I started from this question: How to chain a Celery task that returns a list into a group?
But I want to expand twice. So in my use case I have:
So each step I'm expanding the number of items of the next step. I can do it by looping through the results in my task and calling .delay()
on the next task function. But I thought I'd try to not make my main tasks do that. Instead they'd return a list of tuples - each tuple would then be expanded into the arguments for a call to the next function.
The above question has an answer that appears to meet my need, but I can't work out the correct way of chaining it for a two level expansion.
Here is a very cut down example of my code:
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()
I've then tried various ways to make my nested mapping work. First, a one level mapping works fine, so:
pp = (task_range.s() | dmap.s(add.s()))
pp(2)
Produces the kind of results I'd expect, so I'm not totally off.
But when I try to add another level:
ppp = (task_range.s() | dmap.s(add.s() | dmap.s(combine_log.s())))
Then in the worker I see the error:
[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'
And I'm not sure why changing the argument to dmap()
from a plain task signature to a chain changes how the arguments get passed into add()
. My impression was that it shouldn't, it just means the return value of add()
would get passed on. But apparently that is not the case ...
Bound tasks A task being bound means the first argument to the task will always be the task instance ( self ), just like Python bound methods: logger = get_task_logger(__name__) @app. task(bind=True) def add(self, x, y): logger.
The ETA (estimated time of arrival) lets you set a specific date and time that is the earliest time at which your task will be executed. countdown is a shortcut to set eta by seconds into the future. >>> result = add. apply_async(args=[10, 10], countdown=3) >>> result. get() # this takes at least 3 seconds to return 20.
Turns out the problem is that the clone()
method on a chain
instance does not pass the arguments through at some point - see https://stackoverflow.com/a/53442344/3189 for the full details. If I use the method in that answer, my dmap()
code becomes:
@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
And then when I do:
ppp = (task_range.s() | dmap.s(add.s() | dmap.s(combine_log.s())))
everything works as expected.
Thanks for the great answer. I had to tweak the code to make sure it could handle tasks with single arguments. I am sure this is awful, but it works! Any improvements appreciated.
@celery_app.task(name='app.worker.dmap')
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)
print(f"ARGS: {args_iter}")
args_list = []
run_in_parallel = group(clone_signature(callback, args if type(args) is list else [args]) for args in args_iter)
print(f"Finished Loops: {run_in_parallel}")
return run_in_parallel.delay()
Specifically - I added:
if type(args) is list else [args]
to this line:
run_in_parallel = group(clone_signature(callback, args ***if type(args) is list else [args]***) for args in args_iter)
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