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

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

Install the CData JDBC Driver for Lakebase

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

Start a Spark Shell and Connect to Lakebase Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Lakebase/lib/cdata.jdbc.lakebase.jar
   

2. With the shell running, you can connect to Lakebase with a JDBC URL and use the SQL Context load() function to read a table. To connect to Databricks Lakebase, start by setting the following properties:
   • DatabricksInstance: The Databricks instance or server hostname, provided in the format instance-abcdef12-3456-7890-abcd-abcdef123456.database.cloud.databricks.com.
   • Server: The host name or IP address of the server hosting the Lakebase database.
   • Port (optional): The port of the server hosting the Lakebase database, set to 5432 by default.
   • Database (optional): The database to connect to after authenticating to the Lakebase Server, set to the authenticating user's default database by default.

   OAuth Client Authentication

   To authenicate using OAuth client credentials, you need to configure an OAuth client in your service principal. In short, you need to do the following:

   1. Create and configure a new service principal
   2. Assign permissions to the service principal
   3. Create an OAuth secret for the service principal

   For more information, refer to the Setting Up OAuthClient Authentication section in the Help documentation.

   OAuth PKCE Authentication

   To authenticate using the OAuth code type with PKCE (Proof Key for Code Exchange), set the following properties:

   • AuthScheme: OAuthPKCE.
   • User: The authenticating user's user ID.

   For more information, refer to the Help documentation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.lakebase.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 Lakebase, using the connection string generated above.

   scala> val lakebase_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:lakebase:DatabricksInstance=lakebase;Server=127.0.0.1;Port=5432;Database=my_database;InitiateOAuth=GETANDREFRESH;").option("dbtable","Orders").option("driver","cdata.jdbc.lakebase.LakebaseDriver").load()
   

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

4. Register the Lakebase data as a temporary table:

   scala> lakebase_df.registerTable("orders")

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

   scala> lakebase_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 Lakebase in Apache Spark, you are able to perform fast and complex analytics on Lakebase 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.