celery-tasktree Celery Tasktree module
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tasks Python library trees Celery execute trees celery tasks

celery-tasktree Description

Celery Tasktree module celery-tasktree is a Python module which helps to execute trees of celery tasks asynchronously in a particular order. Tasktree comes to the rescue when a number of tasks and dependencies grows and when naive callback-based approach becomes hard to understand and maintain.Usage sample:from tasktree import task_with_callbacks, TaskTree@task_with_callbacksdef 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_resultDecorator 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 task11Things to be noted:- There is no way to stop propagation of the execution and there is no way to pass extra arguments from ancestor to child task. In short, there in only one kind of dependency between tasks: the dependency of execution order.- If 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 join() for. To extend 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() Requirements: · Python

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