Access Live Sage 300 Data in AWS Lambda (with IntelliJ IDEA)

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 with Maven in IntelliJ.

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.

Step 1: Gather connection properties and build a connection string

Download the CData JDBC Driver for Sage 300 installer, unzip the package, and run the JAR file to install the driver. Then gather the required connection properties.

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.

java -jar cdata.jdbc.sage300.jar

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Fill in the connection properties (including the RTK) and copy the connection string to the clipboard.

Step 2: Create a project in IntelliJ

1. In IntelliJ IDEA, click New Project.
2. Select "Maven Archetype" from the Generators
3. Name the project and select "maven.archetypes:maven-archetype-quickstart" Archetype.
4. Click "Create" 👁 Image
   

Install the CData JDBC Driver for Sage 300 JAR File

Use the following Maven command from the project's root folder to install JAR file in the project.

mvn install:install-file -Dfile="PATH/TO/CData JDBC Driver for Sage 300 20XX/lib/cdata.jdbc.sage300.jar" -DgroupId="org.cdata.connectors" -DartifactId="cdata-sage300-connector" -Dversion="23" -Dpackaging=jar

Add Dependencies

Within the Maven project's pom.xml file, add AWS and the CData JDBC Driver for Sage 300] as dependencies (within the <dependencies> element) using the following XML.

• AWS

  <dependency>
   <groupId>com.amazonaws</groupId>
   <artifactId>aws-lambda-java-core</artifactId>
   <version>1.2.2</version> <!--Replace with the actual version-->
  </dependency>

• CData JDBC Driver for Sage 300

  <dependency>
   <groupId>org.cdata.connectors</groupId>
   <artifactId>cdata-sage300-connector</artifactId>
   <version>25</version> <!--Replace with the actual version-->
  </dependency>

• Maven Shade Plugin to create a fat JAR

  <build>
   <plugins>
   <plugin>
   <groupId>org.apache.maven.plugins</groupId>
   <artifactId>maven-shade-plugin</artifactId>
   <version>3.4.1</version>
   <executions>
   <execution>
   <phase>package</phase>
   <goals>
   <goal>shade</goal>
   </goals>
   <configuration>
   <createDependencyReducedPom>false</createDependencyReducedPom>
   <transformers>
   <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
   <mainClass>com.example.CDataLambda</mainClass>
   <!-- Change to your actual Lambda handler class -->
   </transformer>
   </transformers>
   </configuration>
   </execution>
   </executions>
   </plugin>
   </plugins>
  </build>

Create an AWS Lambda Function

For this sample project, we create two source files: CDataLambda.java and CDataLambdaTest.java.

Lambda Function Definition

1. Update CDataLambda to implement the RequestHandler interface from the AWS Lambda SDK. You will need to add the handleRequest method, which performs the following tasks when the Lambda function is triggered:
   1. Constructs a SQL query using the input
   2. Registers the CData JDBC Driver for Sage 300
   3. Establishes a connection to Sage 300 using JDBC
   4. Executes the SQL query on Sage 300
   5. Prints the results to the console
   6. Returns an output message

2. Use the complete Lambda class below, which includes the imports, class definition, and handleRequest method. Be sure to fill in your connection string values in the DriverManager.getConnection call.

   package com.example;
   
   import com.amazonaws.services.lambda.runtime.Context;
   import com.amazonaws.services.lambda.runtime.RequestHandler;
   
   import java.sql.Connection;
   import java.sql.DriverManager;
   import java.sql.ResultSet;
   import java.sql.ResultSetMetaData;
   import java.sql.SQLException;
   import java.sql.Statement;
   public class CDataLambda implements RequestHandler < Object, String > {
   
    @Override
    public String handleRequest(Object input, Context context) {
    String query = "SELECT * FROM " + input;
   
    String bucketName = "MY_AWS_BUCKET";
    try {
    Class.forName("cdata.jdbc.sage300.Sage300Driver");
    cdata.jdbc.sage300.Sage300Driver driver = new cdata.jdbc.sage300.Sage300Driver();
    DriverManager.registerDriver(driver);
    } catch (SQLException ex) {
    // Registering the driver failed
    throw new RuntimeException("Failed to register JDBC driver", ex);
    } catch (ClassNotFoundException e) {
    // The driver class was not found in the classpath
    throw new RuntimeException("JDBC Driver class not found", e);
   
    }
    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!
   ");
    }
   
    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("
   ", "") : null);
    }
    System.out.print("
   ");
    }
    } catch (SQLException ex) {
    System.out.println("SQL Exception: " + ex.getMessage());
    } catch (Exception ex) {
    System.out.println("General exception: " + ex.getMessage());
    }
    return "v24 query: " + query + " complete";
    }
   }
   
   

Step 3: Deploy and run the lambda function

Once you build the function in Intellij, you are ready to deploy the entire Maven project as a single JAR file.

1. In IntelliJ, use the mvn install command to build the SNAPSHOT JAR file.

   Note: The Maven Shade Plugin generates two JARs in the target folder. Always upload the larger -shaded.jar file to AWS Lambda, as it contains all required dependencies.

2. Create a new function in AWS Lambda (or open an existing one).
3. 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).
4. Click "Upload from" -> ".zip file" and select your SNAPSHOT JAR file. 👁 Uploading the SNAPSHOT JAR file.
   
5. In the "Runtime settings" section, click "Edit" and set Handler to your "handleRequest" method (e.g. package.class::handleRequest) 👁 Configuring the runtime handler.
   
6. You can now test the function. Set the "Event JSON" field to a table name and click, click "Test" 👁 Viewing the results.
   

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.