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

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

Install the CData JDBC Driver for Highrise

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

Start a Spark Shell and Connect to Highrise Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Highrise/lib/cdata.jdbc.highrise.jar
   

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

   Highrise uses the OAuth authentication standard. To authenticate to Highrise, obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with Highrise. You will also need to set the AccountId to connect to data.

   See the "Getting Started" section in the help documentation for a guide to using OAuth.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Highrise JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.highrise.jar
   

   Fill in the connection properties and copy the connection string to the clipboard.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Highrise, using the connection string generated above.

   scala> val highrise_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:highrise:OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;AccountId=MyAccountId;InitiateOAuth=GETANDREFRESH;").option("dbtable","Deals").option("driver","cdata.jdbc.highrise.HighriseDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Highrise data as a temporary table:

   scala> highrise_df.registerTable("deals")

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

   scala> highrise_df.sqlContext.sql("SELECT Name, Price FROM Deals WHERE GroupId = MyGroupId").collect.foreach(println)

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

   👁 Data in Apache Spark (Salesforce is shown)
   

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