the gremlin in the graph
DESCRIPTION
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.TRANSCRIPT
The Gremlin in the Graph
Marko A. RodriguezGraph Systems Architecthttp://markorodriguez.com
http://twitter.com/twarko
http://slideshare.com/slidarko
First University-Industry Meeting on Graph Databases (UIM-GDB)
Universitat Politecnica de Catalunya – February 8, 2011
February 6, 2011
Abstract
This 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.
GremlinG = (V,E)
http://gremlin.tinkerpop.com
TinkerPop Productions
1
1TinkerPop (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)
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 JDKto Gremlin.
2Groovy available at http://groovy.codehaus.org/.
Gremlin is for Property Graphs
name = "marko"age = 29
1
4
knows
weight = 1.0
name = "josh"age = 32
name = "vadas"age = 27
2
knows
weight = 0.5
created
weight = 0.4
name = "lop"lang = "java"
3created
weight = 0.4
name = "ripple"lang = "java"
5
created
weight = 1.0
name = "peter"age = 35
6
created
weight = 0.2
78
9
11
10
12
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
3Blueprints is available at http://blueprints.tinkerpop.com.
A Blueprints Detour - Basic Example
Graph 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 1knows
name=marko name=petersince=2007
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
4Graph Transactions https://github.com/tinkerpop/blueprints/wiki/Graph-Transactions.5Graph Indices https://github.com/tinkerpop/blueprints/wiki/Graph-Indices.
A Blueprints Detour - Implementations
TinkerGraph
A Blueprints Detour - Implementations
• TinkerGraph implements IndexableGraph
• Neo4jGraph implements TransactionalGraph, IndexableGraph6
• OrientGraph implements TransactionalGraph, IndexableGraph7
• RexsterGraph implements IndexableGraph: Implementation of aRexster REST API.8
• SailGraph implements TransactionalGraph: Turns any Sail-basedRDF store into a Blueprints Graph.9
6Neo4j is available at http://neo4j.org.7OrientDB is available at http://www.orientechnologies.com.8Rexster is available at http://rexster.tinkerpop.com.9Sail is available at http://openrdf.org.
A Blueprints Detour - Ouplementations
JUNGJava Universal Network/Graph Framework
A Blueprints Detour - Ouplementations• GraphSail: Turns any IndexableGraph into an RDF store. 10
• GraphJung: Turns any Graph into a JUNG graph.11 12
10GraphSail https://github.com/tinkerpop/blueprints/wiki/Sail-Ouplementation.11JUNG is available at http://jung.sourceforge.net/.12GraphJung https://github.com/tinkerpop/blueprints/wiki/JUNG-Ouplementation.
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 togetherto create processing pipelines.
• A Pipe can be embedded into another Pipe to allow for nestedprocessing.
Pipes13Pipes is available at http://pipes.tinkerpop.com.
A Pipes Detour - Chained Iterators
• This Pipeline takes objects of type A and turns them into objects oftype D.
Pipe1A B Pipe2 C Pipe3 D
Pipeline
A
AA
A
D
DD
D
Pipe<A,D> pipeline =
new Pipeline<A,D>(Pipe1<A,B>, Pipe2<B,C>, Pipe3<C,D>)
A Pipes Detour - Simple Example
“What are the names of the people that marko knows?”
A C
B
D
knows
knows
created
createdname=marko
name=peter
name=pavel
name=gremlin
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"));
A C
B
D
knows
knows
created
createdname=marko
name=peter
name=pavel
VertexEdgePipe(OUT_EDGES)
LabelFilterPipe("knows")
EdgeVertexPipe(IN_VERTEX)
PropertyPipe("name")
name=gremlin
HINT: The benefit of Gremlin is that this Java verbosity is reduced to g.v(‘A’).outE[[label:‘knows’]].inV.name.
A Pipes Detour - Pipes Library
[ FILTERS ]
AndFilterPipe
CollectionFilterPipe
ComparisonFilterPipe
DuplicateFilterPipe
FutureFilterPipe
ObjectFilterPipe
OrFilterPipe
RandomFilterPipe
RangeFilterPipe
[ GRAPHS ]
EdgeVertexPipe
IdFilterPipe
IdPipe
LabelFilterPipe
LabelPipe
PropertyFilterPipe
PropertyPipe
VertexEdgePipe
[ SIDEEFFECTS ]
AggregatorPipe
GroupCountPipe
CountPipe
SideEffectCapPipe
[ UTILITIES ]
GatherPipe
PathPipe
ScatterPipe
Pipeline
...
A Pipes Detour - Creating Pipes
public 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().
Now onto Gremlin proper...
The Gremlin Architecture
OrientDB TinkerGraphNeo4j
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.
14All examples in this lecture/tutorial are via the command line REPL.
The Seamless Nature of Gremlin/Groovy/JavaSimply Gremlin.load() and add Gremlin to your Groovy class.
// a Groovy class
class 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 class
public class GraphFramework {
public static void main(String[] args) {
System.out.println(GraphAlgorithms.eigenvectorRank(new Neo4jGraph("/tmp/graphdata"));
}
}
Property Graph Model and Gremlin
1
4
knows created
3created
vertex 4 id
vertex 1 out edges
9
8 11
edge 9 labelvertex 3 in edges
edge 9 in vertexedge 9 out vertexedge 9 id
name = "josh"age = 32
vertex 4 properties
• 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.15
15Documentation on atomic steps: https://github.com/tinkerpop/gremlin/wiki/Gremlin-Steps.
Loading a Toy Graph
name = "marko"age = 29
1
4
knows
weight = 1.0
name = "josh"age = 32
name = "vadas"age = 27
2
knows
weight = 0.5
created
weight = 0.4
name = "lop"lang = "java"
3created
weight = 0.4
name = "ripple"lang = "java"
5
created
weight = 1.0
name = "peter"age = 35
6
created
weight = 0.2
78
9
11
10
12
marko$ ./gremlin.sh
\,,,/
(o o)
-----oOOo-(_)-oOOo-----
gremlin> g = TinkerGraphFactory.createTinkerGraph()
==>tinkergraph[vertices:6 edges:6]
gremlin>
16
16Load up the toy 6 vertex/6 edge graph that comes hardcoded with Blueprints.
Basic Gremlin Traversals - Part 1
name = "marko"age = 29
1
4
knows
name = "josh"age = 32
name = "vadas"age = 27
2
knows
created
name = "lop"lang = "java"
3created
5
created
6
created
78
9
11
10
12
weight = 1.0
weight = 0.5
weight = 0.4
gremlin> g.v(1)
==>v[1]
gremlin> g.v(1).map
==>name=marko
==>age=29
gremlin> g.v(1).outE
==>e[7][1-knows->2]
==>e[9][1-created->3]
==>e[8][1-knows->4]
gremlin>
17
17Look at the neighborhood around marko.
Basic Gremlin Traversals - Part 2
name = "marko"age = 29
1
4
knows
2
knows
created
name = "lop"lang = "java"
3created
5
created
6
created
78
9
11
10
12
gremlin> g.v(1)
==>v[1]
gremlin> g.v(1).outE[[label:‘created’]].inV
==>v[3]
gremlin> g.v(1).outE[[label:‘created’]].inV.name
==>lop
gremlin>
18
18What has marko created?
Basic Gremlin Traversals - Part 3
name = "marko"age = 29
1
4
knows
name = "josh"age = 32
name = "vadas"age = 27
2
knows
created
name = "lop"lang = "java"
3created
5
created
6
created
78
9
11
10
12
gremlin> g.v(1)
==>v[1]
gremlin> g.v(1).outE[[label:‘knows’]].inV
==>v[2]
==>v[4]
gremlin> g.v(1).outE[[label:‘knows’]].inV.name
==>vadas
==>josh
gremlin> g.v(1).outE[[label:‘knows’]].inV{it.age < 30}.name
==>vadas
gremlin>
19
19Who does marko know? Who does marko know that is under 30 years of age?
Basic Gremlin Traversals - Part 4
marko
josh
knows
vadas
knows
created
lop
created
ripple
created
peter
created
codeveloper
codeveloper
codeveloper
gremlin> g.v(1)
==>v[1]
gremlin> g.v(1).outE[[label:‘created’]].inV
==>v[3]
gremlin> g.v(1).outE[[label:‘created’]].inV
.inE[[label:‘created’]].outV
==>v[1]
==>v[4]
==>v[6]
gremlin> g.v(1).outE[[label:‘created’]].inV
.inE[[label:‘created’]].outV.except([g.v(1)])
==>v[4]
==>v[6]
gremlin> g.v(1).outE[[label:‘created’]].inV
.inE[[label:‘created’]].outV.except([g.v(1)]).name
==>josh
==>peter
gremlin>
20
20Who 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).
Basic Gremlin Traversals - Part 5
marko
josh
knows
vadas
knows
created
lop
created
ripple
created
peter
created
codeveloper
codeveloper
gremlin> Gremlin.addStep(‘codeveloper’)
==>null
gremlin> c = {_{x = it}.outE[[label:‘created’]].inV
.inE[[label:‘created’]].outV{!x.equals(it)}}
==>groovysh_evaluate$_run_closure1@69e94001
gremlin> [Vertex, Pipe].each{ it.metaClass.codeveloper =
{ Gremlin.compose(delegate, c())}}
==>interface com.tinkerpop.blueprints.pgm.Vertex
==>interface com.tinkerpop.pipes.Pipe
gremlin> g.v(1).codeveloper
==>v[4]
==>v[6]
gremlin> g.v(1).codeveloper.codeveloper
==>v[1]
==>v[6]
==>v[1]
==>v[4]
21
21I 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.
Lets explore more complex traversals...
Grateful Dead Concert Graph
The Grateful Dead were an American band that was born out of the San Francisco,
California psychedelic movement of the 1960s. The band played music together
from 1965 to 1995 and is well known for concert performances containing extended
improvisations and long and unique set lists. [http://arxiv.org/abs/0807.2466]
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.
A Subset of the Grateful Dead Concert Graph
1
type="song"name="Scarlet.."
2
type="song"name="Fire on.."
3
type="song"name="Pass.."
4
type="song"name="Terrap.."
followed_by
followed_by
followed_by
weight=239
weight=1
weight=2
5
type="artist"name="Garcia"
sung_by
sung_by
6
type="artist"name="Lesh"
sung_by
sung_by
7
type="artist"name="Hunter"
written_by
written_by
written_by
Centrality in a Property Graph
Garcia
Dark Star
Terrapin Station
China Doll
Stella Blue
Peggy O
sung_by
...
sung_bysung_by
sung_by
sung_bysung_by
sung_bysung_by
sung_bysung_by
sung_bysung_by
followed_byfollowed_by
followed_by
followed_byfollowed_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?
Loading the GraphML Representation
gremlin> g = new TinkerGraph()
==>tinkergraph[vertices:0 edges:0]
gremlin> GraphMLReader.inputGraph(g,
new FileInputStream(‘data/graph-example-2.xml’))
==>null
gremlin> g.V.name
==>OTHER ONE JAM
==>Hunter
==>HIDEAWAY
==>A MIND TO GIVE UP LIVIN
==>TANGLED UP IN BLUE
...
22
22Load the GraphML (http://graphml.graphdrawing.org/) representation of the Grateful Deadgraph, iterate through its vertices and get the name property of each vertex.
Using Graph Indices
gremlin> v = g.idx(T.v)[[name:‘DARK STAR’]] >> 1
==>v[89]
gremlin> v.outE[[label:‘sung_by’]].inV.name
==>Garcia
23
23Use the default vertex index (T.v) and find all vertices index by the key/value pair name/DARK STAR.Who sung Dark Star?
Emitting Collections
gremlin> 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
24What followed Dark Star in concert?How many times did each song follow Dark Start in concert?
Eigenvector Centrality – Indexing Vertices
gremlin> m = [:]; c = 0
==>0
gremlin> 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
25Emanate 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.
Eigenvector Centrality – Indexing Names
gremlin> m = [:]; c = 0
==>0
gremlin> 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
26Do 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.
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
27What are the paths that are out.inV.name emanating from Dark Star (v[89])?
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
28Get 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.
Shortest Path Between Dark Star and Drums
gremlin> [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
==>1
29
29Given 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.
Integrating the JDK (Java API)
• Groovy is the host language for Gremlin. Thus, the JDK is nativelyavailable 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 schemapresented 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
...
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}
The Concept of Abstract Adjacency – Part 2
}
outE
inVoutE
loop(2){..} back(3){it.salary}
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
30In 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”).
Explicit Graph
Developer CreatedGraph
Software ImportsGraph
FriendshipGraph
EmploymentGraph
Developer Imports Graph
Friend-Of-A-FriendGraph
Friends at WorkGraph
Developer's FOAF ImportGraph
You need not make derived graphs explicit. You can, at runtime, compute them. Moreover, generate them locally, not globally (e.g. ``Marko's friends from work relations").
This concept is related to automated reasoning and whether reasoned relations are inserted into the explicit graph or computed at query time.
The Concept of Abstract Adjacency – Part 3
• Gremlin provides fine grained (Turing-complete) control over how yourtraverser moves through a property graph.
• As such, there are as many shortest paths, eigenvectors/PageRanks,clusters, cliques, etc. as there are “abstract adjacencies” in thegraph.31 32
? This is the power of the property graph over standard unlabeled graphs.There are many ways to slice and dice your data.
31Rodriguez 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]
32Other terms for abstract adjacencies include domain-specific relations, inferred relations, implicit edges,virtual edges, abstract paths, derived adjacencies, higher-order relations, etc.
In the End, Gremlin is Just Pipes
gremlin> (g.v(89).outE[[label:‘followed_by’]].inV.name).toString()
==>[VertexEdgePipe<OUT_EDGES>, LabelFilterPipe<NOT_EQUAL,followed_by>,
EdgeVertexPipe<IN_VERTEX>, PropertyPipe<name>]
33
33Gremlin 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).
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.
GremlinG = (V,E)
http://gremlin.tinkerpop.com
http://groups.google.com/group/gremlin-users
http://tinkerpop.com