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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/02/07 13:38:00 UTC
[jira] [Assigned] (SPARK-23352) Explicitly specify supported types
in Pandas UDFs
[ https://issues.apache.org/jira/browse/SPARK-23352?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-23352:
------------------------------------
Assignee: Apache Spark
> 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: Apache Spark
> Priority: Major
>
> 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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