The document discusses data modeling with Neo4j, focusing on graph structures and their components: nodes, relationships, properties, and labels. It emphasizes the advantages of graph databases in managing complex data and highlights the process of designing a data model based on user queries and application goals. Additionally, it contrasts graph models with traditional relational models and discusses best practices for graph data design.

1. Data Modeling with
Neo4j
1
Michael Hunger, Neo Technology
@neo4j | michael@neo4j.org
Thanks to: Ian Robinson, Mark Needham,Alistair Jones
Samstag, 31. August 13

2. Please ask questions
in the chat
I‘ll answer at the end.
Follow up email with missing answers,
video and slides.
2
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3. (Michael) -[:WORKS_ON]-> (Neo4j)
ME
Spring Cloud
Community
Cypher
console
community
graph
Server
3
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4. This Webinar
๏Graphs are everywhere
๏Graph Model Building Blocks
๏(NOSQL) Data Models
๏Designing a Data Model
๏Embrace the Paradigm
4
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5. Addressing Data Complexity
With Graphs
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6. complexity = f(size, semi-structure, connectedness)
Data Complexity
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7. Are Graphs Everywhere ?
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8. Social Network
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9. (Network) Impact Analysis
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10. Route Finding
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11. Recommendations
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12. Logistics
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13. Access Control
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14. Fraud Analysis
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15. Securities and Debt
Image: orgnet.com
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16. Graphs Are Everywhere !!
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17. Graph Model Building
Blocks
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18. Property Graph Data Model
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19. Four Building Blocks
1.Nodes
2.Relationships
3.Properties
4.Labels
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20. Nodes
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21. Nodes
๏ Used to represent entities in your domain
๏ Can contain properties
• Used to represent entity attributes and/or metadata
(e.g. timestamps, version)
• Key-value pairs
‣Java primitives
‣Arrays
‣null is not a valid value
• Every node can have different properties
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22. Relationships
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23. Relationships
๏ Every relationship has a name and a direction
• Add structure to the graph
• Provide semantic context for nodes
๏ Can contain properties
• Used to represent quality or weight of relationship,
or metadata
๏ Every relationship must have a start node and end node
• No dangling relationships
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24. Relationships (continued)
Nodes can have
more than one
relationship
Self relationships are
allowed
Nodes can be connected by
more than one relationship
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25. Variable Structure
๏ Relationships are defined with regard to node
instances, not classes of nodes
• Different nodes can be connected in different ways
• Allows for structural variation in the domain
• Contrast with relational schemas, where foreign key
relationships apply to all rows in a table
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26. Labels
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27. Labels
๏ Every node can have zero or more labels attached
๏ Used to represent roles (e.g. user, product, company)
• Group nodes
• Allow us to associate indexes and constraints with
groups of nodes
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28. Four Building Blocks
๏ Nodes
• Entities
๏ Relationships
• Connect entities and structure domain
๏ Properties
• Attributes and metadata
๏ Labels
• Group nodes by role
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29. Aggregate vs.
Connected Data-Model
24
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30. NOSQL
Relational
Graph
Document
KeyValue
Riak
Column
oriented
25
Redis
Cassandra
Mongo
Couch
Neo4j
MySQL
Postgres
NOSQL Databases
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31. 26
“There is a significant downside - the whole approach works
really well when data access is aligned with the aggregates, but
what if you want to look at the data in a different way? Order
entry naturally stores orders as aggregates, but analyzing
product sales cuts across the aggregate structure. The
advantage of not using an aggregate structure in the database
is that it allows you to slice and dice your data different ways
for different audiences.
This is why aggregate-oriented stores talk so much about map-
reduce.”
Martin Fowler
Aggregate Oriented Model
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32. 27
The connected data model is based on fine grained elements
that are richly connected, the emphasis is on extracting many
dimensions and attributes as elements.
Connections are cheap and can be used not only for the
domain-level relationships but also for additional structures
that allow efficient access for different use-cases. The fine
grained model requires a external scope for mutating
operations that ensures Atomicity, Consistency, Isolation and
Durability - ACID also known as Transactions.
Michael Hunger
Connected Data Model
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33. 28
Relational vs. Graph
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34. You know relational
28
Relational vs. Graph
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35. You know relational
28
Relational vs. Graph
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36. You know relational
28
foo
Relational vs. Graph
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37. You know relational
28
foo bar
Relational vs. Graph
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38. You know relational
28
foo barfoo_bar
Relational vs. Graph
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39. You know relational
28
foo barfoo_bar
Relational vs. Graph
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40. You know relational
28
foo barfoo_bar
Relational vs. Graph
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41. You know relational
28
foo barfoo_bar
Relational vs. Graph
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42. You know relational
28
... now consider a graph
Relational vs. Graph
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43. You know relational
28
... now consider a graph
Relational vs. Graph
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44. You know relational
28
... now consider a graph
Relational vs. Graph
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45. You know relational
28
... now consider a graph
Relational vs. Graph
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46. You know relational
28
... now consider a graph
Relational vs. Graph
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47. You know relational
28
... now consider a graph
Relational vs. Graph
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48. 28
Relational vs. Graph
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49. Designing a Graph Model
an Example
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50. Models
Images:
en.wikipedia.org
Purposeful abstraction of a domain
designed to satisfy particular application/
end-user goals
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51. Design for Queryability
Model
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52. Query
Design for Queryability
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53. Design for Queryability
Model
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54. Method
1. Identify application/end-user goals
2. Figure out what questions to ask of the domain
3. Identify entities in each question
4. Identify relationships between entities in each
question
5. Convert entities and relationships to paths
These become the basis of the data model
6. Express questions as graph patterns
These become the basis for queries
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55. From User Story to Model and Query
1.
User story
4.
Paths
3.
Entities and
relationships
?2.
Questions we want
to ask
5.
Data model
6.
Query
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56. 1. Application/End-User Goals
As an employee
I want to know who in thecompany has similar skills to meSo that we can exchangeknowledge
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57. 2. Questions To Ask of the Domain
Which people, who work for the same
company as me, have similar skills to me?
As an employee
I want to know who in thecompany has similar skills tome
So that we can exchangeknowledge
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58. Which people, who work for the same
company as me, have similar skills to me?
Person
Company
Skill
3. Identify Entities
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59. Which people, who work for the same
company as me, have similar skills to me?
Person WORKS_FOR Company
Person HAS_SKILL Skill
4. Identify Relationships Between
Entities
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60. 5. Convert to Cypher Paths
Person WORKS_FOR Company
Person HAS_SKILL Skill
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61. 5. Convert to Cypher Paths
Person WORKS_FOR Company
Person HAS_SKILL Skill
Relationship
Label
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62. 5. Convert to Cypher Paths
Person WORKS_FOR Company
Person HAS_SKILL Skill
Relationship
Label
(:Person)-[:WORKS_FOR]->(:Company),
(:Person)-[:HAS_SKILL]->(:Skill)
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63. Consolidate Paths
(:Person)-[:WORKS_FOR]->(:Company),
(:Person)-[:HAS_SKILL]->(:Skill)
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64. Consolidate Paths
(:Person)-[:WORKS_FOR]->(:Company),
(:Person)-[:HAS_SKILL]->(:Skill)
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)
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65. Consolidate Paths
(:Person)-[:WORKS_FOR]->(:Company),
(:Person)-[:HAS_SKILL]->(:Skill)
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)
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66. Candidate Data Model
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)
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67. 6. Express Question as Graph Pattern
Which people, who work for the same
company as me, have similar skills to me?
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68. Cypher Query
Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
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69. Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
Graph Pattern
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70. Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
Anchor Pattern in Graph
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71. Which people, who work for the same
company as me, have similar skills to me?
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
Create Projection of Results
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72. First Match
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73. Second Match
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74. Third Match
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75. Running the Query
+-----------------------------------+
| name | score | skills |
+-----------------------------------+
| "Lucy" | 2 | ["Java","Neo4j"] |
| "Bill" | 1 | ["Neo4j"] |
+-----------------------------------+
2 rows
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76. From User Story to Model and Query
MATCH (company)<-[:WORKS_FOR]-(me:Person)-[:HAS_SKILL]->(skill),
(company)<-[:WORKS_FOR]-(colleague)-[:HAS_SKILL]->(skill)
WHERE me.name = {name}
RETURN colleague.name AS name,
count(skill) AS score,
collect(skill.name) AS skills
ORDER BY score DESC
As an employee
I want to know who in thecompany has similar skills tome
So that we can exchangeknowledge
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)
Person WORKS_FOR Company
Person HAS_SKILL Skill
?Which people, who work for the
same company as me, have similar
skills to me?
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77. Embrace the
paradigm
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78. Use the building blocks
๏ Nodes
๏ Relationships
๏ Properties name: value
RELATIONSHIP_NAME
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79. Anti-pattern: rich properties
name: “Canada”
languages_spoken: “[ ‘English’, ‘French’ ]”
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80. Normalize Nodes
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81. Anti-Pattern: Node represents multiple
concepts
name
age
position
company
department
project
skills
Person
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82. HAS_SKILL
Normalize into separate concepts
name
age
Person
name
number_of_employees
Company
WORKS_FOR
Skill
name
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83. Challenge: Property or Relationship?
๏ Can every property be replaced by a relationship?
• Hint: triple stores. Are they easy to use?
๏ Should every entity with the same property values be
connected?
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84. Object Mapping
๏ Similar to how you would map objects to a relational
database, using an ORM such as Hibernate
๏ Generally simpler and easier to reason about
๏ Examples
• Java: Spring Data Neo4j
• Ruby: Active Model
๏ Why Map?
• Do you use mapping because you are scared of SQL?
• Following DDD, could you write your repositories
directly against the graph API?
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85. CONNECT for fast access
In-Graph Indices
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86. Relationships for querying
๏ like in other databases
• same structure for different use-cases (OLTP and
OLAP) doesn‘t work
• graph allows: add more structures
๏ Relationships should the primary means to access
nodes in the database
๏ Traversing relationships is cheap – that’s the whole
design goal of a graph database
๏ Use lookups only to find starting nodes for a query
Data Modeling examples in Manual
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87. Anti-pattern: unconnected graph
name: “Jones” name: “Jones”
name: “Jones”
name: “Jones”
name: “Jones”
name: “Jones”
name: “Jones” name: “Jones”
name: “Jones”
name: “Jones”
name: “Jones”
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88. Pattern: Linked List
61
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89. Pattern: Multiple Relationships
62
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90. Pattern-Trees:Tags and Categories
63
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91. Pattern-Tree: Multi-Level-Tree
64
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92. Pattern-Trees: R-Tree (spatial)
65
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93. Example:Activity Stream
66
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94. Graph Evolution
67
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95. Evolution: Relationship to Node
68
Peter
SENT_EMAIL
Michael
Peter EMAIL_FROM
Michael
EMAIL_TO
Email
Emil
EMAIL_CC
Community
TAGGED
. . .
see Hyperedges
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96. Combine multiple Domains in a Graph
๏ you start with a single domain
๏ add more connected domains as your system evolves
๏ more domains allow to ask different queries
๏ one domain „indexes“ the other
๏ Example Facebook Graph Search
• social graph
• location graph
• activity graph
• favorite graph
• ...
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97. Notes on the Graph Data Model
๏Schema free, but constraints
๏Model your graph with a whiteboard and a wise man
๏Nodes as main entities but useless without connections
๏Relationships are first level citizens in the model and database
๏Normalize more than in a relational database
๏use meaningful relationship-types, not generic ones like IS_
๏use in-graph structures to allow different access paths
๏evolve your graph to your needs, incremental growth
70
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98. 71
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99. ๏ Worldwide one-day Neo4j Tutorials
๏ Books
• Graph Databases, Neo4j in Action
๏ neo4j.org
• http://neo4j.org/develop/modeling
๏ docs.neo4j.org
• Data Modeling Examples
๏ http://console.neo4j.org
๏ http://gist.neo4j.org
๏ Get Neo4j
• http://neo4j.org/download
๏ Participate
• http://groups.google.com/group/neo4j
How to get started?
72
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100. ThankYou!
Questions ?
73
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101. An Example
74
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102. What language do they speak here?
Language Country
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103. What language do they speak here?
Language Country
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104. What language do they speak here?
Language Country
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105. Tables
language_code
language_name
word_count
Language
country_code
country_name
flag_uri
Country
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106. Need to model the relationship
language_code
language_name
word_count
Language
country_code
country_name
flag_uri
language_code
Country
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107. What if the cardinality changes?
language_code
language_name
word_count
country_code
Language
country_code
country_name
flag_uri
Country
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108. Or we go many-to-many?
language_code
language_name
word_count
Language
country_code
country_name
flag_uri
Country
language_code
country_code
LanguageCountry
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109. Or we want to qualify the relationship?
language_code
language_name
word_count
Language
country_code
country_name
flag_uri
Country
language_code
country_code
primary
LanguageCountry
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110. Start talking about
Graphs
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111. Explicit Relationship
name
word_count
Language
name
flag_uri
Country
IS_SPOKEN_IN
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112. Relationship Properties
name
word_count
Language
name
flag_uri
Country
IS_SPOKEN_IN
as_primary
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113. What’s different?
language_code
language_name
word_count
Language
country_code
country_name
flag_uri
Country
language_code
country_code
primary
LanguageCountry
IS_SPOKEN_IN
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114. What’s different?
๏ Implementation of maintaining relationships is left up
to the database
๏ Artificial keys disappear or are unnecessary
๏ Relationships get an explicit name
• can be navigated in both directions
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115. Relationship specialisation
name
word_count
Language
name
flag_uri
Country
IS_SPOKEN_IN
as_primary
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116. Bidirectional relationships
name
word_count
Language
name
flag_uri
Country
IS_SPOKEN_IN
PRIMARY_LANGUAGE
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117. Weighted relationships
name
word_count
Language
name
flag_uri
Country
POPULATION_SPEAKS
population_fraction
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118. Keep on adding relationships
name
word_count
Language
name
flag_uri
Country
POPULATION_SPEAKS
population_fraction
SIMILAR_TO ADJACENT_TO
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119. Realworld Examples
92
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120. 93
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121. 93
Real World Use Cases:
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122. 93
Real World Use Cases:
•[A] ACL from Hell
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123. 93
Real World Use Cases:
•[A] ACL from Hell
•[B] Timely recommendations
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124. 93
Real World Use Cases:
•[A] ACL from Hell
•[B] Timely recommendations
•[C] Global collaboration
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125. 94
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126. 94
Real World Use Cases:
Samstag, 31. August 13

127. 94
Real World Use Cases:
•[A] ACL from Hell
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128. 94
Real World Use Cases:
•[A] ACL from Hell
•[B] Timely recommendations
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129. 94
Real World Use Cases:
•[A] ACL from Hell
•[B] Timely recommendations
•[C] Global collaboration
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130. [A] ACL from Hell
95
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131. [A] ACL from Hell
๏ Customer:
• leading consumer utility company with tons and
tons of users
๏ Goal:
• comprehensive access control administration
for customers
๏ Benefits:
• Flexible and dynamic architecture
• Exceptional performance
• Extensible data model supports new
applications and features
• Low cost
95
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132. [A] ACL from Hell
๏ Customer:
• leading consumer utility company with tons and
tons of users
๏ Goal:
• comprehensive access control administration
for customers
๏ Benefits:
• Flexible and dynamic architecture
• Exceptional performance
• Extensible data model supports new
applications and features
• Low cost
95
• A Reliable access control administration system for
5 million customers, subscriptions and agreements
• Complex dependencies between groups, companies,
individuals, accounts, products, subscriptions, services and
agreements
• Broad and deep graphs (master customers with 1000s of
customers, subscriptions & agreements)
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133. [A] ACL from Hell
๏ Customer:
• leading consumer utility company with tons and
tons of users
๏ Goal:
• comprehensive access control administration
for customers
๏ Benefits:
• Flexible and dynamic architecture
• Exceptional performance
• Extensible data model supports new
applications and features
• Low cost
95
• A Reliable access control administration system for
5 million customers, subscriptions and agreements
• Complex dependencies between groups, companies,
individuals, accounts, products, subscriptions, services and
agreements
• Broad and deep graphs (master customers with 1000s of
customers, subscriptions & agreements)
name: Andreas
subscription: sports
service: NFL
account: 9758352794
agreement: ultimate
owns
subscribes to
has plan
includes
provides group: graphistas
promotion: fall
member of
offered
discounts
company: Neo
Technologyworks with
gets discount on
subscription: local
subscribes to
provides service: Ravens
includes
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134. [B] Timely Recommendations
96
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135. [B] Timely Recommendations
๏ Customer:
• a professional social network
• 35 millions users, adding 30,000+ each day
๏ Goal: up-to-date recommendations
• Scalable solution with real-time end-user
experience
• Low maintenance and reliable architecture
• 8-week implementation
96
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136. [B] Timely Recommendations
๏ Customer:
• a professional social network
• 35 millions users, adding 30,000+ each day
๏ Goal: up-to-date recommendations
• Scalable solution with real-time end-user
experience
• Low maintenance and reliable architecture
• 8-week implementation
96
๏ Problem:
• Real-time recommendation imperative to attract new
users and maintain positive user retention
• Clustered MySQL solution not scalable or fast enough
to support real-time requirements
๏ Upgrade from running a batch job
• initial hour-long batch job
• but then success happened, and it became a day
• then two days
๏ With Neo4j, real time recommendations
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137. [B] Timely Recommendations
๏ Customer:
• a professional social network
• 35 millions users, adding 30,000+ each day
๏ Goal: up-to-date recommendations
• Scalable solution with real-time end-user
experience
• Low maintenance and reliable architecture
• 8-week implementation
96
๏ Problem:
• Real-time recommendation imperative to attract new
users and maintain positive user retention
• Clustered MySQL solution not scalable or fast enough
to support real-time requirements
๏ Upgrade from running a batch job
• initial hour-long batch job
• but then success happened, and it became a day
• then two days
๏ With Neo4j, real time recommendations
name:Andreas
job: talking
name: Allison
job: plumber
name: Tobias
job: coding
knows
knows
name: Peter
job: building
name: Emil
job: plumber
knows
name: Stephen
job: DJ
knows
knows
name: Delia
job: barking
knows
knows
name: Tiberius
job: dancer
knows
knows
knows
knows
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138. [C] Collaboration on Global Scale
97
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139. [C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
97
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140. [C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
97
• Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
• Infer collaborative relationships through user-
generated content
• Worldwide Availability
Samstag, 31. August 13

141. [C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
97
• Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
• Infer collaborative relationships through user-
generated content
• Worldwide Availability
Asia North America Europe
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142. [C] Collaboration on Global Scale
๏ Customer: a worldwide software leader
• highly collaborative end-users
๏ Goal: offer an online platform for global collaboration
• Highly flexible data analysis
• Sub-second results for large, densely-connected data
• User experience - competitive advantage
97
• Massive amounts of data tied to members, user
groups, member content, etc. all interconnected
• Infer collaborative relationships through user-
generated content
• Worldwide Availability
Asia North America Europe
Asia North America Europe
Samstag, 31. August 13

143. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
San Jose, CA
Cisco.com
Industry: Communications
Use case: Recommendations
• Call center volumes needed to be lowered by improving
the efficacy of online self service
• Leverage large amounts of knowledge stored in service
cases, solutions, articles, forums, etc.
• Problem resolution times, as well as support costs, needed
to be lowered
• Cisco.com serves customer and business customers with
Support Services
• Needed real-time recommendations, to encourage use of
online knowledge base
• Cisco had been successfully using Neo4j for its internal
master data management solution.
• Identified a strong fit for online recommendations
• Cases, solutions, articles, etc. continuously scraped for cross-
reference links, and represented in Neo4j
• Real-time reading recommendations via Neo4j
• Neo4j Enterprise with HA cluster
• The result: customers obtain help faster, with decreased reliance
on customer support
Support
Support
Solution
Message
Samstag, 31. August 13

144. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
San Jose, CA
Cisco HMP
Industry: Communications
Use case: Master Data Management
• Sales compensation system had become unable to meet
Cisco’s needs
• Existing Oracle RAC system had reached its limits:
• Insufficient flexibility for handling complex
organizational hierarchies and mappings
• “Real-time” queries were taking > 1 minute!
• Business-critical “P1” system needs to be continually
available, with zero downtime
• One of the world’s largest communications equipment
manufacturers
• #91 Global 2000. $44B in annual sales.
• Needed a system that could accommodate its master data
hierarchies in a performant way
• HMP is a Master Data Management system at whose heart
is Neo4j. Data access services available 24x7 to
applications companywide
• Cisco created a new system: the Hierarchy Management
Platform (HMP)
• Allows Cisco to manage master data centrally, and centralize
data access and business rules
• Neo4j provided “Minutes to Milliseconds” performance over
Oracle RAC, serving master data in real time
• The graph database model provided exactly the flexibility
needed to support Cisco’s business rules
• HMP so successful that it has expanded to
include product hierarchy
Samstag, 31. August 13

145. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Industry: Logistics
Use case: Parcel Routing
• 24x7 availability, year round
• Peak loads of 2500+ parcels per second
• Complex and diverse software stack
• Need predictable performance & linear scalability
• Daily changes to logistics network: route from any point, to
any point
• One of the world’s largest logistics carriers
• Projected to outgrow capacity of old system
• New parcel routing system
• Single source of truth for entire network
• B2C & B2B parcel tracking
• Real-time routing: up to 5M parcels per day
• Neo4j provides the ideal domain fit:
• a logistics network is a graph
• Extreme availability & performance with Neo4j clustering
• Hugely simplified queries, vs. relational for complex routing
• Flexible data model can reflect real-world data variance much
better than relational
• “Whiteboard friendly” model easy to understand
Samstag, 31. August 13

146. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Sausalito, CA
GlassDoor
Industry: Online Job Search
Use case: Social / Recommendations
• Wanted to leverage known fact that most jobs are found
through personal & professional connections
• Needed to rely on an existing source of social network
data. Facebook was the ideal choice.
• End users needed to get instant gratification
• Aiming to have the best job search service, in a very
competitive market
• Online jobs and career community, providing anonymized
inside information to job seekers
• First-to-market with a product that let users find jobs through
their network of Facebook friends
• Job recommendations served real-time from Neo4j
• Individual Facebook graphs imported real-time into Neo4j
• Glassdoor now stores > 50% of the entire Facebook social
graph
• Neo4j cluster has grown seamlessly, with new instances being
brought online as graph size and load have increased
KNOW
S
KNOWS
KNOWS
WORKS_AT
WORKS_AT
Samstag, 31. August 13

147. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
San Jose, CA
Adobe
Industry: Web/ISV
Use case: Content Management, Social,Access Control
• Adobe needed a highly robust and available, 24x7
distributed global system, supporting collaboration for
users of its highest revenue product line
• Storing creative artifacts in the cloud meant managing
access rights for (eventually) millions of users, groups,
collections, and pieces of content
• Complex access control rules controlling who was
connected to whom, and who could see or edit what,
proved a significant technical challenge
• One of the ten largest software companies globally
• $4B+ in revenue. Over 11,000 employees.
• Launched Creative Cloud in 2012, allowing its Creative
Suite users to collaborate via the Cloud
• Selected Neo4j to meet very aggressive project deadlines.The
flexibility of the graph model, and performance, were the two
major selection factors.
• Easily evolve the system to meet tomorrow’s needs
• Extremely high availability and transactional performance
requirements. 24x7 with no downtime.
• Neo4j allows consistently fast response times with complex
queries, even as the system grows
• First (and possibly still only) database cluster to run across three
Amazon EC2 regions: U.S., Europe,Asia
User-Content-Access
Samstag, 31. August 13

148. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Paris, France
SFR
Industry: Communications
Use case: Network Management
• Infrastructure maintenance took one full week to plan,
because of the need to model network impacts
• Needed rapid, automated “what if” analysis to ensure
resilience during unplanned network outages
• Identify weaknesses in the network to uncover the need
for additional redundancy
• Network information spread across > 30 systems, with
daily changes to network infrastructure
• Business needs sometimes changed very rapidly
• Second largest communications company in France
• Part ofVivendi Group, partnering withVodafone
• Flexible network inventory management system, to support
modeling, aggregation & troubleshooting
• Single source of truth (Neo4j) representing the entire network
• Dynamic system loads data from 30+ systems, and allows new
applications to access network data
• Modeling efforts greatly reduced because of the near 1:1
mapping between the real world and the graph
• Flexible schema highly adaptable to changing business
requirements
Router
DEPENDS_O
N
Switch Switch
Router
Fiber
Fiber
Fiber
DEPENDS_ON
DEPENDS_ON
DEPEN
DS_O
N
DEPENDS_ON
DEPENDS_ON
DEPENDS_ON
DEPENDS_ON
DEPENDS_ON
DEPENDS_ON
LINKE
D
LINKE
D
LIN
KE
D
DEPENDS_ON
Samstag, 31. August 13

149. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Frankfurt, Germany
Deutsche Telecom
Industry: Communications
Use case: Social gaming
• The Fanorakel application allows fans to have an interactive
experience while watching sports
• Fans can vote for referee decisions and interact with other
fans watching the game
• Highly connected dataset with real-time updates
• Queries need to be served real-time on rapidly changing
data
• One technical challenge is to handle the very high spikes of
activity during popular games
• Europe’s largest communications company
• Provider of mobile & land telephone lines to consumers
and businesses, as well as internet services, television, and
other services
• Interactive, social offering gives fans a way to experience the
game more closely
• Increased customer stickiness for Deutsche Telekom
• A completely new channel for reaching customers with
information, promotions, and ads
• Clear competitive advantage
Interactive Television
Samstag, 31. August 13

150. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Global (U.S., France)
Hewlett Packard
Industry: Web/ISV, Communications
Use case: Network Management
• Use network topology information to identify root
problems causes on the network
• Simplify alarm handling by human operators
• Automate handling of certain types of alarms Help
operators respond rapidly to network issues
• Filter/group/eliminate redundant Network Management
System alarms by event correlation
• World’s largest provider of IT infrastructure, software &
services
• HP’s Unified Correlation Analyzer (UCA) application is a
key application inside HP’s OSS Assurance portfolio
• Carrier-class resource & service management, problem
determination, root cause & service impact analysis
• Helps communications operators manage large, complex
and fast changing networks
• Accelerated product development time
• Extremely fast querying of network topology
• Graph representation a perfect domain fit
• 24x7 carrier-grade reliability with Neo4j HA clustering
• Met objective in under 6 months
Samstag, 31. August 13

151. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Oslo, Norway
Telenor
Industry: Communications
Use case: Resource Authorization & Access Control
• Degrading relational performance. User login taking
minutes while system retrieved access rights
• Millions of plans, customers, admins, groups.
Highly interconnected data set w/massive joins
• Nightly batch workaround solved the performance
problem, but meant data was no longer current
• Primary system was Sybase. Batch pre-compute
workaround projected to reach 9 hours by 2014: longer
than the nightly batch window
• 10th largest Telco provider in the world, leading in the
Nordics
• Online self-serve system where large business admins
manage employee subscriptions and plans
• Mission-critical system whose availability and
responsiveness is critical to customer satisfaction
• Moved authorization functionality from Sybase to Neo4j
• Modeling the resource graph in Neo4j was straightforward, as
the domain is inherently a graph
• Able to retire the batch process, and move to real-time
responses: measured in milliseconds
• Users able to see fresh data, not yesterday’s snapshot
• Customer retention risks fully mitigated
SUBSCRIBED_BY
CONTROLLED_B
PART_O
User
USER_ACCESS
Samstag, 31. August 13

152. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Silicon Valley & France
Viadeo
Industry: Professional Social Network
Use case: Social, Recommendations
• Business imperative for real-time recommendations: to
attract new users and retain existing ones
• Key differentiator: show members how they are connected
to any other member
• Real-time traversals of social graph not feasible with
MySQL cluster. Batch precompute meant stale data.
• Process taking longer & longer: > 1 week!
• World’s second-largest professional network
(after LinkedIn)
• 50M members. 30K+ new members daily.
• Over 400 staff with offices in 12 countries
• Neo4j solution implemented in 8 weeks with 3 part-time
programmers
• Able to move from batch to real-time: improved responsiveness
with up-to-date data.
• Viadeo (at the time) had 8M members and 35M relationships.
• Neo4j cluster now sits at the heart of Viadeo’s professional
network, connecting 50M+ professionals
Samstag, 31. August 13

153. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Hong Kong
Maaii
Industry: Communications
Use case: Social, Mobile
• Launched a new mobile communication app “Maaii”
allowing consumers to communicate by voice & text
(Similar to Line,Viber, Rebtel,VoxOx...)
• Needed to store & relate devices, users, and contacts
• Import phone numbers from users’ address books. Rapidly
serve up contacts from central database
to the mobile app
• Currently around 3M users w/200M nodes in the graph
• Hong Kong based telephony infrastructure provider
(aka M800 aka Pop Media)
• Exclusive China Mobile partner for international toll-free
services. SMS Hub & other offerings
• 2012 Red Herring Top 100 Global Winner
• Quick transactional performance for key operations:
• friend suggestions (“friend of friend”)
• updating contacts, blocking calls, etc.
• etc.
• High availability telephony app uses Neo4j clustering
• Strong architecture fit: Scala w/Neo4j embedded
Samstag, 31. August 13

154. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Zürich, Switzerland
Junisphere
Industry: Web/ISV, Communications
Use case: Data Center Management
• “Business Service Management” requires mapping of
complex graph, covering: business processes--> business
services--> IT infrastructure
• Embed capability of storing and retrieving this information
into OEM application
• Re-architecting outdated C++ application based on
relational database, with Java
• Junisphere AG is a Zurich-based IT solutions provider
• Founded in 2001. Profitable. Self funded.
• Software & services.
• Novel approach to infrastructure monitoring:
Starts with the end user, mapped to business processes
and services, and dependent infrastructure
• Actively sought out a Java-based solution that could store data
as a graph
• Domain model is reflected directly in the database:
• “No time lost in translation”
• “Our business and enterprise consultants now speak the
same language, and can model the domain with the database
on a 1:1 ratio.”
• Spring Data Neo4j strong fit for Java architecture
Samstag, 31. August 13

155. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
San Francisco, CA
Teachscape
Industry: Education
Use case: Resource Authorization & Access Control
• Neo4j was selected to be at the heart of a new
architecture.
• The user management system, centered around Neo4j, will
be used to support single sign-on, user management,
contract management, and end-user access to their
subscription entitlements.
• Teachscape, Inc. develops online learning tools for K-12
teachers, school principals, and other instructional leaders.
• Teachscape evaluated relational as an option, considering
MySQL and Oracle.
• Neo4j was selected because the graph data model
provides a more natural fit for managing organizational
hierarchy and access to assets.
• Domain and technology fit
• simple domain model where the relationships are relatively complex. Secondary
factors included support for transactions, strong Java support, and well-
implemented Lucene indexing integration
• Speed and Flexibility
• The business depends on being able to do complex walks quickly and efficiently.
This was a major factor in the decision to use Neo4j.
• Ease of Use
• accommodate efficient access for home-grown and commercial off-the-shelf
applications, as well as ad-hoc use.
• Extreme availability & performance with Neo4j clustering
• Hugely simplified queries, vs. relational for complex routing
• Flexible data model can reflect real-world data variance much better than
Samstag, 31. August 13

156. © All Rights Reserved 2013 | Neo
Technology, Inc.
Background
Business problem
Neo Technology Confidential
Solution & Benefits
Cambridge, Massachusetts
SevenBridges Genomics
Industry: Life Sciences
Use case: Content Management
• Neo4j is used to store metadata about each sequenced
genome (including a pointer to the sequenced genome
itself, which is a binary file stored on Amazon S3), and to
support search and other forms of information processing
against the genomic data.
• graph database was chosen because “Our specific domain
maps naturally onto graph paradigm”.
• Bioinformatics company offering gene sequencing
"as a service" (over the web)
• Provider of genomic information services
• Needed a new platform to support storage & retrieval of
sequenced genomes in the cloud
•Domain fit
• Domain naturally lends itself to a graph representation.
• Graph model determined to be a perfect fit.
•Agility & Performance
• Saved time with Neo4j as compared to the alternatives.
• Queries “practically write themselves.”
•Solution Completeness
• “Neo4j is incomparably better than other graph databases.”
Samstag, 31. August 13

157. 112
Samstag, 31. August 13

158. 112
Really, once you start
thinking in graphs
it's hard to stop
Recommendations MDM
Systems
Management
Geospatial
Social computing
Business intelligence
Biotechnology
Making Sense of all that
data
your brain
access control
linguistics
catalogs
genealogyrouting
compensation market vectors
Samstag, 31. August 13

159. 112
Really, once you start
thinking in graphs
it's hard to stop
Recommendations MDM
Systems
Management
Geospatial
Social computing
Business intelligence
Biotechnology
Making Sense of all that
data
your brain
access control
linguistics
catalogs
genealogyrouting
compensation market vectors
What will you build?
Samstag, 31. August 13

160. 113
Samstag, 31. August 13

161. is a
114
Samstag, 31. August 13

162. 115
NOSQL
Samstag, 31. August 13

163. Graph Database
116
Samstag, 31. August 13

164. A graph database...
117
Samstag, 31. August 13

165. A graph database...
117
NO: not for charts & diagrams, or vector artwork
Samstag, 31. August 13

166. A graph database...
117
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
Samstag, 31. August 13

167. A graph database...
117
NO: not for charts & diagrams, or vector artwork
YES: for storing data that is structured as a graph
remember linked lists, trees?
Samstag, 31. August 13

168. A graph database...
117
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
Samstag, 31. August 13

169. A graph database...
117
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 place,
but a graph database will tell you who is most likely to buy you a
beer.”
Samstag, 31. August 13

170. Data Modeling
118
Samstag, 31. August 13

171. Why Data Modeling
119
๏What is modeling?
๏Aren‘t we schema free?
๏How does it work in a
graph?
๏Where should modeling
happen? DB or Application
Samstag, 31. August 13

172. Data Models
120
Samstag, 31. August 13

173. Model mis-match
Real World Model
Samstag, 31. August 13

174. Model mis-match
Application Model Database Model
Samstag, 31. August 13

175. Trinity of models
Samstag, 31. August 13

176. Whiteboard --> Data
124
Samstag, 31. August 13

177. Whiteboard --> Data
124
Andreas
Peter
Emil
Allison
Samstag, 31. August 13

178. Whiteboard --> Data
124
Andreas
Peter
Emil
Allison
knows
knows knows
knows
Samstag, 31. August 13

179. Whiteboard --> Data
124
Andreas
Peter
Emil
Allison
knows
knows knows
knows
Samstag, 31. August 13

180. Whiteboard --> Data
124
Andreas
Peter
Emil
Allison
knows
knows knows
knows
// Cypher query - friend of a friend
start n=node(0)
match (n)-->()-[:KNOWS|LIKES]->(foaf)
WHERE NOT((n)[:KNOWS]-->(foaf))
return foaf
Samstag, 31. August 13

181. 125
Samstag, 31. August 13

182. You traverse the graph
125
Samstag, 31. August 13

183. You traverse the graph
125
Samstag, 31. August 13

184. // lookup starting point in an index
START n=node:People(name = ‘Andreas’)
Andreas
You traverse the graph
125
Samstag, 31. August 13

185. // lookup starting point in an index
START n=node:People(name = ‘Andreas’)
Andreas
You traverse the graph
125
// then traverse to find results
START me=node:People(name = ‘Andreas’
MATCH (me)-[:FRIEND]-(friend)-[:FRIEND]-(friend2)
RETURN friend2
Samstag, 31. August 13

186. 125
Samstag, 31. August 13

187. SELECT skills.*, user_skill.*
FROM users
JOIN user_skill ON users.id = user_skill.user_id
JOIN skills ON user_skill.skill_id = skill.id WHERE users.id = 1
126
START user = node(1)
MATCH user -[user_skill]-> skill
RETURN skill, user_skill
Samstag, 31. August 13