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Posted to issues@spark.apache.org by "Sameer Agarwal (JIRA)" <ji...@apache.org> on 2018/01/08 20:39:00 UTC
[jira] [Updated] (SPARK-17939) Spark-SQL Nullability: Optimizations
vs. Enforcement Clarification
[ https://issues.apache.org/jira/browse/SPARK-17939?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sameer Agarwal updated SPARK-17939:
-----------------------------------
Target Version/s: 2.4.0 (was: 2.3.0)
> 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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