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https://issues.apache.org/jira/browse/SPARK-5097?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14278819#comment-14278819
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Hamel Ajay Kothari commented on SPARK-5097:
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Am I correct in interpreting that this would allow us to trivially select
columns at runtime since we'd just use {{SchemaRDD(stringColumnName)}}? In the
world of catalyst selecting columns known only at runtime was a real pain
because the only defined way to do it in the docs was to use quasiquotes or use
{{SchemaRDD.baseLogicalPlan.resolve()}}. The first couldn't be defined at
runtime (as far as I know) and the second required you to depend on expressions.
Also, is there any way to control the name of the resulting columns from
groupby+aggregate (or similar methods that add columns) in this plan?
> Adding data frame APIs to SchemaRDD
> -----------------------------------
>
> Key: SPARK-5097
> URL: https://issues.apache.org/jira/browse/SPARK-5097
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Reynold Xin
> Assignee: Reynold Xin
> Priority: Critical
> Attachments: DesignDocAddingDataFrameAPIstoSchemaRDD.pdf
>
>
> SchemaRDD, through its DSL, already provides common data frame
> functionalities. However, the DSL was originally created for constructing
> test cases without much end-user usability and API stability consideration.
> This design doc proposes a set of API changes for Scala and Python to make
> the SchemaRDD DSL API more usable and stable.
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