PySpark UDF is taking long to process

[sanjay]

Hi,

I have UDF which runs for each spark dataframe row, does some complex processing and return string output. But it takes very long if data is 15000 rows. I have configured cluster with autoscaling, but its not spinning more servers.

Please suggest how to make UDF fasters or any reference implementations.

Regards,

Sanjay

[pvignesh92]

@Sanjay Jain​ Hi Sanjay. You did not mention what kind of processing you are doing in UDF. Python UDF definitely will create performance issues as Spark optimizer does not apply optimization on what you are doing within the UDF. Please see if you can do any of those processing using Spark native functions.

If still, you need to use python UDF, see if you can try with Pandas UDF. This can provide significant performance improvements for certain types of operations. Pandas UDFs use Apache Arrow to transfer data between Python and Spark, which can result in faster processing times.

[Rishabh-Pandey]

Write ...whether you can perform the same things by using pyspark native logics and functions then no need to use a UDF. Because in most cases we can do by using pyspark also because UDF will definitely create a performance issues ​

[Lakshay · Accepted solution]

Hi @Sanjay Jain​ , Python UDFs are generally slower to process because it runs mostly in the driver which can also lead to OOM errors on Driver. To resolve this issue, please consider the below:

1. Use spark built-in functions to do the same functionality.
2. Use pandas UDF instead of python UDFs.
3. If above 2 options are not suitable, use the configuration : spark.databricks.execution.pythonUDF.arrow.enabled = True

[Kaniz_Fatma · Accepted solution]

Hi @Sanjay Jain​ ​​, We haven't heard from you since the last response from @Lakshay Goel​, @rishabh and @Vigneshraja Palaniraj​​, and I was checking back to see if their suggestions helped you.

Or else, If you have any solution, please share it with the community, as it can be helpful to others. 

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