What’s new in Celery 2.5

What’s new in Celery 2.5

Celery aims to be a flexible and reliable, best-of-breed solution to process vast amounts of messages in a distributed fashion, while providing operations with the tools to maintain such a system.

Celery has a large and diverse community of users and contributors, you should come join us on IRC or our mailing-list.

To read more about Celery you should visit our website.

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

If you use Celery in combination with Django you must also read the django-celery changelog <djcelery:version-2.5.0> and upgrade to django-celery 2.5.

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

Important Notes
Optimizations
Deprecations
- Removals
- Deprecations
News
Fixes

Important Notes

Broker connection pool now enabled by default

The default limit is 10 connections, if you have many threads/green-threads using connections at the same time you may want to tweak this limit to avoid contention.

See the BROKER_POOL_LIMIT setting for more information.

Also note that publishing tasks will be retried by default, to change this default or the default retry policy see CELERY_TASK_PUBLISH_RETRY and CELERY_TASK_PUBLISH_RETRY_POLICY.

Rabbit Result Backend: Exchange is no longer auto delete

The exchange used for results in the Rabbit (AMQP) result backend used to have the auto_delete flag set, which could result in a race condition leading to an annoying warning.

For RabbitMQ users

Old exchanges created with the auto_delete flag enabled has to be removed.

The camqadm command can be used to delete the previous exchange:

$ camqadm exchange.delete celeryresults

As an alternative to deleting the old exchange you can configure a new name for the exchange:

CELERY_RESULT_EXCHANGE = 'celeryresults2'

But you have to make sure that all clients and workers use this new setting, so they are updated to use the same exchange name.

Solution for hanging workers (but must be manually enabled)

The CELERYD_FORCE_EXECV setting has been added to solve a problem with deadlocks that originate when threads and fork is mixed together:

CELERYD_FORCE_EXECV = True

This setting is recommended for all users using the prefork pool, but especially users also using time limits or a max tasks per child setting.

See Python Issue 6721 to read more about this issue, and why resorting to execv`() is the only safe solution.

Enabling this option will result in a slight performance penalty when new child worker processes are started, and it will also increase memory usage (but many platforms are optimized, so the impact may be minimal). Considering that it ensures reliability when replacing lost worker processes, it should be worth it.

It’s already the default behavior on Windows.
It will be the default behavior for all platforms in a future version.

Optimizations

The code path used when the worker executes a task has been heavily optimized, meaning the worker is able to process a great deal more tasks/second compared to previous versions. As an example the solo pool can now process up to 15000 tasks/second on a 4 core MacBook Pro when using the pylibrabbitmq transport, where it previously could only do 5000 tasks/second.
The task error tracebacks are now much shorter.
Fixed a noticeable delay in task processing when rate limits are enabled.

Deprecations

Removals

The old TaskSet signature of (task_name, list_of_tasks) can no longer be used (originally scheduled for removal in 2.4). The deprecated .task_name and .task attributes has also been removed.
The functions celery.execute.delay_task, celery.execute.apply, and celery.execute.apply_async has been removed (originally) scheduled for removal in 2.3).
The built-in ping task has been removed (originally scheduled for removal in 2.3). Please use the ping broadcast command instead.
It is no longer possible to import subtask and TaskSet from celery.task.base, please import them from celery.task instead (originally scheduled for removal in 2.4).

Deprecations

The celery.decorators module has changed status from pending deprecation to deprecated, and is scheduled for removal in version 4.0. The celery.task module must be used instead.

News

Timezone support

Celery can now be configured to treat all incoming and outgoing dates as UTC, and the local timezone can be configured.

This is not yet enabled by default, since enabling time zone support means workers running versions pre 2.5 will be out of sync with upgraded workers.

To enable UTC you have to set CELERY_ENABLE_UTC:

CELERY_ENABLE_UTC = True

When UTC is enabled, dates and times in task messages will be converted to UTC, and then converted back to the local timezone when received by a worker.

You can change the local timezone using the CELERY_TIMEZONE setting. Installing the pytz library is recommended when using a custom timezone, to keep timezone definition up-to-date, but it will fallback to a system definition of the timezone if available.

UTC will enabled by default in version 3.0.

注解

django-celery will use the local timezone as specified by the TIME_ZONE setting, it will also honor the new USE_TZ setting introuced in Django 1.4.

New security serializer using cryptographic signing

A new serializer has been added that signs and verifies the signature of messages.

The name of the new serializer is auth, and needs additional configuration to work (see Security).

参见

Security

Contributed by Mher Movsisyan.

Experimental support for automatic module reloading

Starting celeryd with the --autoreload option will enable the worker to watch for file system changes to all imported task modules imported (and also any non-task modules added to the CELERY_IMPORTS setting or the -I|—include option).

This is an experimental feature intended for use in development only, using auto-reload in production is discouraged as the behavior of reloading a module in Python is undefined, and may cause hard to diagnose bugs and crashes. Celery uses the same approach as the auto-reloader found in e.g. the Django runserver command.

When auto-reload is enabled the worker starts an additional thread that watches for changes in the file system. New modules are imported, and already imported modules are reloaded whenever a change is detected, and if the prefork pool is used the child processes will finish the work they are doing and exit, so that they can be replaced by fresh processes effectively reloading the code.

File system notification backends are pluggable, and Celery comes with three implementations:

inotify (Linux)
Used if the pyinotify library is installed. If you are running on Linux this is the recommended implementation, to install the pyinotify library you have to run the following command:
```
$ pip install pyinotify
```
kqueue (OS X/BSD)
stat

The fallback implementation simply polls the files using stat and is very expensive.

You can force an implementation by setting the CELERYD_FSNOTIFY environment variable:

$ env CELERYD_FSNOTIFY=stat celeryd -l info --autoreload

Contributed by Mher Movsisyan.

New CELERY_ANNOTATIONS setting

This new setting enables the configuration to modify task classes and their attributes.

The setting can be a dict, or a list of annotation objects that filter for tasks and return a map of attributes to change.

As an example, this is an annotation to change the rate_limit attribute for the tasks.add task:

CELERY_ANNOTATIONS = {'tasks.add': {'rate_limit': '10/s'}}

or change the same for all tasks:

CELERY_ANNOTATIONS = {'*': {'rate_limit': '10/s'}}

You can change methods too, for example the on_failure handler:

def my_on_failure(self, exc, task_id, args, kwargs, einfo):
    print('Oh no! Task failed: %r' % (exc, ))
CELERY_ANNOTATIONS = {'*': {'on_failure': my_on_failure}}

If you need more flexibility then you can also create objects that filter for tasks to annotate:

class MyAnnotate(object):
    def annotate(self, task):
        if task.name.startswith('tasks.'):
            return {'rate_limit': '10/s'}
CELERY_ANNOTATIONS = (MyAnnotate(), {…})

current provides the currently executing task

The new celery.task.current proxy will always give the currently executing task.

Example:

from celery.task import current, task
@task
def update_twitter_status(auth, message):
    twitter = Twitter(auth)
    try:
        twitter.update_status(message)
    except twitter.FailWhale, exc:
        # retry in 10 seconds.
        current.retry(countdown=10, exc=exc)

Previously you would have to type update_twitter_status.retry(…) here, which can be annoying for long task names.

注解

This will not work if the task function is called directly, i.e: update_twitter_status(a, b). For that to work apply must be used: update_twitter_status.apply((a, b)).

In Other News

Now depends on Kombu 2.1.0.
Efficient Chord support for the memcached backend (Issue #533)

This means memcached joins Redis in the ability to do non-polling chords.

Contributed by Dan McGee.
Adds Chord support for the Rabbit result backend (amqp)

The Rabbit result backend can now use the fallback chord solution.
Sending QUIT to celeryd will now cause it cold terminate.

That is, it will not finish executing the tasks it is currently working on.

Contributed by Alec Clowes.
New “detailed” mode for the Cassandra backend.

Allows to have a “detailed” mode for the Cassandra backend. Basically the idea is to keep all states using Cassandra wide columns. New states are then appended to the row as new columns, the last state being the last column.

See the CASSANDRA_DETAILED_MODE setting.

Contributed by Steeve Morin.
The crontab parser now matches Vixie Cron behavior when parsing ranges with steps (e.g. 1-59/2).

Contributed by Daniel Hepper.
celerybeat can now be configured on the command-line like celeryd.

Additional configuration must be added at the end of the argument list followed by --, for example:
```
$ celerybeat -l info -- celerybeat.max_loop_interval=10.0
```
Now limits the number of frames in a traceback so that celeryd does not crash on maximum recursion limit exceeded exceptions (Issue #615).

The limit is set to the current recursion limit divided by 8 (which is 125 by default).

To get or set the current recursion limit use sys.getrecursionlimit() and sys.setrecursionlimit().
More information is now preserved in the pickleable traceback.

This has been added so that Sentry can show more details.

Contributed by Sean O’Connor.
CentOS init script has been updated and should be more flexible.

Contributed by Andrew McFague.
MongoDB result backend now supports forget().

Contributed by Andrew McFague
task.retry() now re-raises the original exception keeping the original stack trace.

Suggested by ojii.
The –uid argument to daemons now uses initgroups() to set groups to all the groups the user is a member of.

Contributed by Łukasz Oleś.
celeryctl: Added shell command.

The shell will have the current_app (celery) and all tasks automatically added to locals.
celeryctl: Added migrate command.
The migrate command moves all tasks from one broker to another. Note that this is experimental and you should have a backup of the data before proceeding.

Examples:
```
$ celeryctl migrate redis://localhost amqp://localhost
$ celeryctl migrate amqp://localhost//v1 amqp://localhost//v2
$ python manage.py celeryctl migrate django:// redis://
```
Routers can now override the exchange and routing_key used to create missing queues (Issue #577).

By default this will always use the name of the queue, but you can now have a router return exchange and routing_key keys to set them.

This is useful when using routing classes which decides a destination at runtime.

Contributed by Akira Matsuzaki.
Redis result backend: Adds support for a max_connections parameter.

It is now possible to configure the maximum number of simultaneous connections in the Redis connection pool used for results.

The default max connections setting can be configured using the CELERY_REDIS_MAX_CONNECTIONS setting, or it can be changed individually by RedisBackend(max_connections=int).

Contributed by Steeve Morin.
Redis result backend: Adds the ability to wait for results without polling.

Contributed by Steeve Morin.
MongoDB result backend: Now supports save and restore taskset.

Contributed by Julien Poissonnier.
There’s a new Security guide in the documentation.
The init scripts has been updated, and many bugs fixed.

Contributed by Chris Streeter.
User (tilde) is now expanded in command-line arguments.
Can now configure CELERYCTL envvar in /etc/default/celeryd.

While not necessary for operation, celeryctl is used for the celeryd status command, and the path to celeryctl must be configured for that to work.

The daemonization cookbook contains examples.

Contributed by Jude Nagurney.
The MongoDB result backend can now use Replica Sets.

Contributed by Ivan Metzlar.
gevent: Now supports autoscaling (Issue #599).

Contributed by Mark Lavin.
multiprocessing: Mediator thread is now always enabled, even though rate limits are disabled, as the pool semaphore is known to block the main thread, causing broadcast commands and shutdown to depend on the semaphore being released.

Fixes

Exceptions that are re-raised with a new exception object now keeps the original stack trace.
Windows: Fixed the no handlers found for multiprocessing warning.
Windows: The celeryd program can now be used.

Previously Windows users had to launch celeryd using python -m celery.bin.celeryd.
Redis result backend: Now uses SETEX command to set result key, and expiry atomically.

Suggested by yaniv-aknin.
celeryd: Fixed a problem where shutdown hanged when Ctrl+C was used to terminate.
celeryd: No longer crashes when channel errors occur.

Fix contributed by Roger Hu.
Fixed memory leak in the eventlet pool, caused by the use of greenlet.getcurrent.

Fix contributed by Ignas Mikalajūnas.
Cassandra backend: No longer uses pycassa.connect() which is deprecated since pycassa 1.4.

Fix contributed by Jeff Terrace.
Fixed unicode decode errors that could occur while sending error emails.

Fix contributed by Seong Wun Mun.
celery.bin programs now always defines __package__ as recommended by PEP-366.
send_task now emits a warning when used in combination with CELERY_ALWAYS_EAGER (Issue #581).

Contributed by Mher Movsisyan.
apply_async now forwards the original keyword arguments to apply when CELERY_ALWAYS_EAGER is enabled.
celeryev now tries to re-establish the connection if the connection to the broker is lost (Issue #574).
celeryev: Fixed a crash occurring if a task has no associated worker information.

Fix contributed by Matt Williamson.
The current date and time is now consistently taken from the current loaders now method.
Now shows helpful error message when given a config module ending in .py that can’t be imported.
celeryctl: The --expires and -eta arguments to the apply command can now be an ISO-8601 formatted string.
celeryctl now exits with exit status EX_UNAVAILABLE (69) if no replies have been received.