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celery-tasktree-0.3.4
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Celery Tasktree module
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | celery-tasktree-0.3.4 |
| نام | celery-tasktree |
| نسخه کتابخانه | 0.3.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | NetAngels team |
| ایمیل نویسنده | info@netangels.ru |
| آدرس صفحه اصلی | https://github.com/NetAngels/celery-tasktree |
| آدرس اینترنتی | https://pypi.org/project/celery-tasktree/ |
| مجوز | BSD License |
Celery tasktree module
======================
celery-tasktree is a module which helps to execute trees of celery tasks
asynchronously in a particular order. Tasktree comes to the rescue when the
number of tasks and dependencies grows and when a naive callback-based approach
becomes hard to understand and maintain.
Usage sample
-------------
::
from celery_tasktree import task_with_callbacks, TaskTree
@task_with_callbacks
def some_action(...):
...
def execute_actions():
tree = TaskTree()
task0 = tree.add_task(some_action, args=[...], kwargs={...})
task1 = tree.add_task(some_action, args=[...], kwargs={...})
task10 = task1.add_task(some_action, args=[...], kwargs={...})
task11 = task1.add_task(some_action, args=[...], kwargs={...})
task110 = task11.add_task(some_action, args=[...], kwargs={...})
async_result = tree.apply_async()
return async_result
Decorator named ``task_with_callbacks`` should be used instead of simple celery
``task`` decorator.
According to the code:
- task0 and task1 are executed simultaniously
- task10 and task11 are executed simultaniously after task1
- task110 is executed after task11
Things to be noted:
- There is no way to stop propagation of the execution and there is no way to
pass extra arguments from an ancestor to a child task. In short, there in only one
kind of dependency between tasks: the dependency of execution order.
- If the subtask (function) return value is an object, then a property named
"async_result" will be added to that object so that it will be possible to
use ``join()`` to gather the ordered task results. To extend the previous example::
async_result = execute_actions()
task0_result, task1_result = async_result.join()
task10_result, task11_result = task1_result.async_result.join()
task110_result = task11_result.async_result.join()
Subclassing `celery.task.Task` with callbacks
----------------------------------------------
Decorating functions with ``@task`` decorator is the easiest, but not the only
one way to create new ``Task`` subclasses. Sometimes it is more convenient to
subclass the generic ``celery.task.Task`` class and re-define its ``run()`` method.
To make such a class compatible with TaskTree, ``run`` should be wrapped with
``celery_tasktree.run_with_callbacks`` decorator. The example below
illustrates this approach::
from celery.task import Task
from celery_tasktree import run_with_callbacks, TaskTree
class SomeActionTask(Task):
@run_with_callbacks
def run(self, ...):
...
def execute_actions():
tree = TaskTree()
task0 = tree.add_task(SomeActionTask, args=[...], kwargs={...})
task01 = task0.add_task(SomeActionTask, args=[...], kwargs={...})
tree.apply_async()
Using TaskTree as a simple queue
-----------------------------------
In many cases a fully fledged tree of tasks would be overkill for you. All you
need is to add two or more tasks to a queue to make sure that they will be
executed in order. To allow this TaskTree has ``push()`` and ``pop()``
methods which in fact are nothing but wrappers around ``add_task()``.
The ``push()`` method adds a new task as a child to the perviously created one
whereas ``pop()`` removes and returns the task from the tail of the task stack.
Usage sample looks like::
# create the tree
tree = TaskTree()
# push a number of tasks into it
tree.push(action1, args=[...], kwargs={...})
tree.push(action2, args=[...], kwargs={...})
tree.push(actionX, args=[...], kwargs={...})
tree.pop() # get back action X from the queue
tree.push(action3, args=[...], kwargs={...})
# apply asynchronously
tree.apply_async()
Actions will be executed in order ``action1 -> action2 -> action3``.
Task with callbacks outside TaskTree
---------------------------------------
The ``task_with_callbacks`` decorator can be useful in itself. It decorates
functions the same way the ordinary ``task`` celery decorator does, but also
adds an optional ``callback`` parameter.
Callback can be a subtask or a list of subtasks (not the TaskSet). Behind the
scenes, when a task with a callback is invoked, it executes the function's main code,
then builds a TaskSet, invokes it asynchronously and attaches the
``TaskSetResut`` as the attribute named ``async_result`` to the function's return
value.
Simple example is provided below::
from celery_tasktree import task_with_callbacks
@task_with_callbacks
def some_action(...):
...
cb1 = some_action.subtask(...)
cb2 = some_action.subtask(...)
async_result = some_action.delay(..., callback=[cb1, cb2])
main_result = async_result.wait()
cb1_result, cb2_result = main_result.async_result.join()
نحوه نصب
نصب پکیج whl celery-tasktree-0.3.4:
pip install celery-tasktree-0.3.4.whl
نصب پکیج tar.gz celery-tasktree-0.3.4:
pip install celery-tasktree-0.3.4.tar.gz