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

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

Install the CData JDBC Driver for ConvertAPI

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

Start a Spark Shell and Connect to ConvertAPI Data

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

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

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

   ConvertAPI uses API Secret Key authentication. Your ConvertAPI Secret is used to authenticate requests. You can find your API Secret in the ConvertAPI dashboard under your account settings.

   Using APIKey Authentication

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your ConvertAPI API secret key.

   Example connection string:

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

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

   scala> api_df.registerTable("user")

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

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

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

   

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