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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/07/20 08:42:20 UTC
[jira] [Assigned] (SPARK-16628) OrcConversions should not convert
an ORC table represented by MetastoreRelation to HadoopFsRelation if
metastore schema does not match schema stored in ORC files
[ https://issues.apache.org/jira/browse/SPARK-16628?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-16628:
------------------------------------
Assignee: (was: Apache Spark)
> OrcConversions should not convert an ORC table represented by MetastoreRelation to HadoopFsRelation if metastore schema does not match schema stored in ORC files
> -----------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-16628
> URL: https://issues.apache.org/jira/browse/SPARK-16628
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Reporter: Yin Huai
>
> When {{spark.sql.hive.convertMetastoreOrc}} is enabled, we will convert a ORC table represented by a MetastoreRelation to HadoopFsRelation that uses Spark's OrcFileFormat internally. This conversion aims to make table scanning have a better performance since at runtime, the code path to scan HadoopFsRelation's performance is better. However, OrcFileFormat's implementation is based on the assumption that ORC files store their schema with correct column names. However, before Hive 2.0, an ORC table created by Hive does not store column name correctly in the ORC files (HIVE-4243). So, for this kind of ORC datasets, we cannot really convert the code path.
> Right now, if ORC tables are created by Hive 1.x or 0.x, enabling {{spark.sql.hive.convertMetastoreOrc}} will introduce a runtime exception for non-partitioned ORC tables and drop the metastore schema for partitioned ORC tables.
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