A PostgreSQL Interface for Spark Data (MySQL Remoting via JDBC)

There are a vast number of PostgreSQL clients available on the Internet. From standard Drivers to BI and Analytics tools, PostgreSQL is a popular interface for data access. Using the remoting features of our JDBC Drivers, you can now create PostgreSQL entry-points that you can connect to from any standard client.

To access Spark data as a PostgreSQL database, use the Remoting feature of the CData JDBC Driver for Spark and the MySQL foreign data wrapper (FDW) from EnterpriseDB. In this article, we install the FDW and query Spark data from PostgreSQL Server.

Configure the Connection to Spark

Follow the steps below to configure the driver's MySQL daemon to use the credentials and other connection properties needed to connect to Spark. The MySQL daemon exposes Spark data as a MySQL database named CDataSparkSQL. Add connection properties to the databases section of the configuration file for the daemon. The configuration file for the daemon is located in the lib subfolder of the installation directory for the driver.

Below is a typical connection string:

[databases]
spark = "Server=127.0.0.1;"

Additionally, create a user in the users section.

You can find all of the configuration options for the MySQL daemon in the help documentation.

Start the Remoting Service

Follow the steps below to enable the MySQL Remoting feature of the CData JDBC Driver for Spark.

1. The driver creates a default configuration suitable for testing: Simply start the service to connect to Spark data.

2. Start the MySQL Remoting Service with the following command:

   java -jar cdata.jdbc.sparksql.jar -f cdata.jdbc.sparksql.remoting.ini 
   

Build and Install the MySQL Foreign Data Wrapper

The Foreign Data Wrapper can be installed as an extension to PostgreSQL, without recompiling PostgreSQL.

If pgxn is available for your operating system, you can install with the following:

pgxn install mysql_fdw USE_PGXS=1

Otherwise, follow the steps below to build it yourself:

1. Install the MySQL C client library and obtain the source for the EnterpriseDB FDW for MySQL; from GitHub, for example.
2. Build the FDW. Add the pg_config and mysql_config executables to your PATH:

   env PATH=/usr/local/pgsql/bin:/usr/local/mysql/bin:$PATH make USE_PGXS=1
   

3. Install the FDW:

   make USE_PGXS=1 install
   

To complete the installation, you will need to load the libmysqlclient library into the environment; for example by adding it to the path.

Query Spark Data as a PostgreSQL Database

After you have installed the extension, follow the steps below to start executing queries to Spark data:

1. Log into your database.
2. Load the extension for the database:

   postgres=#CREATE EXTENSION mysql_fdw;
   

3. Create a server object for Spark data:

   postgres=# CREATE SERVER SparkSQL FOREIGN DATA WRAPPER mysql_fdw OPTIONS (host
   '127.0.0.1', port '3309');
   

4. Create a user mapping for the username and password of a user known to the MySQL daemon.

   postgres=# CREATE USER MAPPING for postgres SERVER SparkSQL OPTIONS (username
   'admin', password 'test');
   

5. Create the local schema:

   postgres=# CREATE SCHEMA SparkSQL_db;

6. Import all the tables in the Spark database you defined in the daemon configuration file:

   postgres=# IMPORT FOREIGN SCHEMA "SparkSQL" FROM SERVER SparkSQL INTO SparkSQL_db;
   

You can now execute read/write commands to Spark:

postgres=# SELECT * FROM SparkSQL_db."customers";