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https://issues.apache.org/jira/browse/SPARK-24735?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16578719#comment-16578719
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holdenk commented on SPARK-24735:
---------------------------------
So [~bryanc]what do you think of if we add a AggregatePythonUDF and use it for
grouped_map / grouped_agg so we get treated the correct way by the Scala SQL
engine?
> Improve exception when mixing up pandas_udf types
> -------------------------------------------------
>
> Key: SPARK-24735
> URL: https://issues.apache.org/jira/browse/SPARK-24735
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 2.3.0
> Reporter: Bryan Cutler
> Priority: Major
>
> From the discussion here
> https://github.com/apache/spark/pull/21650#discussion_r199203674, mixing up
> Pandas UDF types, like using GROUPED_MAP as a SCALAR {{foo =
> pandas_udf(lambda x: x, 'v int', PandasUDFType.GROUPED_MAP)}} produces an
> exception which is hard to understand. It should tell the user that the UDF
> type is wrong. This is the full output:
> {code}
> >>> foo = pandas_udf(lambda x: x, 'v int', PandasUDFType.GROUPED_MAP)
> >>> df.select(foo(df['v'])).show()
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File
> "/Users/icexelloss/workspace/upstream/spark/python/pyspark/sql/dataframe.py",
> line 353, in show
> print(self._jdf.showString(n, 20, vertical))
> File
> "/Users/icexelloss/workspace/upstream/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py",
> line 1257, in __call__
> File
> "/Users/icexelloss/workspace/upstream/spark/python/pyspark/sql/utils.py",
> line 63, in deco
> return f(*a, **kw)
> File
> "/Users/icexelloss/workspace/upstream/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py",
> line 328, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling o257.showString.
> : java.lang.UnsupportedOperationException: Cannot evaluate expression:
> <lambda>(input[0, bigint, false])
> at
> org.apache.spark.sql.catalyst.expressions.Unevaluable$class.doGenCode(Expression.scala:261)
> at
> org.apache.spark.sql.catalyst.expressions.PythonUDF.doGenCode(PythonUDF.scala:50)
> at
> org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:108)
> at
> org.apache.spark.sql.catalyst.expressions.Expression$$anonfun$genCode$2.apply(Expression.scala:105)
> at scala.Option.getOrElse(Option.scala:121)
> ...
> {code}
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