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

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

Install the CData JDBC Driver for Pinecone

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

Start a Spark Shell and Connect to Pinecone Data

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

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

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

   Authentication

   To authenticate to Pinecone, and connect to your own data or to allow other users to connect to their data, you can use API Key authentication.

   Using API Key Authentication

   To authenticate using an API Key, you need to obtain your API Key from your Pinecone console at https://app.pinecone.io/.

   You can then connect by setting the AuthScheme to APIKey and providing your API key:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your API key from Pinecone.

   Example connection strings:

   Standard API Key Configuration:

   Profile=C:\profiles\Pinecone.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key;APIVersion=2025-10';
   

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Pinecone.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key;APIVersion=2025-10';").option("dbtable","Indexes").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 Pinecone data as a temporary table:

   scala> api_df.registerTable("indexes")

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

   scala> api_df.sqlContext.sql("SELECT , FROM Indexes WHERE Name = my-index").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 Pinecone in Apache Spark, you are able to perform fast and complex analytics on Pinecone 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.