Graph databases are a type of NoSQL database that uses nodes and relationships to represent and store data. Nodes can have properties and be connected to other nodes via relationships. This allows for complex queries of connected data. Neo4j is an example of a graph database that uses these concepts to store and query data. Code examples are shown for how to programmatically create nodes and relationships in Neo4j and traverse the graph to find connected nodes.

2. No SQL?
Image credit: http://browsertoolkit.com/fault-tolerance.png

3. Neo4j the benefits of
graph databases
#neo4j
Peter Neubauer @peterneubauer
COO, Neo Technology peter@neotechnology.com

4. Why NoSQL 2009?
Trend 1: Size.
Trend 2: Connectivity.
Trend 3: Semi-structure.
Trend 4: Architecture.

5. NOSQL data models
Key-value stores
Data size
Bigtable clones
Document
databases
Graph databases
Data complexity

6. NOSQL data models
Key-value stores
Data size
Bigtable clones
Document
databases
Graph databases
(This is still b i l l i on s of
90% nodes & relationships)
of
use
cases
Data complexity

7. Graph DBs
& Neo4j intro

8. The Graph DB model: representation
Core abstractions:
name = “Emil”
Nodes
age = 29
sex = “yes”
Relationships between nodes
Properties on both 1 2
type = KNOWS
time = 4 years 3
type = car
vendor = “SAAB”
model = “95 Aero”

9. Example: The Matrix
name = “The Architect”
name = “Morpheus”
rank = “Captain”
name = “Thomas Anderson”
occupation = “Total badass” 42
age = 29
disclosure = public
KNOWS KNOWS CODED_BY
KNO
1 7 3 W S
KN
13
S
OW name = “Cypher”
KNOW
S last name = “Reagan”
name = “Agent Smith”
disclosure = secret version = 1.0b
age = 3 days age = 6 months language = C++
2
name = “Trinity”

10. Code (1): Building a node space
GraphDatabaseService graphDb = ... // Get factory
// Create Thomas 'Neo' Anderson
Node mrAnderson = graphDb.createNode();
mrAnderson.setProperty( "name", "Thomas Anderson" );
mrAnderson.setProperty( "age", 29 );
// Create Morpheus
Node morpheus = graphDb.createNode();
morpheus.setProperty( "name", "Morpheus" );
morpheus.setProperty( "rank", "Captain" );
morpheus.setProperty( "occupation", "Total bad ass" );
// Create a relationship representing that they know each other
mrAnderson.createRelationshipTo( morpheus, RelTypes.KNOWS );
// ...create Trinity, Cypher, Agent Smith, Architect similarly

11. Code (1): Building a node space
GraphDatabaseService graphDb = ... // Get factory
Transaction tx = graphdb.beginTx();
// Create Thomas 'Neo' Anderson
Node mrAnderson = graphDb.createNode();
mrAnderson.setProperty( "name", "Thomas Anderson" );
mrAnderson.setProperty( "age", 29 );
// Create Morpheus
Node morpheus = graphDb.createNode();
morpheus.setProperty( "name", "Morpheus" );
morpheus.setProperty( "rank", "Captain" );
morpheus.setProperty( "occupation", "Total bad ass" );
// Create a relationship representing that they know each other
mrAnderson.createRelationshipTo( morpheus, RelTypes.KNOWS );
// ...create Trinity, Cypher, Agent Smith, Architect similarly
tx.commit();

12. Code (1b): Def ning RelationshipTypes
i
// In package org.neo4j.graphdb
public interface RelationshipType
{
String name();
}
// In package org.yourdomain.yourapp
// Example on how to roll dynamic RelationshipTypes
RelationshipsType myRelType = DynamicRelatoinshipType.
withName(“myRelType”);
// Example on how to kick it, static-RelationshipType-like
enum MyStaticRelTypes implements RelationshipType
{
KNOWS,
WORKS_FOR,
}

13. Whiteboard friendly
owns
Björn Big Car
build drives
DayCare

15. The Graph DB model: traversal
Traverser framework for
high-performance traversing name = “Emil”
age = 31
across the node space sex = “yes”
1 2
type = KNOWS
time = 4 years 3
type = car
vendor = “SAAB”
model = “95 Aero”

16. Example: Mr Anderson’s friends
name = “The Architect”
name = “Morpheus”
rank = “Captain”
name = “Thomas Anderson”
occupation = “Total badass” 42
age = 29
disclosure = public
KNOWS KNOWS CODED_BY
KNO
1 7 3 W S
KN
13
S
OW name = “Cypher”
KNOW
S last name = “Reagan”
name = “Agent Smith”
disclosure = secret version = 1.0b
age = 3 days age = 6 months language = C++
2
name = “Trinity”

17. Code (2): Traversing a node space
// Instantiate a traverser that returns Mr Anderson's friends
Traverser friendsTraverser = mrAnderson.traverse(
Traverser.Order.BREADTH_FIRST,
StopEvaluator.END_OF_GRAPH,
ReturnableEvaluator.ALL_BUT_START_NODE,
RelTypes.KNOWS,
Direction.OUTGOING );
// Traverse the node space and print out the result
System.out.println( "Mr Anderson's friends:" );
for ( Node friend : friendsTraverser )
{
System.out.printf( "At depth %d => %s%n",
friendsTraverser.currentPosition().getDepth(),
friend.getProperty( "name" ) );
}

18. name = “The Architect”
name = “Morpheus”
rank = “Captain”
name = “Thomas Anderson”
occupation = “Total badass” 42
age = 29
disclosure = public
KNOWS KNOWS CODED_BY
KNO
1 7 3 W S
KN 13
S
OW name = “Cypher”
KNOW
S last name = “Reagan”
name = “Agent Smith”
disclosure = secret version = 1.0b
age = 3 days age = 6 months language = C++
2
name = “Trinity”
$ bin/start-neo-example
Mr Anderson's friends:
At depth 1 => Morpheus
friendsTraverser = mrAnderson.traverse(
Traverser.Order.BREADTH_FIRST, At depth 1 => Trinity
StopEvaluator.END_OF_GRAPH, At depth 2 => Cypher
ReturnableEvaluator.ALL_BUT_START_NODE,
RelTypes.KNOWS,
At depth 3 => Agent Smith
Direction.OUTGOING ); $

19. Example: Friends in love?
name = “The Architect”
name = “Morpheus”
rank = “Captain”
name = “Thomas Anderson”
occupation = “Total badass” 42
age = 29
disclosure = public
KNOWS KNOWS CODED_BY
KNO
1 7 3 W S
KN 13
S
KNOW
OW name = “Cypher”
S last name = “Reagan”
name = “Agent Smith”
LO disclosure = secret version = 1.0b
VE age = 6 months language = C++
S
2
name = “Trinity”

20. Code (3a): Custom traverser
// Create a traverser that returns all “friends in love”
Traverser loveTraverser = mrAnderson.traverse(
Traverser.Order.BREADTH_FIRST,
StopEvaluator.END_OF_GRAPH,
new ReturnableEvaluator()
{
public boolean isReturnableNode( TraversalPosition pos )
{
return pos.currentNode().hasRelationship(
RelTypes.LOVES, Direction.OUTGOING );
}
},
RelTypes.KNOWS,
Direction.OUTGOING );

21. Code (3a): Custom traverser
// Traverse the node space and print out the result
System.out.println( "Who’s a lover?" );
for ( Node person : loveTraverser )
{
System.out.printf( "At depth %d => %s%n",
loveTraverser.currentPosition().getDepth(),
person.getProperty( "name" ) );
}

22. name = “The Architect”
name = “Morpheus”
rank = “Captain”
name = “Thomas Anderson”
occupation = “Total badass” 42
age = 29
disclosure = public
KNOWS KNOWS KNO CODED_BY
1 7 3 W S
KN 13
S
name = “Cypher”
KNOW
OW
S last name = “Reagan”
name = “Agent Smith”
LO disclosure = secret version = 1.0b
VE age = 6 months language = C++
S
2
name = “Trinity”
$ bin/start-neo-example
new ReturnableEvaluator()
Who’s a lover?
{
public boolean isReturnableNode(
TraversalPosition pos)
At depth 1 => Trinity
{ $
return pos.currentNode().
hasRelationship( RelTypes.LOVES,
Direction.OUTGOING );
}
},

23. Domain layer frameworks
Spring (SpringDM, Grails, Griffon etc), Sling, Karaf
Rails, Django
Qi4j (www.qi4j.org)
Framework for doing DDD in pure Java5
Def nes Entities / Associations / Properties
i
Sound familiar? Nodes / Rel’s / Properties!
Neo4j is an “EntityStore” backend
Jo4neo (http://code.google.com/p/jo4neo)
Annotation driven
Weaves Neo4j-backed persistence into domain objects
at runtime

24. Code (4): Groovy/Grails
import grails.plugins.neo4j.*
@Neo4jEntity
class Author {
@Neo4jIndex
String name
}

25. Result: Groovy/Grails

26. Code (5): JRuby
require "rubygems"
require "neo4j"
class Person
include Neo4j::NodeMixin
property :name, :salary, :age, :country
has_n :friends
index :name, :salary, :age, :country
end
neo = Person.new :name => 'Neo'
morpheus = Person.new :name => 'Morpheus'
neo.friends << morpheus

27. Neo4j system characteristics
Disk-based
Native graph storage engine with custom binary on-disk
format
Robust
6+ years in 24/7 production
Fast
1-2 million nodes per second
Transactional
JTA/JTS, XA, 2PC, Tx recovery, deadlock detection,
MVCC, etc
Scales up
Many b i l l i on s of nodes/rels/props on single JVM

28. What do I use this stuff for?
Modeling, productivity, stability
Bindings, migrations, robustness
Fast graph algos on critical path
Shortest path (Spatial, GIS, Social Web, Routing)
Fast traversals
Hierarchies, Networks (Automation, ERP, Intelligence,
Finance)
Recommendation engines (social, bioinfo)
Multidimensional indexes (BI, Analytics, versioning)
Activity Streams, Write Load -> Read Load
RDF, SAIL, SemWeb

29. Query languages
SPARQL – “SQL for linked data”
Ex: ”SELECT ?person WHERE {
?person neo4j:KNOWS ?friend .
?friend neo4j:KNOWS ?foe .
?foe neo4j:name “Larry Ellison” .
}”
Gremlin – “perl for graphs”
Ex: ”./outE[@label='KNOWS']/inV[@age > 30]/@name”

30. The Neo4j ecosystem
Neo4j-kernel is an embedded database
Tiny teeny lil jar f le
i
Component ecosystem
Index
REST
meta-model
graph-matching
remote-graphdb
sparql-engine
...
See http://components.neo4j.org

31. Example: Neo4j-RDF
Neo4j-RDF triple/quad store
REST
OWL SPARQL
RDF
Metamodel Graph
match
Neo4j

32. Language bindings
Neo4j.py – bindings for Jython and CPython
http://components.neo4j.org/neo4j.py
Neo4jrb – bindings for JRuby (incl RESTful API)
http://wiki.neo4j.org/content/Ruby
Clojure
http://wiki.neo4j.org/content/Clojure
Scala (incl RESTful API)
http://wiki.neo4j.org/content/Scala
PHP
http://wiki.neo4j.org/content/PHP
Erlang, Compojure ...

33. Scale out – replication
Rolling out Neo4j HA... soon :)
Master-slave replication, 1st configuration
MySQL style... ish
Except all instances can write, synchronously between
writing slave & master (strong consistency)
Updates are asynchronously propagated to the other
slaves (eventual consistency)
This can handle billions of entities...
… but not 100B

34. Scale out – partitioning
Sharding possible today
… but you have to do manual work
… just as with MySQL
Great option: shard on top of resilient, scalable OSS app
server , see: www.codecauldron.org
Transparent partitioning? Neo4j 2.0
100B? Easy to say. Sliiiiightly harder to do.
Fundamentals: BASE & eventual consistency
Generic clustering algorithm as base case, but give lots of
knobs for developers
Cross-shard edges->Node migration/replication->Partition
schemes

35. Conclusion
Graphs && Neo4j => teh awesome!
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 first one is free!
Download
http://neo4j.org
Feedback
http://lists.neo4j.org

36. Looking ahead: polyglot persistence
SQL && N oSQL

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

38. http://neotechnology.com