Spark JDBC Driver

Because the Spark 2.x ThriftServer doesn't implement the Hive JDBC Driver's Catalog API, when using Spark's version of the Hive JDBC driver (org.spark-project.hive:hive-jdbc:1.2.1.spark2) within BI tools such as DataGrip, the tools are unable to introspect the catalog schema (databases, tables, views, columns, etc).

This driver fixes that by wrapping the default Spark Hive Driver (org.spark-project.hive:hive-jdbc:1.2.1.spark2) and adds introspection support by implementing the missing calls with calls to Spark's SHOW/DESCRIBE commands.

Download

Go to Releases and download the latest JAR version.

Configuration

Add the new Driver to your BI tool using the downloaded jar.

The driver class is com.hindog.spark.jdbc.SparkDriver.

JDBC url patterns are same as the Hive Driver. You can also use spark for the driver scheme, so the JDBC url jdbc:hive2://<hostname>:<port>/ would be equivalent to jdbc:spark://<hostname>:<port>/

Building

Run sbt assembly:assembly to build the driver jar.

The output jar will be written to target/scala-2.11/spark-jdbc-<version>.jar and it will include all the required dependencies and so it will be quite large.

Limitations

• Spark 2.x does not support multiple "catalogs", so a hard-coded catalog named Spark will be used. You can override this by setting the system property com.hindog.spark.jdbc.catalog.name with the desired catalog name.
• Spark does not support column sizes, so the driver will return the value of the column's defaultSize value.
• Spark DESCRIBE & SHOW commands return a limited amount of metadata, so most JDBC metadata will not be populated.
• Introspection can take some time to run as a separate SQL statement must be issued to introspect each database/table/column/function, etc.