Performance / speed issue with pandas 1.5.0

[kormoczi]

Hi,

I have a python script node with a fairly large input table.
When I did run this node, the processing / run time was around 20 seconds, and most of the time was the initialization (transferring the inputs from KNIME to the python script), not the the script itself.
Previously I have used pandas version 1.4.4.
But now I have updated pandas to the new 1.5.0 version, and the running time of this script has changed dramatically, it became more than 10 minutes!
(And again, from the logs it looks like that most of the time was the initialization, the running time of the script itself is around one second…)
Have anyone experienced similar issue? Is there something I can do to fix this?
(Downgrading back to pandas 1.4.4 solves the problem, of course…)
I am using KNIME Analytics Platform 4.6.2 with all the latest upgrades (but I have tested version 4.6.1 as well, the issue is the same), the OS is Ubuntu 22.04.

Best regards,
Csaba

[mlauber71]

@kormoczi you might want to try the Columnar Storage to communicate between KNIME and Python.

KNIME Columnar Table Backend Boosts Performance | KNIME

Other option would be to use an external file (database) to bring data in and out (maybe not that elegang but still possible).

Export data from KNIME for processing in Python (externaly)

@TheLeo you could use Parquet to keep the column types. Or SQLite database: https://kni.me/w/G1kX4pbOlCeq56cH ORC could also be an option but it might be more complicated. Both ORC and Parquet would allow to read and write data in chunks. https://kni.me/w/uW1d55taxEotKFHO If you absolutely must have a text only file. ARFF is another option: https://kni.me/w/mp7Pv607rD0biVI5 If you want to explore more about exchange between KNIME and Python you might have a look here: https://forum.knim…