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https://issues.apache.org/jira/browse/SPARK-12436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15212671#comment-15212671
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Reynold Xin commented on SPARK-12436:
-------------------------------------

I don't have everything page in, but why isn't an empty string just a string 
type?


> If all values of a JSON field is null, JSON's inferSchema should return 
> NullType instead of StringType
> ------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-12436
>                 URL: https://issues.apache.org/jira/browse/SPARK-12436
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>            Reporter: Reynold Xin
>              Labels: starter
>
> Right now, JSON's inferSchema will return {{StringType}} for a field that 
> always has null values or an {{ArrayType(StringType)}}  for a field that 
> always has empty array values. Although this behavior makes writing JSON data 
> to other data sources easy (i.e. when writing data, we do not need to remove 
> those {{NullType}} or {{ArrayType(NullType)}} columns), it makes downstream 
> application hard to reason about the actual schema of the data and thus makes 
> schema merging hard. We should allow JSON's inferSchema returns {{NullType}} 
> and {{ArrayType(NullType)}}. Also, we need to make sure that when we write 
> data out, we should remove those {{NullType}} or {{ArrayType(NullType)}} 
> columns first. 
> Besides  {{NullType}} and {{ArrayType(NullType)}}, we may need to do the same 
> thing for empty {{StructType}}s (i.e. a {{StructType}} having 0 fields). 
> To finish this work, we need to finish the following sub-tasks:
> * Allow JSON's inferSchema returns {{NullType}} and {{ArrayType(NullType)}}.
> * Determine whether we need to add the operation of removing {{NullType}} and 
> {{ArrayType(NullType)}} columns from the data that will be write out for all 
> data sources (i.e. data sources based our data source API and Hive tables). 
> Or, we should just add this operation for certain data sources (e.g. 
> Parquet). For example, we may not need this operation for Hive because Hive 
> has VoidObjectInspector.
> * Implement the change and get it merged to Spark master.



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