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https://issues.apache.org/jira/browse/SPARK-30319?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17003718#comment-17003718
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Farooq Qaiser commented on SPARK-30319:
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I have written similar variants of this feature (using scala's
implicit-conversion technique to monkey-patch the Dataset class) across
multiple organizations/codebases now and wanted to share my thoughts in case
its helpful to the discussion.
I can affirm that this would be a valuable feature to have in Spark. Without
this feature, our developers would nearly always have to pair an {{as}}
operation with a {{select}} operation. As such, my preference would be to
change the existing Dataset {{as[T]}} method to add this strict-ness by default
when {{T}} is a class. This would be a breaking change but since the next
version of Spark is a major release (3.0.0), this should be okay.
Also, I saw that in your PR you included eager-casting-of-Column-types. I'm not
sure if this is a good idea although I can't think of any concrete objections.
In my own implementations of this feature, I've always just raised an exception
if the column types don't match what's specified in {{T}}, leaving it to the
developer to explicitly cast Columns to the correct types prior to using this
feature.
> Adds a stricter version of as[T]
> --------------------------------
>
> Key: SPARK-30319
> URL: https://issues.apache.org/jira/browse/SPARK-30319
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 2.4.4
> Reporter: Enrico Minack
> Priority: Major
> Fix For: 3.0.0
>
>
> The behaviour of as[T] is not intuitive when you read code like
> df.as[T].write.csv("data.csv"). The result depends on the actual schema of
> df, where def as[T](): Dataset[T] should be agnostic to the schema of df. The
> expected behaviour is not provided elsewhere:
> * Extra columns that are not part of the type {{T}} are not dropped.
> * Order of columns is not aligned with schema of {{T}}.
> * Columns are not cast to the types of {{T}}'s fields. They have to be cast
> explicitly.
> A method that enforces schema of T on a given Dataset would be very
> convenient and allows to articulate and guarantee above assumptions about
> your data with the native Spark Dataset API. This method plays a more
> explicit and enforcing role than as[T] with respect to columns, column order
> and column type.
> Possible naming of a stricter version of {{as[T]}}:
> * {{as[T](strict = true)}}
> * {{toDS[T]}} (as in {{toDF}})
> * {{selectAs[T]}} (as this is merely selecting the columns of schema {{T}})
> The naming {{toDS[T]}} is chosen here.
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