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https://issues.apache.org/jira/browse/SPARK-9340?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Cheng Lian updated SPARK-9340:
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Summary: CatalystSchemaConverter and CatalystRowConverter don't handle
unannotated repeated fields correctly (was: ParquetTypeConverter incorrectly
handling of repeated types results in schema mismatch)
> CatalystSchemaConverter and CatalystRowConverter don't handle unannotated
> repeated fields correctly
> ---------------------------------------------------------------------------------------------------
>
> 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, 1.5.0
> Reporter: Damian Guy
> 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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