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

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

Getting Started

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Install the CData JDBC Driver for Smartsheet

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

Start a Spark Shell and Connect to Smartsheet Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Smartsheet/lib/cdata.jdbc.smartsheet.jar
   

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

   Smartsheet uses the OAuth authentication standard. To authenticate using OAuth, register an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties.

   However, for testing purposes you can instead use the Personal Access Token you get when you create an application; set this to the OAuthAccessToken connection property.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.smartsheet.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 Smartsheet, using the connection string generated above.

   scala> val smartsheet_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:smartsheet:OAuthClientId=MyOauthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;").option("dbtable","Sheet_Event_Plan_Budget").option("driver","cdata.jdbc.smartsheet.SmartsheetDriver").load()
   

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

4. Register the Smartsheet data as a temporary table:

   scala> smartsheet_df.registerTable("sheet_event_plan_budget")

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

   scala> smartsheet_df.sqlContext.sql("SELECT TaskName, Progress FROM Sheet_Event_Plan_Budget WHERE Assigned = Ana Trujilo").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 Smartsheet in Apache Spark, you are able to perform fast and complex analytics on Smartsheet 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.