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

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

Install the CData JDBC Driver for Perigon

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

Start a Spark Shell and Connect to Perigon Data

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

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

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

   Using API Key Authentication

   To use the Perigon API, you need to obtain an API key from your Perigon account. Navigate to the Perigon dashboard and generate an API key from your account settings.

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Perigon API key.

   Example connection string:

   Profile=C:\profiles\Perigon.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key"
   

   Available Tables

   The Perigon profile provides access to the following tables:

   • Articles - News articles retrieved from the Perigon news intelligence API
   • Headlines - Story clusters grouping related headline articles
   • Sources - News sources tracked by the Perigon news intelligence API
   • Journalists - Journalist profiles tracked by the Perigon news intelligence API

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Perigon 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.

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

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Perigon.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key"").option("dbtable","Articles").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 Perigon data as a temporary table:

   scala> api_df.registerTable("articles")

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

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

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

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Perigon in Apache Spark, you are able to perform fast and complex analytics on Perigon 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.