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From "Michael Armbrust (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-17939) Spark-SQL Nullability: Optimizations vs. Enforcement Clarification
Date Wed, 03 May 2017 23:55:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-17939?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Michael Armbrust updated SPARK-17939:
-------------------------------------
    Target Version/s: 2.3.0  (was: 2.2.0)

> Spark-SQL Nullability: Optimizations vs. Enforcement Clarification
> ------------------------------------------------------------------
>
>                 Key: SPARK-17939
>                 URL: https://issues.apache.org/jira/browse/SPARK-17939
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Aleksander Eskilson
>            Priority: Critical
>
> The notion of Nullability of of StructFields in DataFrames and Datasets creates some
confusion. As has been pointed out previously [1], Nullability is a hint to the Catalyst optimizer,
and is not meant to be a type-level enforcement. Allowing null fields can also help the reader
successfully parse certain types of more loosely-typed data, like JSON and CSV, where null
values are common, rather than just failing. 
> There's already been some movement to clarify the meaning of Nullable in the API, but
also some requests for a (perhaps completely separate) type-level implementation of Nullable
that can act as an enforcement contract.
> This bug is logged here to discuss and clarify this issue.
> [1] - [https://issues.apache.org/jira/browse/SPARK-11319|https://issues.apache.org/jira/browse/SPARK-11319?focusedCommentId=15014535&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15014535]
> [2] - https://github.com/apache/spark/pull/11785



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