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

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

Install the CData JDBC Driver for Perplexity

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

Start a Spark Shell and Connect to Perplexity Data

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

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

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

   Start by setting the Profile connection property to the location of the Perplexity Profile on disk (e.g. C:\profiles\Perplexity.apip). Next, set the ProfileSettings connection property to the connection string for Perplexity (see below).

   Perplexity API Profile Settings

   Visit the Perplexity API dashboard at https://www.perplexity.ai/settings/api to generate an API key.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("asyncjobresults")

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

   scala> api_df.sqlContext.sql("SELECT RequestId, Model FROM AsyncJobResults WHERE Status = COMPLETED").collect.foreach(println)

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

   

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