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Posted to issues@spark.apache.org by "Jim Fulton (Jira)" <ji...@apache.org> on 2019/10/07 14:58:00 UTC
[jira] [Updated] (SPARK-28978) PySpark: Can't pass more than 256
arguments to a UDF
[ https://issues.apache.org/jira/browse/SPARK-28978?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Jim Fulton updated SPARK-28978:
-------------------------------
Affects Version/s: 2.3.2
2.4.4
> PySpark: Can't pass more than 256 arguments to a UDF
> ----------------------------------------------------
>
> Key: SPARK-28978
> URL: https://issues.apache.org/jira/browse/SPARK-28978
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.3.2, 2.4.0, 2.4.4
> Reporter: Jim Fulton
> Priority: Major
> Labels: koalas, mlflow, pyspark
>
> This code:
> [https://github.com/apache/spark/blob/712874fa0937f0784f47740b127c3bab20da8569/python/pyspark/worker.py#L367-L379]
> Creates Python lambdas that call UDF functions passing arguments singly, rather than using varargs. For example: `lambda a: f(a[0], a[1], ...)`.
> This fails when there are more than 256 arguments.
> mlflow, when generating model predictions, uses an argument for each feature column. I have a model with > 500 features.
> I was able to easily hack around this by changing the generated lambdas to use varargs, as in `lambda a: f(*a)`.
> IDK why these lambdas were created the way they were. Using varargs is much simpler and works fine in my testing.
>
>
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