A walk in graph databases v1.0

A Walk in Graph DatabasesPierre De Wilde4 May 2012Global Brain InstituteVUB - ECCO Group

The Law of the Hammer If the only tool you have is a hammer, everything looks like a nail. Abraham Maslow - The Psychology of Science - 1966

The Law of the Relational Database If the only tool you have is a relational database, everything looks like a table. A Walk in Graph Databases - 2012

doesntOne size fits all Scalability issue Scale up Scale out Index-intensive issue Find data Join data

NoSQL ?! No SQL ? Not only SQL ! Scalability solutions Key-value stores Column databases Document databases Index-intensive solution Graph databases

Query language for relational databases SQL ISUD or CRUD

Traversal graphQuery language for relational databases Gremlin is a graph traversal language

Map of walk Warm up Property Graph Graph Database Walk with Gremlin Graph Manipulation Graph Traversal Stretching Linked Data Global Graph

Graph G = (V, E) --. .-. .- .--. ....

One graph doesnt fit all Marko A. Rodriguez and Peter Neubauer - Constructions from Dots and Lines - 2010

Property graph A property graph is a directed, labeled, attributed, multi graph.

Anatomy of a vertex A vertex is composed of - an unique identifier (id) - a collection of properties - a set of incoming edges (inE) - a set of outgoing edges (outE)

Anatomy of an edge An edge is composed of - an unique identifier (id) - an outgoing vertex (outV) - a label - an incoming vertex (inV) - a collection of properties

Graph database

Key feature of a graph database Index-free adjacency

Some graph database vendors Neo4j from Neo Technology http://neo4j.org/ OrientDB from Orient Technologies http://www.orientdb.org/ Dex from Sparsity-Technologies http://www.sparsity-technologies.com/dex InfiniteGraph from Objectivity, Inc. http://www.infinitegraph.com/

TinkerPop Open source project in the graph space

TinkerPop family https://github.com/tinkerpop

Gremlin $ gremlin.sh ,,,/ (o o) -----oOOo-(_)-oOOo----- gremlin> Gremlin is a graph traversal language

Connect to a graph database gremlin> g = new TinkerGraph(name) gremlin> g = new Neo4jGraph(name) gremlin> g = new OrientGraph(name) gremlin> g = new DexGraph(name) gremlin> g = new IGGraph(name)

Add a vertex / an edge gremlin> v1 = g.addVertex() gremlin> v2 = g.addVertex() ... gremlin> g.addEdge(v1, knows, v2) ... gremlin> g.loadGraphML(url)

Update a vertex gremlin> v = g.getVertex(1) ==>v[1] gremlin> v.getPropertyKeys() ==>age ==>name gremlin> v.getProperty(name) ==>marko gremlin> v.getProperty(age) ==>29 gremlin> v.setProperty(age,32) ==>32 gremlin> v.age ==>32 gremlin> v.name ==>marko

Update an edge gremlin> e = g.getEdge(8) ==>e[8][1-knows->4] gremlin> e.getPropertyKeys() ==>weight gremlin> e.getProperty(weight) ==>1.0 gremlin> e.setProperty(weigth,0.9) ==>0.9 gremlin> e.map() ==>weigth=0.9 ==>weight=1.0 gremlin> e.removeProperty(weigth) ==>0.9

Remove a vertex gremlin> v = g.getVertex(3) ==>v[3] gremlin> g.removeVertex(v) ==>null

Remove an edge gremlin> e = g.getEdge(10) ==>e[10][4-created->5] gremlin> g.removeEdge(e) ==>null

Disconnect from the graph database gremlin> g.shutdown()

Graph traversal Jump - from vertex to edge - from edge to vertex - from vertex to vertex

Graph traversal: starting the traversal gremlin> g.v(1) ==>v[1]

Graph traversal: outgoing edges gremlin> g.v(1).outE ==>e[7][1-knows->2] ==>e[9][1-created->3] ==>e[8][1-knows->4]

Graph traversal: incoming vertices gremlin> g.v(1).outE.inV ==>v[2] ==>v[4] ==>v[3]

Graph traversal: outgoing edges (cont.) gremlin> g.v(1).outE.inV.outE ==>e[10][4-created->5] ==>e[11][4-created->3]

Graph traversal: incoming vertices (cont.) gremlin> g.v(1).outE.inV.outE.inV ==>v[5] ==>v[3]

Graph traversal: ending the traversal gremlin> g.v(1).outE.inV.outE.inV.outE

Graph traversal: starting vertex gremlin> g.v(1) ==>v[1]

Graph traversal: adjacent vertices gremlin> g.v(1).out ==>v[2] ==>v[4] ==>v[3]

Graph traversal: adjacent vertices (cont.) gremlin> g.v(1).out.out ==>v[5] ==>v[3]

Graph traversal: starting vertex gremlin> g.v(1) ==>v[1]

Graph traversal: labeled outgoing edges gremlin> g.v(1).outE(created) ==>e[9][1-created->3]

Graph traversal: labeled adjacent vertices gremlin> g.v(1).outE(created).inV ==>v[3] gremlin> g.v(1).out(created) ==>v[3]

Graph traversal: labeled adjacent (cont.) gremlin> g.v(1).out(created).in(created) ==>v[1] ==>v[4] ==>v[6]

Graph traversal and ... index transform filter compute manipulate loop path

Graph traversal and index gremlin> g.idx(vertices)[[name:marko]] ==>v[1]

Graph traversal and transform gremlin> g.v(1).outE.label.dedup ==>knows ==>created gremlin> g.v(1).out(knows).name ==>vadas ==>josh

Graph traversal and filter gremlin> g.v(1).out(knows).age ==>27 ==>32 gremlin> g.v(1).out(knows).filter{it .age>30}.age ==>32

Graph traversal and compute gremlin> g.v(1).outE.weight ==>0.5 ==>1.0 ==>0.4 gremlin> g.v(1).outE.weight.mean() ==>0.6333333353201548

Graph traversal and manipulate gremlin> g.v(1).outE.sideEffect{it. weight+=0.1}.weight ==>0.6 ==>1.1 ==>0.5

Graph traversal and loop gremlin> g.v(1).out.loop(1){it.loops<3} ==>v[5] ==>v[3]

Graph traversal and path gremlin> g.v(1).outE.inV.path ==>[v[1], e[7][1-knows->2], v[2]] ==>[v[1], e[8][1-knows->4], v[4]] ==>[v[1], e[9][1-created->3], v[3]] gremlin> g.v(1).out.path ==>[v[1], v[2]] ==>[v[1], v[4]] ==>[v[1], v[3]]

Global traversal: in-degree distribution gremlin> m=[:].withDefault{0}; g.V. sideEffect{m[it.in.count()]+=1}. iterate(); m.sort() ==>0=2 ==>1=3 ==>3=1

Walk is ending flexible Gremlin is a graph traversal language

Linked Data http://www.w3.org/DesignIssues/LinkedData.html

Linked Data cloud Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

Linked Data initiative http://freeyourmetadata.org/

Linked Data and Gremlin + +gremlin> g = new SparqlRepositorySailGraph("http://dbpedia.org/sparql")gremlin> v = g.v( http://dbpedia.org/resource/Global_brain)==>v[http://dbpedia.org/resource/Global_brain]gremlin> v.out(http://www.w3.org/2000/01/rdf-schema#comment).has(lang,en).value==>The Global Brain is a metaphor for the worldwide intelligent network...gremlin> v.inE(http://dbpedia.org/ontology/knownFor).outV==>v[http://dbpedia.org/resource/Francis_Heylighen]gremlin> v.inE(http://dbpedia.org/ontology/knownFor).outV.outE(http://dbpedia.org/ontology/knownFor).inV==>v[http://dbpedia.org/resource/Self-organization]==>v[http://dbpedia.org/resource/Memetics]==>v[http://dbpedia.org/resource/Global_brain]

Graph and Brain

Global Graph Internet => net of computers Word Wide Web => web of documentsGiant Global Graph => graph of metadata I called this graph the Semantic Web, but maybe it should have been Giant Global Graph. Tim Berners-Lee - timbls blog - 2007

Thank you

http://tinkerpop.com Logos created by Ketrina Yim for TinkerPop geeks Images created by Flickr Creative Commons Artists Graphs created by Memotive Concept Mapping tool

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A walk in graph databases v1.0

by Pierre De Wilde on May 05, 2012

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