How to work with MariaDB Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for MariaDB, Spark can work with live MariaDB data. This article describes how to connect to and query MariaDB data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live MariaDB data due to optimized data processing built into the driver. When you issue complex SQL queries to MariaDB, the driver pushes supported SQL operations, like filters and aggregations, directly to MariaDB and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze MariaDB data using native data types.

Install the CData JDBC Driver for MariaDB

Download the CData JDBC Driver for MariaDB installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to MariaDB Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for MariaDB JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for MariaDB/lib/cdata.jdbc.mariadb.jar
   

2. With the shell running, you can connect to MariaDB with a JDBC URL and use the SQL Context load() function to read a table.

   The Server and Port properties must be set to a MariaDB server. If IntegratedSecurity is set to false, then User and Password must be set to valid user credentials. Optionally, Database can be set to connect to a specific database. If not set, the tables from all databases are reported.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the MariaDB JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.mariadb.jar
   

   Fill in the connection properties and copy the connection string to the clipboard.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to MariaDB, using the connection string generated above.

   scala> val mariadb_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:mariadb:User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;").option("dbtable","Orders").option("driver","cdata.jdbc.mariadb.MariaDBDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the MariaDB data as a temporary table:

   scala> mariadb_df.registerTable("orders")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> mariadb_df.sqlContext.sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = USA").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for MariaDB in Apache Spark, you are able to perform fast and complex analytics on MariaDB data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.