How to work with PhantomBuster 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 PhantomBuster, Spark can work with live PhantomBuster data. This article describes how to connect to and query PhantomBuster data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live PhantomBuster data due to optimized data processing built into the driver. When you issue complex SQL queries to PhantomBuster, the driver pushes supported SQL operations, like filters and aggregations, directly to PhantomBuster 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 PhantomBuster data using native data types.

Install the CData JDBC Driver for PhantomBuster

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

Start a Spark Shell and Connect to PhantomBuster Data

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

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

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

   Using API Key Authentication

   To use the Phantombuster API, you need to obtain an API key from your Phantombuster account settings. Navigate to phantombuster.com, click your profile icon, select Settings, and copy the API key from the API section.

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Phantombuster API key from the account settings page.

   Multi-Organization Accounts

   If your API key is associated with multiple organizations, you can target a specific organization by setting the OrganizationId connection property to the desired organization identifier. When set, it is sent as the X-Phantombuster-Org request header.

   Example connection string:

   Profile=C:\profiles\Phantombuster.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key_here"
   

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the PhantomBuster 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 PhantomBuster, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Phantombuster.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key_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 PhantomBuster 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 PhantomBuster in Apache Spark, you are able to perform fast and complex analytics on PhantomBuster 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.