Slow Job Processing due to Concurrency Architecture about faktory_worker_python HOT 5 OPEN

The 0.25s delay is only applied when the worker is either disconnecting or at the concurrency limit.

If the worker has capacity to be running jobs then the delay is not used and new jobs will be processed near instantly.

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I'm running the example producer and worker code with some small tweaks to queue the jobs faster and add additional logging:
Producer
Worker

The Producer code queues ~6 jobs a second. However the Worker cannot keep up with that with a Concurrency of one.

Here are the log outputs of the worker:

DEBUG:faktory.worker:Running task: add(1, 2)
2024-05-21 15:16:10.826007 - add: 1 + 2 = 3
DEBUG:faktory.worker:Running task: subtract(10, 5)
2024-05-21 15:16:11.082362 subtract: 10 - 5 = 5
DEBUG:faktory.worker:Running task: multiply(8, 8)
2024-05-21 15:16:11.338471 multiply: 8 * 8 = 64
DEBUG:faktory.worker:Running task: add(1, 2)
2024-05-21 15:16:11.595590 - add: 1 + 2 = 3
DEBUG:faktory.worker:Running task: subtract(10, 5)
2024-05-21 15:16:11.852505 subtract: 10 - 5 = 5
DEBUG:faktory.worker:Running task: multiply(8, 8)
2024-05-21 15:16:12.109596 multiply: 8 * 8 = 64
DEBUG:faktory.worker:Running task: add(1, 2)
2024-05-21 15:16:12.361325 - add: 1 + 2 = 3
DEBUG:faktory.worker:Running task: subtract(10, 5)
2024-05-21 15:16:12.618927 subtract: 10 - 5 = 5
DEBUG:faktory.worker:Running task: multiply(8, 8)
2024-05-21 15:16:12.871962 multiply: 8 * 8 = 64

Each job is roughly taking ~0.25s to process, even though there enqueued jobs in Faktory is increasing in size.

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Increase your concurrency and you will get a better throughput.

The 0.25s delay is there to stop the worker process from spinning the CPU, allowing the in flight jobs to complete, when you are at the concurrency limit. When your workers are scaled appropriately you won't hit that delay at all and everything will be near instant.

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I'm looking to run this for jobs that are heavy CPU intensive with very minimal IO (machine learning), so I wasn't planning on increasing concurrency for my use case.

I just tested increasing the concurrency to 2, and this made the processing of the jobs nearly instantaneous (which is what I originally expected).

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To answer your original question:

Would you be open to a pull request to refactor this code to match the Ruby implementation where the Executor Threads are the ones doing the fetching?

I would, but I think this would not be trivial. Jobs can be processed in other processes, so the socket would need to be shared. I think it would be difficult to get this right.

The easy path would be to make the 250ms timeout configurable, so you can tune to something more suitable for your use case.

A better alternative would be to change the logic where the time.sleep is to be cancellable. If Worker._process could "cancel" the sleep(0.25) when a job is complete, then the worker would instantly fetch the next job. That would remove the delay when the worker is at the concurrency limit without spinning the CPU.

I think this could be implemented with threading.Event fairly easily.

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