To view the full-length video and tutorial, visit: https://academy.datastax.com/demos/getting-started-graph-databases Getting Started with Graph Databases contains a brief overview of RDBMS architecture in comparison to graph, basic graph terminology, a real-world use case for graph, and an overview of Gremlin, the standard graph query language found in TinkerPop.

1. ©2013 DataStax Confidential. Do not distribute without consent.
@rustyrazorblade
Jon Haddad
Technical Evangelist, DataStax
Graph Databases 101
1

2. A Quick Look at Relational
View the Full-Length Video & Tutorial
https://academy.datastax.com/demos/
getting-started-graph-databases

3. Tables
• Very structured data
• Relational calculus
• Various normal forms
id name title country
1 jon evangelist usa
2 patrick chief evangelist usa
3 rebecca jr evangelist canada
4 billy ceo usa

4. Selecting and Filtering
id name title country
1 jon evangelist usa
2 patrick chief evangelist usa
3 rebecca jr evangelist canada
4 billy ceo usa
select * from people where country = 'canada';

5. Foreign Keys
id name title country
1 jon evangelist 1
2 patrick chief evangelist 1
3 rebecca jr evangelist 2
4 billy ceo 1
id name
1 usa
2 canada
3 germany
4 japan

6. Joins
select * from people join country on people.country = country.id;
id name title country name
1 jon evangelist 1 usa
2 patrick chief evangelist 1 usa
3 rebecca jr evangelist 2 canada
4 billy ceo 1 usa

7. Many to Many Relationships
id name title
1 jon evangelist
2 patrick chief evangelist
3 rebecca jr evangelist
4 billy ceo
id name
1 usa
2 canada
3 germany
4 japan
user country
1 1
1 2
2 1
3 2
4 1

8. Joining
select * from people
join people_country on people.id = people_country.user
join country on people_country.country = country.id;
id name title country name
1 jon evangelist 1 usa
1 jon evangelist 2 canada
2 patrick chief evangelist 1 usa
3 rebecca jr evangelist 2 canada
4 billy ceo 1 usa

9. A Fun, Real World Example

10. movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob
episode
tv_show
season
episode
actor_episode
episode
actor

11. Problems
• Every entity gets a table
• Lots of many to many tables
• Very rigid structure
• Going from one to many requires a
migration and new data model

12. How is this Solved with a Graph?

13. Vertex
• A vertex represents a "thing"
• For example: Movie, Person
JCVD
Time
cop

14. JCVD
Time
cop
Edge
• Edges are labeled relationships
• Edges have direction
ActedIn

15. Edges are always many to many
JCVD
Time
copActedIn
Blood
sport
ActedIn

16. Properties
• Similar to fields in a table
• Much more flexible
• Meta properties - properties on
properties
• Can be on an edge or a vertex
• Special property - a label
JCVD
status amazing
charm infinite
odds of rendezvous 99.6%
enemies decapitated 108747

17. Tinkerpop 3
• Cross database graph query
language
• API for graph
• Gremlin Server

18. Adding Data
1 graph = TitanFactory.build().set('storage.backend', 'inmemory').open()
2
3 g = graph.traversal()
4
5 jcvd = graph.addVertex(label, "actor",
6 "name", "jean claude van damme")
7
8 kick = graph.addVertex(label, "movie", "name", "Kickboxer",
9 "year", 1989)
10
11 blood = graph.addVertex(label, "movie", "name", "Bloodsport",
12 "year", 1988)
13
14 jcvd.addEdge("acted_in", kick)
15 jcvd.addEdge("acted_in", blood)

19. Summary
• We don't define tables
• We create vertices to represent real
world objects
• Relationships don't need to be
explicitly modeled
• Expressing complex relationships is
not hard
• Adding new edge types is easy

20. Traversing the Graph

21. Finding Vertices
single vertex g.V(4160)
matching a
property
g.V().has("name", "jean claude van damme")
range filtering
g.V().has("year",
between(1980, 1990))

22. Traversals
Bloodsport (1988)
Kickboxer (1989)
Timecop (1994)jcvd.out()

23. Traversals
Bloodsport (1988)
Kickboxer (1989)
Timecop (1994)
jcvd.outE()

24. Traversals
to other vertices g.V().has("name", "jean claude van damme").out()
to edges g.V().has("name", "jean claude van damme").outE()
filtering with
traversals
g.V().has("name", "jean claude van damme").out().
has("year", between(1980, 1990))

26. Use Cases

27. Recommendation Engines
• What do I like?
• What do others like?
• What do others listen to?
• Find correlations

28. Fraud & Anomaly Detection

29. Complex Taxonomies

30. Titan
• Open Source
• Works with Cassandra to scale to massive
graphs
• OLTP + OLAP graph queries via Spark
• Works with Solr or Elastic Search

31. DSE Graph
• Next generation of distributed graph
• Benefits of Titan but tightly integrated
with DataStax Enterprise

32. ©2013 DataStax Confidential. Do not distribute without consent. 32