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        "author": "NetAngels team",
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        "description": "Celery tasktree module\n======================\n\ncelery-tasktree is a module which helps to execute trees of celery tasks\nasynchronously in a particular order. Tasktree comes to the rescue when the\nnumber of tasks and dependencies grows and when a naive callback-based approach\nbecomes hard to understand and maintain.\n\nUsage sample\n-------------\n\n::\n\n    from celery_tasktree import task_with_callbacks, TaskTree\n\n    @task_with_callbacks\n    def some_action(...):\n        ...\n\n    def execute_actions():\n        tree = TaskTree()\n        task0 = tree.add_task(some_action, args=[...], kwargs={...})\n        task1 = tree.add_task(some_action, args=[...], kwargs={...})\n        task10 = task1.add_task(some_action, args=[...], kwargs={...})\n        task11 = task1.add_task(some_action, args=[...], kwargs={...})\n        task110 = task11.add_task(some_action, args=[...], kwargs={...})\n        async_result = tree.apply_async()\n        return async_result\n\n\nDecorator named ``task_with_callbacks`` should be used instead of simple celery\n``task`` decorator.\n\nAccording to the code:\n\n- task0 and task1 are executed simultaniously\n- task10 and task11 are executed simultaniously after task1\n- task110 is executed after task11\n\nThings to be noted:\n\n- There is no way to stop propagation of the execution and there is no way to\n  pass extra arguments from an ancestor to a child task. In short, there in only one\n  kind of dependency between tasks: the dependency of execution order.\n- If the subtask (function) return value is an object, then a property named\n  \"async_result\" will be added to that object so that it will be possible to\n  use ``join()`` to gather the ordered task results. To extend the previous example::\n\n      async_result = execute_actions() \n      task0_result, task1_result = async_result.join()\n      task10_result, task11_result = task1_result.async_result.join()\n      task110_result = task11_result.async_result.join() \n\nSubclassing `celery.task.Task` with callbacks\n----------------------------------------------\n\nDecorating functions with ``@task`` decorator is the easiest, but not the only\none way to create new ``Task`` subclasses. Sometimes it is more convenient to\nsubclass the generic ``celery.task.Task`` class and re-define its ``run()`` method.\nTo make such a class compatible with TaskTree, ``run`` should be wrapped with\n``celery_tasktree.run_with_callbacks`` decorator. The example below\nillustrates this approach::\n\n    from celery.task import Task\n    from celery_tasktree import run_with_callbacks, TaskTree\n\n    class SomeActionTask(Task):\n\n        @run_with_callbacks\n        def run(self, ...):\n            ...\n\n    def execute_actions():\n        tree = TaskTree()\n        task0 = tree.add_task(SomeActionTask, args=[...], kwargs={...})\n        task01 = task0.add_task(SomeActionTask, args=[...], kwargs={...})\n        tree.apply_async()\n\n\nUsing TaskTree as a simple queue\n-----------------------------------\n\nIn many cases a fully fledged tree of tasks would be overkill for you. All you\nneed is to add two or more tasks to a queue to make sure that they will be\nexecuted in order. To allow this TaskTree has ``push()`` and ``pop()``\nmethods which in fact are nothing but wrappers around ``add_task()``.\nThe ``push()`` method adds a new task as a child to the perviously created one\nwhereas ``pop()`` removes and returns the task from the tail of the task stack.\nUsage sample looks like::\n\n    # create the tree\n    tree = TaskTree()\n    # push a number of tasks into it\n    tree.push(action1, args=[...], kwargs={...})\n    tree.push(action2, args=[...], kwargs={...})\n    tree.push(actionX, args=[...], kwargs={...})\n    tree.pop() # get back action X from the queue\n    tree.push(action3, args=[...], kwargs={...})\n    # apply asynchronously\n    tree.apply_async()\n\nActions will be executed in order ``action1 -> action2 -> action3``.\n\n\nTask with callbacks outside TaskTree\n---------------------------------------\n\nThe ``task_with_callbacks`` decorator can be useful in itself. It decorates\nfunctions the same way the ordinary ``task`` celery decorator does, but also\nadds an optional ``callback`` parameter.\n\nCallback can be a subtask or a list of subtasks (not the TaskSet). Behind the\nscenes, when a task with a callback is invoked, it executes the function's main code,\nthen builds a TaskSet, invokes it asynchronously and attaches the\n``TaskSetResut`` as the attribute named ``async_result`` to the function's return\nvalue.\n\nSimple example is provided below::\n\n    from celery_tasktree import task_with_callbacks\n\n    @task_with_callbacks\n    def some_action(...):\n        ...\n\n    cb1 = some_action.subtask(...)\n    cb2 = some_action.subtask(...)\n    async_result = some_action.delay(..., callback=[cb1, cb2])\n    main_result = async_result.wait()\n    cb1_result, cb2_result = main_result.async_result.join()",
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