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

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

Install the CData JDBC Driver for Bright Data

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

Start a Spark Shell and Connect to Bright Data Data

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

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

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

   Using API Key Authentication

   To use the Bright Data API, you need an API key from the Bright Data Control Panel. Navigate to Account Settings > API to generate or retrieve your API key.

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Bright Data API key from the Control Panel.

   Example connection string:

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

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("accountstatus")

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

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

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

   

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