Multiple Celery Remote workers

3
votes

This question is regarding the use of multiple remote Celery workers on separate machines. The implementation of the App can be conceptualized as:

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My App (Producer) will be adding multiple tasks (say 50) to the queue every 5 mins (imagine a python for loop iterating over a list of tasks to be performed asynchronously at every 5 min interval). I want the celery workers (which will be remote machines) to pick these tasks up as soon as they are pushed.

My question is will Celery/RabbitMQ automatically handle task distribution (so no Worker picks up a task that has already been picked up by a worker from the queue - i.e. to ensure work is not duplicated) and distribute the tasks evenly so no worker is left lazying about while other workers are working hard or do these have to be configured/programmed in the settings?*

I would most appreciate it if someone could forward me relevant documentation (I was checking out Celery docs but couldn't find this specific info regarding remote celery workers in this context.)

2
pythonrabbitmqcelery

2 Answers

3
votes

Automatically but you need to be aware of prefetching feature which is described here: http://docs.celeryproject.org/en/latest/userguide/optimizing.html#prefetch-limits, read until the end of the page.

In short, prefetching works on two levels: worker level and process level, since a worker may have multiple processes. To disable prefetch on worker level you need to specify worker_prefetch_multiplier = 1 in celery settings, to disable on the process level you need to specify -Ofair option in worker's command line.

0
votes

So after digging around in RabbitMQ docs it seems that the default exchange method is Direct Exchange (ref https://www.rabbitmq.com/tutorials/amqp-concepts.html) which means that tasks will be distributed to workers in a round-robin manner.