Xiangrui Meng reassigned SPARK-23772:

    Assignee: Takeshi Yamamuro

> 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
> 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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