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

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

Install the CData JDBC Driver for Invoiced

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

Start a Spark Shell and Connect to Invoiced Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Invoiced JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Invoiced/lib/cdata.jdbc.api.jar
2. With the shell running, you can connect to Invoiced with a JDBC URL and use the SQL Context load() function to read a table.

   Start by setting the Profile connection property to the location of the Invoiced Profile on disk (e.g. C:\profiles\Invoiced.apip). Next, set the ProfileSettings connection property to the connection string for Invoiced (see below).

   Invoiced API Profile Settings

   In order to authenticate to Invoiced, you'll need to provide your API Key. An API key can be obtained by signing in to your account, and then going to Settings > Developers > API Keys. Set the API Key in the ProfileSettings property to connect.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("invoices")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Invoices WHERE Paid = false").collect.foreach(println)

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

Using the CData JDBC Driver for Invoiced in Apache Spark, you are able to perform fast and complex analytics on Invoiced data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.