How to work with MySQL 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 MySQL, Spark can work with live MySQL data. This article describes how to connect to and query MySQL data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live MySQL data due to optimized data processing built into the driver. When you issue complex SQL queries to MySQL, the driver pushes supported SQL operations, like filters and aggregations, directly to MySQL 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 MySQL data using native data types.

Install the CData JDBC Driver for MySQL

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

Start a Spark Shell and Connect to MySQL Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for MySQL/lib/cdata.jdbc.mysql.jar
   

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

   The CData Provider supports connecting to on-premises and cloud-hosted versions of MySQL such as Amazon RDS for MySQL, Google Cloud SQL for MySQL, Azure Database for MySQL, or Oracle MySQL HeatWave. The Server and Port properties must be set to a MySQL 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, tables from all databases will be returned.

   SSH Connectivity for MySQL

   You can use SSH (Secure Shell) to authenticate with MySQL, whether the instance is hosted on-premises or in supported cloud environments. SSH authentication ensures that access is encrypted (as compared to direct network connections).

   SSH Connections to MySQL in Password Auth Mode

   To connect to MySQL via SSH in Password Auth mode, set the following connection properties:

   • User: MySQL User name
   • Password: MySQL Password
   • Database: MySQL database name
   • Server: MySQL Server name
   • Port: MySQL port number like 3306
   • UserSSH: "true"
   • SSHAuthMode: "Password"
   • SSHPort: SSH Port number
   • SSHServer: SSH Server name
   • SSHUser: SSH User name
   • SSHPassword: SSH Password

   SSH Connections to MySQL in Public Key Auth Mode

   To connect to MySQL via SSH in Password Auth mode, set the following connection properties:

   • User: MySQL User name
   • Password: MySQL Password
   • Database: MySQL database name
   • Server: MySQL Server name
   • Port: MySQL port number like 3306
   • UserSSH: "true"
   • SSHAuthMode: "Public_Key"
   • SSHPort: SSH Port number
   • SSHServer: SSH Server name
   • SSHUser: SSH User name
   • SSHClientCret: the path for the public key certificate file

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.mysql.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 MySQL, using the connection string generated above.

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

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

4. Register the MySQL data as a temporary table:

   scala> mysql_df.registerTable("orders")

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

   scala> mysql_df.sqlContext.sql("SELECT ShipName, Freight 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 MySQL in Apache Spark, you are able to perform fast and complex analytics on MySQL 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.