app.scala

import org.apache.spark.sql.DataFrame
import org.opencypher.morpheus.api.MorpheusSession
import org.opencypher.morpheus.api.io.{MorpheusNodeTable, MorpheusRelationshipTable}

implicit val morpheus: MorpheusSession = MorpheusSession.local()
val spark = morpheus.sparkSession

import spark.sqlContext.implicits._

// 2) Generate some DataFrames that we'd like to interpret as a property graph.
val nodesDF = spark.createDataset(Seq(
  (0L, "Alice", 42L),
  (1L, "Bob", 23L),
  (2L, "Eve", 84L)
)).toDF("id", "name", "age")
val relsDF = spark.createDataset(Seq(
  (0L, 0L, 1L, "23/01/1987"),
  (1L, 1L, 2L, "12/12/2009")
)).toDF("id", "source", "target", "since")

// 3) Generate node- and relationship tables that wrap the DataFrames. The mapping between graph elements and columns
//    is derived using naming conventions for identifier columns.
val personTable = MorpheusNodeTable(Set("Person"), nodesDF)
val friendsTable = MorpheusRelationshipTable("KNOWS", relsDF)

// 4) Create property graph from graph scans
val graph = morpheus.readFrom(personTable, friendsTable)

// 5) Execute Cypher query and print results
val result = graph.cypher("MATCH (n:Person) RETURN n.name")

// 6) Collect results into string by selecting a specific column.
//    This operation may be very expensive as it materializes results locally.
val names: Set[String] = result.records.table.df.collect().map(_.getAs[String]("n_name")).toSet

println(names)