How to work with Neo4J 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 Neo4J, Spark can work with live Neo4J data. This article describes how to connect to and query Neo4J data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Neo4J data due to optimized data processing built into the driver. When you issue complex SQL queries to Neo4J, the driver pushes supported SQL operations, like filters and aggregations, directly to Neo4J 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 Neo4J data using native data types.

Install the CData JDBC Driver for Neo4J

Download the CData JDBC Driver for Neo4J installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Neo4J Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Neo4J JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Neo4J/lib/cdata.jdbc.neo4j.jar
   

2. With the shell running, you can connect to Neo4J with a JDBC URL and use the SQL Context load() function to read a table.

   To connect to Neo4j, set the following connection properties:

   • Server: The server hosting the Neo4j instance.
   • Port: The port on which the Neo4j service is running. The provider connects to port 7474 by default.
   • User: The username of the user using the Neo4j instance.
   • Password: The password of the user using the Neo4j instance.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Neo4J JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.neo4j.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 Neo4J, using the connection string generated above.

   scala> val neo4j_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:neo4j:Server=localhost;Port=7474;User=my_user;Password=my_password;").option("dbtable","ProductCategory").option("driver","cdata.jdbc.neo4j.Neo4jDriver").load()
   

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

4. Register the Neo4J data as a temporary table:

   scala> neo4j_df.registerTable("productcategory")

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

   scala> neo4j_df.sqlContext.sql("SELECT CategoryId, CategoryName FROM ProductCategory WHERE CategoryOwner = CData Software").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 Neo4J in Apache Spark, you are able to perform fast and complex analytics on Neo4J 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.