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https://issues.apache.org/jira/browse/SPARK-25801?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Bryan Cutler resolved SPARK-25801.
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Resolution: Fixed
Fix Version/s: 2.4.0
> pandas_udf grouped_map fails with input dataframe with more than 255 columns
> ----------------------------------------------------------------------------
>
> Key: SPARK-25801
> URL: https://issues.apache.org/jira/browse/SPARK-25801
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.3.0
> Environment: python 2.7
> pyspark 2.3.0
> Reporter: Frederik
> Priority: Major
> Fix For: 2.4.0
>
>
> Hi,
> I'm using a pandas_udf to deploy a model to predict all samples in a spark
> dataframe,
> for this I use a udf as follows:
> @pandas_udf("scores double", PandasUDFType.GROUPED_MAP) def
> predict_scores(pdf): score_values = model.predict_proba(pdf)[:,1] return
> pd.DataFrame({'scores': score_values})
> So it takes a dataframe and predicts the probability of being positive
> according to an sklearn model for each row and returns this as single column.
> This works great on a random groupBy, e.g.:
> sdf_to_score.groupBy(sf.col('age')).apply(predict_scores)
> as long as the dataframe has <255 columns. When the input dataframe has more
> than 255 columns (thus features in my model), I get:
> org.apache.spark.api.python.PythonException: Traceback (most recent call
> last):
> File "path/to/lib/spark2/python/lib/pyspark.zip/pyspark/worker.py", line
> 219, in main
> func, profiler, deserializer, serializer = read_udfs(pickleSer, infile,
> eval_type)
> File "path/to/lib/spark2/python/lib/pyspark.zip/pyspark/worker.py", line
> 148, in read_udfs
> mapper = eval(mapper_str, udfs)
> File "<string>", line 1
> SyntaxError: more than 255 arguments
> Which seems to be related with Python's general limitation of having not
> allowing more than 255 arguments for a function?
>
> Is this a bug or is there a straightforward way around this problem?
>
> Regards,
> Frederik
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