1. Graph databases are suited for modeling connected data and querying relationships. 2. A graph database stores data in nodes, edges and properties and allows for flexible querying of relationships. 3. Examples of using graph databases include social networks, knowledge graphs, and fraud detection by analyzing entities and their connections.

1. Introduction to
Graph Databases
Andreas Kollegger
@akollegger
#neo4j
1

2. Introduction to
Graph Databases
Andreas Kollegger
@akollegger
#neo4j
1

3. Introduction to
Graph Databases
Andreas Kollegger
@akollegger
#neo4j
1

4. 1

5. 2

6. The Path Forward
2

7. The Path Forward
1.Why graphs?
2

8. The Path Forward
1.Why graphs?
2.What's a graph database?
2

9. The Path Forward
1.Why graphs?
2.What's a graph database?
3.How do people use Neo4j?
2

10. 3

11. Why graphs, why now?
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1.Big Data is the trend
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13. Why graphs, why now?
1.Big Data is the trend
2.NOSQL is the answer
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1.Big Data is the trend
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16. 3

17. 4

18. A Graph?
4

19. A Graph?
Yes, a graph
4

20. A graph database...
5

21. A graph database...
๏ no: not for charts & diagrams, or vector artwork
5

22. A graph database...
๏ no: not for charts & diagrams, or vector artwork
๏ yes: for storing data that is structured as a graph
5

23. 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?
5

24. 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
5

25. 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 traditional relational database may tell you the
average age of everyone in this conference,
but a graph database will tell you who is most
likely to buy you a beer (spoiler: it's me).”
5

26. 6

27. Some well-known named graphs
6

28. Some well-known named graphs
diamond
6

29. Some well-known named graphs
diamond butterfly
6

30. Some well-known named graphs
diamond butterfly star
6

31. Some well-known named graphs
diamond butterfly star bull
6

32. Some well-known named graphs
diamond butterfly star bull
franklin
6

33. Some well-known named graphs
diamond butterfly star bull
franklin robertson
6

34. Some well-known named graphs
diamond butterfly star bull
franklin robertson horton
6

35. Some well-known named graphs
diamond butterfly star bull
franklin robertson horton hall-janko
6

36. 6

37. 7

38. Most well-known named graph
7

39. Most well-known named graph
7

40. Graph DB 101
8

41. 9

42. You know relational
9

43. You know relational
9

44. You know relational
foo
9

45. You know relational
foo bar
9

46. You know relational
foo foo_bar bar
9

47. You know relational
foo foo_bar bar
9

48. You know relational
foo foo_bar bar
9

49. You know relational
foo foo_bar bar
9

50. You know relational
now consider relationships...
9

51. You know relational
now consider relationships...
9

52. You know relational
now consider relationships...
9

53. You know relational
now consider relationships...
9

54. You know relational
now consider relationships...
9

55. You know relational
now consider relationships...
9

56. 9

57. 10

58. We're talking about a
Property Graph
10

59. We're talking about a
Property Graph
Nodes
10

60. We're talking about a
Property Graph
Nodes
Relationships
10

61. 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)
10

62. 10

63. 11

64. Looks different, fine. Who cares?
11

65. Looks different, fine. Who cares?
๏ a sample social graph
11

66. Looks different, fine. Who cares?
๏ a sample social graph
•with ~1,000 persons
11

67. Looks different, fine. Who cares?
๏ a sample social graph
•with ~1,000 persons
๏ average 50 friends per person
11

68. 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
11

69. 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
11

70. 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
11

71. 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
11

72. 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
11

73. 11

74. 12

75. And, but, so how do you
query this "graph" database?
12

76. 13

77. Cypher - a graph query language
๏ a pattern-matching query language
๏ declarative grammar with clauses (like SQL)
๏ aggregation, ordering, limits
๏ create, read, update, delete
13

78. Cypher - a graph query language
๏ a pattern-matching query language
๏ declarative grammar with clauses (like SQL)
๏ aggregation, ordering, limits
๏ create, read, update, delete
// get node with id 0
start a=node(0) return a
// traverse from node 1
start a=node(1) match (a)-->(b) return b
// return friends of friends
start a=node(1) match (a)--()--(c) return c
13

79. Neo4j - the Graph Database
14

80. With love, from Sweden
๏ 2001 - a Swedish media asset project
•CTO Emil prototyped a proper graph interface
•first SQL-backed, then revised down to bare metal
•(just like Amazon->Dynamo, Facebook->Cassandra,
Google-> Big Table, Sweden->Neo4j)
๏ 2003 Neo4j went into 24/7 production
๏ 2006-2007 - Neo4j was spun off as an open source project
๏ 2009 seed funding for the company
๏ 2010 Neo4j Server was created (previously only an embedded DB)
๏ 2011 Series-A Funding, Top-Tier customers
(gratuitous name dropping)
15

81. Neo4j is a Graph Database
16

82. Neo4j is a Graph Database
๏ A Graph Database:
16

83. Neo4j is a Graph Database
๏ A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
16

84. Neo4j is a Graph Database
๏ A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
• perfect for complex, highly connected data
16

85. Neo4j is a Graph Database
๏ A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
• perfect for complex, highly connected data
๏ A Graph Database:
16

86. Neo4j is a Graph Database
๏ A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
16

87. Neo4j is a Graph Database
๏ A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
• scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion
Properties
16

88. Neo4j is a Graph Database
๏ A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
• scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion
Properties
• Server with REST API, or Embeddable on the JVM
16

89. Neo4j is a Graph Database
๏ A Graph Database:
• a Property Graph with Nodes, Relationships
and Properties on both
• perfect for complex, highly connected data
๏ A Graph Database:
• reliable with real ACID Transactions
• scalable: 32 Billion Nodes, 32 Billion Relationships, 64 Billion
Properties
• Server with REST API, or Embeddable on the JVM
• high-performance with High-Availability (read scaling) 16

90. the Real World
17

91. 18

92. Real World Use Cases:
18

93. Real World Use Cases:
• [A] Mmm Pancakes
18

94. Real World Use Cases:
•[A] Mmm Pancakes
• [B] ACL from Hell
18

95. Real World Use Cases:
•[A] Mmm Pancakes
• [B] ACL from Hell
• [C] Master of your Domain
18

97. [A] Mmm Pancakes

98. [A] Mmm Pancakes

100. [A] Mozilla Pancake
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

101. [A] Mozilla Pancake
• Experimental cloud-based browser
• Built to improve how users Discover,
Collect, Share & Organize things on
the web
• Goal: help users better access &
curate information on the net, on
any device
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

102. This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

103. Why Neo4J?
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

104. Why Neo4J?
• The internet is a network of pages
connected to each other. What
better way to model that than in
graphs?
• No time lost fighting with less
expressive datastores
• Easy to implement experimental
features
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

105. Cute meta + data
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

106. Cute meta + data
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

107. Neo4J Co-Existence
• Node uuids as refs in external
ElasticSearch also in internal Lucene
• Custom search ranking for user
history based on node relationship
data
• MySQL for user data, Redis for
metrics
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

108. Mozilla Pancake
Available on BitBucket:
https://bitbucket.org/
mozillapancake/pancake
Questions?
Olivier Yiptong:
oyiptong@mozilla.com
This Material is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the
MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/

110. [B] ACL from Hell
One of the top 10 telcos worldwide

111. Background
Telenor calculated in 2010 that it´s self-service web solution for
companies, MinBedrift, would not scale with the projected
customer and subscription growth beyond 2012.
Limit
th
G row
ted
jec
Pro
2010
2015

112. [B] Telenor Background
Background
Telenor calculated in 2010 that it´s self-service web solution for
companies, MinBedrift, would not scale with the projected
customer and subscription growth beyond 2012.
Limit
th
G row
ted
jec
Pro
2010
2015

113. [B] Telenor Background
• MinBedrif, a self service web
solution for companies
Background
• 2010 - calculated that it would not
Telenor calculated in 2010 that it´s self-service web solution for
scale with projected growth
companies, MinBedrift, would not scale with the projected
customer and subscription growth beyond 2012.
Limit
th
G row
ted
jec
Pro
2010
2015

114. Business Case
The business case is built on the negative consequence of NOT
addressing the problem.
Loss of customers (income)
Reduced sales transactions (income)
Increased manual support (expenses)
Other

115. 28

116. Current ACL Service
28

117. Current ACL Service
๏ Stored procedure in DB calculating all access
•cached results for up to 24 hours
•minutes to calculate for large customers
•extremely complex to understand (1500 lines)
•depends on temporary tables
•joins across multiple tables
28

118. Example Access Authorization
User
Access may be given directly or by inheritance Customer
Account
U Subscription
U Inherit = true
Inherit =
false
In
C
he
rit
=
C C C
tru
e
A A A A A
S S S S S S S S S S

119. 30

120. ACL With Neo4j
30

121. ACL With Neo4j
๏ Faster than current solution
๏ Simpler to understand the logic
๏ Avoid large temporary tables
๏ Tailored for service (resource authorization)
30

123. [C] Master of your Domain

124. [C] MDM within Cisco
master data management, sales compensation management, online customer support
Description Benefits
Real-time conflict detection in sales compensation management. Performance : “Minutes to Milliseconds”
Business-critical “P1” system. Neo4j allows Cisco to model complex Outperforms Oracle RAC, serving complex queries in real time
algorithms, which still maintaining high performance over a large Flexibility
dataset. Allows for Cisco to model interconnected data and complex queries with
ease
Background
Robustness
Neo4j replaces Oracle RAC, which was not performant enough for the
With 9+ years of production experience, Neo4j brings a solid product.
use case.
Architecture
3-node Enterprise cluster with mirrored
disaster recovery cluster
Dedicated hardware in own datacenter
Embedded in custom webapp
Sizing
35 million nodes
50 million relationships
600 million properties

125. 33

126. Really, once you start
thinking in graphs
it's hard to stop
Recommendations MDM
Business intelligence
Geospatial
catalogs Systems
access control Social computingManagement
your brain
Biotechnology
routing genealogy
linguistics
Making Sense of all that
compensation
data market vectors
33

127. Really, once you start
thinking in graphs
it's hard to stop
What will you MDM
Recommendations build?
Business intelligence
Geospatial
catalogs Systems
access control Social computingManagement
your brain
Biotechnology
routing genealogy
linguistics
Making Sense of all that
compensation
data market vectors
33

128. 34

129. Neo4j
and, Thanks :)
34