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https://issues.apache.org/jira/browse/SPARK-23352?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16360658#comment-16360658
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Apache Spark commented on SPARK-23352:
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User 'HyukjinKwon' has created a pull request for this issue:
https://github.com/apache/spark/pull/20588
> Explicitly specify supported types in Pandas UDFs
> -------------------------------------------------
>
> Key: SPARK-23352
> URL: https://issues.apache.org/jira/browse/SPARK-23352
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 2.3.0
> Reporter: Hyukjin Kwon
> Assignee: Hyukjin Kwon
> Priority: Major
> Fix For: 2.4.0
>
>
> Currently, we don't support {{BinaryType}} in Pandas UDFs:
> {code}
> >>> from pyspark.sql.functions import pandas_udf
> >>> pudf = pandas_udf(lambda x: x, "binary")
> >>> df = spark.createDataFrame([[bytearray("a")]])
> >>> df.select(pudf("_1")).show()
> ...
> TypeError: Unsupported type in conversion to Arrow: BinaryType
> {code}
> Also, the grouped aggregate Pandas UDF fail fast on {{ArrayType}} but seems
> we can support this case.
> We should better clarify it in doc in Pandas UDFs, and fail fast with type
> checking ahead, rather than execution time.
> Please consider this case:
> {code}
> pandas_udf(lambda x: x, BinaryType()) # we can fail fast at this stage
> because we know the schema ahead
> {code}
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