The Gremlin in the Graph

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This tutorial/lecture addresses various aspects of the graph traversal language Gremlin. In particular, the presentation focuses on Gremlin 0.7 and its application to graph analysis and manipulation.

This tutorial/lecture addresses various aspects of the graph traversal language Gremlin. In particular, the presentation focuses on Gremlin 0.7 and its application to graph analysis and manipulation.

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  • 1. The Gremlin in the Graph Marko A. Rodriguez Graph Systems Architect http://markorodriguez.com http://twitter.com/twarko http://slideshare.com/slidarkoFirst University-Industry Meeting on Graph Databases (UIM-GDB) Universitat Polit´cnica de Catalunya – February 8, 2011 e February 6, 2011
  • 2. AbstractThis tutorial/lecture addresses various aspects of the graph traversallanguage Gremlin. In particular, the presentation focuses on Gremlin 0.7and its application to graph analysis and processing. Gremlin G = (V, E) http://gremlin.tinkerpop.com
  • 3. TinkerPop Productions1 1 TinkerPop (http://tinkerpop.com), Marko A. Rodriguez (http://markorodriguez.com), PeterNeubauer (http://www.linkedin.com/in/neubauer), Joshua Shinavier (http://fortytwo.net/), PavelYaskevich (https://github.com/xedin), Darrick Wiebe (http://ofallpossibleworlds.wordpress.com/), Stephen Mallette (http://stephen.genoprime.com/), Alex Averbuch (http://se.linkedin.com/in/alexaverbuch)
  • 4. Gremlin is a Domain Specific Language• A domain specific language for graph analysis and manipulation.• Gremlin 0.7+ uses Groovy has its host language.2• Groovy is a superset of Java and as such, natively exposes the full JDK to Gremlin. 2 Groovy available at http://groovy.codehaus.org/.
  • 5. Gremlin is for Property Graphs name = "lop" lang = "java" weight = 0.4 3 name = "marko" age = 29 created weight = 0.2 9 1 created 8 created 12 7 weight = 1.0weight = 0.5 weight = 0.4 6 knows knows 11 name = "peter" age = 35 name = "josh" 4 age = 32 2 10 name = "vadas" age = 27 weight = 1.0 created 5 name = "ripple" lang = "java"
  • 6. Gremlin is for Blueprints-enabled Graph Databases• Blueprints can be seen as the JDBC for property graph databases.3• Provides a collection of interfaces for graph database providers to implement.• Provides tests to ensure the operational semantics of any implementation are correct.• Provides support for GraphML and other “helper” utilities. Blueprints 3 Blueprints is available at http://blueprints.tinkerpop.com.
  • 7. A Blueprints Detour - Basic ExampleGraph graph = new Neo4jGraph("/tmp/my_graph");Vertex a = graph.addVertex(null);Vertex b = graph.addVertex(null);a.setProperty("name","marko");b.setProperty("name","peter");Edge e = graph.addEdge(null, a, b, "knows");e.setProperty("since", 2007);graph.shutdown(); 0 knows 1 since=2007 name=marko name=peter
  • 8. A Blueprints Detour - Sub-Interfaces• TransactionalGraph extends Graph For transaction based graph databases.4• IndexableGraph extends Graph For graph databases that support the indexing of properties.5 4 Graph Transactions https://github.com/tinkerpop/blueprints/wiki/Graph-Transactions. 5 Graph Indices https://github.com/tinkerpop/blueprints/wiki/Graph-Indices.
  • 9. A Blueprints Detour - ImplementationsTinkerGraph
  • 10. A Blueprints Detour - Implementations• TinkerGraph implements IndexableGraph• Neo4jGraph implements TransactionalGraph, IndexableGraph6• OrientGraph implements TransactionalGraph, IndexableGraph7• RexsterGraph implements IndexableGraph: Implementation of a Rexster REST API.8• SailGraph implements TransactionalGraph: Turns any Sail-based RDF store into a Blueprints Graph.9 6 Neo4j is available at http://neo4j.org. 7 OrientDB is available at http://www.orientechnologies.com. 8 Rexster is available at http://rexster.tinkerpop.com. 9 Sail is available at http://openrdf.org.
  • 11. A Blueprints Detour - Ouplementations JUNG Java Universal Network/Graph Framework
  • 12. A Blueprints Detour - Ouplementations 10• GraphSail: Turns any IndexableGraph into an RDF store.• GraphJung: Turns any Graph into a JUNG graph.11 1210 GraphSail https://github.com/tinkerpop/blueprints/wiki/Sail-Ouplementation.11 JUNG is available at http://jung.sourceforge.net/.12 GraphJung https://github.com/tinkerpop/blueprints/wiki/JUNG-Ouplementation.
  • 13. Gremlin Compiles Down to Pipes• Pipes is a data flow framework for evaluating lazy graph traversals.13• A Pipe extends Iterator, Iterable and can be chained together to create processing pipelines.• A Pipe can be embedded into another Pipe to allow for nested processing. Pipes 13 Pipes is available at http://pipes.tinkerpop.com.
  • 14. A Pipes Detour - Chained Iterators• This Pipeline takes objects of type A and turns them into objects of type D. D D A A A Pipe1 B Pipe2 C Pipe3 D D A D A PipelinePipe<A,D> pipeline = new Pipeline<A,D>(Pipe1<A,B>, Pipe2<B,C>, Pipe3<C,D>)
  • 15. A Pipes Detour - Simple Example“What are the names of the people that marko knows?” B name=peter knows A knows C name=pavel name=marko created created D name=gremlin
  • 16. A Pipes Detour - Simple ExamplePipe<Vertex,Edge> pipe1 = new VertexEdgePipe(Step.OUT_EDGES);Pipe<Edge,Edge> pipe2= new LabelFilterPipe("knows",Filter.NOT_EQUAL);Pipe<Edge,Vertex> pipe3 = new EdgeVertexPipe(Step.IN_VERTEX);Pipe<Vertex,String> pipe4 = new PropertyPipe<String>("name");Pipe<Vertex,String> pipeline = new Pipeline(pipe1,pipe2,pipe3,pipe4);pipeline.setStarts(new SingleIterator<Vertex>(graph.getVertex("A")); EdgeVertexPipe(IN_VERTEX) VertexEdgePipe(OUT_EDGES) PropertyPipe("name") B name=peter knows A knows C name=pavel name=marko created created D name=gremlin LabelFilterPipe("knows")HINT: The benefit of Gremlin is that this Java verbosity is reduced to g.v(‘A’).outE[[label:‘knows’]].inV.name.
  • 17. A Pipes Detour - Pipes Library [ GRAPHS ] [ SIDEEFFECTS ][ FILTERS ] EdgeVertexPipe AggregatorPipeAndFilterPipe IdFilterPipe GroupCountPipeCollectionFilterPipe IdPipe CountPipeComparisonFilterPipe LabelFilterPipe SideEffectCapPipeDuplicateFilterPipe LabelPipeFutureFilterPipe PropertyFilterPipe [ UTILITIES ]ObjectFilterPipe PropertyPipe GatherPipeOrFilterPipe VertexEdgePipe PathPipeRandomFilterPipe ScatterPipeRangeFilterPipe Pipeline ...
  • 18. A Pipes Detour - Creating Pipespublic class NumCharsPipe extends AbstractPipe<String,Integer> { public Integer processNextStart() { String word = this.starts.next(); return word.length(); }}When extending the base class AbstractPipe<S,E> all that is required isan implementation of processNextStart().
  • 19. Now onto Gremlin proper...
  • 20. The Gremlin Architecture Neo4j OrientDB TinkerGraph
  • 21. The Many Ways of Using Gremlin• Gremlin has a REPL to be run from the shell.14• Gremlin can be natively integrated into any Groovy class.• Gremlin can be interacted with indirectly through Java, via Groovy.• Gremlin has a JSR 223 ScriptEngine as well.14 All examples in this lecture/tutorial are via the command line REPL.
  • 22. The Seamless Nature of Gremlin/Groovy/JavaSimply Gremlin.load() and add Gremlin to your Groovy class.// a Groovy classclass GraphAlgorithms { static { Gremlin.load(); } public static Map<Vertex, Integer> eigenvectorRank(Graph g) { Map<Vertex,Integer> m = [:]; int c = 0; g.V.outE.inV.groupCount(m).loop(3) {c++ < 1000} >> -1; return m; }}Writing software that mixes Groovy and Java is simple...dead simple.// a Java classpublic class GraphFramework { public static void main(String[] args) { System.out.println(GraphAlgorithms.eigenvectorRank(new Neo4jGraph("/tmp/graphdata")); }}
  • 23. Property Graph Model and Gremlin vertex 1 out edges vertex 3 in edges edge 9 out vertex edge 9 label edge 9 in vertex edge 9 id 1 9 created 3 8 11 knows created 4 vertex 4 id vertex 4 properties name = "josh" age = 32• outE: outgoing edges from a vertex.• inE: incoming edge to a vertex.• inV: incoming/head vertex of an edge.• outV: outgoing/tail vertex of an edge.1515 Documentation on atomic steps: https://github.com/tinkerpop/gremlin/wiki/Gremlin-Steps.
  • 24. Loading a Toy Graph name = "lop" marko$ ./gremlin.sh lang = "java" weight = 0.4 3 ,,,/ name = "marko" age = 29 created (o o) weight = 0.2 9 -----oOOo-(_)-oOOo----- 1 created gremlin> g = TinkerGraphFactory.createTinkerGraph() 8 created 12 ==>tinkergraph[vertices:6 edges:6] 7 weight = 1.0weight = 0.5 weight = 0.4 6 gremlin> knows knows 11 name = "peter" age = 35 4 name = "josh" age = 32 16 2 10 name = "vadas" age = 27 weight = 1.0 created 5 name = "ripple" lang = "java" 16 Load up the toy 6 vertex/6 edge graph that comes hardcoded with Blueprints.
  • 25. Basic Gremlin Traversals - Part 1 name = "lop" gremlin> g.v(1) lang = "java" ==>v[1] weight = 0.4 gremlin> g.v(1).map name = "marko" 3 age = 29 ==>name=marko created 9 ==>age=29 1 created gremlin> g.v(1).outE 8 created 7 12 ==>e[7][1-knows->2]weight = 0.5 6 ==>e[9][1-created->3] knows ==>e[8][1-knows->4] knows 11 weight = 1.0 gremlin> name = "josh" 4 age = 32 2 10 17 name = "vadas" age = 27 created 5 17 Look at the neighborhood around marko.
  • 26. Basic Gremlin Traversals - Part 2 name = "lop" gremlin> g.v(1) lang = "java" ==>v[1] gremlin> g.v(1).outE[[label:‘created’]].inVname = "marko" 3 ==>v[3]age = 29 created gremlin> g.v(1).outE[[label:‘created’]].inV.name 9 ==>lop created 1 gremlin> 8 created 12 7 6 18 knows knows 11 4 2 10 created 5 18 What has marko created?
  • 27. Basic Gremlin Traversals - Part 3 name = "lop" gremlin> g.v(1) lang = "java" ==>v[1] gremlin> g.v(1).outE[[label:‘knows’]].inVname = "marko" 3 ==>v[2]age = 29 created ==>v[4] 9 gremlin> g.v(1).outE[[label:‘knows’]].inV.name 1 created 8 created ==>vadas 12 7 ==>josh 6 gremlin> g.v(1).outE[[label:‘knows’]].inV{it.age < 30}.name knows knows 11 ==>vadas gremlin> name = "josh" 4 age = 32 2 10 19name = "vadas"age = 27 created 5 19 Who does marko know? Who does marko know that is under 30 years of age?
  • 28. Basic Gremlin Traversals - Part 4 codeveloper gremlin> g.v(1) ==>v[1] gremlin> g.v(1).outE[[label:‘created’]].inV lop codeveloper ==>v[3] created gremlin> g.v(1).outE[[label:‘created’]].inV codeveloper created .inE[[label:‘created’]].outV marko ==>v[1] peter ==>v[4] created ==>v[6] knows knows gremlin> g.v(1).outE[[label:‘created’]].inV .inE[[label:‘created’]].outV.except([g.v(1)]) josh ==>v[4] vadas ==>v[6] gremlin> g.v(1).outE[[label:‘created’]].inV created .inE[[label:‘created’]].outV.except([g.v(1)]).name ==>josh ripple ==>peter gremlin>20 20 Who are marko’s codevelopers? That is, people who have created the same software as him, but arenot him (marko can’t be a codeveloper of himeself).
  • 29. Basic Gremlin Traversals - Part 5 gremlin> Gremlin.addStep(‘codeveloper’) ==>null lop gremlin> c = {_{x = it}.outE[[label:‘created’]].inV codeveloper .inE[[label:‘created’]].outV{!x.equals(it)}} created ==>groovysh_evaluate$_run_closure1@69e94001 created marko gremlin> [Vertex, Pipe].each{ it.metaClass.codeveloper = created { Gremlin.compose(delegate, c())}} codeveloper peter ==>interface com.tinkerpop.blueprints.pgm.Vertex knows knows ==>interface com.tinkerpop.pipes.Pipe gremlin> g.v(1).codeveloper ==>v[4] josh vadas ==>v[6] gremlin> g.v(1).codeveloper.codeveloper created ==>v[1] ==>v[6] ==>v[1] ripple ==>v[4]21 21 I don’t want to talk in terms of outE, inV, etc. Given my domain model, I want to talk in terms ofhigher-order, abstract adjacencies — e.g. codeveloper.
  • 30. Lets explore more complex traversals...
  • 31. Grateful Dead Concert GraphThe Grateful Dead were an American band that was born out of the San Francisco,California psychedelic movement of the 1960s. The band played music togetherfrom 1965 to 1995 and is well known for concert performances containing extendedimprovisations and long and unique set lists. [http://arxiv.org/abs/0807.2466]
  • 32. Grateful Dead Concert Graph Schema• vertices song vertices ∗ type: always song for song vertices. ∗ name: the name of the song. ∗ performances: the number of times the song was played in concert. ∗ song type: whether the song is a cover song or an original. artist vertices ∗ type: always artist for artist vertices. ∗ name: the name of the artist.• edges followed by (song → song): if the tail song was followed by the head song in concert. ∗ weight: the number of times these two songs were paired in concert. sung by (song → artist): if the tail song was primarily sung by the head artist. written by (song → artist): if the tail song was written by the head artist.
  • 33. A Subset of the Grateful Dead Concert Graph type="artist" type="artist" name="Hunter" name="Garcia" type="song" name="Scarlet.." 7 5 written_by 1 sung_by weight=239 followed_by type="song" name="Fire on.." sung_by sung_by written_by 2 type="artist" weight=1 name="Lesh" type="song" followed_by name="Pass.." 6 written_by 3 sung_by followed_by type="song" weight=2 name="Terrap.." 4
  • 34. Centrality in a Property Graph ... Garcia sung_by sung_by sung_by sung_by sung_by sung_by sung_by sung_by sung_by sung_by sung_by sung_by Dark Terrapin China Stella Peggy Star Station Doll Blue O ... followed_by followed_by followed_by followed_by followed_by followed_by• What is the most central vertex in this graph? – Garcia.• What is the most central song? What do you mean “central?”• How do you calculate centrality when there are numerous ways in which vertices can be related?
  • 35. Loading the GraphML Representationgremlin> g = new TinkerGraph()==>tinkergraph[vertices:0 edges:0]gremlin> GraphMLReader.inputGraph(g, new FileInputStream(‘data/graph-example-2.xml’))==>nullgremlin> g.V.name==>OTHER ONE JAM==>Hunter==>HIDEAWAY==>A MIND TO GIVE UP LIVIN==>TANGLED UP IN BLUE...22 22 Load the GraphML (http://graphml.graphdrawing.org/) representation of the Grateful Deadgraph, iterate through its vertices and get the name property of each vertex.
  • 36. Using Graph Indicesgremlin> v = g.idx(T.v)[[name:‘DARK STAR’]] >> 1==>v[89]gremlin> v.outE[[label:‘sung_by’]].inV.name==>Garcia23 23 Use the default vertex index (T.v) and find all vertices index by the key/value pair name/DARK STAR.Who sung Dark Star?
  • 37. Emitting Collectionsgremlin> v.outE[[label:‘followed_by’]].inV.name==>EYES OF THE WORLD==>TRUCKING==>SING ME BACK HOME==>MORNING DEW...gremlin> v.outE[[label:‘followed_by’]].emit{[it.inV.name >> 1, it.weight]}==>[EYES OF THE WORLD, 9]==>[TRUCKING, 1]==>[SING ME BACK HOME, 1]==>[MORNING DEW, 11]...24 24 What followed Dark Star in concert?How many times did each song follow Dark Start in concert?
  • 38. Eigenvector Centrality – Indexing Verticesgremlin> m = [:]; c = 0==>0gremlin> g.V.outE[[label:‘followed_by’]].inV.groupCount(m) .loop(4){c++ < 1000}.filter{false}gremlin> m.sort{a,b -> b.value <=> a.value}==>v[13]=762==>v[21]=668==>v[50]=661==>v[153]=630==>v[96]=609==>v[26]=586...25 25 Emanate from each vertex the followed by path. Index each vertex by how many times its beentraversed over in the map m. Do this for 1000 times. The final filter{false} will ensure nothing isoutputted.
  • 39. Eigenvector Centrality – Indexing Namesgremlin> m = [:]; c = 0==>0gremlin> g.V.outE[[label:‘followed_by’]].inV.name.groupCount(m) .back(2).loop(4){c++ < 1000}.filter{false}gremlin> m.sort{a,b -> b.value <=> a.value}==>PLAYING IN THE BAND=762==>TRUCKING=668==>JACK STRAW=661==>SUGAR MAGNOLIA=630==>DRUMS=609==>PROMISED LAND=586...26 26 Do the same, index the names of the songs in the map m. However, since you can get the outgoingedges of a string, jump back 2 steps, then loop.
  • 40. Paths in a Graphgremlin> g.v(89).outE.inV.name==>EYES OF THE WORLD==>TRUCKING==>SING ME BACK HOME==>MORNING DEW==>HES GONE...gremlin> g.v(89).outE.inV.name.paths==>[v[89], e[7021][89-followed_by->83], v[83], EYES OF THE WORLD]==>[v[89], e[7022][89-followed_by->21], v[21], TRUCKING]==>[v[89], e[7023][89-followed_by->206], v[206], SING ME BACK HOME]==>[v[89], e[7006][89-followed_by->127], v[127], MORNING DEW]==>[v[89], e[7024][89-followed_by->49], v[49], HES GONE]...27 27 What are the paths that are out.inV.name emanating from Dark Star (v[89])?
  • 41. Paths of Length < 4 Between Dark Star and Drumsgremlin> g.v(89).outE.inV.loop(2){it.loops < 4 & !(it.object.equals(g.v(96)))}[[id:‘96’]].paths==>[v[89], e[7014][89-followed_by->96], v[96]]==>[v[89], e[7021][89-followed_by->83], v[83], e[1418][83-followed_by->96], v[96]]==>[v[89], e[7022][89-followed_by->21], v[21], e[6320][21-followed_by->96], v[96]]==>[v[89], e[7006][89-followed_by->127], v[127], e[6599][127-followed_by->96], v[96]]==>[v[89], e[7024][89-followed_by->49], v[49], e[6277][49-followed_by->96], v[96]]==>[v[89], e[7025][89-followed_by->129], v[129], e[5751][129-followed_by->96], v[96]]==>[v[89], e[7026][89-followed_by->149], v[149], e[2017][149-followed_by->96], v[96]]==>[v[89], e[7027][89-followed_by->148], v[148], e[1937][148-followed_by->96], v[96]]==>[v[89], e[7028][89-followed_by->130], v[130], e[1378][130-followed_by->96], v[96]]==>[v[89], e[7019][89-followed_by->39], v[39], e[6804][39-followed_by->96], v[96]]==>[v[89], e[7034][89-followed_by->91], v[91], e[925][91-followed_by->96], v[96]]==>[v[89], e[7035][89-followed_by->70], v[70], e[181][70-followed_by->96], v[96]]==>[v[89], e[7017][89-followed_by->57], v[57], e[2551][57-followed_by->96], v[96]]...28 28 Get the adjacent vertices to Dark Star (v[89]). Loop on outE.inV while the amount of loops is lessthan 4 and the current path hasn’t reached Drums (v[96]). Return the paths traversed.
  • 42. Shortest Path Between Dark Star and Drumsgremlin> [g.v(89).outE.inV .loop(2){!(it.object.equals(g.v(96)))}[[id:‘96’]].paths >> 1]==>[v[89], e[7014][89-followed_by->96], v[96]]gremlin> (g.v(89).outE.inV.loop(2) {!(it.object.equals(g.v(96)))}[[id:‘96’]].paths >> 1).size() / 3==>129 29 Given the nature of the loop step, the paths are emitted in order of length. Thus, just “pop off” thefirst path (>> 1) and that is the shortest path. You can then gets its length if you want to know the lengthof the shortest path. Be sure to divide by 3 as the path has the start vertex, edge, and end vertex.
  • 43. Integrating the JDK (Java API)• Groovy is the host language for Gremlin. Thus, the JDK is natively available in a path expression.gremlin> v.outE[[label:‘followed_by’]].inV{it.name.matches(‘D.*’)}.name==>DEAL==>DRUMS• Examples below are not with respect to the Grateful Dead schema presented previously. They help to further articulate the point at hand.gremlin> x.outE.inV.emit{JSONParser.get(it.uri).reviews}.mean()...gremlin> y.outE[[label:‘rated’]].filter{TextAnalysis.isElated(it.review)}.inV...
  • 44. The Concept of Abstract Adjacency – Part 1• Loop on outE.inV. g.v(89).outE.inV.loop(2){it.loops < 4}• Loop on followed by. g.v(89).outE[[label:‘followed_by’]].inV.loop(3){it.loops < 4}• Loop on cofollowed by. g.v(89).outE[[label:‘followed_by’]].inV .inE[[label:‘followed_by’]].outV.loop(6){it.loops < 4}
  • 45. The Concept of Abstract Adjacency – Part 2 outE in outE V loop(2){..} back(3) {i t. sa la } ry } friend_of_a_friend_who_earns_less_than_friend_at_work• outE, inV, etc. is low-level graph speak (the domain is the graph).• codeveloper is high-level domain speak (the domain is software development).30 30 In this way, Gremlin can be seen as a DSL (domain-specific language) for creating DSL’s for yourgraph applications. Gremlin’s domain is “the graph.” Build languages for your domain on top of Gremlin(e.g. “software development domain”).
  • 46. Developers FOAF Import Graph Friend-Of-A-Friend GraphDeveloper Imports Friends at Work Graph Graph You need not make Software Imports Friendship derived graphs explicit. Graph Graph You can, at runtime, compute them. Moreover, generate them locally, not Developer Created globally (e.g. ``Markos Employment Graph friends from work Graph relations"). This concept is related to automated reasoning and whether reasoned relations are inserted into the explicit graph or Explicit Graph computed at query time.
  • 47. The Concept of Abstract Adjacency – Part 3• Gremlin provides fine grained (Turing-complete) control over how your traverser moves through a property graph.• As such, there are as many shortest paths, eigenvectors/PageRanks, clusters, cliques, etc. as there are “abstract adjacencies” in the graph.31 32 This is the power of the property graph over standard unlabeled graphs. There are many ways to slice and dice your data. 31 Rodriguez M.A., Shinavier, J., “Exposing Multi-Relational Networks to Single-Relational NetworkAnalysis Algorithms, Journal of Informetrics, 4(1), pp. 29–41, 2009. [http://arxiv.org/abs/0806.2274] 32 Other terms for abstract adjacencies include domain-specific relations, inferred relations, implicit edges,virtual edges, abstract paths, derived adjacencies, higher-order relations, etc.
  • 48. In the End, Gremlin is Just Pipesgremlin> (g.v(89).outE[[label:‘followed_by’]].inV.name).toString()==>[VertexEdgePipe<OUT_EDGES>, LabelFilterPipe<NOT_EQUAL,followed_by>, EdgeVertexPipe<IN_VERTEX>, PropertyPipe<name>]33 33 Gremlin is a domain specific language for constructing lazy evaluation, data flow Pipes (http://pipes.tinkerpop.com). In the end, what is evaluated is a pipeline process in pure Java over aBlueprints-enabled property graph (http://blueprints.tinkerpop.com).
  • 49. Credits• Marko A. Rodriguez: designed, developed, and documented Gremlin.• Pavel Yaskevich: heavy development on earlier versions of Gremlin.• Darrick Wiebe: development on Pipes, Blueprints, and inspired the move to using Groovy as the host language of Gremlin.• Peter Neubauer: promoter and evangelist of Gremlin.• Ketrina Yim: designed the Gremlin logo. Gremlin G = (V, E) http://gremlin.tinkerpop.com http://groups.google.com/group/gremlin-users http://tinkerpop.com