如何递归地将一个返回列表的 Celery 任务链接到一个组中？

Question

我从这个问题开始：如何将一个返回列表的 Celery 任务链接到一个组中？

但我想扩展两次。所以在我的用例中，我有：

• 任务 A：确定给定日期的项目总数
• 任务 B：下载该日期的 1000 个元数据条目
• 任务 C：下载一项的内容

所以每一步我都在扩大下一步的项目数量。我可以通过循环遍历任务中的结果并调用.delay()下一个任务函数来实现。但我想我会尽量不让我的主要任务这样做。相反，他们会返回一个元组列表——然后每个元组将被扩展为调用下一个函数的参数。

上述问题的答案似乎满足了我的需要，但我无法找出将其链接到两级扩展的正确方法。

这是我的代码的一个非常精简的示例：

from celery import group
from celery.task import subtask
from celery.utils.log import get_task_logger

from .celery import app

logger = get_task_logger(__name__)

@app.task
def task_range(upper=10):
    # wrap in list to make JSON serializer work
    return list(zip(range(upper), range(upper)))

@app.task
def add(x, y):
    logger.info(f'x is {x} and y is {y}')
    char = chr(ord('a') + x)
    char2 = chr(ord('a') + x*2)
    result = x + y
    logger.info(f'result is {result}')
    return list(zip(char * result, char2 * result))

@app.task
def combine_log(c1, c2):
    logger.info(f'combine log is {c1}{c2}')

@app.task
def dmap(args_iter, celery_task):
    """
    Takes an iterator of argument tuples and queues them up for celery to run with the function.
    """
    logger.info(f'in dmap, len iter: {len(args_iter)}')
    callback = subtask(celery_task)
    run_in_parallel = group(callback.clone(args) for args in args_iter)
    return run_in_parallel.delay()

然后我尝试了各种方法来使我的嵌套映射工作。首先，一级映射工作正常，所以：

pp = (task_range.s() | dmap.s(add.s()))
pp(2)

产生了我期望的那种结果，所以我并没有完全离开。

但是当我尝试添加另一个级别时：

ppp = (task_range.s() | dmap.s(add.s() | dmap.s(combine_log.s())))

然后在工人中我看到错误：

[2019-11-23 22:34:12,024: ERROR/ForkPoolWorker-2] Task proj.tasks.dmap[e92877a9-85ce-4f16-88e3-d6889bc27867] raised unexpected: TypeError("add() missing 2 required positional arguments: 'x' and 'y'",)
Traceback (most recent call last):
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/app/trace.py", line 385, in trace_task
    R = retval = fun(*args, **kwargs)
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/app/trace.py", line 648, in __protected_call__
    return self.run(*args, **kwargs)
  File "/home/hdowner/dev/playground/celery/proj/tasks.py", line 44, in dmap
    return run_in_parallel.delay()
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/canvas.py", line 186, in delay
    return self.apply_async(partial_args, partial_kwargs)
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/canvas.py", line 1008, in apply_async
    args=args, kwargs=kwargs, **options))
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/canvas.py", line 1092, in _apply_tasks
    **options)
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/canvas.py", line 578, in apply_async
    dict(self.options, **options) if options else self.options))
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/canvas.py", line 607, in run
    first_task.apply_async(**options)
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/canvas.py", line 229, in apply_async
    return _apply(args, kwargs, **options)
  File "/home/hdowner/.venv/play_celery/lib/python3.6/site-packages/celery/app/task.py", line 532, in apply_async
    check_arguments(*(args or ()), **(kwargs or {}))
TypeError: add() missing 2 required positional arguments: 'x' and 'y'

而且我不确定为什么将参数dmap()从普通任务签名更改为链会改变参数传递到add(). 我的印象是它不应该，它只是意味着 的返回值add()会被传递。但显然事实并非如此......

Answer 1

事实证明，问题在于实例clone()上的方法chain在某些时候不会传递参数 -有关完整详细信息，请参阅/sf/answers/3740964111/ 。如果我使用该答案中的方法，我的dmap()代码将变为：

@app.task
def dmap(args_iter, celery_task):
    """
    Takes an iterator of argument tuples and queues them up for celery to run with the function.
    """
    callback = subtask(celery_task)
    run_in_parallel = group(clone_signature(callback, args) for args in args_iter)
    return run_in_parallel.delay()


def clone_signature(sig, args=(), kwargs=(), **opts):
    """
    Turns out that a chain clone() does not copy the arguments properly - this
    clone does.
    From: https://stackoverflow.com/a/53442344/3189
    """
    if sig.subtask_type and sig.subtask_type != "chain":
        raise NotImplementedError(
            "Cloning only supported for Tasks and chains, not {}".format(sig.subtask_type)
        )
    clone = sig.clone()
    if hasattr(clone, "tasks"):
        task_to_apply_args_to = clone.tasks[0]
    else:
        task_to_apply_args_to = clone
    args, kwargs, opts = task_to_apply_args_to._merge(args=args, kwargs=kwargs, options=opts)
    task_to_apply_args_to.update(args=args, kwargs=kwargs, options=deepcopy(opts))
    return clone

然后当我这样做时：

ppp = (task_range.s() | dmap.s(add.s() | dmap.s(combine_log.s())))

一切都按预期进行。