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Neo4j is a native graph database that leverages data relationships as first-class entities. You can connect a Databricks cluster to a Neo4j cluster using the neo4j-spark-connector, which offers Apache Spark APIs for RDD, DataFrame, and GraphFrames. The neo4j-spark-connector uses the binary Bolt protocol to transfer data to and from the Neo4j server.
This article describes how to deploy and configure Neo4j, and configure Databricks to access Neo4j.
Neo4j deployment and configuration
You can deploy Neo4j on various cloud providers.
Change the Neo4j password from the default (you should be prompted when you first access Neo4j) and modify
conf/neo4j.conf to accept remote connections.
# conf/neo4j.conf # Bolt connector dbms.connector.bolt.enabled=true #dbms.connector.bolt.tls_level=OPTIONAL dbms.connector.bolt.listen_address=0.0.0.0:7687 # HTTP Connector. There must be exactly one HTTP connector. dbms.connector.http.enabled=true #dbms.connector.http.listen_address=0.0.0.0:7474 # HTTPS Connector. There can be zero or one HTTPS connectors. dbms.connector.https.enabled=true #dbms.connector.https.listen_address=0.0.0.0:7473
For more information, see Configuring Neo4j Connectors.
Install two libraries: neo4j-spark-connector and graphframes as Spark Packages. See the libraries guide for instructions.
Create a cluster with these Spark configurations.
spark.neo4j.bolt.url bolt://<ip-of-neo4j-instance>:7687 spark.neo4j.bolt.user <username> spark.neo4j.bolt.password <password>
Import libraries and test the connection.
import org.neo4j.spark._ import org.graphframes._ val neo = Neo4j(sc) // Dummy Cypher query to check connection val testConnection = neo.cypher("MATCH (n) RETURN n;").loadRdd[Long]