#Step 1
%python
#write a file to DBFS using Python I/O APIs
with open("/dbfs/tmp/neo4_test.csv", 'w') as f:
  f.write("id,name,emp_id,employer\n")
  for x in range(500):
    f.write(str(x) + ",name_" + str(x) + "," + str(x) + ",emp_name_" + str(x) + "\n")
  f.close()

#Step 2 Load to Dataframe

%scala
val sampleNeo4jCSV = spark.read.format("csv")
  .option("sep", ",")
  .option("inferSchema", "true")
  .option("header", "true")
  .load("/tmp/neo4_test.csv")

sampleNeo4jCSV.createOrReplaceTempView("personList")

#Step 3 - Run Cypher queries
%scala
import org.neo4j.spark._
import org.graphframes._
import org.neo4j.spark.Neo4jDataFrame
import scala.collection.JavaConverters._

val config = Neo4jConfig(sc.getConf)

val neo = Neo4j(sc)

var queries: Array[String]=Array(
	"MATCH (a) DETACH DELETE a RETURN count(a);",
	"CREATE CONSTRAINT ON (p:Person) ASSERT p.id IS UNIQUE;",
	"CREATE INDEX ON :Person(name);",
    "CREATE INDEX ON :Employer(emp_id);",
    "CREATE INDEX ON :Device(id);"
)

for (q <- queries) {
	Neo4jDataFrame.execute(config,q,Map().asJava);
}

# Step 4 - Create Nodes
%scala
import org.neo4j.spark._
val neo = Neo4j(sc)
val my_dataframe = spark.sql("""select id, name from personList""")
Neo4jDataFrame.createNodes(sc, my_dataframe, ("Person",Seq("id", "name")))

# Step 5 - Create Nodes
%scala
import org.neo4j.spark._
val neo = Neo4j(sc)
val my_dataframe = spark.sql("""select emp_id,employer from personList""")
Neo4jDataFrame.createNodes(sc, my_dataframe, ("Employer",Seq("emp_id", "employer")))

# Step 6 - Create Relationships
%scala
import org.neo4j.spark._
val neo = Neo4j(sc)
val my_dataframe = spark.sql("""select emp_id,id from personList""")
Neo4jDataFrame.mergeEdgeList(sc, my_dataframe, ("Employer",Seq("emp_id")),("HAS_EMPLOYEE",Seq()),("Person",Seq("id")))

# Step 7 - Load from JSON
%scala
import spark.implicits._
import org.neo4j.spark._

// A JSON dataset is pointed to by path.
// The path can be either a single text file or a directory storing text files
val path = "/databricks-datasets/iot/iot_devices.json"
val peopleDF = spark.read.json(path)

// The inferred schema can be visualized using the printSchema() method
peopleDF.printSchema()

peopleDF.createOrReplaceTempView("people")

// SQL statements can be run by using the sql methods provided by spark
val teenagerNamesDF = spark.sql("SELECT ip,device_id, device_name FROM people")
val neo = Neo4j(sc)
Neo4jDataFrame.createNodes(sc, teenagerNamesDF, ("Device",Seq("ip", "device_id","device_name")))
teenagerNamesDF.show()

# Other info - List sample files
%python
dbutils.fs.ls("/databricks-datasets/")