Query Lakebase Data as a SQL Server Database in Node.js

You can use CData Connect AI to query Lakebase data through a SQL Server interface. Follow the procedure below to create a virtual database for Lakebase in Connect AI and start querying using Node.js.

CData Connect AI provides a pure MySQL, cloud-to-cloud interface for Lakebase, allowing you to easily query live Lakebase data in Node.js — without replicating the data to a natively supported database. As you query data in Node.js, CData Connect AI pushes all supported SQL operations (filters, JOINs, etc) directly to Lakebase, leveraging server-side processing to quickly return Lakebase data.

Configure Lakebase Connectivity for NodeJS

Connectivity to Lakebase from NodeJS is made possible through CData Connect AI. To work with Lakebase data from NodeJS, we start by creating and configuring a Lakebase connection.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Lakebase" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Lakebase. 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.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Lakebase Connection page and update the User-based permissions.

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to Lakebase data from Node.js.

Query Lakebase from Node.js

The following example shows how to define a connection and execute queries to Lakebase with the SQL Server module. You will need the following information:

• server: tds.cdata.com
• port: 14333
• user: a Connect AI user (e.g. [email protected])
• password: the PAT for the above user
• database: The connection you configured for Lakebase (Lakebase1)

Connect to Lakebase data and start executing queries with the code below:

var sql = require('mssql')
var config = {
	server: 'tds.cdata.com',
	port: 14333, 
	user: '[email protected]', //update me
	password: 'CONNECT_USER_PAT', //update me	
	options: {
		encrypt: true,
		database: 'Lakebase1'
	}
}

sql.connect(config, err => { 
    if(err){
        throw err ;
    }
    new sql.Request().query('SELECT * FROM Orders', (err, result) => {
        console.dir(result)
    })
        
});

sql.on('error', err => {
    console.log("SQL Error: " ,err);
})