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From "Tomasz Bartczak (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-23478) Inconsistent behaviour of union when columns have conflicting metadata
Date Wed, 21 Feb 2018 11:40:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-23478?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Tomasz Bartczak updated SPARK-23478:
------------------------------------
    Priority: Minor  (was: Major)

> Inconsistent behaviour of union when columns have conflicting metadata
> ----------------------------------------------------------------------
>
>                 Key: SPARK-23478
>                 URL: https://issues.apache.org/jira/browse/SPARK-23478
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.2.1
>            Reporter: Tomasz Bartczak
>            Priority: Minor
>
> When columns have different metadata and we union dataframes with them - the end result
of metadata depends on union ordering:
> {code:java}
> df = spark.createDataFrame([{'a':1}])
> a = df
> b = df.select(col('a').alias('a',metadata={'description':'xxx'}))
> print("a.union(b) gives {}".format(a.union(b).schema.fields[0].metadata))
> print("b.union(a) gives {}".format(b.union(a).schema.fields[0].metadata))
> {code}
> gives:
> {code:java}
> a.union(b) gives {}
> b.union(a) gives {'description': 'xxx'}{code}
>  
> And I wonder if this kind of union should be allowed at all - when fields with different
metadata are inside a struct - union fails, which can be seen in https://issues.apache.org/jira/projects/SPARK/issues/SPARK-23477



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