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

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

Install the CData JDBC Driver for PayPal

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

Start a Spark Shell and Connect to PayPal Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for PayPal/lib/cdata.jdbc.paypal.jar
   

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

   The provider surfaces tables from two PayPal APIs. The APIs use different authentication methods.

   • The REST API uses the OAuth standard. To authenticate to the REST API, set the OAuthClientId, OAuthClientSecret, and CallbackURL properties.
   • The Classic API requires Signature API credentials. To authenticate to the Classic API, obtain an API username, password, and signature.

   See the "Getting Started" chapter of the help documentation for a guide to obtaining the necessary API credentials.

   To select the API you want to work with, you can set the Schema property to REST or SOAP. By default the SOAP schema will be used.

   For testing purposes you can set UseSandbox to true and use sandbox credentials.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.paypal.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 PayPal, using the connection string generated above.

   scala> val paypal_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:paypal:Schema=SOAP;Username=sandbox-facilitator_api1.test.com;Password=xyz123;Signature=zx2127;InitiateOAuth=GETANDREFRESH;").option("dbtable","Transactions").option("driver","cdata.jdbc.paypal.PayPalDriver").load()
   

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

4. Register the PayPal data as a temporary table:

   scala> paypal_df.registerTable("transactions")

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

   scala> paypal_df.sqlContext.sql("SELECT Date, GrossAmount FROM Transactions WHERE TransactionClass = Received").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 PayPal in Apache Spark, you are able to perform fast and complex analytics on PayPal 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.