Simeon Simeonov created SPARK-10186:
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Summary: Inconsistent handling of complex column types in external
databases
Key: SPARK-10186
URL: https://issues.apache.org/jira/browse/SPARK-10186
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 1.4.1
Environment: Ubuntu on AWS
Reporter: Simeon Simeonov
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 throw 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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