Access Live Lakebase Data in AWS Lambda

AWS Lambda is a compute service that lets you build applications that respond quickly to new information and events. AWS Lambda functions can work with live Lakebase data when paired with the CData JDBC Driver for Lakebase. This article describes how to connect to and query Lakebase data from an AWS Lambda function built in Eclipse.

At the time this article was written (June 2022), Eclipse version 2019-12 and Java 8 were the highest versions supported by the AWS Toolkit for Eclipse.

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

Gather Connection Properties and Build a Connection String

• 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.

NOTE: To use the JDBC driver in an AWS Lambda function, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

Built-in Connection String Designer

For assistance constructing the JDBC URL, use the connection string designer built into the Lakebase JDBC Driver. 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 (including the RTK) and copy the connection string to the clipboard.

Create an AWS Lambda Function

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

2. Create a new AWS Lambda Java Project in Eclipse using the AWS Toolkit for Eclipse. You can follow the tutorial from AWS (amazon.com).

   For this article, set the Input Type for the project to "Custom" so we can enter a table name as the input.

   
3. Add the CData JDBC Driver for Lakebase JAR file (cdata.jdbc.lakebase.jar) to the build path. The file is found in INSTALL_PATH\lib\.
4. Add the following import statements to the Java class:

   import java.sql.Connection;
   import java.sql.DriverManager;
   import java.sql.ResultSet;
   import java.sql.ResultSetMetaData;
   import java.sql.SQLException;
   import java.sql.Statement;
   

5. Replace the body of the handleRequest method with the code below. Be sure to fill in the connection string in the DriverManager.getConnection method call.

   String query = "SELECT * FROM " + input;
   
   try {
   	Class.forName("cdata.jdbc.lakebase.LakebaseDriver");
   } catch (ClassNotFoundException ex) {
   	context.getLogger().log("Error: class not found");
   }
   
   Connection connection = null;
    
   try {
   	connection = DriverManager.getConnection("jdbc:cdata:lakebase:RTK=52465...;DatabricksInstance=lakebase;Server=127.0.0.1;Port=5432;Database=my_database;InitiateOAuth=GETANDREFRESH;");
   } catch (SQLException ex) {
   	context.getLogger().log("Error getting connection: " + ex.getMessage());
   } catch (Exception ex) {
   	context.getLogger().log("Error: " + ex.getMessage());
   }
    
   if(connection != null)
   {
   	context.getLogger().log("Connected Successfully!\n");
   }
    
   ResultSet resultSet = null;
   try
   {
   	//executing query
   	Statement stmt = connection.createStatement();
   	resultSet = stmt.executeQuery(query);
   
   	ResultSetMetaData metaData = resultSet.getMetaData();
   	int numCols = metaData.getColumnCount();
   
   	//printing the results
   	while(resultSet.next())
   	{
   		for(int i = 1; i <= numCols; i++)
   		{
   			System.out.printf("%-25s", (resultSet.getObject(i) != null) ? resultSet.getObject(i).toString().replaceAll("\n", "") : null );
   		}
   		System.out.print("\n");
   	}
   }
   catch (SQLException ex)
   {
   	System.out.println("SQL Exception: " + ex.getMessage());
   }
   catch (Exception ex)
   {
   	System.out.println("General exception: " + ex.getMessage());
   }
    
   String output = "query: " + query + " complete";
   return output;
   

Deploy and Run the Lambda Function

Once you build the function in Eclipse, you are ready to upload and run the function. In this article, the output is written to the AWS logs, but you can use this is a template to implement you own custom business logic to work with Lakebase data in AWS Lambda functions.

1. Right-click the Package and select Amazon Web Services -> Upload function to AWS Lamba.
2. Name the function, select an IAM role, and set the timeout value to a high enough value to ensure the function completes (depending on the result size of your query).
3. Right-click the Package and select Amazon Web Services -> Run function on AWS Lambda and set the input to the name of the Lakebase object you wish to query (i.e. "Orders").
4. After the job runs, you can view the output in the CloudWatch logs.

Free Trial & More Information

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