hey everyone, I noticed very high memory consumption when using mapped tasks in conjunction with the 

CloudTaskRunner

 but not the plain 

TaskRunner

 (using Dask Deployment). I was observing that during the "mapping" procedure the worker that was doing the actual mapping was continuously using more and more memory. Which seemed reasonable since mapping constitutes copying the mapped task. However, I noticed that when using the

CloudTaskRunner

memory consumption is much much higher during this step. To be specific, mapping from a list that only contained approximately 8000 elements has eaten up more than 4 GB of memory on the worker. I did some debugging and found out that the same mapped task has a serialized size of 15 200 bytes using

TaskRunner

, but 122 648 bytes using the

CloudTaskRunner

. This is almost a 10 fold increase which makes the mapping function pretty unusable for me. The increased size is ultimately coming from pickling this function: https://github.com/PrefectHQ/prefect/blob/master/src/prefect/engine/task_runner.py#L788 and I think the serialized size of the

CloudTaskRunner

class is the cause of the different sizes. Is this behavior something that is known? Or is it worth a bug report? I will stick to the plain

TaskRunner

for now which will, unfortunately, prevent me from using the cloud UI which I really like. It would be great if this could be fixed. I'm using the latest prefect (v 0.10.7)

IIRC @Chris White looking into sizing at some point so tagging him in this thread for visibility. Also cc @Jim Crist-Harif for the Dask clout

Yeah, there's some inefficiencies here. We could do some things to fix this now, but a lot of the issues here will go away with the upcoming mapping refactor that Chris is working on. Once that's done I plan to revisit the serialized size issue.

@Jim Crist-Harif understood and thank you for the response. When do you plan to release this refactor? I will keep an eye on it and try to retest it then. I think I can manage without the UI for the time being.

The refactor is on me; right now I’m focused on our 0.11.0 release (to go out next week) but the minute that work is complete I’m going to switch to the mapping refactor

cool, I will keep eye on the updates then and wait patiently :)