Access Live Sage 300 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 Sage 300 data when paired with the CData JDBC Driver for Sage 300. This article describes how to connect to and query Sage 300 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 Sage 300 data. When you issue complex SQL queries to Sage 300, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 300 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 Sage 300 data using native data types.

Gather Connection Properties and Build a Connection String

Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

• Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
• Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
• Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

Authenticate to Sage 300 using Basic authentication.

Connect Using Basic Authentication

You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

• Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
• User: Set this to the username of your account.
• Password: Set this to the password of your account.

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 Sage 300 JDBC Driver. Double-click the JAR file or execute the jar file from the command line.

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 Sage 300 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 Sage 300 JAR file (cdata.jdbc.sage300.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.sage300.Sage300Driver"); } catch (ClassNotFoundException ex) { context.getLogger().log("Error: class not found"); } Connection connection = null; try { connection = DriverManager.getConnection("jdbc:cdata:sage300:RTK=52465...;User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;"); } 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 Sage 300 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 Sage 300 object you wish to query (i.e. "OEInvoices").
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 Sage 300 and start working with your live Sage 300 data in AWS Lambda. Reach out to our Support Team if you have any questions.