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https://issues.apache.org/jira/browse/SPARK-16483?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Michael Armbrust updated SPARK-16483:
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Target Version/s: 2.1.0
> Unifying struct fields and columns
> ----------------------------------
>
> Key: SPARK-16483
> URL: https://issues.apache.org/jira/browse/SPARK-16483
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Reporter: Simeon Simeonov
> Labels: sql
>
> This issue comes as a result of an exchange with Michael Armbrust outside of
> the usual JIRA/dev list channels.
> DataFrame provides a full set of manipulation operations for top-level
> columns. They have be added, removed, modified and renamed. The same is not
> true about fields inside structs yet, from a logical standpoint, Spark users
> may very well want to perform the same operations on struct fields,
> especially since automatic schema discovery from JSON input tends to create
> deeply nested structs.
> Common use-cases include:
> - Remove and/or rename struct field(s) to adjust the schema
> - Fix a data quality issue with a struct field (update/rewrite)
> To do this with the existing API by hand requires manually calling
> {{named_struct}} and listing all fields, including ones we don't want to
> manipulate. This leads to complex, fragile code that cannot survive schema
> evolution.
> It would be far better if the various APIs that can now manipulate top-level
> columns were extended to handle struct fields at arbitrary locations or,
> alternatively, if we introduced new APIs for modifying any field in a
> dataframe, whether it is a top-level one or one nested inside a struct.
> Purely for discussion purposes, here is the skeleton implementation of an
> update() implicit that we've use to modify any existing field in a dataframe.
> (Note that it depends on various other utilities and implicits that are not
> included). https://gist.github.com/ssimeonov/f98dcfa03cd067157fa08aaa688b0f66
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