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https://issues.apache.org/jira/browse/SPARK-10186?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Michael Armbrust resolved SPARK-10186.
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Resolution: Fixed
Fix Version/s: 1.6.0
> Add support for more postgres column types
> ------------------------------------------
>
> Key: SPARK-10186
> URL: https://issues.apache.org/jira/browse/SPARK-10186
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 1.4.1
> Environment: Ubuntu on AWS
> Reporter: Simeon Simeonov
> Fix For: 1.6.0
>
>
> The specific observations below are based on Postgres 9.4 tables accessed via
> the postgresql-9.4-1201.jdbc41.jar driver. However, based on the behavior, I
> would expect the problem to exists for all external SQL databases.
> - *json and jsonb columns generate {{java.sql.SQLException: Unsupported type
> 1111}}*. While it is reasonable to not support dynamic schema discovery of
> JSON columns automatically (it requires two passes over the data), a better
> behavior would be to create a String column and return the JSON.
> - *Array columns generate {{java.sql.SQLException: Unsupported type 2003}}*.
> This is true even for simple types, e.g., {{text[]}}. A better behavior would
> be be create an Array column.
> - *Custom type columns are mapped to a String column.* This behavior is
> harder to understand as the schema of a custom type is fixed and therefore
> mappable to a Struct column. The automatic conversion to a string is also
> inconsistent when compared to json and array column handling.
> The exceptions are thrown by
> {{org.apache.spark.sql.jdbc.JDBCRDD$.org$apache$spark$sql$jdbc$JDBCRDD$$getCatalystType(JDBCRDD.scala:100)}}
> so this definitely looks like a Spark SQL and not a JDBC problem.
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