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https://issues.apache.org/jira/browse/SPARK-53330?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon reassigned SPARK-53330:
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Assignee: Ben Hurdelhey
> Fix Arrow UDF support for DayTimeIntervalType with bounds != start-end
> ----------------------------------------------------------------------
>
> Key: SPARK-53330
> URL: https://issues.apache.org/jira/browse/SPARK-53330
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 4.1.0
> Reporter: Ben Hurdelhey
> Assignee: Ben Hurdelhey
> Priority: Major
> Labels: pull-request-available
>
> When a pyspark udf (useArrow=true) returns interval type data, it currently
> errors with below error when the resultType (e.g., DayTimeIntervalType) has
> begin/end that don't span the maximum range.
> org.apache.spark.SparkException: [ARROW_TYPE_MISMATCH] Invalid schema from
> pandas_udf(): expected DayTimeIntervalType(1,3), got
> DayTimeIntervalType(0,3). SQLSTATE: 42K0G
>
> Repro:
> ```
> from pyspark.sql.types import DayTimeIntervalType
> from pyspark.sql.functions import udf
>
> @udf(useArrow=True, returnType=DayTimeIntervalType(0, 3)) # this works
> def return_interval1(x):
> return x
> @udf(useArrow=True, returnType=DayTimeIntervalType(1, 3)) # this fails
> def return_interval2(x):
> return x
> spark.sql("SELECT INTERVAL '1 10:30:45.123' DAY TO SECOND as
> value").select(return_interval2("value")).collect()
> ```
>
> YearToMonthIntervalType is not supported in arrow udfs, so that it's
> currently not a concern.
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