Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1uRYaAR. Volker Pacher, Sam Phillips present key differences between relational databases and graph databases, and how they use the later to model a complex domain and to gain insights into their data. Filmed at qconlondon.com. Sam Phillips is Head of Engineering for eBay's Local Delivery team, bringing super fast delivery to customers in the UK and US. Volker Pacher is a Senior Developer at eBay Local Delivery. Before its acquisition by eBay, he was a member of the core team at Shutl helping to transition from a monolithic application to SOA and introducing new technologies, among them Neo4j.

2. Watch the video with slide
synchronization on InfoQ.com!
http://www.infoq.com/presentations
/shutl-neo4j-graph-db
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4. How Shutl Delivers Even Faster Using Neo4j
Sam Phillips and Volker Pacher
@samsworldofno @vpacher

5. Sam Phillips
Volker Pacher

6. Graphs at Shutl

7. Graphs at Shutl
• Graph databases are awesome

8. Graphs at Shutl
• Graph databases are awesome
• We’ve seen lots of the talks about modelling

9. Graphs at Shutl
• Graph databases are awesome
• We’ve seen lots of the talks about modelling
• But querying is important too

10. Graphs at Shutl
• Graph databases are awesome
• We’ve seen lots of the talks about modelling
• But querying is important too
• So let’s talk about querying too!

11. Show of hands

12. Show of hands
• Who has used graph databases before?

13. Show of hands
• Who has used graph databases before?
• Who has used Neo4j before?

14. Shutl

15. Shutl

16. ECOMMERCE IS QUICK & CONVENIENT

17. ECOMMERCE IS QUICK & CONVENIENT

18. PAYPAL FOR AWESOME DELIVERY

19. PAYPAL FOR AWESOME DELIVERY

20. PAYPAL FOR AWESOME DELIVERY
Branded, super quick delivery that people trust, embedded in merchant websites

21. HUB & SPOKE
A B

22. HUB & SPOKE
A B

23. HUB & SPOKE
A B
Only cost effective means to deliver 10+ miles but slow and unpredictable

24. HUB & SPOKE
Only cost effective means to deliver 10+ miles but slow and unpredictable
A B

25. HUB & SPOKE
Only cost effective means to deliver 10+ miles but slow and unpredictable
A B
POINT TO POINT
A
B

26. HUB & SPOKE
Only cost effective means to deliver 10+ miles but slow and unpredictable
A B
POINT TO POINT
A
B
Fast and predictable but cost prohibitive over longer distances

27. HUB & SPOKE
97% Courier, Express & Parcel Market

28. POINT TO POINT
3% Courier, Express & Parcel Market

29. POINT TO POINT
+7,500 more!
3% Courier, Express & Parcel Market

30. POINT TO POINT

31. SHOP

32. Shutl generates a quote from
each relevant carrier within
platform
SHOP
$$
$$$
$
$$
$
$

33. Shutl generates a quote from
each relevant carrier within
platform
Optimum picked based
on price & quality rating
SHOP
$$
$$$
$
$$
$
$$

34. SHOP
SHOP

35. On checkout, delivery sent via API into
chosen carrier’s transportation system
SHOP
$$
SHOP

36. On checkout, delivery sent via API into
chosen carrier’s transportation system
Courier collects from nearest
store and delivers to shopper
SHOP
$$

38. Delivery status updated in
real-time, performance
compared against SLA &
carrier quality rating updated
Better performing carriers
get more deliveries & can
demand higher prices

39. Delivery status updated in
real-time, performance
compared against SLA &
carrier quality rating updated
Better performing carriers
get more deliveries & can
demand higher prices

40. Delivery status updated in
real-time, performance
compared against SLA &
carrier quality rating updated
Better performing carriers
get more deliveries & can
demand higher prices

41. Track your order online…

42. FEEDBACK
Quality paramount since we are motivated by LTV of shopper

43. FEEDBACK
Quality paramount since we are motivated by LTV of shopper

44. FEEDBACK
Shutl sends feedback email to consumer seconds after they have received
delivery asking to rate qualitative aspects of experience

45. FEEDBACK
Feedback streamed unedited to shutl.com/feedback & facebook

46. FEEDBACK

47. FEEDBACK

48. FEEDBACK

49. FEEDBACK

51. SHUTL IS NOW AN COMPANY

52. Version One
Ruby 1.8, Rails 2.3 and MySQL

53. Version One
Ruby 1.8, Rails 2.3 and MySQL

54. Version One
Ruby 1.8, Rails 2.3 and MySQL
• Well-known tale: built quickly, worked slowly, tough to maintain
• Getting a quote for an hour time-slot took over 4 seconds

55. Here is the Shutl price calendar

56. Here is the Shutl price calendar
To generate this in V1, the merchant site would have had to call Shutl to get
available slots (2 seconds)

57. Here is the Shutl price calendar
To generate this in V1, the merchant site would have had to call Shutl to get
available slots (2 seconds)
Then, they would have to call Shutl to generate a quote for each slot - for
two days of store opening, that’s 20+ slots

58. Here is the Shutl price calendar
To generate this in V1, the merchant site would have had to call Shutl to get
available slots (2 seconds)
Then, they would have to call Shutl to generate a quote for each slot - for
two days of store opening, that’s 20+ slots
So, that’s 2 + (20 x 4) seconds, 1:22 to generate the data for this calendar

59. Here is the Shutl price calendar
To generate this in V1, the merchant site would have had to call Shutl to get
available slots (2 seconds)
Then, they would have to call Shutl to generate a quote for each slot - for
two days of store opening, that’s 20+ slots
So, that’s 2 + (20 x 4) seconds, 1:22 to generate the data for this calendar
In V1, this UX could never have happened.

60. V2

61. • Broke app into services
V2
• Services focused around functions like quoting, booking, and
giving feedback
• Key goal for the project was improving the speed of the quoting
operation, which is where we used graph databases

62. V1
V2

63. V1
V2
• Quoting for 20 windows down
from 82000 ms to 800 ms

64. V1
V2
• Quoting for 20 windows down
from 82000 ms to 800 ms
• Code complexity much
reduced

65. V1
V2
• Quoting for 20 windows down
from 82000 ms to 800 ms
• Code complexity much
reduced

66. A large part of the success of our rewrite was
down to the graph database.

67. What is a graph anyway?

69. a simple graph
a collection of vertices (nodes)
connected by edges (relationships)

70. a short history
the seven bridges of Königsberg (1735)!
Leonard Euler

71. the seven bridges of Königsberg (1735)!

72. the seven bridges of Königsberg (1735)!

73. the seven bridges of Königsberg (1735)!

74. the seven bridges of Königsberg (1735)!

75. the seven bridges of Königsberg (1735)!

76. Euler walk
each node has an even degree

77. Euler walk

78. Euler walk

79. Euler walk
two nodes have an odd degree

80. Euler walk
two nodes have an odd degree

81. Euler walk
no
two nodes have an odd degree

82. directed graph
each relationship has a direction or
one start node and one end node

83. property graph
nodes contain properties (key, value)
relationships have a type and are always directed
relationships can contain properties too
Person
name: Sam
Person
name: Volker
:friends
:knows
since: 2005
Person
name: Megan
:friends
Company
name: eBay
:works_for
:works_for

84. The Case for Graph Databases

85. relationships are explicit stored

86. additive domain modelling

87. whiteboard friendly

88. traversals of relationships are easy and very fast

89. DB performance remains relatively constant as
queries are localised to its portion of the graph.
O(1) for same query

90. a graph is its own index (constant query performance)

91. a graph is its own index (constant query performance)

92. a graph is its own index (constant query performance)

93. the case for Neo4j

94. standalone or embedded in jvm

95. ruby/jruby

96. ruby libraries - neo4j gem by Andreas Ronge
(https://github.com/andreasronge/neo4j)

97. cypher

98. the neotech guys are awesome

99. Querying the graph: Cypher
declarative query language specific to neo4j
easy to learn and intuitive
use specific patterns to query for (something that looks like ‘this’)
inspired partly by SQL (WHERE and ORDER BY) and SPARQL (pattern matching)
focuses on what to query for and not how to query for it
switch from a mySQl world is made easier by the use of cypher instead of having to learn
a traversal framework straight away

100. cypher clauses
START: Starting points in the graph, obtained via index lookups or by element IDs.
MATCH: The graph pattern to match, bound to the starting points in START.
WHERE: Filtering criteria.
RETURN: What to return.
CREATE: Creates nodes and relationships.
DELETE: Removes nodes, relationships and properties.
SET: Set values to properties.
FOREACH: Performs updating actions once per element in a list.
WITH: Divides a query into multiple, distinct parts

101. cypher clauses
START: Starting points in the graph, obtained via index lookups or by element IDs.
MATCH: The graph pattern to match, bound to the starting points in START.
WHERE: Filtering criteria.
RETURN: What to return.
CREATE: Creates nodes and relationships.
DELETE: Removes nodes, relationships and properties.
SET: Set values to properties.
FOREACH: Performs updating actions once per element in a list.
WITH: Divides a query into multiple, distinct parts

102. an example
Person
name: Sam
Person
name: Volker
:friends
:knows
since: 2005
Person
name: Megan
:friends
Company
name: eBay
:works_for
:works_for
Person
name: Jim
:friends
:works_for
Company
name: neotech

103. find all the companies my friends work for
MATCH (person{ name:’Volker’ }) -[:friends]
- (person) - [:works_for]-> company
RETURN company

104. find all the companies my friends work for
MATCH (person{ name:’Volker’ }) -[:friends]
- (person) - [:works_for]-> company
RETURN company

105. find all the companies my friends work for
MATCH (person{ name:’Volker’ }) -[:friends]
- (person) - [:works_for]-> company
RETURN company
Person
name: Sam
Person
name: Volker
:friends
:knows
since: 2005
Person
name: Megan
:friends
Company
name: eBay
:works_for
:works_for
Person
name: Jim
:friends
:works_for
Company
name: neotech

106. find all the companies my friend’s friends work for
MATCH (person{ name:’Volker’ }) -
[:friends*2..2]-(person) - [:works_for]
-> company
RETURN company

107. find all the companies my friend’s friends work for
MATCH (person{ name:’Volker’ }) -
[:friends*2..2]-(person) - [:works_for]
-> company
RETURN company

108. find all the companies my friend’s friends work for
MATCH (person{ name:’Volker’ }) -
[:friends*2..2]-(person) - [:works_for]
-> company
RETURN company
Person
name: Sam
Person
name: Volker
:friends
:knows
since: 2005
Person
name: Megan
:friends
Company
name: eBay
:works_for
:works_for
Person
name: Jim
:friends
:works_for
Company
name: neotech

109. find all my friends who work for neotech
MATCH (person{ name:’Volker’ }) -[:friends]
-(friends) - [:works_for]-> company
WHERE company.name = ‘neotech’
RETURN friends

110. find all my friends who work for neotech
MATCH (person{ name:’Volker’ }) -[:friends]
-(friends) - [:works_for]-> company
WHERE company.name = ‘neotech’
RETURN friends

111. find all my friends who work for neotech
MATCH (person{ name:’Volker’ }) -[:friends]
-(friends) - [:works_for]-> company
WHERE company.name = ‘neotech’
RETURN friends
Person
name: Sam
Person
name: Volker
:friends
:knows
since: 2005
Person
name: Megan
:friends
Company
name: eBay
:works_for
:works_for
Person
name: Jim
:friends
:works_for
Company
name: neotech

112. a good place to try it out:
!
http://console.neo4j.org/
!
http://gist.neo4j.org/

113. coverage example
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902

114. coverage example
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902
:operates :operates
Carrier
id = carrier_1

115. coverage example
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902
:operates :operates
Carrier
id = carrier_1

116. coverage example
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902
:operates Carrier
id = carrier_2
:operates :operates
Carrier
id = carrier_1

117. MATCH (store{ id:’ebay_store’ }) -[:located]
-> (locality) <- [:operates]- carrier
RETURN carrier
the query

118. the query
MATCH (store{ id:’ebay_store’ }) -[:located]
-> (locality) <- [:operates]- carrier
RETURN carrier

119. MATCH (store{ id:’ebay_store’ }) -[:located]
-> (locality) <- [:operates]- carrier
RETURN carrier
the query
Locality
id = california
Locality
id = 94902
Locality
id = marin_county
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:contains
Locality
id = 94903
:contains :contains
:operates :operates
Carrier
id = carrier_1

120. MATCH (store{ id:’ebay_store’ }) -[:located]
-> () <- [:contains*0..2] - (locality)
<- [:operates]- carrier
RETURN carrier
the query

121. MATCH (store{ id:’ebay_store’ }) -[:located]
-> () <- [:contains*0..2] - (locality)
<- [:operates]- carrier
RETURN carrier
the query

122. MATCH (store{ id:’ebay_store’ }) -[:located]
-> () <- [:contains*0..2] - (locality)
<- [:operates]- carrier
RETURN carrier
the query
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902
:operates Carrier
id = carrier_2
:operates :operates
Carrier
id = carrier_1

123. MATCH (store{ id:’ebay_store’ }) -[:located]
-> () <- [:contains*0..2] - (locality)
<- [:operates]- carrier
RETURN carrier
the query
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902
:operates Carrier
id = carrier_2
:operates :operates
Carrier
id = carrier_1

124. MATCH (store{ id:’ebay_store’ }) -[:located]
-> () <- [:contains*0..2] - (locality)
<- [:operates]- carrier
RETURN carrier
the query
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902
:operates Carrier
id = carrier_2
:operates :operates
Carrier
id = carrier_1

125. MATCH (store{ id:’ebay_store’ }) -[:located]
-> () <- [:contains*0..2] - (locality)
<- [:operates]- carrier
RETURN carrier
the query
Locality
id = california
Locality
id = marin_county
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:contains
Locality
id = 94903
:contains :contains
Locality
id = 94902
:operates Carrier
id = carrier_2
:operates :operates
Carrier
id = carrier_1

126. SELECT * FROM carriers
LEFT JOIN locations ON carrier.location_id = location.id
LEFT JOIN stores ON stores.location_id = carrier.location_id
WHERE stores.name = ‘ebay_store’

127. SELECT * FROM carriers
LEFT JOIN locations ON carrier.location_id = location.id OR
carrier.location_id = location.parent_id
LEFT JOIN stores ON stores.location_id = carrier.location_id
WHERE stores.name = ‘ebay_store’

128. ?

129. MATCH (store{ id:’ebay_store’ }) -[:located]
-> () <- [:contains*0..2] - (locality)
<- [:operates]- carrier
RETURN carrier

130. representing dates/times
root (0)
:year_2014
Year: 2013
:year_2015
Year: 2015
:month_06
:month_05 :month_01
Month: 05 Month: 01
Month: 06
:day_24 :day_25
Day: 24 Day: 25
:day_26
Day: 26
:happens :happens :happens :happens
Event 1 Event 2 Event 3

131. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event

132. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event

133. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event root (0)
:year_2014
Year: 2013
:year_2015
Year: 2015
:month_06
:month_05 :month_01
Month: 05 Month: 01
Month: 06
:day_24 :day_25
Day: 24 Day: 25
:day_26
Day: 26
:happens :happens :happens :happens
Event 1 Event 2 Event 3

134. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event root (0)
:year_2014
Year: 2013
:year_2015
Year: 2015
:month_06
:month_05 :month_01
Month: 05 Month: 01
Month: 06
:day_24 :day_25
Day: 24 Day: 25
:day_26
Day: 26
:happens :happens :happens :happens
Event 1 Event 2 Event 3

135. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event root (0)
:year_2014
Year: 2013
:year_2015
Year: 2015
:month_06
:month_05 :month_01
Month: 05 Month: 01
Month: 06
:day_24 :day_25
Day: 24 Day: 25
:day_26
Day: 26
:happens :happens :happens :happens
Event 1 Event 2 Event 3

136. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event root (0)
:year_2014
Year: 2013
:year_2015
Year: 2015
:month_06
:month_05 :month_01
Month: 05 Month: 01
Month: 06
:day_24 :day_25
Day: 24 Day: 25
:day_26
Day: 26
:happens :happens :happens :happens
Event 1 Event 2 Event 3

137. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event root (0)
:year_2014
Year: 2013
:year_2015
Year: 2015
:month_06
:month_05 :month_01
Month: 05 Month: 01
Month: 06
:day_24 :day_25
Day: 24 Day: 25
:day_26
Day: 26
:happens :happens :happens :happens
Event 1 Event 2 Event 3

138. find all events on a specific day
START root=node(0)
MATCH root - [:year_2014] -> () -[:month_05] ->
()- [:day_24] -> () - [:happens] -> event
RETURN event root (0)
:year_2014
Year: 2013
:year_2015
Year: 2015
:month_06
:month_05 :month_01
Month: 05 Month: 01
Month: 06
:day_24 :day_25
Day: 24 Day: 25
:day_26
Day: 26
:happens :happens :happens :happens
Event 1 Event 2 Event 3

139. all together
Locality
id = california
Locality
id = marin_county
:contains
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:available {premium: 1.5}
Carrier
id = carrier_1
:operates
root (0)
:year_2014
Year: 2013
:month_05
Month: 05
:day_24
Day: 24
hour 09
:hour_09
hour 10
:hour_10
hour 11 :hour_11
:available {premium: 1}

140. all together
MATCH (store{ id:’ebay_store’ }) -[:located]
-> (locality) <- [:operates]- carrier -
[available:available] -> () <-
[:hour_10] - () <- [:day_24] - ()
[:month_05] - () [:year_2014] - ()
RETURN carrier, available.premium as premium

141. all together
MATCH (store{ id:’ebay_store’ }) -[:located]
-> (locality) <- [:operates]- carrier -
[available:available] -> () <-
[:hour_10] - () <- [:day_24] - ()
[:month_05] - () [:year_2014] - ()
RETURN carrier, available.premium as premium

142. all together
MATCH (store{ id:’ebay_store’ }) -[:located]
-> (locality) <- [:operates]- carrier -
[available:available] -> () <-
[:hour_10] - () <- [:day_24] - ()
[:month_05] - () [:year_2014] - ()
RETURN carrier, available.premium as premium
Locality
id = california
Locality
id = marin_county
:contains
Locality
id = 94901
:contains
Store
id = ebay_store
:located
:available {premium: 1.5}
Carrier
id = carrier_1
:operates
root (0)
:year_2014
Year: 2013
:month_05
Month: 05
:day_24
Day: 24
hour 09
:hour_09
hour 10
:hour_10
hour 11 :hour_11
:available {premium: 1}

143. Other graph uses

144. Other graph uses
• Recommendation engines

145. Other graph uses
• Recommendation engines
• Organisational analysis

146. Other graph uses
• Recommendation engines
• Organisational analysis
• Graphing your infrastructure

147. Some gotchas

148. Some gotchas
• There was a learning curve in switching from a relational
mentality to a graph one

149. Some gotchas
• There was a learning curve in switching from a relational
mentality to a graph one
• Tooling not as mature as in the relational world

150. Some gotchas
• There was a learning curve in switching from a relational
mentality to a graph one
• Tooling not as mature as in the relational world
• No out of the box solution for db migrations

151. Some gotchas
• There was a learning curve in switching from a relational
mentality to a graph one
• Tooling not as mature as in the relational world
• No out of the box solution for db migrations
• Seeding an embedded database was unfamiliar

152. Testing was a challenge

153. Testing was a challenge
• Setting up scenarios for tests was tedious

154. Testing was a challenge
• Setting up scenarios for tests was tedious
• Built our own tool based on the geoff syntax developed by Nigel Small

155. Testing was a challenge
• Setting up scenarios for tests was tedious
• Built our own tool based on the geoff syntax developed by Nigel Small
• Geoff allows modelling of graphs in textual form and provides an
interface to insert them into an existing graph

156. Testing was a challenge
• Setting up scenarios for tests was tedious
• Built our own tool based on the geoff syntax developed by Nigel Small
• Geoff allows modelling of graphs in textual form and provides an
interface to insert them into an existing graph
(A) {“name”: “Alice”}

157. Testing was a challenge
• Setting up scenarios for tests was tedious
• Built our own tool based on the geoff syntax developed by Nigel Small
• Geoff allows modelling of graphs in textual form and provides an
interface to insert them into an existing graph
(A) {“name”: “Alice”}
(B) {“name”: “Bob”}

158. Testing was a challenge
• Setting up scenarios for tests was tedious
• Built our own tool based on the geoff syntax developed by Nigel Small
• Geoff allows modelling of graphs in textual form and provides an
interface to insert them into an existing graph
(A) {“name”: “Alice”}
(B) {“name”: “Bob”}
(A) -[:KNOWS] -> (B)

159. Testing was a challenge
• Setting up scenarios for tests was tedious
• Built our own tool based on the geoff syntax developed by Nigel Small
• Geoff allows modelling of graphs in textual form and provides an
interface to insert them into an existing graph
(A) {“name”: “Alice”}
(B) {“name”: “Bob”}
(A) -[:KNOWS] -> (B)
• We created a Ruby dsl for modelling a graph and inserting it into the db
that works with factory_girl

160. Testing was a challenge
• Setting up scenarios for tests was tedious
• Built our own tool based on the geoff syntax developed by Nigel Small
• Geoff allows modelling of graphs in textual form and provides an
interface to insert them into an existing graph
(A) {“name”: “Alice”}
(B) {“name”: “Bob”}
(A) -[:KNOWS] -> (B)
• We created a Ruby dsl for modelling a graph and inserting it into the db
that works with factory_girl
• Open source - https://github.com/shutl/geoff

161. Wrap Up

162. Wrap Up
• Neo4j and graph theory enabled Shutl to
achieve big performance increases in its
most important operation - calculating
delivery prices

163. Wrap Up
• Neo4j and graph theory enabled Shutl to
achieve big performance increases in its
most important operation - calculating
delivery prices
• It’s a new tool based on tested theory, and
cypher is the first language that allows you to
query graphs in a declarative way (like SQL)

164. Wrap Up
• Neo4j and graph theory enabled Shutl to
achieve big performance increases in its
most important operation - calculating
delivery prices
• It’s a new tool based on tested theory, and
cypher is the first language that allows you to
query graphs in a declarative way (like SQL)
• Tooling and adoption is immature but getting
better all the time

165. Thank you!
!
Any questions?
Sam Phillips
Head of Engineering
!
@samsworldofno
http://samsworldofno.com
sam@shutl.com
Volker Pacher
Senior Developer
!
@vpacher
https://github.com/vpacher
volker@shutl.com

167. our

168. Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/shutl-neo4j-
graph-db