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

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

Install the CData JDBC Driver for SAS Data Sets

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

Start a Spark Shell and Connect to SAS Data Sets Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for SAS Data Sets/lib/cdata.jdbc.sasdatasets.jar
   

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

   Set the following connection properties to connect to your SAS DataSet files:

   Connecting to Local Files

   • Set the Connection Type to "Local." Local files support SELECT, INSERT, and DELETE commands.
   • Set the URI to a folder containing SAS files, e.g. C:\PATH\TO\FOLDER\.

   Connecting to Cloud-Hosted SAS DataSet Files

   While the driver is capable of pulling data from SAS DataSet files hosted on a variety of cloud data stores, INSERT, UPDATE, and DELETE are not supported outside of local files in this driver.

   Set the Connection Type to the service hosting your SAS DataSet files. A unique prefix at the beginning of the URI connection property is used to identify the cloud data store and the remainder of the path is a relative path to the desired folder (one table per file) or single file (a single table). For more information, refer to the Getting Started section of the Help documentation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.sasdatasets.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 SAS Data Sets, using the connection string generated above.

   scala> val sasdatasets_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sasdatasets:URI=C:/myfolder;").option("dbtable","restaurants").option("driver","cdata.jdbc.sasdatasets.SASDataSetsDriver").load()
   

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

4. Register the SAS Data Sets data as a temporary table:

   scala> sasdatasets_df.registerTable("restaurants")

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

   scala> sasdatasets_df.sqlContext.sql("SELECT name, borough FROM restaurants WHERE cuisine = American").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 SAS Data Sets in Apache Spark, you are able to perform fast and complex analytics on SAS Data Sets 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.