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

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

Install the CData JDBC Driver for TigerGraph

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

Start a Spark Shell and Connect to TigerGraph Data

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

   To authenticate with your TigerGraph instance, set the User, Password, and URL properties to valid TigerGraph credentials. By default connections are made on port 14240.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.tigergraph.jar

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

   Configure the connection to TigerGraph, using the connection string generated above.

   scala> val tigergraph_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:tigergraph:User=MyUserName;Password=MyPassword;URL=MyURL;").option("dbtable","person").option("driver","cdata.jdbc.tigergraph.TigerGraphDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the TigerGraph data as a temporary table:

   scala> tigergraph_df.registerTable("person")

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

   scala> tigergraph_df.sqlContext.sql("SELECT id, locationId FROM person WHERE locationId = chn").collect.foreach(println)

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

Using the CData JDBC Driver for TigerGraph in Apache Spark, you are able to perform fast and complex analytics on TigerGraph 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.