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

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

Install the CData JDBC Driver for Factorial

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

Start a Spark Shell and Connect to Factorial Data

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

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

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

   Authentication

   Factorial uses OAuth 2.0 for authentication to connect to your HR data or to allow other users to connect to their data.

   Using OAuth Authentication

   To connect using OAuth, follow these steps:

   1. Navigate to your Factorial admin panel and create a new OAuth application.
   2. Copy the Client ID and Client Secret from your application configuration.
   3. Configure the following connection properties:

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

   • AuthScheme: Set this to OAuth.
   • OAuthClientId: Set this to your OAuth Client ID.
   • OAuthClientSecret: Set this to your OAuth Client Secret.
   • Scope: Set this to specify the data access permissions (default: "read write").

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Factorial.apip;AuthScheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;").option("dbtable","Agreements").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 Factorial data as a temporary table:

   scala> api_df.registerTable("agreements")

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

   scala> api_df.sqlContext.sql("SELECT ,  FROM Agreements WHERE ProcessId = 123").collect.foreach(println)

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

   

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