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

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

Install the CData JDBC Driver for Printify

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

Start a Spark Shell and Connect to Printify Data

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

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

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

   Printify API Profile Settings

   In order to authenticate to Printify, you'll need to provide your API Key. To get your API Key navigate to My Profile, then Connections. In the Connections section you will be able to generate your Personal Access Token (API Key) and set your Token Access Scopes. Personal Access Tokens are valid for one year. An expired Personal Access Token can be re-generated using the same steps after it expires. Set the API Key to your Personal Access Token in the ProfileSettings property to connect.

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Printify.apip;ProfileSettings='APIKey=your_personal_token';").option("dbtable","Tags").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 Printify data as a temporary table:

   scala> api_df.registerTable("tags")

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

   scala> api_df.sqlContext.sql("SELECT Id, ShippingMethod FROM Tags WHERE Status = pending").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 Printify in Apache Spark, you are able to perform fast and complex analytics on Printify 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.