how to Connect to NEO4J in Spark worker nodes?

Question

I need to get a small subgraph in a spark map function. I have tried to use AnormCypher and NEO4J-SPARK-CONNECTOR, but neither works. AnormCypher will lead to a java IOException Error (I build the connection in a mapPartition function, test at localhost server). And Neo4j-spark-connector will cause TASK NOT SERIALIZABLE EXCEPTION below.

Is there a good way to get a subgraph(or connect to graph data base like neo4j) in the Spark worker node?

Exception in thread "main" org.apache.spark.SparkException: Task not serializable
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2094)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:793)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:792)
    at ....

my code snippet using neo4j-spark-connector 2.0.0-m2:

val neo = Neo4j(sc) // this runs on the driver

// this runs by a map function
def someFunctionToBeMapped(p: List[Long]) = { 
  val metaGraph = neo.cypher("match p = (a:TourPlace) -[r:could_go_to] -> (b:TourPlace)" +
    "return a.id ,r.distance, b.id").loadRowRdd.map( row => ((row(0).asInstanceOf[Long],row(2).asInstanceOf[Long]), row(1).asInstanceOf[Double]) ).collect().toList

The AnromCypher code is :

def partitionMap(partition: Iterator[List[Long]]) = {
  import org.anormcypher._
  import play.api.libs.ws._
  // Provide an instance of WSClient
  val wsclient = ning.NingWSClient()
  // Setup the Rest Client
  // Need to add the Neo4jConnection type annotation so that the default
  // Neo4jConnection -> Neo4jTransaction conversion is in the implicit scope
  implicit val connection: Neo4jConnection = Neo4jREST("127.0.0.1", 7474, "neo4j", "000000")(wsclient)
  //
  // Provide an ExecutionContext
  implicit val ec = scala.concurrent.ExecutionContext.global

  val res = partition.filter( placeList => {

    val startPlace = Cypher("match p = (a:TourPlace) -[r:could_go_to] -> (b:TourPlace)"  +
      "return p")().flatMap( row => row.data )
  })
  wsclient.close()
  res
}

Answer 1

I have used spark standalone mode and able to connect neo4j database

Version used :

spark 2.1.0

neo4j-spark-connector 2.1.0-m2

My code:-

val sparkConf = new SparkConf().setAppName("Neo$j").setMaster("local")
    val sc = new SparkContext(sparkConf)
    println("***Getting Started ****")
    val neo = Neo4j(sc)
    val rdd = neo.cypher("MATCH (n) RETURN id(n) as id").loadDataFrame
    println(rdd.count)

Spark submit:- spark-submit --class package.classname --jars pathofneo4jsparkconnectoryJAR --conf spark.neo4j.bolt.password=***** targetJarFile.jar