[jira] [Assigned] (SPARK-18254) UDFs don't see aliased column names

["Davies Liu (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-18254?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Davies Liu reassigned SPARK-18254:
----------------------------------

    Assignee: Davies Liu

> UDFs don't see aliased column names
> -----------------------------------
>
>                 Key: SPARK-18254
>                 URL: https://issues.apache.org/jira/browse/SPARK-18254
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.1
>         Environment: Python 3.5, Java 8
>            Reporter: Nicholas Chammas
>            Assignee: Davies Liu
>              Labels: correctness
>
> Dunno if I'm misinterpreting something here, but this seems like a bug in how UDFs work, or in how they interface with the optimizer.
> Here's a basic reproduction. I'm using {{length_udf()}} just for illustration; it could be any UDF that accesses fields that have been aliased.
> {code}
> import pyspark
> from pyspark.sql import Row
> from pyspark.sql.functions import udf, col, struct
> def length(full_name):
>     # The non-aliased names, FIRST and LAST, show up here, instead of
>     # first_name and last_name.
>     # print(full_name)
>     return len(full_name.first_name) + len(full_name.last_name)
> if __name__ == '__main__':
>     spark = (
>         pyspark.sql.SparkSession.builder
>         .getOrCreate())
>     length_udf = udf(length)
>     names = spark.createDataFrame([
>         Row(FIRST='Nick', LAST='Chammas'),
>         Row(FIRST='Walter', LAST='Williams'),
>     ])
>     names_cleaned = (
>         names
>         .select(
>             col('FIRST').alias('first_name'),
>             col('LAST').alias('last_name'),
>         )
>         .withColumn('full_name', struct('first_name', 'last_name'))
>         .select('full_name'))
>     # We see the schema we expect here.
>     names_cleaned.printSchema()
>     # However, here we get an AttributeError. length_udf() cannot
>     # find first_name or last_name.
>     (names_cleaned
>     .withColumn('length', length_udf('full_name'))
>     .show())
> {code}
> When I run this I get a long stack trace, but the relevant portions seem to be:
> {code}
>   File ".../udf-alias.py", line 10, in length
>     return len(full_name.first_name) + len(full_name.last_name)
>   File "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/lib/pyspark.zip/pyspark/sql/types.py", line 1502, in __getattr__
>     raise AttributeError(item)
> AttributeError: first_name
> {code}
> {code}
> Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
>   File "/usr/local/Cellar/apache-spark/2.0.1/libexec/python/lib/pyspark.zip/pyspark/sql/types.py", line 1497, in __getattr__
>     idx = self.__fields__.index(item)
> ValueError: 'first_name' is not in list
> {code}
> Here are the relevant execution plans:
> {code}
> names_cleaned.explain()
> == Physical Plan ==
> *Project [struct(FIRST#0 AS first_name#5, LAST#1 AS last_name#6) AS full_name#10]
> +- Scan ExistingRDD[FIRST#0,LAST#1]
> {code}
> {code}
> (names_cleaned
> .withColumn('length', length_udf('full_name'))
> .explain())
> == Physical Plan ==
> *Project [struct(FIRST#0 AS first_name#5, LAST#1 AS last_name#6) AS full_name#10, pythonUDF0#21 AS length#17]
> +- BatchEvalPython [length(struct(FIRST#0, LAST#1))], [FIRST#0, LAST#1, pythonUDF0#21]
>    +- Scan ExistingRDD[FIRST#0,LAST#1]
> {code}
> It looks like from the second execution plan that {{BatchEvalPython}} somehow gets the unaliased column names, whereas the {{Project}} right above it gets the aliased names.



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