mpollmeier / gremlin-scala

Scala wrapper for Apache TinkerPop 3 Graph DSL


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Gremlin-Scala for Apache Tinkerpop 3

A wrapper to use Apache Tinkerpop3 - a JVM graph traversal library - from Scala.

  • Scala friendly function signatures, aiming to be close to the Scala collection library.
    • use standard Scala functions - no need to worry about how to implement things like java.util.function.BiPredicate
  • Beautiful DSL to create vertices and edges
  • Type safe traversals
  • Minimal runtime overhead - only allocates additional instances if absolutely necessary
  • Nothing is hidden away, you can always easily access the underlying Gremlin-Java objects if needed, e.g. to access graph db specifics things like indexes

Getting started

The examples project comes with working examples for different graph databases. Typically you just need to add a dependency on "com.michaelpollmeier" %% "gremlin-scala" % "SOME_VERSION" and one for the graph db of your choice to your build.sbt. The latest version is displayed at the top of this readme in the maven badge.

Using the sbt console

  • sbt gremlin-scala/test:console
import gremlin.scala._
import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerFactory
val graph = TinkerFactory.createModern.asScala
// List(marko, vadas, josh, peter)

Simple traversals

The below create traversals, which are lazy computations. To run a traversal, you can use e.g. toSet, toList, head, headOption etc.

import gremlin.scala._
import org.apache.tinkerpop.gremlin.process.traversal.{Order, P}
import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerFactory

val graph = TinkerFactory.createModern.asScala

graph.V //all vertices
graph.E //all edges

graph.V(1).outE("knows") //follow outgoing edges
graph.V(1).out("knows") //follow outgoing edges to incoming vertex

for {
  person <- graph.V.hasLabel("person")
  favorite <- person.outE("likes").orderBy("weight", Order.decr).limit(1).inV
} yield (person, favorite.label)

// remove all people over 30 from the graph - also removes corresponding edges
val Age = Key[Int]("age")
graph.V.hasLabel("person").has(Age, P.gte(30)).drop.iterate

Warning: GremlinScala is not a monad, because the underlying Tinkerpop GraphTraversal is not. I.e. while GremlinScala offers map, flatMap etc. and you can use it in a for comprehension for syntactic sugar, it does not fulfil all monad laws.

More working examples in TraversalSpec.

Vertices and edges with type safe properties

import gremlin.scala._
import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph
val graph =

// Keys for properties which can later be used for type safe traversals
val Founded = Key[String]("founded")
val Distance = Key[Int]("distance")

// create labelled vertex
val paris = graph + "Paris"

// create vertex with typed properties
val london = graph + ("London", Founded  "43 AD")

// create labelled edges 
paris --- "OneWayRoad" --> london
paris <-- "OtherWayAround" --- london
paris <-- "Eurostar" --> london

// create edge with typed properties
paris --- ("Eurostar", Distance  495) --> london

// type safe access to properties
paris.out("Eurostar").value(Founded).head //43 AD
paris.outE("Eurostar").value(Distance).head //495
london.valueOption(Founded) //Some(43 AD)
london.valueOption(Distance) //None
paris.setProperty(Founded, "300 BC")

val Name = Key[String]("name")
val Age = Key[Int]("age")

val v1 = graph + ("person", Name -> "marko", Age -> 29) asScala

v1.keys // Set(Key("name"), Key("age")) // "marko"
v1.valueMap // Map("name" → "marko", "age" → 29)
v1.valueMap("name", "age") // Map("name" → "marko", "age" → 29)

More working examples in SchemaSpec, ArrowSyntaxSpec and ElementSpec.

Compiler helps to eliminate invalid traversals

Gremlin-Scala aims to helps you at compile time as much as possible. Take this simple example:

import gremlin.scala._
import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph
val graph =
graph.V.outE.inV  //compiles
graph.V.outE.outE //does _not_ compile

In Gremlin-Groovy there's nothing stopping you to create the second traversal - it will explode at runtime, as outgoing edges do not have outgoing edges. In Gremlin-Scala this simply doesn't compile.

Type safe traversals

You can label any step using as(StepLabel) and the compiler will infer the correct types for you in the select step using an HList (a type safe list, i.e. the compiler knows the types of the elements of the list). In Gremlin-Java and Gremlin-Groovy you get a Map[String, Any], so you have to cast to the type you think it will be, which is ugly and error prone. For example:

// use :paste in Scala REPL
import gremlin.scala._
import shapeless._
import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerFactory
import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph
def g = TinkerFactory.createModern.asScala

// select all labelled steps
// returns a `(Vertex, Edge)` for each path

// select subset of labelled steps
val a = StepLabel[Vertex]()
val b = StepLabel[Edge]()
val c = StepLabel[Double]()

.select((b, c)) //step labels parsed as tuple of any size
// returns a `(Edge, Double)`

More working examples in SelectSpec. Kudos to shapeless and Scala's sophisticated type system that made this possible.

A note on predicates

tl;dr: use gremlin.scala.P to create predicates of type P.

Many steps in take a tinkerpop3 predicate of type org.apache.tinkerpop.gremlin.process.traversal.P. Creating Ps that take collection types is dangerous though, because you need to ensure you're creating the correct P. For example P.within(Set("a", "b")) would be calling the wrong overload (which checks if the value IS the given set). In that instance you actually wanted to create P.within(Set("a", "b").asJava: java.util.Collection[String]). To avoid that confusion, it's best to just import gremlin.scala._ and create it as P.within(Set("a", "b")).

Build a custom DSL on top of Gremlin-Scala

You can now build your own domain specific language, which is super helpful if you don't want to expose your users to the world of graphs and tinkerpop, but merely build an API for them. All you need to do is setup your ADT as case classes, define your DSL as Steps and create one implicit constructor (the only boilerplate code). The magic in gremlin.scala.dsl._ allows you to even write for comprehensions like this (DSL for tinkerpop testgraph):

case class Person  (name: String, age: Integer) extends DomainRoot
case class Software(name: String, lang: String) extends DomainRoot

val traversal = for {
  person   <- PersonSteps(graph)
  software <- person.created
} yield (, software)

// note: `traversal` is inferred by the compiler as `gremlin.scala.dsl.Steps[(String, Software)]`

traversal.toSet // returns: 
  ("marko", Software("lop", "java")),
  ("josh", Software("lop", "java")),
  ("peter", Software("lop", "java")),
  ("josh", Software("ripple", "java"))

// DSL also supports typesafe as/select:
// inferred return type is `List[(Person, Software)]`

See the full setup and more tests in DslSpec.

Common and useful steps

// get a vertex by id

// get all vertices

// group all vertices by their label

// group vertices by a property

// order by property decreasing
graph.V.has("age").orderBy("age", Order.decr)

More working examples in TraversalSpec.

Mapping vertices from/to case classes

You can save and load case classes as a vertex - implemented with a blackbox macro. You can optionally annotate the id and label of your case class. Scala's Option types will be automatically unwrapped, i.e. a Some[A] will be stored as the value of type A in the database, or null if it's None. If we wouldn't unwrap it, the database would have to understand Scala's Option type itself. The same goes for value classes, i.e. a case class ShoeSize(value: Int) extends AnyVal will be stored as an Integer. Note: your classes must be defined outside the scope where they are being used (e.g. in the code below the class Example cannot be inside object Main).

// this does _not_ work in a REPL
case class Example(@id id: Option[Int],
                   longValue: Long,
                   stringValue: Option[String])

object Main {
  import gremlin.scala._
  import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph

  val graph =
  val example = Example(None, Long.MaxValue, Some("optional value"))
  val v = graph + example
  v.toCC[Example] // equal to `example`, but with id set

  // find all vertices with the label of the case class `Example`
  // modify the vertex like a case class
  v.updateAs[Example](_.copy(longValue = 0L))

You can also define your own marshaller, if the macro generated one doesn't quite cut it. For that and more examples check out the MarshallableSpec.

Some more advanced traversals

Here are some examples of more complex traversals from the examples repo. If you want to run them yourself, check out the tinkergraph examples in there.

What is Die Hard's average rating?

// use :paste in Scala REPL
graph.V.has("movie", "name", "Die Hard")

Get the maximum number of movies a single user rated

// use :paste in Scala REPL

What 80's action movies do 30-something programmers like? Group count the movies by their name and sort the group count map in decreasing order by value.

// use :paste in Scala REPL
  .`match`("a").hasLabel("movie"),"a").out("hasGenre").has("name", "Action"),"a").has("year", P.between(1980, 1990)),"a").inE("rated").as("b"),"b").has("stars", 5),"b").outV().as("c"),"c").out("hasOccupation").has("name", "programmer"),"c").has("age", P.between(30, 40))
  .limit(Scope.local, 10)

What is the most liked movie in each decade?

// use :paste in Scala REPL
  .groupBy { movie =>
    val year = movie.value2(Year)
    val decade = (year / 10)
    (decade * 10): Integer
  .map { moviesByDecade ⇒
    val highestRatedByDecade = moviesByDecade.mapValues { movies ⇒
        .sortBy { _.inE(Rated).value(Stars).mean().head }
        .reverse.head //get the movie with the highest mean rating

Serialise to and from files

Currently graphML, graphson and gryo/kryo are supported file formats, it is very easy to serialise and deserialise into those: see GraphSerialisationSpec. An easy way to visualise your graph is to export it into graphML and import it into gephi.

Help - it's open source!

If you would like to help, here's a list of things that needs to be addressed:

Why such a long version number?

The first three digits is the TP3 version number, only the last digit is incremented on every release of gremlin-scala.

Further reading

For more information about Gremlin see the Gremlin docs and the Gremlin users mailinglist. Please note that while Gremlin-Scala is very close to the original Gremlin, there are differences to Gremlin-Groovy - don't be afraid, they hopefully all make sense to a Scala developer ;)

Random links:

Random things worth knowing

Release a new version of gremlin-scala

  • release #will do a release for each crossScalaVersions
  • sonatypeRelease

after release: upgrade gremlin-examples

  • find . -name build.sbt | xargs grep gremlin-scala
  • git grep -l | xargs sed -i 's/'
  • bash

Breaking changes

The filter step changed it's signature and now takes a traversal: filter(predicate: GremlinScala[End, _] ⇒ GremlinScala[_, _]). The old filter(predicate: End ⇒ Boolean) is now called filterOnEnd, in case you still need it. This change might affect your for comprehensions.

The reasoning for the change is that it's discouraged to use lambdas (see Instead we are now creating anonymous traversals, which can be optimised by the driver, sent over the wire as gremlin binary for remote execution etc.