[jira] [Commented] (SPARK-15856) Revert API breaking changes made in SQLContext.range

["Reynold Xin (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-15856?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15326065#comment-15326065 ] 

Reynold Xin commented on SPARK-15856:
-------------------------------------

Note that we have decided to only revert the SQLContext.range API in this ticket.

> Revert API breaking changes made in SQLContext.range
> ----------------------------------------------------
>
>                 Key: SPARK-15856
>                 URL: https://issues.apache.org/jira/browse/SPARK-15856
>             Project: Spark
>          Issue Type: Sub-task
>          Components: SQL
>            Reporter: Cheng Lian
>            Assignee: Wenchen Fan
>             Fix For: 2.0.0
>
>
> In Spark 2.0, after unifying Datasets and DataFrames, we made two API breaking changes:
> # {{DataFrameReader.text()}} now returns {{Dataset\[String\]}} instead of {{DataFrame}}
> # {{SQLContext.range()}} now returns {{Dataset\[java.lang.Long\]}} instead of {{DataFrame}}
> However, these two changes introduced several inconsistencies and problems:
> # {{spark.read.text()}} silently discards partitioned columns when reading a partitioned table in text format since {{Dataset\[String\]}} only contains a single field. Users have to use {{spark.read.format("text").load()}} to workaround this, which is pretty confusing and error-prone.
> # All data source shortcut methods in `DataFrameReader` return {{DataFrame}} (aka {{Dataset\[Row\]}}) except for {{DataFrameReader.text()}}.
> # When applying typed operations over Datasets returned by {{spark.range()}}, weird schema changes may happen. Please refer to SPARK-15632 for more details.
> Due to these reasons, we decided to revert these two changes.



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