Officially supported, Apache 2 licensend Neo4j Connector for Apache Spark.
@neo4j-contrib / (0)
The Neo4j Connector for Apache Spark is intended to make integrating graphs together with spark easy. There are effectively two ways of using the connector:
As a data source: read any set of nodes or relationships as a DataFrame in Spark
As a sink: write any DataFrame to Neo4j as a collection of nodes or relationships, or alternatively; use a Cypher statement to process records in a DataFrame into the graph pattern of your choice.
Because the connector is based on the new Spark DataSource API, other spark interpreters for languages such as Python and R will work.
The API remains the same, and mostly only slight syntax changes are necessary to accomodate the differences between (for example) Python and Scala.
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Include this package in your Spark Applications using:
spark-shell, pyspark, or spark-submit
> $SPARK_HOME/bin/spark-shell --packages neo4j-contrib:neo4j-connector-apache-spark_2.11:4.0.1
If you use the sbt-spark-package plugin, in your sbt build file, add:
spDependencies += "neo4j-contrib/neo4j-connector-apache-spark_2.11:4.0.1"
resolvers += "Spark Packages Repo" at "https://repos.spark-packages.org/" libraryDependencies += "neo4j-contrib" % "neo4j-connector-apache-spark_2.11" % "4.0.1"
MavenIn your pom.xml, add:
<dependencies> <!-- list of dependencies --> <dependency> <groupId>neo4j-contrib</groupId> <artifactId>neo4j-connector-apache-spark_2.11</artifactId> <version>4.0.1</version> </dependency> </dependencies> <repositories> <!-- list of other repositories --> <repository> <id>SparkPackagesRepo</id> <url>https://repos.spark-packages.org/</url> </repository> </repositories>