This document discusses graph databases and provides examples of how the Neo4j graph database can be used. It shows how Neo4j supports social, spatial, financial and other types of connected data. It also summarizes Neo4j's REST API, support for object-oriented programming, routing algorithms, multiple indexes, recommendation systems, and other use cases. The document advocates for graph databases for any problem involving multiple relationships and connections between entities.

1. Neo4j
And some graph problems
#neo4j
Peter Neubauer @peterneubauer
COO, Neo Technology peter@neotechnology.com

2. Any sufficiently advanced technology is
indistinguishable from magic.
Arthur C. Clarke
@kymerawand

3. First, a recap: NOSQL data models
Key-value stores
Data size
Column Family
Document
databases
Graph databases
We are here
Data complexity

4. What makes a good DB?
Separate Logic and Data
Support Ad-Hoc queries
Be persistent
Scale good enough

5. The property graph model
Core abstractions:
name = “Emil”
Nodes
age = 29
sex = “yes”
Relationships between nodes
Properties on both 1 2
Traversal framework
type = KNOWS
High performance time = 4 years 3
queries on connected
data sets type = car
vendor = “SAAB”
Other bindings model = “95 Aero”
Langs, REST, Gremlin, RDF

6. Social data (customer: brand-name social network)
name = “Marcy Runkle”
name = “Hank”
last_name = “Moody”
name = “Mike”
age = 42 42
age = 29 S
W
NO
disclosure = public
K
KNOWS KNOWS KNO
1 7 3 W S
KN
13
S
OW name = “Charlie”
KNOW
S last_name = “Runkle”
name = “Dani”
last_name = “California”
age = 3 days age = 27
2
name = “Karen”

7. Just a social graph?

8. Spatial data (customer: large telecom company)
name = ...
name = “The Tavern”
lat = 1295238237
name = “Omni Hotel”
long = 234823492 42
lat = 3492848
long = 283823423 length = 7 miles AD
RO
ROAD ROAD ROO
1 7 3 OAD
RO
13
AD name = ...
ROAD
lat, long = ...
name = “Swedland”
lat = 23410349
length = 3 miles long = 2342348852
2
name = ...

9. Social? Spatial? … Social AND spatial!

10. Social AND spatial data (customer: LBS)
name = “Peter”
name = “The Tavern”
lat = 1295238237
name = “Omni Hotel”
long = 234823492 42
lat = 3492848 S
W
NO
long = 283823423 weight = 10
K
ROAD LIKES SIBL
1 7 3 IN G
RO
13
AD name = “Emil”
ROAD
beer_qual = expert
name = “Maria”
age = 30
length = 3 miles beer_qual = non-existant
2
name = ...

11. Financial data (customer: international bank)
name = ...
name = “The Tavern”
lat = 1295238237
long = 234823492 42
name = “Mr Godfather” AW
karma = veeeery-low
HDR
cash = more-than-you amount = $1000
IT
W
OWNS TRANSFER WIT
1 7 3 HDR
AW
13
S FE R
DE name = “Emil”
P OS cash = always-too-li'l
IT TRAN
title = “ATM @ Wall St”
id = 230918484233
amount = $1000 cash_left = 384204
2
name = ...

12. Use Cases
REST in Neo4j
The graph as an OODB – Roo, Grails
Jruby, Jo4neo
Deep graph algos – Routing with A*
Multiple indexes in the graph – GIS
Recommendation systems – Gremlin
and LinkedData

13. REST in Neo4j
Build on self describing JSON
Indexing
Querying
Bindings to PHP, C#, JS
OpenGraph API
Jruby, Scala

14. The Graph for objects
Keep data clean!
Annotation based
Grails
Jo4neo
Roo
JPA
Mixin based
Django
JRuby
C# (REST)
PHP (REST)

15. The Graph as an OODB

16. Routing with Neo4j and A*

17. Routing with Neo4j and A*

18. Multiple indexes - GIS
QuadTree (2D)
Hierarchy
Time (1D)
On-demand index

19. Multiple indexes - GIS

20. Multiple indexes - GIS

21. Recommendation - Gremlin
gremlin.tinkerpop.com
XPath based “Perl for Graphs”
Property Graph Model
Neo4j, RDF-SAIL, Tinkergraph, MongoDB, FS
LinkedData SAIL

22. Recommendation - Gremlin

23. Recommendation - Gremlin

24. Other use cases
Activity streams
Process automation
Life-filtering of event streams
Web-of-Things data f ows
l
Object oriented reporting
Jasper Reports
ReportAnywhere
Scripting

25. Why graph databases?
“For anything with multiple
relationships, multiple
connections N e o4 j
ab s ol u t e l y R OCK S!”
http://bit.ly/dys4nx
A web where the default is social →
an explosion of graph-y data
“Just 3 years ago, the data sets our product
handled were very simple. But the past 9-12
months have seen an e x p l os i on of the number
of relationships between things.”
Manager
of an
enterprise
product
for the
Fortune
5000 Werner Vogels
CTO, Amazon

26. How ego are you? (aka other impls?)
Franz’ Al l e g roGrap h (http://agraph.franz.com)
Proprietary, Lisp, RDF-oriented but real graphdb
Sones g rap h DB (http://sones.com)
Proprietary, .NET, in beta
Twitter's Fl oc k DB (http://github.com/twitter/flockdb)
Twitter's (graph) database for large and shallow graphs
Google P re g e l (http://bit.ly/dP9IP)
We are oh-so-secret
Objectivity's I n fi n i te Gra p h (http://infinitegraph.com)
New, closed OODB with Graph Layer on top

27. Why Neo4j?
Most widely deployed graph db in the world
Robust: 24/7 production since 2003
Mature: lots of production deployments
Community: ecosystem of tools, bindings, frameworks
Available NOW under AGPLv3 / commercial license
AGPLv3: “if you’re open source, we’re open source”
If you have proprietary software? Must buy a commercial
license
But the f rst one is free! For ALL use-cases.
i
Download
http://neo4j.org
Feedback
http://lists.neo4j.org

28. Th e N e o4 j te a m
i s h i ri n g !
Do you see the Matrix?
Apply now.

29. Questions?
Image credit: lost again! Sorry :(

30. http://neotechnology.com