Chain a celery task's results into a distributed group

问题

Like in this other question, I want to create a celery group from a list that's returned by a celery task. The idea is that the first task will return a list, and the second task will explode that list into concurrent tasks for every item in the list.

The plan is to use this while downloading content. The first task gets links from a website, and the second task is a chain that downloads the page, processes it, and then uploads it to s3. Finally, once all the subpages are done, the website is marked as done in our DB. Something like:

chain(
    get_links_from_website.si('https://www.google.com'),
    dmap.s(  # <-- Distributed map
        download_sub_page.s() | 
        process_sub_page.s() | 
        upload_sub_page_to_s3.s()
    ),
    mark_website_done.s()
)

The solution I've seen so far seems to do an adequate job of this, but fails when the second task is a chain, due to issues with clone not doing a deepcopy (see the comments on this answer for details):

@task
def dmap(it, callback):
    # Map a callback over an iterator and return as a group
    callback = subtask(callback)
    return group(callback.clone([arg,]) for arg in it)()

It also has the problem that if the iterable is 10,000 items long, it will create a group with 10,000 items. That is blowing up our memory usage, as you can imagine.

So, what I'm looking for is a way to do dmap that:

Doesn't blow up RAM by creating monstrous groups (maybe there's a way to chunk through the iterable?)
Works on celery chains without issues with deepcopy.

回答1:

celery canvas provides chunks to split a task into chunks. Unfortunately, this won't work with primitives like chain, group.

You can use celery signals to prevent issues with dmap/clone.

ch = chain(
    download_sub_page.s(),
    process_sub_page.s(),
    upload_sub_page.s(),
)

@task_success.connect(sender='get_links_from_website')
def task_success_handler(sender=None, headers=None, body=None, **kwargs):
    result = kwargs['result']    
    header = [ch(i) for i in result]
    callback = mark_website_done.si()
    chord(header)(callback)

Create a chain for processing pages and hook the last task to it using a chord. This function gets executed whenever get_links_from_website runs succcessfully.

Depending on the time taken by chain, you can also save results of get_links_from_website somewhere. Then iterate over a batch of them to queue up chains and with the last batch, you can hook a callback to last task.

回答2:

This is a bit hacky but we're using deepcopy to clone the callback, this fixes the bug with Signature's shallow copy

def dmap(it, callback, final=None):
    # Map a callback over an iterator and return as a group
    callback = subtask(callback)

    run_in_parallel = group(subtask(copy.deepcopy(dict(callback))).clone([arg, ]) for arg in it)

    if len(run_in_parallel.tasks) == 0:
        return []

    if final:
        return chord(run_in_parallel)(final)

    return run_in_parallel.delay()

Note that this will only work for one nesting level (i.e. callback is a chain/group/chord) but will not work for deeply nested callbacks

For deeply nested callback graphs we use this hack which is a bit slower but works flawlessly

# Hack to completely clone a signature with possibly complex subtasks (chains, chords, etc...)
run_in_parallel = group(pickle.loads(pickle.dumps(callback)).clone([arg, ]) for arg in it)

And for the size of the groups you can always split the iterator to chunks

来源：https://stackoverflow.com/questions/43106099/chain-a-celery-tasks-results-into-a-distributed-group

标签

python

celery

chain