Defining Tasks - celery.task.base¶

class celery.task.base.AsynchronousMapTask¶

Task used internally by dmap_async() and TaskSet.map_async().

run(serfunc, args, **kwargs)¶

The method run by celeryd.

class celery.task.base.ExecuteRemoteTask¶

Execute an arbitrary function or object.

Note You probably want execute_remote() instead, which this is an internal component of.

The object must be pickleable, so you can’t use lambdas or functions defined in the REPL (that is the python shell, or ipython).

run(ser_callable, fargs, fkwargs, **kwargs)¶

Parameters:	ser_callable – A pickled function or callable object. fargs – Positional arguments to apply to the function. fkwargs – Keyword arguments to apply to the function.

class celery.task.base.PeriodicTask¶

A periodic task is a task that behaves like a cron job.

run_every¶

REQUIRED Defines how often the task is run (its interval), it can be either a datetime.timedelta object or an integer specifying the time in seconds.

Raises NotImplementedError:
	if the run_every attribute is not defined.

You have to register the periodic task in the task registry.

Example

>>> from celery.task import tasks, PeriodicTask
>>> from datetime import timedelta
>>> class MyPeriodicTask(PeriodicTask):
...     name = "my_periodic_task"
...     run_every = timedelta(seconds=30)
...
...     def run(self, **kwargs):
...         logger = self.get_logger(**kwargs)
...         logger.info("Running MyPeriodicTask")
>>> tasks.register(MyPeriodicTask)

class celery.task.base.Task¶

A task that can be delayed for execution by the celery daemon.

All subclasses of Task must define the run() method, which is the actual method the celery daemon executes.

The run() method supports both positional, and keyword arguments.

name¶

REQUIRED All subclasses of Task has to define the name attribute. This is the name of the task, registered in the task registry, and passed to delay_task().

type¶

The type of task, currently this can be regular, or periodic, however if you want a periodic task, you should subclass PeriodicTask instead.

routing_key¶

Override the global default routing_key for this task.

exchange¶

Override the global default exchange for this task.

mandatory¶

If set, the message has mandatory routing. By default the message is silently dropped by the broker if it can’t be routed to a queue. However - If the message is mandatory, an exception will be raised instead.

immediate:

Request immediate delivery. If the message cannot be routed to a task worker immediately, an exception will be raised. This is instead of the default behaviour, where the broker will accept and queue the message, but with no guarantee that the message will ever be consumed.

priority:

The message priority. A number from 0 to 9.

max_retries¶

Maximum number of retries before giving up.

default_retry_delay¶

Defeault time in seconds before a retry of the task should be executed. Default is a 1 minute delay.

ignore_result¶

Don’t store the status and return value. This means you can’t use the celery.result.AsyncResult to check if the task is done, or get its return value. Only use if you need the performance and is able live without these features. Any exceptions raised will store the return value/status as usual.

disable_error_emails¶

Disable all error e-mails for this task (only applicable if settings.SEND_CELERY_ERROR_EMAILS is on.)

Raises NotImplementedError:
	if the name attribute is not set.

The resulting class is callable, which if called will apply the run() method.

Examples

This is a simple task just logging a message,

>>> from celery.task import tasks, Task
>>> class MyTask(Task):
...
...     def run(self, some_arg=None, **kwargs):
...         logger = self.get_logger(**kwargs)
...         logger.info("Running MyTask with arg some_arg=%s" %
...                     some_arg))
...         return 42
... tasks.register(MyTask)

You can delay the task using the classmethod delay()...

>>> result = MyTask.delay(some_arg="foo")
>>> result.status # after some time
'DONE'
>>> result.result
42

...or using the delay_task() function, by passing the name of the task.

>>> from celery.task import delay_task
>>> result = delay_task(MyTask.name, some_arg="foo")

exception MaxRetriesExceededError¶

The tasks max restart limit has been exceeded.

classmethod Task.apply(args=None, kwargs=None, **options)¶

Execute this task at once, by blocking until the task has finished executing.

Parameters:	args – positional arguments passed on to the task. kwargs – keyword arguments passed on to the task.
Return type:	celery.result.EagerResult

See celery.execute.apply().

classmethod Task.apply_async(args=None, kwargs=None, **options)¶

Delay this task for execution by the celery daemon(s).

Parameters:	args – positional arguments passed on to the task. kwargs – keyword arguments passed on to the task. **options – Any keyword arguments to pass on to celery.execute.apply_async().

See celery.execute.apply_async() for more information.

Return type:	celery.result.AsyncResult

classmethod Task.delay(*args, **kwargs)¶

Delay this task for execution by the celery daemon(s).

Parameters:	*args – positional arguments passed on to the task. **kwargs – keyword arguments passed on to the task.
Return type:	celery.result.AsyncResult

See celery.execute.delay_task().

Task.get_consumer(connect_timeout=4)¶

Get a celery task message consumer.

Return type:	celery.messaging.TaskConsumer.

Please be sure to close the AMQP connection when you’re done with this object. i.e.:

>>> consumer = self.get_consumer()
>>> # do something with consumer
>>> consumer.connection.close()

Task.get_logger(**kwargs)¶

Get process-aware logger object.

See celery.log.setup_logger().

Task.get_publisher(connect_timeout=4)¶

Get a celery task message publisher.

Return type:	celery.messaging.TaskPublisher.

Please be sure to close the AMQP connection when you’re done with this object, i.e.:

>>> publisher = self.get_publisher()
>>> # do something with publisher
>>> publisher.connection.close()

Task.on_failure(exc)¶

Error handler.

This is run by the worker when the task fails.

Parameter:	exc – The exception raised by the task.

Task.on_retry(exc)¶

Retry handler.

This is run by the worker when the task is to be retried.

Parameter:	exc – The exception sent to retry().

Task.on_success(retval)¶

Success handler.

This is run by the worker when the task executed successfully.

Parameter:	retval – The return value of the task.

Task.retry(args, kwargs, exc=None, throw=True, **options)¶

Retry the task.

Parameters:	args – Positional arguments to retry with. kwargs – Keyword arguments to retry with. exc – Optional exception to raise instead of MaxRestartsExceededError when the max restart limit has been exceeded. throw – Do not raise the celery.exceptions.RetryTaskError exception, that tells the worker that the task is to be retried. countdown – Time in seconds to delay the retry for. eta – Explicit time and date to run the retry at (must be a datetime.datetime instance). **options – Any extra options to pass on to meth:apply_async. See celery.execute.apply_async().
Raises celery.exceptions.RetryTaskError:
	To tell the worker that the task has been re-sent for retry. This always happens except if the throw keyword argument has been explicitly set to False.

Example

>>> class TwitterPostStatusTask(Task):
... 
...     def run(self, username, password, message, **kwargs):
...         twitter = Twitter(username, password)
...         try:
...             twitter.post_status(message)
...         except twitter.FailWhale, exc:
...             # Retry in 5 minutes.
...             self.retry([username, password, message], kwargs,
...                        countdown=60 * 5, exc=exc)

Task.run(*args, **kwargs)¶

REQUIRED The actual task.

All subclasses of Task must define the run method.

Raises NotImplementedError:
	by default, so you have to override this method in your subclass.

class celery.task.base.TaskSet(task, args)¶

A task containing several subtasks, making it possible to track how many, or when all of the tasks has been completed.

Parameters:	task – The task class or name. Can either be a fully qualified task name, or a task class. args – A list of args, kwargs pairs. e.g. [[args1, kwargs1], [args2, kwargs2], ..., [argsN, kwargsN]]

task_name¶

The name of the task.

arguments¶

The arguments, as passed to the task set constructor.

total¶

Total number of tasks in this task set.

Example

>>> from djangofeeds.tasks import RefreshFeedTask
>>> taskset = TaskSet(RefreshFeedTask, args=[
...                 [], {"feed_url": "http://cnn.com/rss"},
...                 [], {"feed_url": "http://bbc.com/rss"},
...                 [], {"feed_url": "http://xkcd.com/rss"}])

>>> taskset_result = taskset.run()
>>> list_of_return_values = taskset.join()

join(timeout=None)¶

Gather the results for all of the tasks in the taskset, and return a list with them ordered by the order of which they were called.

Parameter:	timeout – The time in seconds, how long it will wait for results, before the operation times out.
Raises TimeoutError:
	if timeout is not None and the operation takes longer than timeout seconds.

If any of the tasks raises an exception, the exception will be reraised by join().

Returns:	list of return values for all tasks in the taskset.

classmethod map(func, args, timeout=None)¶

Distribute processing of the arguments and collect the results.

classmethod map_async(func, args, timeout=None)¶

Distribute processing of the arguments and collect the results asynchronously.

Returns:	celery.result.AsyncResult instance.

classmethod remote_execute(func, args)¶

Apply args to function by distributing the args to the celery server(s).

run(connect_timeout=4)¶

Run all tasks in the taskset.

Returns:	A celery.result.TaskSetResult instance.

Example

>>> ts = TaskSet(RefreshFeedTask, [
...         ["http://foo.com/rss", {}],
...         ["http://bar.com/rss", {}],
... )
>>> result = ts.run()
>>> result.taskset_id
"d2c9b261-8eff-4bfb-8459-1e1b72063514"
>>> result.subtask_ids
["b4996460-d959-49c8-aeb9-39c530dcde25",
"598d2d18-ab86-45ca-8b4f-0779f5d6a3cb"]
>>> result.waiting()
True
>>> time.sleep(10)
>>> result.ready()
True
>>> result.successful()
True
>>> result.failed()
False
>>> result.join()
[True, True]