Using the Apache Spark Connector

In this tutorial we use the Neo4j Connector for Apache Spark to write to and read graph data from an Aura instance.


  1. Download Spark from the Download page (latest version, for example 3.3.0, pre-built for Apache Hadoop 3.3 and later).

  2. Download the Spark Neo4j Connector from the GitHub release page.

    Make sure to match both the Spark version and the Scala version.

  3. Decompress the downloaded file and launch the Spark shell as follows:

    $ spark-3.3.0-bin-hadoop3/bin/spark-shell --jars neo4j-connector-apache-spark_2.12-4.1.3_for_spark_3.jar

Running code in Spark

Scala code can be copy-pasted in the Spark shell by activating the paste mode with the :paste command.

First of all, we need to create a Spark session and set the Aura connection credentials:

import org.apache.spark.sql.{SaveMode, SparkSession}

val spark = SparkSession.builder().getOrCreate()

// Replace with the actual connection URI and credentials
val url = "neo4j+s://"
val username = "neo4j"
val password = ""

Then, we create the Person class and a Spark Dataset with some example data:

case class Person(name: String, surname: String, age: Int)

// Create example Dataset
val ds = Seq(
    Person("John", "Doe", 42),
    Person("Jane", "Doe", 40)

We can now write the data to Aura:

// Write to Neo4j
    .option("url", url)
    .option("authentication.basic.username", username)
    .option("authentication.basic.password", password)
    .option("labels", ":Person")
    .option("node.keys", "name,surname")

The written data can be queried and visualized using the Neo4j Browser. We can also read the data back from Aura:

// Read from Neo4j
val data ="org.neo4j.spark.DataSource")
    .option("url", url)
    .option("authentication.basic.username", username)
    .option("authentication.basic.password", password)
    .option("labels", "Person")

For further information on how to use the connector, check the Neo4j Spark Connector docs.