How to work with Landbot Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Landbot, Spark can work with live Landbot data. This article describes how to connect to and query Landbot data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Landbot data due to optimized data processing built into the driver. When you issue complex SQL queries to Landbot, the driver pushes supported SQL operations, like filters and aggregations, directly to Landbot and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Landbot data using native data types.

Install the CData JDBC Driver for Landbot

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

Start a Spark Shell and Connect to Landbot Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Landbot JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Landbot/lib/cdata.jdbc.api.jar
   

2. With the shell running, you can connect to Landbot with a JDBC URL and use the SQL Context load() function to read a table.

   Using API Key Authentication

   Landbot uses token-based authentication. Obtain your agent token from Settings > Account in your Landbot account.

   Set the following connection properties:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Landbot agent token.

   Sample Connection String

   Profile=C:\profiles\Landbot.apip;AuthScheme=APIKey;APIKey=your_agent_token_here;
   

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Landbot JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.api.jar
   

   Fill in the connection properties and copy the connection string to the clipboard.

   

   Configure the connection to Landbot, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Landbot.apip;AuthScheme=APIKey;APIKey=your_agent_token_here;").option("dbtable","Agents").option("driver","cdata.jdbc.api.APIDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Landbot data as a temporary table:

   scala> api_df.registerTable("agents")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> api_df.sqlContext.sql("SELECT ,  FROM Agents WHERE  = ").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   

Using the CData JDBC Driver for Landbot in Apache Spark, you are able to perform fast and complex analytics on Landbot data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.