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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-9340) ParquetTypeConverter incorrectly handling of repeated types results in schema mismatch
Date Fri, 07 Aug 2015 13:35:46 GMT

     [ https://issues.apache.org/jira/browse/SPARK-9340?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-9340:
-----------------------------------

    Assignee: Apache Spark

> ParquetTypeConverter incorrectly handling of repeated types results in schema mismatch
> --------------------------------------------------------------------------------------
>
>                 Key: SPARK-9340
>                 URL: https://issues.apache.org/jira/browse/SPARK-9340
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.2.0, 1.3.0, 1.4.0
>            Reporter: Damian Guy
>            Assignee: Apache Spark
>         Attachments: ParquetTypesConverterTest.scala
>
>
> The way ParquetTypesConverter handles primitive repeated types results in an incompatible
schema being used for querying data. For example, given a schema like so:
> message root {
>    repeated int32 repeated_field;
>  }
> Spark produces a read schema like:
> message root {
>    optional int32 repeated_field;
>  }
> These are incompatible and all attempts to read fail.
> In ParquetTypesConverter.toDataType:
>  if (parquetType.isPrimitive) {
>       toPrimitiveDataType(parquetType.asPrimitiveType, isBinaryAsString, isInt96AsTimestamp)
>     } else {...}
> The if condition should also have !parquetType.isRepetition(Repetition.REPEATED)
>  
> And then this case will need to be handled in the else 



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