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Artem  Chebotko  
Solution  Architect
Traversing  Graphs  with  Gremlin
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.
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
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
Graph  Technology
• Graph  Databases
• Graph  Processors
©  DataStax,  All  Rights  Reserved. 5
Graph  Data  Models
• Property  Graph
• RDF  Graph
• Other
©  DataStax,  All  Rights  Reserved. 6
Graph  Query  Languages
• Gremlin
• SPARQL
• Other
©  DataStax,  All  Rights  Reserved. 7
Apache  TinkerPopTM
Graph  computing  framework
©  DataStax,  All  Rights  Reserved. 8
tinkerpop.apache.org
Apache  TinkerPopTM
Designing  TinkerPop-­enabled  graph  systems
©  DataStax,  All  Rights  Reserved. 9
tinkerpop.apache.org
SparkGraphComputer
GiraphGraphComputer
Apache  TinkerPopTM
Designing  TinkerPop-­enabled  graph  systems
©  DataStax,  All  Rights  Reserved. 10
tinkerpop.apache.org
SparkGraphComputer
GiraphGraphComputer
Apache  TinkerPopTM
Designing  TinkerPop-­enabled  graph  systems
©  DataStax,  All  Rights  Reserved. 11
tinkerpop.apache.org
SparkGraphComputer
GiraphGraphComputer
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.
Vertices
©  DataStax,  All  Rights  Reserved. 13
movie
user
user
genremovie
person
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")
Edges
©  DataStax,  All  Rights  Reserved. 15
movie
user
rated rated
user
knows
genre
belongsTo belongsTo
actor
movie
person
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)
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
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)
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
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"))
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
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.
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
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()
Graph  Traversal  Steps
©  DataStax,  All  Rights  Reserved. 25
Simple  Traversal  Steps
©  DataStax,  All  Rights  Reserved. 26
Simple  Traversal  Steps
©  DataStax,  All  Rights  Reserved. 27
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
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")
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.
Demo  Setup
• Real-­time  Graph  DBMS
• Superior  scalability
• High  throughput
• Search  and  analytics  
capabilities
©  DataStax,  All  Rights  Reserved. 31
How  DSE  Graph  Works
©  DataStax,  All  Rights  Reserved. 32
Graph  Applications
DSE  Graph
DSE  Graph
Property  Graph  and  Gremlin
DSE  schema  API
How  DSE  Graph  Works
©  DataStax,  All  Rights  Reserved. 33
Graph  Applications
DSE  Graph
Property  Graph  and  Gremlin
DSE  schema  API
How  DSE  Graph  Works
©  DataStax,  All  Rights  Reserved. 34
Fully  integrated
backend  technologies
Graph  Applications
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
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
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
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
©  DataStax,  All  Rights  Reserved. 39
Thank  You
©  DataStax,  All  Rights  Reserved. 40
Artem Chebotko
achebotko@datastax.com
www.linkedin.com/in/artemchebotko

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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