[jira] [Commented] (SPARK-23772) Provide an option to ignore column of all null values or empty map/array during JSON schema inference

["Apache Spark (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-23772?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16569566#comment-16569566 ] 

Apache Spark commented on SPARK-23772:
--------------------------------------

User 'MaxGekk' has created a pull request for this issue:
https://github.com/apache/spark/pull/22002

> Provide an option to ignore column of all null values or empty map/array during JSON schema inference
> -----------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-23772
>                 URL: https://issues.apache.org/jira/browse/SPARK-23772
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.3.0
>            Reporter: Xiangrui Meng
>            Assignee: Takeshi Yamamuro
>            Priority: Major
>             Fix For: 2.4.0
>
>
> It is common that we convert data from JSON source to structured format periodically. In the initial batch of JSON data, if a field's values are always null, Spark infers this field as StringType. However, in the second batch, one non-null value appears in this field and its type turns out to be not StringType. Then merge schema failed because schema inconsistency.
> This also applies to empty arrays and empty objects. My proposal is providing an option in Spark JSON source to omit those fields until we see a non-null value.
> This is similar to SPARK-12436 but the proposed solution is different.
> cc: [~rxin] [~smilegator]



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