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

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

Install the CData JDBC Driver for Scrapfly

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

Start a Spark Shell and Connect to Scrapfly Data

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

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

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

   The Scrapfly API uses API Key authentication. The API key is passed as the key query parameter on every request.

   Using API Key Authentication

   Your Scrapfly API key is required to create a connection. To obtain your API key:

   1. Log into your Scrapfly account at scrapfly.io.
   2. Navigate to Dashboard and select API Keys.
   3. Copy your API key (begins with scp-live- for production or scp-test- for the test environment).

   After obtaining your API key, set the following connection properties:

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

   Example connection string:

   Profile=C:\profiles\Scrapfly.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 Scrapfly 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 Scrapfly, using the connection string generated above.

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

   scala> api_df.registerTable("account")

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

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

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

   

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