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From "Xiao Li (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-23352) Explicitly specify supported types in Pandas UDFs
Date Tue, 13 Feb 2018 00:49:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-23352?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Xiao Li updated SPARK-23352:
----------------------------
    Fix Version/s: 2.3.1

> 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: 2.3.1, 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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