How to connect and process Redis 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 Redis data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Redis data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Redis data in Databricks, install the driver on your Azure 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.redis.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to Redis from Databricks

With the JAR file installed, we are ready to work with live Redis 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 Redis, and create a basic report.

Configure the Connection to Redis

Connect to Redis 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 RTK 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.redis.RedisDriver"
url = "jdbc:redis:RTK=5246...;Server=127.0.0.1;Port=6379;Password=myPassword;"

Built-in Connection String Designer

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

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

Set the following connection properties to connect to a Redis instance:

• Server: Set this to the name or address of the server your Redis instance is running on. You can specify the port in Port.
• Password: Set this to the password used to authenticate with a password-protected Redis instance , using the Redis AUTH command.

Set UseSSL to negotiate SSL/TLS encryption when you connect.

Load Redis Data

Once the connection is configured, you can load Redis 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" , "Customers") \
	.load ()

Display Redis Data

Check the loaded Redis data by calling the display function.

display (remote_table.select ("City"))

Analyze Redis 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 Redis data for analysis.

% sql

SELECT City, CompanyName FROM Customers

The data from Redis 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 Redis and start working with your live Redis data in Azure Databricks. Reach out to our Support Team if you have any questions.