TinkerPop API support in DseGraphFrame

Supported methods

DseGraphFrame mimics the TinkerPop graph traversal source by defining two methods: E() and V(). These methods return a GraphTraversal that has all methods defined below. Only a limit set of TinkerPop's Step classes are supported. Steps other than the ones in the following table will throw an UnsupportedException.

DseGraphFrame can be used to drop millions of vertices or edges at once, and is much faster for bulk property updates than Gremlin OLAP or OLTP.

For example this query drops all person vertices and their associated edges:

Using DseGraphFrame in Scala

GraphTraversal is a Java interface, and extends the Java Iterator interface. To iterate through the results of a traversal as a DataFrame use the df() method. DseGraphFrame supports implicit conversion to DataFrame.

The following example will traverse the vertices of a graph using TinkerPop and then show the result as a DataFrame.

g.V().out().show

In some cases you may need to use the TinkerPop Java API to get the correct TinkerPop objects.

For example, to extract the DSE Graph Id object the Traversal Java iterator can be converted to a Scala iterator which allows direct access to the TinkerPop representation of the Id. This method allows you to use the original Id instead of the DataFrame methods which return the DataFrame String representation of the Id, you can also use the toList() and toSet() methods to set the appropriate ID.

import scala.collection.JavaConverters._
// convert the iterator to a Scala iterator to get the native Id object
for(i <-g.V().id().asScala) println (i)

{~label=vertex, community_id=748226688, member_id=0}
{~label=custom, name=Name, value=1}

// convert to a Set
g.V.id.toSet

res18: java.util.Set[Object] = [{~label=demigod, community_id=224391936, member_id=0}, ...

The TinkerPop P (predicate) and T (constant) classes are imported by the Spark shell automatically.

g.E().groupCount().by(T.label)
g.V().has("age", P.gt(30)).show

For standalone applications, import theses classes.

import org.apache.tinkerpop.gremlin.structure.T
import org.apache.tinkerpop.gremlin.process.traversal.P
import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.__

Scala is not always able to infer the return type, especially in the Spark shell. The property values of the type should be provided explicitly.

g.V().values[Any]("name").next()

Or similarly:

val n: String = g.V().values("name").next()

Explicitly set the type when dropping properties.

g.V().properties[Any]("age", "name").drop().iterate()

In this case, using the DataFrame API is easier as you do not need to specify the type.

g.V().properties("age", "name").drop().show()

++
||
++
++

g.V().values("age").show()

+-----+
|  age|
+-----+
|10000|

g.V().has("name", "Alice").outE("created").hasNext()

g.V().groupCount().by(label).next()

g.V().property("age", 10).iterate()

g.V().has("name", "Alice").out("knows").toList()