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https://issues.apache.org/jira/browse/SPARK-13309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin resolved SPARK-13309.
---------------------------------
Resolution: Fixed
Assignee: Rahul Tanwani
Fix Version/s: 2.0.0
> Incorrect type inference for CSV data.
> --------------------------------------
>
> Key: SPARK-13309
> URL: https://issues.apache.org/jira/browse/SPARK-13309
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.0
> Reporter: Rahul Tanwani
> Assignee: Rahul Tanwani
> Priority: Minor
> Fix For: 2.0.0
>
>
> Type inference for CSV data does not work as expected when the data is
> sparse.
> For instance: Consider the following datasets and the inferred schema:
> {code}
> A,B,C,D
> 1,,,
> ,1,,
> ,,1,
> ,,,1
> {code}
> {code}
> root
> |-- A: integer (nullable = true)
> |-- B: integer (nullable = true)
> |-- C: string (nullable = true)
> |-- D: string (nullable = true)
> {code}
> Here all the fields should have been inferred as Integer types, but clearly
> the inferred schema is different.
> Another dataset:
> {code}
> A,B,C,D
> 1,,1,
> {code}
> and the inferred schema:
> {code}
> root
> |-- A: string (nullable = true)
> |-- B: string (nullable = true)
> |-- C: string (nullable = true)
> |-- D: string (nullable = true)
> {code}
> Here, fields A & C should be inferred as Integer types.
> Same issue has been discussed on spark-csv package. Please take a look at
> https://github.com/databricks/spark-csv/issues/216 for reference.
> The issue was fixed with
> https://github.com/databricks/spark-csv/commit/8704b26030da88ac6e18b955a81d5c22ca3b480d.
> I will try to submit PR with the patch soon.
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