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Traversing Graphs with Gremlin
1.
Artem Chebotko Solution
Architect Traversing Graphs with Gremlin
2.
1 Introduction to
Graphs 2 Modeling Data as Property Graphs 3 Graph Traversal Language “Gremlin” 4 Gremlin Traversals – Live Demo 5 Q&A 2© DataStax, All Rights Reserved.
3.
Why Graph? • Everything
is connected to everything else • Connections or relationships are important © DataStax, All Rights Reserved. 3 k v k v Data Complexity and Value in Relationships Key-Value Tabular Document Relation Graph v v
4.
Graph Use Cases ©
DataStax, All Rights Reserved. 4 Customer 360 Internet of Things Asset management Recommendations Fraud detection Social networks Communication networks Genomics Epidemiology Web Semantic Web Data integration
5.
Graph Technology • Graph
Databases • Graph Processors © DataStax, All Rights Reserved. 5
6.
Graph Data Models •
Property Graph • RDF Graph • Other © DataStax, All Rights Reserved. 6
7.
Graph Query Languages •
Gremlin • SPARQL • Other © DataStax, All Rights Reserved. 7
8.
Apache TinkerPopTM Graph computing
framework © DataStax, All Rights Reserved. 8 tinkerpop.apache.org
9.
Apache TinkerPopTM Designing TinkerPop-enabled
graph systems © DataStax, All Rights Reserved. 9 tinkerpop.apache.org SparkGraphComputer GiraphGraphComputer
10.
Apache TinkerPopTM Designing TinkerPop-enabled
graph systems © DataStax, All Rights Reserved. 10 tinkerpop.apache.org SparkGraphComputer GiraphGraphComputer
11.
Apache TinkerPopTM Designing TinkerPop-enabled
graph systems © DataStax, All Rights Reserved. 11 tinkerpop.apache.org SparkGraphComputer GiraphGraphComputer
12.
1 Introduction to
Graphs 2 Modeling Data as Property Graphs 3 Graph Traversal Language “Gremlin” 4 Gremlin Traversals – Live Demo 5 Q&A 12© DataStax, All Rights Reserved.
13.
Vertices © DataStax, All
Rights Reserved. 13 movie user user genremovie person
14.
Vertices © DataStax, All
Rights Reserved. 14 Vertex m1 = graph.addVertex("movie") Vertex m2 = graph.addVertex("movie") Vertex u1 = graph.addVertex("user") Vertex u2 = graph.addVertex("user") Vertex p = graph.addVertex("person") Vertex g = graph.addVertex("genre")
15.
Edges © DataStax, All
Rights Reserved. 15 movie user rated rated user knows genre belongsTo belongsTo actor movie person
16.
Edges © DataStax, All
Rights Reserved. 16 Edge r1 = u1.addEdge("rated",m1) Edge r2 = u1.addEdge("rated",m2) u2.addEdge("knows",u1) m1.addEdge("actor",p) m1.addEdge("belongsTo",g) m2.addEdge("belongsTo",g)
17.
Properties © DataStax, All
Rights Reserved. 17 movieId: m267 title: Alice in Wonderland year: 2010 duration: 108 country: United States rating: 6rating: 5 genreId: g2 name: Adventure userId: u75 age: 17 gender: F movieId: m16 title: Alice in Wonderland year: 1951 duration: 75 country: United States userId: u185 age: 12 gender: M movie user rated rated user knows genrebelongsTo belongsTo actor movie personId: p4361 name: Johnny Depp person
18.
Properties © DataStax, All
Rights Reserved. 18 m1.property("movieId","m267") m1.property("title","Alice in Wonderland") m1.property("year",2010) m1.property("duration",108) m1.property("country","United States") r1.property("rating",5)
19.
Multi- and Meta-Properties ©
DataStax, All Rights Reserved. 19 movieId: m267 title: Alice in Wonderland year: 2010 duration: 108 country: United States production: [Tim Burton Animation Co., Walt Disney Productions] budget: [$150M, $200M] m267 movie source: Bloomberg Businessweek date: March 5, 2010 source: Los Angeles Times date: March 7, 2010
20.
Multi- and Meta-Properties ©
DataStax, All Rights Reserved. 20 m1.property(list,"production","Tim Burton Animation Co.") m1.property(list,"production","Walt Disney Productions") Property b1 = m1.property(list,"budget","$150M") b1.property("source","Bloomberg Businessweek") b1.property("date",Date.parse("yyyy-MM-dd", "2010-03-05")) Property b2 = m1.property(list,"budget","$200M") b2.property("source","Los Angeles Times") b2.property("date",Date.parse("yyyy-MM-dd", "2010-03-07"))
21.
Generalizing Graph Data
Model © DataStax, All Rights Reserved. 21 movieId :text title :text year :int duration :int country :text production :text* personId:text name :text genreId :text name :text userId :text age :int gender :text rating :int genrebelongsTomovieuser rated person cinematographer actor director composer screenwriter knows
22.
1 Introduction to
Graphs 2 Modeling Data as Property Graphs 3 Graph Traversal Language “Gremlin” 4 Gremlin Traversals – Live Demo 5 Q&A 22© DataStax, All Rights Reserved.
23.
Gremlin Graph Traversal
Language • Gremlin is defined in Apache TinkerPop™ • Expressive language to define traversals • Functional language with a fluent syntax • Bindings in Groovy, Java8, Scala, Clojure, and more © DataStax, All Rights Reserved. 23
24.
Gremlin Traversal • Traversal
source • Traversal steps • Traverser © DataStax, All Rights Reserved. 24 g.V().has("title","Alice in Wonderland") .has("year",2010) .out("director") .values("name") g = graph.traversal()
25.
Graph Traversal Steps ©
DataStax, All Rights Reserved. 25
26.
Simple Traversal Steps ©
DataStax, All Rights Reserved. 26
27.
Simple Traversal Steps ©
DataStax, All Rights Reserved. 27
28.
Gremlin Traversal Execution ©
DataStax, All Rights Reserved. 28 g.V().has("title", "Alice in Wonderland") .has("year",2010) .out("director") .values("name") movie personId: p8153 name: Tim Burton actor persondirector movieId: m267 title: Alice in Wonderland year: 2010 ... personId: p4361 name: Johnny Depp person screenwriter personId: p5206 name: Linda Woolverton person
29.
movie personId: p8153 name: Tim
Burton actor persondirector movieId: m267 title: Alice in Wonderland year: 2010 ... personId: p4361 name: Johnny Depp person screenwriter personId: p5206 name: Linda Woolverton person Gremlin Traversal Execution (cont.) © DataStax, All Rights Reserved. 29 g.V().has("title", "Alice in Wonderland") .has("year",2010) .out("director", "screenwriter") .values("name")
30.
1 Introduction to
Graphs 2 Modeling Data as Property Graphs 3 Graph Traversal Language “Gremlin” 4 Gremlin Traversals – Live Demo 5 Q&A 30© DataStax, All Rights Reserved.
31.
Demo Setup • Real-time
Graph DBMS • Superior scalability • High throughput • Search and analytics capabilities © DataStax, All Rights Reserved. 31
32.
How DSE Graph
Works © DataStax, All Rights Reserved. 32 Graph Applications DSE Graph
33.
DSE Graph Property Graph
and Gremlin DSE schema API How DSE Graph Works © DataStax, All Rights Reserved. 33 Graph Applications
34.
DSE Graph Property Graph
and Gremlin DSE schema API How DSE Graph Works © DataStax, All Rights Reserved. 34 Fully integrated backend technologies Graph Applications
35.
Property Graph and
Gremlin DSE schema API DSE Graph How DSE Graph Works © DataStax, All Rights Reserved. 35 Schema, data, and query mappings OLTP and OLAP engines Fully integrated backend technologies Graph Applications
36.
DSE Graph vs.
Other Graph Databases © DataStax, All Rights Reserved. 36 DSE Graph Neo4j Titan (with C*) Titan (other) Open development language Yes Yes Yes Yes Architecture Masterless Master Masterless Master Consistency Model Tunable Tunable Tunable Non-tunable Scaling Method Scale out read/write Scale up Scale out read/write Scale out for reads Read, Write, Active- Anywhere Yes No Yes No Auto Multi-Data Center and Cloud Zone Support Yes No Yes No
37.
DSE Graph vs.
Other Graph Databases (cont’d) © DataStax, All Rights Reserved. 37 DSE Graph Neo4j Titan (with C*) Titan (other) Advanced Security Features Yes No No No Integrated Analytics Yes No No No Integrated Search Yes No No No Automatic workload management and ETL not needed between OLTP, OLAP, and Search systems Yes No No No Integrated Multi-Model Platform Yes No No No
38.
Demo Agenda • Performing
statistical analysis • Navigating the graph • Extracting vertex neighborhoods • Finding paths between vertices • Estimating degrees of separation • Finding top 10 movies • Profiling traversals © DataStax, All Rights Reserved. 38
39.
© DataStax, All
Rights Reserved. 39
40.
Thank You © DataStax,
All Rights Reserved. 40 Artem Chebotko achebotko@datastax.com www.linkedin.com/in/artemchebotko
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