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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2018/02/07 13:14:00 UTC
[jira] [Updated] (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 ]
Hyukjin Kwon updated SPARK-23352:
---------------------------------
Description:
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}
was:
Currently, we don't support {{BinaryType}} in Pandas UDFs:
{code}
>>> from pyspark.sql.functions import pandas_udf
>>> pudf = pandas_udf(lambda x: x, "binary")
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>> 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}
> 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
> 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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