An introduction of the Neo4j Graph database. Introduces the NOSQL space, the Graph Database concept, and Neo4j with examples.

1. 1

2. Introduction to
Neo4j
Michael Hunger
@mesirii
#neo4j
2

3. Introduction to
Neo4j
Michael Hunger
@mesirii
#neo4j
2

4. Neo4j
Introduction to
Neo4j
Michael Hunger
@mesirii
#neo4j
2

5. 2

6. (Michael) -[:WORKS_ON]-> (Neo4j)
console
Cypher community
graph
Community
ME
Server
Spring Cloud
3

7. 4

8. The Path Forward
4

9. The Path Forward
1.No .. NO .. NOSQL
4

10. The Path Forward
1.No .. NO .. NOSQL
2.Why graphs?
4

11. The Path Forward
1.No .. NO .. NOSQL
2.Why graphs?
3.What's a graph database?
4

12. The Path Forward
1.No .. NO .. NOSQL
2.Why graphs?
3.What's a graph database?
4.Some things about Neo4j.
4

13. The Path Forward
1.No .. NO .. NOSQL
2.Why graphs?
3.What's a graph database?
4.Some things about Neo4j.
5.How do people use Neo4j?
4

14. NOSQL
5

15. NOSQL Neo4j
5

16. Neo4j
5

17. What is NOSQL?
6

18. What is NOSQL?
6

19. What is NOSQL?
It’s not “No to SQL”
6

20. What is NOSQL?
It’s not “No to SQL”
It’s not “Never SQL”
6

21. What is NOSQL?
It’s not “No to SQL”
It’s not “Never SQL”
It’s “Not Only SQL”
6

22. What is NOSQL?
It’s not “No to SQL”
It’s not “Never SQL”
It’s “Not Only SQL”
NOSQL no-seek-wool n. Describes ongoing
trend where developers increasingly opt for
non-relational databases to help solve their
problems, in an effort to use the right tool for
the right job.
6

23. NOSQL Databases
Riak
Couch
KeyValue
Redis Document
NOSQL Mongo
Column
oriented
Graph Neo4j
Cassandra Relational
MySQL
Postgres 7

24. 8

25. Living in a NOSQL World
8

26. Living in a NOSQL World
Volume ~= Size
8

27. Living in a NOSQL World
Density ~= Complexity
Volume ~= Size
8

28. Living in a NOSQL World
Density ~= Complexity
Key-Value
Store
Volume ~= Size
8

29. Living in a NOSQL World
Density ~= Complexity
Column
Family
Key-Value
Store
Volume ~= Size
8

30. Living in a NOSQL World
Density ~= Complexity
Document
Databases
Column
Family
Key-Value
Store
Volume ~= Size
8

31. Living in a NOSQL World
RDBMS
Density ~= Complexity
Document
Databases
Column
Family
Key-Value
Store
Volume ~= Size
8

32. Living in a NOSQL World
Graph
Databases
RDBMS
Density ~= Complexity
Document
Databases
Column
Family
Key-Value
Store
Volume ~= Size
8

33. Living in a NOSQL World
Graph
Databases
RDBMS
Density ~= Complexity
Document
Databases
Column
Family
Key-Value
Store
90% Volume ~= Size
of
use
cases
8

34. Living in a NOSQL World
Graph
Databases
RDBMS
Density ~= Complexity
Document
Databases
Column
Family
Key-Value
Store
90% Volume ~= Size
of
use
cases
8

35. Trends in BigData & NOSQL
1. increasing data size (big data)
• “Every 2 -days we create as much information as we did up to
2003” Eric Schmidt
2. increasingly connected data (graph data)
• for example, text documents to html
3. semi-structured data
• individualization of data, with common sub-set
4. architecture - a facade over multiple services
• from monolithic to modular, distributed applications
9

36. 10

37. A Graph?
10

38. A Graph?
Yes, a graph
10

39. They are everywhere
Flight Patterns in Europe
11

40. Graphs Everywhere
๏ Relationships in
• Politics, Economics, History, Science,Transportation
๏ Biology, Chemistry, Physics, Sociology
• Body, Ecosphere, Reaction, Interactions
๏ Internet
• Hardware, Software, Interaction
๏ Social Networks
• Family, Friends
• Work, Communities
• Neighbours, Cities, Society 12

41. Good Relationships
๏ the world is rich, messy and related data
๏ relationships are as least as important as the things they connect
๏ Graphs = Whole > Σ parts
๏ complex interactions
๏ always changing, change of structures as well
๏ Graph: Relationships are part of the data
๏ RDBMS: Relationships part of the fixed schema
13

42. Questions and Answers
๏ Complex Questions
๏ Answers lie between the lines (things)
๏ Locality of the information
๏ Global searches / operations very expensive
๏ constant query time, regardless of data volume
14

43. Categories ?
๏ Categories == Classes, Trees ?
๏ What if more than one category fits?
๏ Tags
๏ Categories vi relationships like „IS_A“
๏ any number, easy change
๏ „virtual“ Relationships - Traversals
๏ Category dynamically derived from queries
15

44. Fowler & Christakis „Connected“
16

45. New York Times R&D „Cascade“
17

46. Deb Roy - MIT & Bluefin Labs
„Birth of a Word“ TED Talk
Researches Social Reactions
to (Media) Events
18

47. 19

48. Everyone is talking about graphs...
19

49. Everyone is talking about graphs...
Facebook Open Graph
19

50. Everyone is talking about graphs...
Facebook Open Graph
19

51. 20

52. Each of us has not only one graph, but many!
20

53. Graph DB 101
21

54. A graph database...
22

55. A graph database...
NO: not for charts & diagrams, or vector artwork
22

56. A graph database...
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
22

57. A graph database...
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
remember linked lists, trees?
22

58. A graph database...
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
remember linked lists, trees?
graphs are the general-purpose data structure
22

59. A graph database...
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
remember linked lists, trees?
graphs are the general-purpose data structure
“A relational database may tell you the average age of everyone
in this session,
but a graph database will tell you who is most likely to buy you a
beer.”
22

60. 23

61. You know relational
23

62. You know relational
23

63. You know relational
foo
23

64. You know relational
foo bar
23

65. You know relational
foo foo_bar bar
23

66. You know relational
foo foo_bar bar
23

67. You know relational
foo foo_bar bar
23

68. You know relational
foo foo_bar bar
23

69. You know relational
now consider relationships...
23

70. You know relational
now consider relationships...
23

71. You know relational
now consider relationships...
23

72. You know relational
now consider relationships...
23

73. You know relational
now consider relationships...
23

74. You know relational
now consider relationships...
23

75. 23

76. 24

77. We're talking about a
Property Graph
24

78. We're talking about a
Property Graph
Nodes
24

79. We're talking about a
Property Graph
Nodes
Relationships
24

80. We're talking about a
Property Graph
Em Joh
il a n
knows knows
Alli Tob Lar
Nodes
son ias knows s
knows
And And knows
knows rea rés
s
knows knows knows
Pet Miic
Mc knows Ian
er knows a
a
knows knows
De Mic
lia h ael
Relationships
Properties (each a key+value)
+ Indexes (for easy look-ups)
24

81. 24

82. 25

83. Looks different, fine. Who cares?
25

84. Looks different, fine. Who cares?
๏ a sample social graph
25

85. Looks different, fine. Who cares?
๏ a sample social graph
• with ~1,000 persons
25

86. Looks different, fine. Who cares?
๏ a sample social graph
• with ~1,000 persons
๏ average 50 friends per person
25

87. Looks different, fine. Who cares?
๏ a sample social graph
• with ~1,000 persons
๏ average 50 friends per person
๏ pathExists(a,b) limited to depth 4
25

88. Looks different, fine. Who cares?
๏ a sample social graph
• with ~1,000 persons
๏ average 50 friends per person
๏ pathExists(a,b) limited to depth 4
๏ caches warmed up to eliminate disk I/O
25

89. Looks different, fine. Who cares?
๏ a sample social graph
• with ~1,000 persons
๏ average 50 friends per person
๏ pathExists(a,b) limited to depth 4
๏ caches warmed up to eliminate disk I/O
# persons query time
Relational database 1,000 2000ms
Neo4j 1,000 2ms
Neo4j 1,000,000 2ms
25

90. Looks different, fine. Who cares?
๏ a sample social graph
• with ~1,000 persons
๏ average 50 friends per person
๏ pathExists(a,b) limited to depth 4
๏ caches warmed up to eliminate disk I/O
# persons query time
Relational database 1,000 2000ms
Neo4j 1,000 2ms
Neo4j 1,000,000 2ms
25

91. Looks different, fine. Who cares?
๏ a sample social graph
• with ~1,000 persons
๏ average 50 friends per person
๏ pathExists(a,b) limited to depth 4
๏ caches warmed up to eliminate disk I/O
# persons query time
Relational database 1,000 2000ms
Neo4j 1,000 2ms
Neo4j 1,000,000 2ms
25

92. 25

93. Graph Database: Pros & Cons
๏ Strengths
• Powerful data model, as general as RDBMS
• Fast, for connected data
• Easy to query
๏ Weaknesses:
• Sharding (though they can scale reasonably well)
‣also, stay tuned for developments here
• Requires conceptual shift
‣though graph-like thinking becomes addictive
26

94. 27

95. And, but, so how do you
query this "graph" database?
27

96. 28

97. Query a graph with a traversal
28

98. Query a graph with a traversal
28

99. Query a graph with a traversal
// lookup starting point in an index
start n=node:People(name = ‘Andreas’)
And
rea
s
28

100. Query a graph with a traversal
// lookup starting point in an index
then traverse to find results
start n=node:People(name = ‘Andreas’)
match (n)--()--(foaf) return foaf
And
rea
s
28

101. 28

102. Neo4j - the Graph Database
29

103. 30

104. (Neo4j) -[:IS_A]-> (Graph Database)
Lucene
Sharding 1 M/s
Master/
Index
LS
Slave
TRAVERSA
HIG
TES
H_A
VA RA
G
IL. TE
IN
PROVIDES ACID
Server RUN
S_A LI TX
S CE
NS
ED
_L
ES_T
Ruby IK
RU
JS E
MySQL
S
_A
NS
SC AL
O
Clojure
_O
NS
.net
RU
N Mongo
34bn
embedded Heroku
Nodes 31

105. Neo4j is a Graph Database
32

106. Neo4j is a Graph Database
๏ A Graph Database:
32

107. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
32

108. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
• perfect for complex, highly connected data
32

109. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
• perfect for complex, highly connected data
๏ A Graph Database:
32

110. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
32

111. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
• scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion
Properties
32

112. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
• scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion
Properties
• fast with more than 1M traversals / second
32

113. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
• scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion
Properties
• fast with more than 1M traversals / second
• Server with REST API, or Embeddable on the JVM
32

114. Neo4j is a Graph Database
๏ A Graph Database:
• a schema-free Property Graph
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
• scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion
Properties
• fast with more than 1M traversals / second
• Server with REST API, or Embeddable on the JVM
• higher-performance with High-Availability (read scaling) 32

115. Whiteboard --> Data
33

116. Whiteboard --> Data
Peter
Andreas
Allison
Emil
33

117. Whiteboard --> Data
Peter
Andreas knows
knows knows
Allison
knows
Emil
33

118. Whiteboard --> Data
Peter
Andreas knows
knows knows
Allison
knows
Emil
33

119. Whiteboard --> Data
Peter
Andreas knows
knows knows
Allison
knows
Emil
// Cypher query - friend of a friend
start n=node(0)
match (n)--()--(foaf)
return foaf
33

120. 34

121. Two Ways to Work with Neo4j
34

122. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
34

123. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
34

124. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
• Tomcat, Rails, Akka, etc.
34

125. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
• Tomcat, Rails, Akka, etc.
• great for testing
34

126. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
• Tomcat, Rails, Akka, etc.
• great for testing
34

127. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
• Tomcat, Rails, Akka, etc.
• great for testing
34

128. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
• Tomcat, Rails, Akka, etc.
• great for testing
34

129. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
• Tomcat, Rails, Akka, etc.
• great for testing
34

130. Two Ways to Work with Neo4j
๏ 1. Embeddable on JVM
• Java, JRuby, Scala...
• Tomcat, Rails, Akka, etc.
• great for testing
34

131. Two Ways to Work with Neo4j
34

132. Show me some code, please
GraphDatabaseService graphDb =
new EmbeddedGraphDatabase(“var/neo4j”);
Transaction tx = graphDb.beginTx();
try {
Node steve = graphDb.createNode();
Node michael = graphDb.createNode();
steve.setProperty(“name”, “Steve Vinoski”);
michael.setProperty(“name”, “Michael Hunger”);
Relationship presentedWith = steve.createRelationshipTo(
michael, PresentationTypes.PRESENTED_WITH);
presentedWith.setProperty(“date”, today);
tx.success();
} finally {
tx.finish();
}

133. Spring Data Neo4j
@NodeEntity
public class Movie {
@Indexed private String title;
@RelatedToVia(type = “ACTS_IN”, direction=INCOMING)
private Set<Role> cast;
private Director director;
}
@NodeEntity
public class Actor {
@RelatedTo(type = “ACTS_IN”)
private Set<Movies> movies;
}
@RelationshipEntity
public class Role {
@StartNode private Actor actor;
@EndNode private Movie movie;
private String roleName;
}

134. neo4j.rb
gem install neo4j
require 'rubygems'
require 'neo4j'
class Person
include Neo4j::NodeMixin
property :name, :age, :rank
index :name
has_n :friends
end
Neo4j::Transaction.run do
neo = Person.new :name=>'Neo', :age=>29
morpheus =
Person.new :name=>'Morpheus', :rank=>'Captain'
neo.friends << morpheus
end
neo.friends.each {|p|...}

135. Cypher Query Language
๏ Declarative query language
• Describe what you want, not how
• Based on pattern matching
๏ Examples:
START david=node:people(name=”David”) # index lookup
MATCH david-[:knows]-friends-[:knows]-new_friends
WHERE new_friends.age > 18
RETURN new_friends
START user=node(5, 15, 26, 28) # node IDs
MATCH user--friend
RETURN user, COUNT(friend), SUM(friend.money)
38

136. Create Graph with Cypher
CREATE
(steve {name: “Steve Vinoski”})
-[:PRESENTED_WITH {date:{day}}]->
(michael {name: “Michael Hunger”})

137. Two Ways to Work with Neo4j
40

138. Two Ways to Work with Neo4j
๏ 2. Server with REST API
40

139. Two Ways to Work with Neo4j
๏ 2. Server with REST API
• every language on the planet
40

140. Two Ways to Work with Neo4j
๏ 2. Server with REST API
• every language on the planet
• flexible deployment scenarios
40

141. Two Ways to Work with Neo4j
๏ 2. Server with REST API
• every language on the planet
• flexible deployment scenarios
• DIY server, or cloud managed
40

142. Two Ways to Work with Neo4j
๏ 2. Server with REST API
• every language on the planet
• flexible deployment scenarios
• DIY server, or cloud managed
40

143. Two Ways to Work with Neo4j
๏ 2. Server with REST API
• every language on the planet
• flexible deployment scenarios
• DIY server, or cloud managed
40

144. Two Ways to Work with Neo4j
๏ 2. Server with REST API
• every language on the planet
• flexible deployment scenarios
• DIY server, or cloud managed
40

145. Two Ways to Work with Neo4j
๏ 2. Server with REST API
• every language on the planet
• flexible deployment scenarios
• DIY server, or cloud managed
40

146. Two Ways to Work with Neo4j
40

147. Bindings
REST://
41

148. Two Ways to Work with Neo4j
42

149. Two Ways to Work with Neo4j
๏ Server capability == Embedded capability
42

150. Two Ways to Work with Neo4j
๏ Server capability == Embedded capability
• same scalability, transactionality, and availability
42

151. Two Ways to Work with Neo4j
๏ Server capability == Embedded capability
• same scalability, transactionality, and availability
42

152. Two Ways to Work with Neo4j
๏ Server capability == Embedded capability
• same scalability, transactionality, and availability
42

153. 42

154. How to get started?
43

155. How to get started?
๏ Documentation
43

156. How to get started?
๏ Documentation
• docs.neo4j.org - tutorials+reference
43

157. How to get started?
๏ Documentation
• docs.neo4j.org - tutorials+reference
• Neo4j in Action
43

158. How to get started?
๏ Documentation
• docs.neo4j.org - tutorials+reference
• Neo4j in Action
• Good Relationships
43

159. How to get started?
๏ Documentation
• docs.neo4j.org - tutorials+reference
• Neo4j in Action
• Good Relationships
๏ Get Neo4j
43

160. How to get started?
๏ Documentation
• docs.neo4j.org - tutorials+reference
• Neo4j in Action
• Good Relationships
๏ Get Neo4j
• http://neo4j.org/download
43

161. How to get started?
๏ Documentation
• docs.neo4j.org - tutorials+reference
• Neo4j in Action
• Good Relationships
๏ Get Neo4j
• http://neo4j.org/download
• http://addons.heroku.com/neo4j/
43

162. How to get started?
๏ Documentation
• docs.neo4j.org - tutorials+reference
• Neo4j in Action
• Good Relationships
๏ Get Neo4j
• http://neo4j.org/download
• http://addons.heroku.com/neo4j/
๏ Participate
• http://groups.google.com/group/neo4j
• http://neo4j.meetup.com
• a session like this one ;)
43

163. Thank you!
44