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https://issues.apache.org/jira/browse/SPARK-34805?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17478610#comment-17478610
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Kevin Wallimann commented on SPARK-34805:
-----------------------------------------
The problem happens in Scala as well. I attached a scala file
[^nested_columns_metadata.scala] to demonstrate the issue. I tried it in the
spark-shell of versions 2.4.7, 3.1.2 and 3.2.0, always with the same result.
This behavior is a bug, because the documentation for {{StructField}} clearly
says that the "metadata should be preserved during transformation if the
content of the column is not modified, e.g, in selection"
> PySpark loses metadata in DataFrame fields when selecting nested columns
> ------------------------------------------------------------------------
>
> Key: SPARK-34805
> URL: https://issues.apache.org/jira/browse/SPARK-34805
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.0.1, 3.1.1
> Reporter: Mark Ressler
> Priority: Major
> Attachments: jsonMetadataTest.py, nested_columns_metadata.scala
>
>
> For a DataFrame schema with nested StructTypes, where metadata is set for
> fields in the schema, that metadata is lost when a DataFrame selects nested
> fields. For example, suppose
> {code:java}
> df.schema.fields[0].dataType.fields[0].metadata
> {code}
> returns a non-empty dictionary, then
> {code:java}
> df.select('Field0.SubField0').schema.fields[0].metadata{code}
> returns an empty dictionary, where "Field0" is the name of the first field in
> the DataFrame and "SubField0" is the name of the first nested field under
> "Field0".
>
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