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Celery Tasktree module
Python
Branch: master

Merge pull request #4 from bersegosx/master

Replace `celery.task.sets.TaskSet` to `celery.group`
latest commit f79da22ff7
@bersegosx bersegosx authored

README.rst

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()
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