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

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

Install the CData JDBC Driver for CloudConvert

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

Start a Spark Shell and Connect to CloudConvert Data

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

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

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

   CloudConvert uses API key authentication. Your CloudConvert API key is used to authenticate requests as a Bearer token. You can generate or view your keys at https://cloudconvert.com/dashboard/api/v2/keys.

   Using API Key Authentication

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

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

   Example connection string:

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

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

   scala> api_df.registerTable("jobs")

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

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

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

   

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