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https://issues.apache.org/jira/browse/SPARK-5472?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14303961#comment-14303961
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Tor Myklebust commented on SPARK-5472:
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It probably does not handle SQL ARRAY types in a sane way. I would guess that
type mapping would throw an error if you try to read from a table that has an
ARRAY column. I would also guess that type mapping would throw an error if you
try to write a DataFrame that has an ARRAY column.
JDBCRDD handles partitioning however you instruct it to. If you give no
instructions, the entire table is a single partition. If you give it a
JDBCPartitioningInfo object, it divides the specified range of the specified
column into the appropriate number of slices. If you give it a list of WHERE
clauses, each WHERE clause corresponds to one partition.
> Add support for reading from and writing to a JDBC database
> -----------------------------------------------------------
>
> Key: SPARK-5472
> URL: https://issues.apache.org/jira/browse/SPARK-5472
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Reporter: Tor Myklebust
> Assignee: Tor Myklebust
> Priority: Blocker
> Fix For: 1.3.0
>
>
> It would be nice to be able to make a table in a JDBC database appear as a
> table in Spark SQL. This would let users, for instance, perform a JOIN
> between a DataFrame in Spark SQL with a table in a Postgres database.
> It might also be nice to be able to go the other direction -- save a
> DataFrame to a database -- for instance in an ETL job.
> Edited to clarify: Both of these tasks are certainly possible to accomplish
> at the moment with a little bit of ad-hoc glue code. However, there is no
> fundamental reason why the user should need to supply the table schema and
> some code for pulling data out of a ResultSet row into a Catalyst Row
> structure when this information can be derived from the schema of the
> database table itself.
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