How to connect and process MySQL data from Azure Databricks

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 explains how to host the CData JDBC Driver in Azure, as well as connect to and process 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 Azure

To work with live MySQL data in Databricks, install the driver through Azure Data Lake Storage (ADLS). (Please note that the method of connecting through DBFS, which previous versions of this article described, has been deprecated, but has not published an end-of-life.)

1. Upload the JDBC JAR file to a blob container of your choice (i.e. "jdbcjars" container of the "databrickslibraries" storage account).
2. Fetch the Account Key from the storage account by expanding "Security + networking" and clicking on "Access Keys". Show and copy whichever of the two keys you wish to use. 👁 Get Access Key
   
3. Get the JDBC JAR file's URL by navigating to Containers, opening the specific container storing the JAR, and selecting the entry for the JDBC JAR file. This should open the file's details, where there should be a convenient button to copy the URL button to clipboard. This value will look similar to the below, though the "blob" component may vary depending on storage account type:

   https://databrickslibraries.blob.core.windows.net/jdbcjars/cdata.jdbc.salesforce.jar

   👁 Get JAR URL
   
4. In the Configuration tab of your Databricks cluster, click on the Edit button and expand "Advanced options". From there, add the following Spark option (derived from the JAR URL's domain name) with your copied Account key as its value and click Confirm: spark.hadoop.fs.azure.account.key.databrickslibraries.blob.core.windows.net 👁 Apply Account Key
   
5. In the Libraries tab of your Databricks cluster, click on "Install new", and select the ADLS option. Specify the ABFSS URL for the driver JAR (also derived from the JAR URL's domain name), and click Install. The ABFSS URL should resemble the below:

   abfss://[email protected]/cdata.jdbc.salesforce.jar

   👁 Install ADLS Library
   

Connect to MySQL from Databricks

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 workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

👁 Attaching to an existing compute resource

Configure the Connection to MySQL

Connect to MySQL by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

driver = "cdata.jdbc.mysql.MySQLDriver"
url = "jdbc:mysql:RTK=5246...;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 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

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

Load MySQL Data

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

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.

display (remote_table.select ("ShipName"))

👁 Displaying MySQL Data

Analyze MySQL Data in Azure Databricks

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

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the MySQL data for analysis.

result = spark.sql("SELECT ShipName, Freight FROM SAMPLE_VIEW")

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" )

👁 Displaying MySQL Data

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