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Posted to issues@spark.apache.org by "Michael Armbrust (JIRA)" <ji...@apache.org> on 2014/12/05 21:23:12 UTC

[jira] [Updated] (SPARK-4768) Add Support For Impala Encoded Timestamp (INT96)

     [ https://issues.apache.org/jira/browse/SPARK-4768?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Michael Armbrust updated SPARK-4768:
------------------------------------
    Target Version/s: 1.3.0

> Add Support For Impala Encoded Timestamp (INT96)
> ------------------------------------------------
>
>                 Key: SPARK-4768
>                 URL: https://issues.apache.org/jira/browse/SPARK-4768
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>            Reporter: Pat McDonough
>
> Impala is using INT96 for timestamps. Spark SQL should be able to read this data despite the fact that it is not part of the spec.
> Perhaps adding a flag to act like impala when reading parquet (like we do for strings already) would be useful.
> Here's an example of the error you might see:
> {code}
> Caused by: java.lang.RuntimeException: Potential loss of precision: cannot convert INT96
>         at scala.sys.package$.error(package.scala:27)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.toPrimitiveDataType(ParquetTypes.scala:61)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.toDataType(ParquetTypes.scala:113)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$convertToAttributes$1.apply(ParquetTypes.scala:314)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$convertToAttributes$1.apply(ParquetTypes.scala:311)
>         at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>         at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>         at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>         at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>         at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
>         at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
>         at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>         at scala.collection.AbstractTraversable.map(Traversable.scala:105)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.convertToAttributes(ParquetTypes.scala:310)
>         at org.apache.spark.sql.parquet.ParquetTypesConverter$.readSchemaFromFile(ParquetTypes.scala:441)
>         at org.apache.spark.sql.parquet.ParquetRelation.<init>(ParquetRelation.scala:66)
>         at org.apache.spark.sql.SQLContext.parquetFile(SQLContext.scala:141)
> {code}



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