to implement custom revoke-like functionality, or manually store the result of a task.
Example keeping revoked tasks in a Redis set:
from celery.exceptions import Ignore
@app.task(bind=True)
def some_task(self):
if redis.ismember('tasks.revoked', self.request.id):
raise Ignore()Example that stores results manually:
from celery import states
from celery.exceptions import Ignore
@app.task(bind=True)
def get_tweets(self, user):
timeline = twitter.get_timeline(user)
if not self.request.called_directly:
self.update_state(state=states.SUCCESS, meta=timeline)
raise Ignore()The task may raise :exc:`~@Reject` to reject the task message using
AMQPs basic_reject method. This won't have any effect unless
:attr:`Task.acks_late` is enabled.
Rejecting a message has the same effect as acking it, but some brokers may implement additional functionality that can be used. For example RabbitMQ supports the concept of Dead Letter Exchanges where a queue can be configured to use a dead letter exchange that rejected messages are redelivered to.
Reject can also be used to re-queue messages, but please be very careful when using this as it can easily result in an infinite message loop.
Example using reject when a task causes an out of memory condition:
import errno
from celery.exceptions import Reject
@app.task(bind=True, acks_late=True)
def render_scene(self, path):
file = get_file(path)
try:
renderer.render_scene(file)
# if the file is too big to fit in memory
# we reject it so that it's redelivered to the dead letter exchange
# and we can manually inspect the situation.
except MemoryError as exc:
raise Reject(exc, requeue=False)
except OSError as exc:
if exc.errno == errno.ENOMEM:
raise Reject(exc, requeue=False)
# For any other error we retry after 10 seconds.
except Exception as exc:
raise self.retry(exc, countdown=10)Example re-queuing the message:
from celery.exceptions import Reject
@app.task(bind=True, acks_late=True)
def requeues(self):
if not self.request.delivery_info['redelivered']:
raise Reject('no reason', requeue=True)
print('received two times')Consult your broker documentation for more details about the basic_reject
method.
The :exc:`~@Retry` exception is raised by the Task.retry method
to tell the worker that the task is being retried.
All tasks inherit from the :class:`@Task` class. The :meth:`[email protected]` method becomes the task body.
As an example, the following code,
@app.task
def add(x, y):
return x + ywill do roughly this behind the scenes:
class _AddTask(app.Task):
def run(self, x, y):
return x + y
add = app.tasks[_AddTask.name]A task is not instantiated for every request, but is registered in the task registry as a global instance.
This means that the __init__ constructor will only be called
once per process, and that the task class is semantically closer to an
Actor.
If you have a task,
from celery import Task
class NaiveAuthenticateServer(Task):
def __init__(self):
self.users = {'george': 'password'}
def run(self, username, password):
try:
return self.users[username] == password
except KeyError:
return FalseAnd you route every request to the same process, then it will keep state between requests.
This can also be useful to cache resources, For example, a base Task class that caches a database connection:
from celery import Task
class DatabaseTask(Task):
_db = None
@property
def db(self):
if self._db is None:
self._db = Database.connect()
return self._dbThe above can be added to each task like this:
from celery.app import task
@app.task(base=DatabaseTask, bind=True)
def process_rows(self: task):
for row in self.db.table.all():
process_row(row)The db attribute of the process_rows task will then
always stay the same in each process.
You can also use your custom class in your whole Celery app by passing it as
the task_cls argument when instantiating the app. This argument should be
either a string giving the python path to your Task class or the class itself:
from celery import Celery
app = Celery('tasks', task_cls='your.module.path:DatabaseTask')This will make all your tasks declared using the decorator syntax within your
app to use your DatabaseTask class and will all have a db attribute.
The default value is the class provided by Celery: 'celery.app.task:Task'.
.. method:: before_start(self, task_id, args, kwargs)
Run by the worker before the task starts executing.
.. versionadded:: 5.2
:param task_id: Unique id of the task to execute.
:param args: Original arguments for the task to execute.
:param kwargs: Original keyword arguments for the task to execute.
The return value of this handler is ignored.
.. method:: after_return(self, status, retval, task_id, args, kwargs, einfo)
Handler called after the task returns.
:param status: Current task state.
:param retval: Task return value/exception.
:param task_id: Unique id of the task.
:param args: Original arguments for the task that returned.
:param kwargs: Original keyword arguments for the task
that returned.
:keyword einfo: :class:`~billiard.einfo.ExceptionInfo`
instance, containing the traceback (if any).
The return value of this handler is ignored.
.. method:: on_failure(self, exc, task_id, args, kwargs, einfo)
This is run by the worker when the task fails.
:param exc: The exception raised by the task.
:param task_id: Unique id of the failed task.
:param args: Original arguments for the task that failed.
:param kwargs: Original keyword arguments for the task
that failed.
:keyword einfo: :class:`~billiard.einfo.ExceptionInfo`
instance, containing the traceback.
The return value of this handler is ignored.
.. method:: on_retry(self, exc, task_id, args, kwargs, einfo)
This is run by the worker when the task is to be retried.
:param exc: The exception sent to :meth:`[email protected]`.
:param task_id: Unique id of the retried task.
:param args: Original arguments for the retried task.
:param kwargs: Original keyword arguments for the retried task.
:keyword einfo: :class:`~billiard.einfo.ExceptionInfo`
instance, containing the traceback.
The return value of this handler is ignored.
.. method:: on_success(self, retval, task_id, args, kwargs)
Run by the worker if the task executes successfully.
:param retval: The return value of the task.
:param task_id: Unique id of the executed task.
:param args: Original arguments for the executed task.
:param kwargs: Original keyword arguments for the executed task.
The return value of this handler is ignored.
Upon receiving a message to run a task, the `worker <guide-workers>`:ref: creates a `request <celery.worker.request.Request>`:class: to represent such demand.
Custom task classes may override which request class to use by changing the attribute `celery.app.task.Task.Request`:attr:. You may either assign the custom request class itself, or its fully qualified name.
The request has several responsibilities. Custom request classes should cover them all -- they are responsible to actually run and trace the task. We strongly recommend to inherit from `celery.worker.request.Request`:class:.
When using the `pre-forking worker <worker-concurrency>`:ref:, the methods `~celery.worker.request.Request.on_timeout`:meth: and `~celery.worker.request.Request.on_failure`:meth: are executed in the main worker process. An application may leverage such facility to detect failures which are not detected using `celery.app.task.Task.on_failure`:meth:.
As an example, the following custom request detects and logs hard time limits, and other failures.
import logging
from celery import Task
from celery.worker.request import Request
logger = logging.getLogger('my.package')
class MyRequest(Request):
'A minimal custom request to log failures and hard time limits.'
def on_timeout(self, soft, timeout):
super(MyRequest, self).on_timeout(soft, timeout)
if not soft:
logger.warning(
'A hard timeout was enforced for task %s',
self.task.name
)
def on_failure(self, exc_info, send_failed_event=True, return_ok=False):
super().on_failure(
exc_info,
send_failed_event=send_failed_event,
return_ok=return_ok
)
logger.warning(
'Failure detected for task %s',
self.task.name
)
class MyTask(Task):
Request = MyRequest # you can use a FQN 'my.package:MyRequest'
@app.task(base=MyTask)
def some_longrunning_task():
# use your imaginationHere come the technical details. This part isn't something you need to know, but you may be interested.
All defined tasks are listed in a registry. The registry contains a list of task names and their task classes. You can investigate this registry yourself:
>>> from proj.celery import app
>>> app.tasks
{'celery.chord_unlock':
<@task: celery.chord_unlock>,
'celery.backend_cleanup':
<@task: celery.backend_cleanup>,
'celery.chord':
<@task: celery.chord>}This is the list of tasks built into Celery. Note that tasks will only be registered when the module they're defined in is imported.
The default loader imports any modules listed in the :setting:`imports` setting.
The :meth:`@task` decorator is responsible for registering your task in the applications task registry.
When tasks are sent, no actual function code is sent with it, just the name of the task to execute. When the worker then receives the message it can look up the name in its task registry to find the execution code.
This means that your workers should always be updated with the same software as the client. This is a drawback, but the alternative is a technical challenge that's yet to be solved.
If you don't care about the results of a task, be sure to set the :attr:`[email protected]_result` option, as storing results wastes time and resources.
@app.task(ignore_result=True)
def mytask():
something()Results can even be disabled globally using the :setting:`task_ignore_result` setting.
Results can be enabled/disabled on a per-execution basis, by passing the ignore_result boolean parameter,
when calling apply_async.
@app.task
def mytask(x, y):
return x + y
# No result will be stored
result = mytask.apply_async(1, 2, ignore_result=True)
print result.get() # -> None
# Result will be stored
result = mytask.apply_async(1, 2, ignore_result=False)
print result.get() # -> 3By default tasks will not ignore results (ignore_result=False) when a result backend is configured.
The option precedence order is the following:
- Global :setting:`task_ignore_result`
- :attr:`[email protected]_result` option
- Task execution option
ignore_result
You find additional optimization tips in the :ref:`Optimizing Guide <guide-optimizing>`.
Having a task wait for the result of another task is really inefficient, and may even cause a deadlock if the worker pool is exhausted.
Make your design asynchronous instead, for example by using callbacks.
Bad:
@app.task
def update_page_info(url):
page = fetch_page.delay(url).get()
info = parse_page.delay(page).get()
store_page_info.delay(url, info)
@app.task
def fetch_page(url):
return myhttplib.get(url)
@app.task
def parse_page(page):
return myparser.parse_document(page)
@app.task
def store_page_info(url, info):
return PageInfo.objects.create(url, info)Good:
def update_page_info(url):
# fetch_page -> parse_page -> store_page
chain = fetch_page.s(url) | parse_page.s() | store_page_info.s(url)
chain()
@app.task()
def fetch_page(url):
return myhttplib.get(url)
@app.task()
def parse_page(page):
return myparser.parse_document(page)
@app.task(ignore_result=True)
def store_page_info(info, url):
PageInfo.objects.create(url=url, info=info)Here I instead created a chain of tasks by linking together different :func:`~celery.signature`'s. You can read about chains and other powerful constructs at :ref:`designing-workflows`.
By default Celery will not allow you to run subtasks synchronously within a task, but in rare or extreme cases you might need to do so. WARNING: enabling subtasks to run synchronously is not recommended!
@app.task
def update_page_info(url):
page = fetch_page.delay(url).get(disable_sync_subtasks=False)
info = parse_page.delay(page).get(disable_sync_subtasks=False)
store_page_info.delay(url, info)
@app.task
def fetch_page(url):
return myhttplib.get(url)
@app.task
def parse_page(page):
return myparser.parse_document(page)
@app.task
def store_page_info(url, info):
return PageInfo.objects.create(url, info)The task granularity is the amount of computation needed by each subtask. In general it is better to split the problem up into many small tasks rather than have a few long running tasks.
With smaller tasks you can process more tasks in parallel and the tasks won't run long enough to block the worker from processing other waiting tasks.
However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the overhead added probably removes any benefit.
.. seealso::
The book `Art of Concurrency`_ has a section dedicated to the topic
of task granularity [AOC1]_.
| [AOC1] | Breshears, Clay. Section 2.2.1, "The Art of Concurrency". O'Reilly Media, Inc. May 15, 2009. ISBN-13 978-0-596-52153-0. |
The worker processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst would be a full transfer from another continent.
If the data is far away, you could try to run another worker at location, or if that's not possible - cache often used data, or preload data you know is going to be used.
The easiest way to share data between workers is to use a distributed cache system, like memcached.
.. seealso::
The paper `Distributed Computing Economics`_ by Jim Gray is an excellent
introduction to the topic of data locality.
Since Celery is a distributed system, you can't know which process, or on what machine the task will be executed. You can't even know if the task will run in a timely manner.
The ancient async sayings tells us that “asserting the world is the responsibility of the task”. What this means is that the world view may have changed since the task was requested, so the task is responsible for making sure the world is how it should be; If you have a task that re-indexes a search engine, and the search engine should only be re-indexed at maximum every 5 minutes, then it must be the tasks responsibility to assert that, not the callers.
Another gotcha is Django model objects. They shouldn't be passed on as arguments to tasks. It's almost always better to re-fetch the object from the database when the task is running instead, as using old data may lead to race conditions.
Imagine the following scenario where you have an article and a task that automatically expands some abbreviations in it:
class Article(models.Model):
title = models.CharField()
body = models.TextField()
@app.task
def expand_abbreviations(article):
article.body.replace('MyCorp', 'My Corporation')
article.save()First, an author creates an article and saves it, then the author clicks on a button that initiates the abbreviation task:
>>> article = Article.objects.get(id=102)
>>> expand_abbreviations.delay(article)Now, the queue is very busy, so the task won't be run for another 2 minutes. In the meantime another author makes changes to the article, so when the task is finally run, the body of the article is reverted to the old version because the task had the old body in its argument.
Fixing the race condition is easy, just use the article id instead, and re-fetch the article in the task body:
@app.task
def expand_abbreviations(article_id):
article = Article.objects.get(id=article_id)
article.body.replace('MyCorp', 'My Corporation')
article.save()>>> expand_abbreviations.delay(article_id)There might even be performance benefits to this approach, as sending large messages may be expensive.
Let's have a look at another example:
from django.db import transaction
from django.http import HttpResponseRedirect
@transaction.atomic
def create_article(request):
article = Article.objects.create()
expand_abbreviations.delay(article.pk)
return HttpResponseRedirect('/articles/')This is a Django view creating an article object in the database, then passing the primary key to a task. It uses the transaction.atomic decorator, that will commit the transaction when the view returns, or roll back if the view raises an exception.
There's a race condition if the task starts executing before the transaction has been committed; The database object doesn't exist yet!
The solution is to use the on_commit callback to launch your Celery task
once all transactions have been committed successfully.
from django.db import transaction
from django.http import HttpResponseRedirect
@transaction.atomic
def create_article(request):
article = Article.objects.create()
transaction.on_commit(lambda: expand_abbreviations.delay(article.pk))
return HttpResponseRedirect('/articles/')Note
on_commit is available in Django 1.9 and above, if you are using a
version prior to that then the django-transaction-hooks library
adds support for this.
Let's take a real world example: a blog where comments posted need to be filtered for spam. When the comment is created, the spam filter runs in the background, so the user doesn't have to wait for it to finish.
I have a Django blog application allowing comments on blog posts. I'll describe parts of the models/views and tasks for this application.
The comment model looks like this:
from django.db import models
from django.utils.translation import ugettext_lazy as _
class Comment(models.Model):
name = models.CharField(_('name'), max_length=64)
email_address = models.EmailField(_('email address'))
homepage = models.URLField(_('home page'),
blank=True, verify_exists=False)
comment = models.TextField(_('comment'))
pub_date = models.DateTimeField(_('Published date'),
editable=False, auto_add_now=True)
is_spam = models.BooleanField(_('spam?'),
default=False, editable=False)
class Meta:
verbose_name = _('comment')
verbose_name_plural = _('comments')In the view where the comment is posted, I first write the comment to the database, then I launch the spam filter task in the background.
from django import forms
from django.http import HttpResponseRedirect
from django.template.context import RequestContext
from django.shortcuts import get_object_or_404, render_to_response
from blog import tasks
from blog.models import Comment
class CommentForm(forms.ModelForm):
class Meta:
model = Comment
def add_comment(request, slug, template_name='comments/create.html'):
post = get_object_or_404(Entry, slug=slug)
remote_addr = request.META.get('REMOTE_ADDR')
if request.method == 'post':
form = CommentForm(request.POST, request.FILES)
if form.is_valid():
comment = form.save()
# Check spam asynchronously.
tasks.spam_filter.delay(comment_id=comment.id,
remote_addr=remote_addr)
return HttpResponseRedirect(post.get_absolute_url())
else:
form = CommentForm()
context = RequestContext(request, {'form': form})
return render_to_response(template_name, context_instance=context)To filter spam in comments I use Akismet, the service used to filter spam in comments posted to the free blog platform Wordpress. Akismet is free for personal use, but for commercial use you need to pay. You have to sign up to their service to get an API key.
To make API calls to Akismet I use the akismet.py library written by Michael Foord.
from celery import Celery
from akismet import Akismet
from django.core.exceptions import ImproperlyConfigured
from django.contrib.sites.models import Site
from blog.models import Comment
app = Celery(broker='amqp://')
@app.task
def spam_filter(comment_id, remote_addr=None):
logger = spam_filter.get_logger()
logger.info('Running spam filter for comment %s', comment_id)
comment = Comment.objects.get(pk=comment_id)
current_domain = Site.objects.get_current().domain
akismet = Akismet(settings.AKISMET_KEY, 'http://{0}'.format(domain))
if not akismet.verify_key():
raise ImproperlyConfigured('Invalid AKISMET_KEY')
is_spam = akismet.comment_check(user_ip=remote_addr,
comment_content=comment.comment,
comment_author=comment.name,
comment_author_email=comment.email_address)
if is_spam:
comment.is_spam = True
comment.save()
return is_spam