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

Gather Connection Properties and Build a Connection String

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

In order to authenticate, set the following connection properties:

• Host: Set this value to the host of your HDFS installation.
• Port: Set this value to the port of your HDFS installation. Default port: 50070

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 HDFS 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 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"

Install the CData JDBC Driver for HDFS JAR File

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

Add Dependencies

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

• AWS <dependency> <groupId>com.amazonaws</groupId> <artifactId>aws-lambda-java-core</artifaceId> <version>1.2.2</version> <!--Replace with the actual version--> </dependency>
• CData JDBC Driver for HDFS <dependency> <groupId>org.cdata.connectors</groupId> <artifactId>cdata-hdfs-connector</artifaceId> <version>23</version> <!--Replace with the actual version--> </dependency>

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 HDFS.
   3. Establishes a connection to HDFS using JDBC.
   4. Executes the SQL query on HDFS.
   5. Prints the results to the console.
   6. Returns an output message.
2. 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;

3. 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.hdfs.HDFSDriver"); cdata.jdbc.hdfs.HDFSDriver driver = new cdata.jdbc.hdfs.HDFSDriver(); DriverManager.registerDriver(driver); } catch (SQLException ex) { } catch (ClassNotFoundException e) { throw new RuntimeException(e); } Connection connection = null; try { connection = DriverManager.getConnection("jdbc:cdata:hdfs:RTK=52465...;Host=sandbox-hdp.hortonworks.com;Port=50070;Path=/user/root;User=root;"); } 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()); } return "query: " + query + " complete";

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.
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.
5. In the "Runtime settings" section, click "Edit" and set Handler to your "handleRequest" method (e.g. package.class::handleRequest)
6. You can now test the function. Set the "Event JSON" field to a table name and click, click "Test"

Free Trial & More Information

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