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

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

Install the CData JDBC Driver for Harvest

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

Start a Spark Shell and Connect to Harvest Data

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

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

2. With the shell running, you can connect to Harvest 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 Harvest Profile on disk (e.g. C:\profiles\Harvest.apip). Next, set the ProfileSettings connection property to the connection string for Harvest (see below).

   Harvest API Profile Settings

   To authenticate to Harvest, you can use either Token authentication or the OAuth standard. Use Basic authentication to connect to your own data. Use OAuth to allow other users to connect to their data.

   Using Token Authentication

   To use Token Authentication, set the APIKey to your Harvest Personal Access Token in the ProfileSettings connection property. In addition to APIKey, set your AccountId in ProfileSettings to connect.

   Using OAuth Authentication

   First, register an OAuth2 application with Harvest. The application can be created from the "Developers" section of Harvest ID.

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

   • ProfileSettings: Set your AccountId in ProfileSettings.
   • AuthScheme: Set this to OAuth.
   • OAuthClientId: Set this to the client ID that you specified in your app settings.
   • OAuthClientSecret: Set this to the client secret that you specified in your app settings.
   • CallbackURL: Set this to the Redirect URI that you specified in your app settings.
   • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage how the driver obtains and refreshes the OAuthAccessToken.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Harvest 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.

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

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Harvest.apip;ProfileSettings='APIKey=my_personal_key;AccountId=_your_account_id';").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 Harvest 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, ClientName FROM Invoices WHERE State = open").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 Harvest in Apache Spark, you are able to perform fast and complex analytics on Harvest 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.