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

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

Install the CData JDBC Driver for SAP HANA

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

Start a Spark Shell and Connect to SAP HANA Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for SAP HANA JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for SAP HANA/lib/cdata.jdbc.saphana.jar
2. With the shell running, you can connect to SAP HANA with a JDBC URL and use the SQL Context load() function to read a table.

   Set the Server, Database and Port properties to specify the address of your SAP Hana database to interact with. Set the User and the Password properties to authenticate to the server.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.saphana.jar

   Fill in the connection properties and copy the connection string to the clipboard.

   Configure the connection to SAP HANA, using the connection string generated above.

   scala> val saphana_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:saphana:User=system;Password=mypassword;Server=localhost;Database=systemdb;").option("dbtable","Buckets").option("driver","cdata.jdbc.saphana.SAPHANADriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the SAP HANA data as a temporary table:

   scala> saphana_df.registerTable("buckets")

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

   scala> saphana_df.sqlContext.sql("SELECT Name, OwnerId FROM Buckets WHERE Name = TestBucket").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

Using the CData JDBC Driver for SAP HANA in Apache Spark, you are able to perform fast and complex analytics on SAP HANA data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.