Sets of tasks, Subtasks and Callbacks

Subtasks

The subtask class is used to wrap the arguments and execution options for a single task invocation:

subtask(task_name_or_cls, args, kwargs, options)

For convenience every task also has a shortcut to create subtask instances:

task.subtask(args, kwargs, options)

subtask is actually a subclass of dict, which means it can be serialized with JSON or other encodings that doesn’t support complex Python objects.

Also it can be regarded as a type, as the following usage works:

>>> s = subtask("tasks.add", args=(2, 2), kwargs={})

>>> subtask(dict(s))  # coerce dict into subtask

This makes it excellent as a means to pass callbacks around to tasks.

Callbacks

Let’s improve our add task so it can accept a callback that takes the result as an argument:

from celery.decorators import task
from celery.task.sets import subtask

@task
def add(x, y, callback=None):
    result = x + y
    if callback is not None:
        subtask(callback).delay(result)
    return result

See? subtask also knows how it should be applied, asynchronously by delay(), and eagerly by apply().

The best thing is that any arguments you add to subtask.delay, will be prepended to the arguments specified by the subtask itself!

So if you have the subtask:

>>> add.subtask(args=(10, ))

subtask.delay(result) becomes:

>>> add.apply_async(args=(result, 10))

Now let’s execute our new add task with a callback:

>>> add.delay(2, 2, callback=add.subtask((8, )))

As expected this will first launch one task calculating 2 + 2, then another task calculating 4 + 8.

Task Sets

The TaskSet enables easy invocation of several tasks at once, and is then able to join the results in the same order as the tasks were invoked.

A task set takes a list of subtask‘s:

>>> from celery.task.sets import TaskSet
>>> from tasks import add

>>> job = TaskSet(tasks=[
...             add.subtask((4, 4)),
...             add.subtask((8, 8)),
...             add.subtask((16, 16)),
...             add.subtask((32, 32)),
... ])

>>> result = job.apply_async()

>>> result.ready()  # has all subtasks completed?
True
>>> result.successful() # was all subtasks successful?

>>> result.join()
[4, 8, 16, 32, 64]

Results

When a TaskSet is applied it returns a TaskSetResult object.

TaskSetResult takes a list of AsyncResult instances and operates on them as if it was a single task.

It supports the following operations:

• successful()

  Returns True if all of the subtasks finished successfully (e.g. did not raise an exception).

• failed()

  Returns True if any of the subtasks failed.

• waiting()

  Returns True if any of the subtasks is not ready yet.

• ready()

  Return True if all of the subtasks are ready.

• completed_count()

  Returns the number of completed subtasks.

• revoke()

  Revokes all of the subtasks.

• iterate()

  Iterates over the return values of the subtasks as they finish, one by one.

• join()

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