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

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

Install the CData JDBC Driver for Square

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

Start a Spark Shell and Connect to Square Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Square/lib/cdata.jdbc.square.jar
   

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

   Square uses the OAuth authentication standard. To authenticate using OAuth, register an app with Square to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

   Additionally, you must specify the LocationId. You can retrieve the Ids for your Locations by querying the Locations table. Alternatively, you can set the LocationId in the search criteria of your query.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.square.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 Square, using the connection string generated above.

   scala> val square_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:square:OAuthClientId=MyAppId;OAuthClientSecret=MyAppSecret;CallbackURL=http://localhost:33333;LocationId=MyDefaultLocation;InitiateOAuth=GETANDREFRESH;").option("dbtable","Refunds").option("driver","cdata.jdbc.square.SquareDriver").load()
   

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

4. Register the Square data as a temporary table:

   scala> square_df.registerTable("refunds")

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

   scala> square_df.sqlContext.sql("SELECT Reason, RefundedMoneyAmount FROM Refunds WHERE Type = FULL").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 Square in Apache Spark, you are able to perform fast and complex analytics on Square 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.