Process & Analyze MySQL Data in Databricks (AWS)

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MySQL JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with MySQL-compatible database engines.



Host the CData JDBC Driver for MySQL in AWS and use Databricks to perform data engineering and data science on live MySQL data.

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live MySQL data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live MySQL data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live MySQL data. 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 client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze MySQL data using native data types.

Install the CData JDBC Driver in Databricks

To work with live MySQL data in Databricks, install the driver on your Databricks cluster.

  1. Navigate to your Databricks administration screen and select the target cluster.
  2. On the Libraries tab, click "Install New."
  3. Select "Upload" as the Library Source and "Jar" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.mysql.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for MySQL\lib).

Access MySQL Data in your Notebook: Python

With the JAR file installed, we are ready to work with live MySQL data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query MySQL, and create a basic report.

Configure the Connection to MySQL

Connect to MySQL by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL.

Step 1: Connection Information

driver = "cdata.jdbc.mysql.MySQLDriver"
url = "jdbc:mysql:User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;"

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.

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.

Load MySQL Data

Once you configure the connection, you can load MySQL data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Orders") \
	.load ()

Display MySQL Data

Check the loaded MySQL data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("ShipName"))

Analyze MySQL Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the MySQL data for reporting, visualization, and analysis.

% sql

SELECT ShipName, Freight FROM SAMPLE_VIEW ORDER BY Freight DESC LIMIT 5

The data from MySQL is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for MySQL and start working with your live MySQL data in Databricks. Reach out to our Support Team if you have any questions.