Skip to content

Latest commit

 

History

History
770 lines (488 loc) · 22.7 KB

whatsnew-3.0.rst

File metadata and controls

770 lines (488 loc) · 22.7 KB

What's new in Celery 3.0 (Chiastic Slide)

Celery is a simple, flexible and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system.

It's a task queue with focus on real-time processing, while also supporting task scheduling.

Celery has a large and diverse community of users and contributors, you should come join us :ref:`on IRC <irc-channel>` or :ref:`our mailing-list <mailing-list>`.

To read more about Celery you should go read the :ref:`introduction <intro>`.

While this version is backward compatible with previous versions it's important that you read the following section.

If you use Celery in combination with Django you must also read the django-celery changelog and upgrade to django-celery 3.0.

This version is officially supported on CPython 2.5, 2.6, 2.7, 3.2 and 3.3, as well as PyPy and Jython.

Highlights

  • A new and improved API, that is both simpler and more powerful.

    Everyone must read the new :ref:`first-steps` tutorial, and the new :ref:`next-steps` tutorial.

    There are no current plans to deprecate the old API, so you don't have to be in a hurry to port your applications.

  • The worker is now thread-less, giving great performance improvements.

  • This is the last version to support Python 2.5

  • The new "Canvas" makes it easy to define complex workflows.

    Ever wanted to chain tasks together? This is possible, but not just that, now you can even chain together groups and chords, or even combine multiple chains.

    Read more in the :ref:`Canvas <guide-canvas>` user guide.

  • Support for the new librabbitmq C client.

    Celery will automatically use the :mod:`librabbitmq` module if installed, which is a very fast and memory-optimized replacement for the amqplib module.

  • Redis support is more reliable with improved ack emulation.

Important Notes

Eventloop

The worker is now running without threads when used with AMQP or Redis as a broker, resulting in:

- Much better performance overall.
- Fixes several edge case race conditions.
- Sub-millisecond timer precision.
- Faster shutdown times.

The transports supported are: amqplib, librabbitmq, and redis Hopefully this can be extended to include additional broker transports in the future.

For increased reliability the :setting:`CELERY_FORCE_EXECV` setting is enabled by default if the eventloop is not used.

Now depends on :mod:`billiard`.

Billiard is a fork of the multiprocessing containing the no-execv patch by sbt (http://bugs.python.org/issue8713), and also contains the pool improvements previously located in Celery.

This fork was necessary as changes to the C extension code was required for the no-execv patch to work.

Last version to support Python 2.5

The 3.0 series will be last version to support Python 2.5, and starting from 3.1 Python 2.6 and later will be required.

With several other distributions taking the step to discontinue Python 2.5 support, we feel that it is time too.

Python 2.6 should be widely available at this point, and we urge you to upgrade, but if that is not possible you still have the option to continue using the Celery 3.0, and important bug fixes introduced in Celery 3.1 will be back-ported to Celery 3.0 upon request.

News

Chaining Tasks

Tasks can now have callbacks and errbacks, and dependencies are recorded

  • The task message format have been updated with two new extension keys

    Both keys can be empty/undefined or a list of subtasks.

    • callbacks

      Applied if the task exits successfully, with the result of the task as an argument.

    • errbacks

      Applied if an error occurred while executing the task, with the uuid of the task as an argument. Since it may not be possible to serialize the exception instance, it passes the uuid of the task instead. The uuid can then be used to retrieve the exception and traceback of the task from the result backend.

    • link and link_error keyword arguments has been added to apply_async.

      These add callbacks and errbacks to the task, and you can read more about them at :ref:`calling-links`.

    • We now track what subtasks a task sends, and some result backends supports retrieving this information.

      • task.request.children

        Contains the result instances of the subtasks the currently executing task has applied.

      • AsyncResult.children

        Returns the tasks dependencies, as a list of AsyncResult/ResultSet instances.

      • AsyncResult.iterdeps

        Recursively iterates over the tasks dependencies, yielding (parent, node) tuples.

        Raises IncompleteStream if any of the dependencies has not returned yet.

      • AsyncResult.graph

        A DependencyGraph of the tasks dependencies. This can also be used to convert to dot format:

        with open('graph.dot') as fh:
    result.graph.to_dot(fh)

        which can than be used to produce an image:

        $ dot -Tpng graph.dot -o graph.png

  • A new special subtask called chain is also included:

    .. code-block:: python

    >>> from celery import chain

    # (2 + 2) * 8 / 2
    >>> res = chain(add.subtask((2, 2)),
                    mul.subtask((8, )),
                    div.subtask((2,))).apply_async()
    >>> res.get() == 16

    >>> res.parent.get() == 32

    >>> res.parent.parent.get() == 4

  • Adds :meth:`AsyncResult.get_leaf`

    Waits and returns the result of the leaf subtask. That is the last node found when traversing the graph, but this means that the graph can be 1-dimensional only (in effect a list).

  • Adds subtask.link(subtask) + subtask.link_error(subtask)

    Shortcut to s.options.setdefault('link', []).append(subtask)

  • Adds subtask.flatten_links()

    Returns a flattened list of all dependencies (recursively)

group/chord/chain are now subtasks

  • group is no longer an alias to TaskSet, but new alltogether, since it was very difficult to migrate the TaskSet class to become a subtask.

  • A new shortcut has been added to tasks:

        >>> task.s(arg1, arg2, kw=1)

as a shortcut to::

    >>> task.subtask((arg1, arg2), {'kw': 1})

  • Tasks can be chained by using the | operator:

    >>> (add.s(2, 2), pow.s(2)).apply_async()

  • Subtasks can be "evaluated" using the ~ operator:

        >>> ~add.s(2, 2)
    4

    >>> ~(add.s(2, 2) | pow.s(2))

is the same as::

    >>> chain(add.s(2, 2), pow.s(2)).apply_async().get()

  • A new subtask_type key has been added to the subtask dicts

    This can be the string "chord", "group", "chain", "chunks", "xmap", or "xstarmap".

  • maybe_subtask now uses subtask_type to reconstruct the object, to be used when using non-pickle serializers.

  • The logic for these operations have been moved to dedicated tasks celery.chord, celery.chain and celery.group.

  • subtask no longer inherits from AttributeDict.

    It's now a pure dict subclass with properties for attribute access to the relevant keys.

  • The repr's now outputs how the sequence would like imperatively:

    >>> from celery import chord

>>> (chord([add.s(i, i) for i in xrange(10)], xsum.s())
      | pow.s(2))
tasks.xsum([tasks.add(0, 0),
            tasks.add(1, 1),
            tasks.add(2, 2),
            tasks.add(3, 3),
            tasks.add(4, 4),
            tasks.add(5, 5),
            tasks.add(6, 6),
            tasks.add(7, 7),
            tasks.add(8, 8),
            tasks.add(9, 9)]) | tasks.pow(2)

Additional control commands made public

  • add_consumer/cancel_consumer

    Tells workers to consume from a new queue, or cancel consuming from a queue. This command has also been changed so that the worker remembers the queues added, so that the change will persist even if the connection is re-connected.

    These commands are available programmatically as :meth:`@control.add_consumer` / :meth:`@control.cancel_consumer`:

    >>> celery.control.add_consumer(queue_name,
...     destination=['w1.example.com'])
>>> celery.control.cancel_consumer(queue_name,
...     destination=['w1.example.com'])

    or using the :program:`celery control` command:

    $ celery control -d w1.example.com add_consumer queue
$ celery control -d w1.example.com cancel_consumer queue

    Note

    Remember that a control command without destination will be sent to all workers.

  • autoscale

    Tells workers with --autoscale enabled to change autoscale max/min concurrency settings.

    This command is available programmatically as :meth:`@control.autoscale`:

    >>> celery.control.autoscale(max=10, min=5,
...     destination=['w1.example.com'])

    or using the :program:`celery control` command:

    $ celery control -d w1.example.com autoscale 10 5

  • pool_grow/pool_shrink

    Tells workers to add or remove pool processes.

    These commands are available programmatically as :meth:`@control.pool_grow` / :meth:`@control.pool_shrink`:

    >>> celery.control.pool_grow(2, destination=['w1.example.com'])
>>> celery.contorl.pool_shrink(2, destination=['w1.example.com'])

    or using the :program:`celery control` command:

    $ celery control -d w1.example.com pool_grow 2
$ celery control -d w1.example.com pool_shrink 2

  • :program:`celery control` now supports rate_limit & time_limit commands.

    See celery control --help for details.

Crontab now supports Day of Month, and Month of Year arguments

See the updated list of examples at :ref:`beat-crontab`.

Immutable subtasks

subtask's can now be immutable, which means that the arguments will not be modified when calling callbacks:

>>> chain(add.s(2, 2), clear_static_electricity.si())

means it will not receive the argument of the parent task, and .si() is a shortcut to:

>>> clear_static_electricity.subtask(immutable=True)

Logging Improvements

Logging support now conforms better with best practices.

  • Classes used by the worker no longer uses app.get_default_logger, but uses celery.utils.log.get_logger which simply gets the logger not setting the level, and adds a NullHandler.

  • Loggers are no longer passed around, instead every module using logging defines a module global logger that is used throughout.

  • All loggers inherit from a common logger called "celery".

  • Before task.get_logger would setup a new logger for every task, and even set the loglevel. This is no longer the case.

    • Instead all task loggers now inherit from a common "celery.task" logger that is set up when programs call setup_logging_subsystem.
    • Instead of using LoggerAdapter to augment the formatter with the task_id and task_name field, the task base logger now use a special formatter adding these values at runtime from the currently executing task.

  • In fact, task.get_logger is no longer recommended, it is better to add module-level logger to your tasks module.

    For example, like this:

    from celery.utils.log import get_task_logger

logger = get_task_logger(__name__)

@celery.task()
def add(x, y):
    logger.debug('Adding %r + %r' % (x, y))
    return x + y

    The resulting logger will then inherit from the "celery.task" logger so that the current task name and id is included in logging output.

  • Redirected output from stdout/stderr is now logged to a "celery.redirected" logger.

  • In addition a few warnings.warn have been replaced with logger.warn.

  • Now avoids the 'no handlers for logger multiprocessing' warning

Task registry no longer global

Every Celery instance now has its own task registry.

You can make apps share registries by specifying it:

>>> app1 = Celery()
>>> app2 = Celery(tasks=app1.tasks)

Note that tasks are shared between registries by default, so that tasks will be added to every subsequently created task registry. As an alternative tasks can be private to specific task registries by setting the shared argument to the @task decorator:

@celery.task(shared=False)
def add(x, y):
    return x + y

Abstract tasks are now lazily bound.

The :class:`~celery.task.Task` class is no longer bound to an app by default, it will first be bound (and configured) when a concrete subclass is created.

This means that you can safely import and make task base classes, without also initializing the app environment:

from celery.task import Task

class DebugTask(Task):
    abstract = True

    def __call__(self, *args, **kwargs):
        print('CALLING %r' % (self, ))
        return self.run(*args, **kwargs)

>>> DebugTask
<unbound DebugTask>

>>> @celery1.task(base=DebugTask)
... def add(x, y):
...     return x + y
>>> add.__class__
<class add of <Celery default:0x101510d10>>

Lazy task decorators

The @task decorator is now lazy when used with custom apps.

That is, if accept_magic_kwargs is enabled (herby called "compat mode"), the task decorator executes inline like before, however for custom apps the @task decorator now returns a special PromiseProxy object that is only evaluated on access.

All promises will be evaluated when app.finalize is called, or implicitly when the task registry is first used.

Smart --app option

The :option:`--app` option now 'auto-detects'

  • If the provided path is a module it tries to get an attribute named 'celery'.
  • If the provided path is a package it tries to import a submodule named 'celery', and get the celery attribute from that module.

E.g. if you have a project named 'proj' where the celery app is located in 'from proj.celery import celery', then the following will be equivalent:

$ celery worker --app=proj
$ celery worker --app=proj.celery:
$ celery worker --app=proj.celery:celery

In Other News

  • New :setting:`CELERYD_WORKER_LOST_WAIT` to control the timeout in seconds before :exc:`billiard.WorkerLostError` is raised when a worker can not be signalled (Issue #595).

    Contributed by Brendon Crawford.

  • Redis event monitor queues are now automatically deleted (Issue #436).

  • App instance factory methods have been converted to be cached descriptors that creates a new subclass on access.

    This means that e.g. celery.Worker is an actual class and will work as expected when:

    class Worker(celery.Worker):
    ...

  • New signal: :signal:`task-success`.

  • Multiprocessing logs are now only emitted if the :envvar:`MP_LOG` environment variable is set.

  • The Celery instance can now be created with a broker URL

    celery = Celery(broker='redis://')

  • Result backends can now be set using an URL

    Currently only supported by redis. Example use:

    CELERY_RESULT_BACKEND = 'redis://localhost/1'

  • Heartbeat frequency now every 5s, and frequency sent with event

    The heartbeat frequency is now available in the worker event messages, so that clients can decide when to consider workers offline based on this value.

  • Module celery.actors has been removed, and will be part of cl instead.

  • Introduces new celery command, which is an entrypoint for all other commands.

    The main for this command can be run by calling celery.start().

  • Annotations now supports decorators if the key startswith '@'.

    E.g.:

    def debug_args(fun):

    @wraps(fun)
    def _inner(*args, **kwargs):
        print('ARGS: %r' % (args, ))
    return _inner

CELERY_ANNOTATIONS = {
    'tasks.add': {'@__call__': debug_args},
}

    Also tasks are now always bound by class so that annotated methods end up being bound.

  • Bugreport now available as a command and broadcast command

    • Get it from a Python repl:

      >>> import celery
>>> print(celery.bugreport())

    • Using the celery command-line program:

      $ celery report

    • Get it from remote workers:

      $ celery inspect report

  • Module celery.log moved to :mod:`celery.app.log`.

  • Module celery.task.control moved to :mod:`celery.app.control`.

  • New signal: :signal:`task-revoked`

    Sent in the main process when the task is revoked or terminated.

  • AsyncResult.task_id renamed to AsyncResult.id

  • TasksetResult.taskset_id renamed to .id

  • xmap(task, sequence) and xstarmap(task, sequence)

    Returns a list of the results applying the task function to every item in the sequence.

    Example:

    >>> from celery import xstarmap

>>> xstarmap(add, zip(range(10), range(10)).apply_async()
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

  • chunks(task, sequence, chunksize)

  • group.skew(start=, stop=, step=)

    Skew will skew the countdown for the individual tasks in a group, e.g. with a group:

    >>> g = group(add.s(i, i) for i in xrange(10))

    Skewing the tasks from 0 seconds to 10 seconds:

    >>> g.skew(stop=10)

    Will have the first task execute in 0 seconds, the second in 1 second, the third in 2 seconds and so on.

  • 99% test Coverage

  • :setting:`CELERY_QUEUES` can now be a list/tuple of :class:`~kombu.Queue` instances.

    Internally :attr:`@amqp.queues` is now a mapping of name/Queue instances, instead of converting on the fly.

  • Can now specify connection for :class:`@control.inspect`.

    from kombu import Connection

i = celery.control.inspect(connection=Connection('redis://'))
i.active_queues()

  • Module :mod:`celery.app.task` is now a module instead of a package.

    The setup.py install script will try to remove the old package, if that doesn't work for some reason you have to remove it manually, you can do so by executing the command:

    $ rm -r $(dirname $(python -c '
    import celery;print(celery.__file__)'))/app/task/

  • :setting:`CELERY_FORCE_EXECV` is now enabled by default.

    If the old behavior is wanted the setting can be set to False, or the new :option:`--no-execv` to :program:`celery worker`.

  • Deprecated module celery.conf has been removed.

  • The :setting:`CELERY_TIMEZONE` now always require the :mod:`pytz` library to be installed (exept if the timezone is set to UTC).

  • The Tokyo Tyrant backend has been removed and is no longer supported.

  • Now uses :func:`~kombu.common.maybe_declare` to cache queue declarations.

  • There is no longer a global default for the :setting:`CELERYBEAT_MAX_LOOP_INTERVAL` setting, it is instead set by individual schedulers.

  • Worker: now truncates very long message bodies in error reports.

  • :envvar:`CELERY_BENCH` environment variable, will now also list memory usage statistics at worker shutdown.

  • Worker: now only ever use a single timer for all timing needs, and instead set different priorities.

Internals

Experimental

:mod:`celery.contrib.methods`: Task decorator for methods

This is an experimental module containing a task decorator, and a task decorator filter, that can be used to create tasks out of methods:

from celery.contrib.methods import task_method

class Counter(object):

    def __init__(self):
        self.value = 1

    @celery.task(name='Counter.increment', filter=task_method)
    def increment(self, n=1):
        self.value += 1
        return self.value

See :mod:`celery.contrib.methods` for more information.

Unscheduled Removals

Usually we don't make backward incompatible removals, but these removals should have no major effect.

  • The following settings have been renamed:

    • CELERYD_ETA_SCHEDULER -> CELERYD_TIMER
    • CELERYD_ETA_SCHEDULER_PRECISION -> CELERYD_TIMER_PRECISION

Deprecations

See the :ref:`deprecation-timeline`.

The following undocumented API's has been moved:

This way inspect() is only used for commands that do not modify anything, while idempotent control commands that make changes are on the control objects.

Fixes

  • Retry sqlalchemy backend operations on DatabaseError/OperationalError (Issue #634)