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From "Anand Mohan Tumuluri (JIRA)" <>
Subject [jira] [Commented] (SPARK-5472) Add support for reading from and writing to a JDBC database
Date Sat, 31 Jan 2015 03:39:36 GMT


Anand Mohan Tumuluri commented on SPARK-5472:

Pardon my ignorance but I think
JdbcRdd can be given a ResultSet to case class mapper which will yield a RDD[case class]
Any RDD[case class] (RDD[Product]) can be converted into a SchemaRDD by using createSchemaRDD
method of SQL/HiveContext. This SchemaRDD can then be registered as a temp table within Spark
SQL through registerTempTable and then can be joined to other Spark SQL tables.

This solves the use case of loading data from a JDBC data source, isn't it? Am I missing something.
Ofcourse this requires Scala and Spark-shell, meaning it cant be done from spark-sql or thriftserver2.

Howeer there currently is no easy way of saving a RDD into a JDBC data sink. (DbOutputFormat
is way too rigid).
This PR, providing a generic mechanism for saving SchemaRDD into a RDBMS table, will be very
valuable for us.

> Add support for reading from and writing to a JDBC database
> -----------------------------------------------------------
>                 Key: SPARK-5472
>                 URL:
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>            Reporter: Tor Myklebust
>            Assignee: Tor Myklebust
>            Priority: Blocker
> 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.

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