1. In
a
League
of
their
Own:
Neo4j
and
Premiership
Football
Mark
Needham
@markhneedham

2. •
•
•
•
•
•
Intro
to
graphs
When
do
we
need
a
graph?
Property
graph
model
Neo4j’s
query
language
The
football
graph
Using
Neo4j
from
.NET
Outline

3. Let’s
talk
graphs

4. Dancing
With
Michael
Jackson
EaJng
Brains
You
mean
these?

5. !

6.
ts
ar
Ch
e

7.
ar

8.
se
he
T

9.
Dancing
With
Michael
Jackson
EaJng
Brains
N
s!

10.
ph
a

11. Gr
OT
Nope!

12. Node
RelaJonship
Ok
so
what’s
a
graph
then?

13. The
tube

14. The
social
network
(graph)

15. Complexity
What
are
graphs
good
for?

16. complexity = f(size, semi-structure, connectedness)
Data
Complexity

17. Size

18. complexity = f(size
, semi-structure, connectedness)
The
Real
Complexity

19. Semi-­‐Structure

20. USER_ID
315
FIRST_NAME
LAST_NAME
Mark
Needham
EMAIL_1
mark.needham@neotech
nology.com
Email:
mark.needham@neotechnology.com
Email:
m.h.needham@gmail.com
TwiXer:
@markhneedham
Skype:
mk_jnr1984
USER
EMAIL_2
FACEBOOK
m.h.needham@gmail.com
NULL
TWITTER
@markhneedham
CONTACT
CONTACT_TYPE
Semi-­‐Structure
SKYPE
mk_jnr1984

21. complexity = f(size
, semi-structure, connectedness)
The
Real
Complexity

22. Connectedness

23. Connectedness

24. Connectedness

25. Densely
Connected
Semi
Structured
When
do
we
need
a
graph?

26. Lots
of
join
tables
Densely
connected?

27. Lots
of
sparse
tables
Semi-­‐Structured?

28. • Millions
of
‘joins’
per
second
• Consistent
query
Jmes
as
dataset
grows
• Join
Complexity
and
Performance
• Easy
to
evolve
data
model
• Easy
to
‘layer’
different
types
of
data
together
ProperJes
of
graph
databases

29. Property
Graph
Data
Model

30. Nodes

31. • Used
to
represent
enJty
aributes
and/or
metadata
(e.g.
Jmestamps,
version)
• Key-­‐value
pairs
• Java
primiJves
• Arrays
• null
is
not
a
valid
value
• Every
node
can
have
different
properJes
Nodes
can
have
properJes

32. What’s
a
node?

33. RelaJonships

34. • RelaJonships
are
first
class
ciJzens
• Every
relaJonship
has
a
name
and
a
direc.on
– Add
structure
to
the
graph
– Provide
semanJc
context
for
nodes
• ProperJes
used
to
represent
quality
or
weight
of
relaJonship,
or
metadata
• Every
relaJonship
must
have
a
start
node
and
end
node
RelaJonships

35. Nodes
can
be
connected
by
more
than
one
relaJonship
Nodes
can
have
more
than
one
relaJonship
Self
relaJonships
are
allowed
RelaJonships

36. Labels

37. Think
Gmail
labels

38. • Nodes
– EnJJes
• RelaJonships
– Connect
enJJes
and
structure
domain
• ProperJes
– EnJty
aXributes,
relaJonship
qualiJes,
and
metadata
• Labels
– Group
nodes
by
role
Four
Building
Blocks

39. Purposeful
abstracJon
of
a
domain
designed
to
saJsfy
parJcular
applicaJon/end-­‐user
goals
Models

40. Model
Query
Design
for
Queryability

41. Model
Design
for
Queryability

42. Model
Query
Design
for
Queryability

43. • DeclaraJve
PaXern-­‐Matching
language
• SQL-­‐like
syntax
• Designed
for
graphs
Introducing
Cypher

44. A
B
C
PaXerns,
paXerns,
everywhere

45. a
b
(a) -- (b)
It’s
all
about
the
ASCII
art!

46. MATCH (a)--(b)
RETURN a, b
a
b
The
most
basic
query

47. a
ACTED IN
m
(a)–[:ACTED_IN]-(m)
Adding
in
a
relaJonship
type

48. MATCH (a)-[:ACTED_IN]-(m)
RETURN a.name, m.name
a
ACTED IN
m
Adding
in
a
relaJonship
type

49. The
football
graph

50. The
football
graph

51. Find
Arsenal’s
away
matches

52. Find
Arsenal’s
away
matches

53. MATCH (team:Team)-[:away_team]-(game)
WHERE team.name = Arsenal
RETURN game
Find
Arsenal’s
away
matches

54. MATCH (team:Team)-[:away_team]-(game)
WHERE team.name = Arsenal
RETURN game.name
Graph
PaXern

55. MATCH (team:Team)-[:away_team]-(game)
WHERE team.name = Arsenal
RETURN game.name
Anchor
paXern
in
graph

56. MATCH (team:Team)-[:away_team]-(game)
WHERE team.name = Arsenal
RETURN game.name
Create
projecJon
of
results

57. Find
Arsenal’s
away
matches

58. Evolving
the
football
graph

59. Find
the
top
away
goal
scorers

60. MATCH (team)-[:away_team]-(game:Game),
(game)-[:contains_match]-(season:Season),
(team)-[:for]-(stats)-[:played]-(player),
(stats)-[:in]-(game)
WHERE season.name = 2012-2013
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
Find
the
top
away
goal
scorers

61. MATCH (team)-[:away_team]-(game:Game),
(game)-[:contains_match]-(season:Season),
(team)-[:for]-(stats)-[:played]-(player),
(stats)-[:in]-(game)
WHERE season.name = 2012-2013
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
MulJple
graph
paXerns

62. MATCH (team)-[:away_team]-(game:Game),
(game)-[:contains_match]-(season:Season),
(team)-[:for]-(stats)-[:played]-(player),
(stats)-[:in]-(game)
WHERE season.name = 2012-2013
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
Anchor
paXern
in
the
graph

63. MATCH (team)-[:away_team]-(game:Game),
(game)-[:contains_match]-(season:Season),
(team)-[:for]-(stats)-[:played]-(player),
(stats)-[:in]-(game)
WHERE season.name = 2012-2013
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
Group
by
player

64. Find
the
top
away
goal
scorers

65. • Goals
scored
in
each
month
by
Michu
• ToXenham
results
when
Gareth
Bale
scores
• What
did
Wayne
Rooney
do
in
April?
• Which
players
only
score
when
a
game
is
televised?
Other
football
queries

66. Graph
Query
Design

67. The
relaJonal
version

68. Rela%onal
Graphs
Tables
Nodes
-­‐
no
need
to
set
a
property
if
it
-­‐
assume
records
all
have
the
same
structure
doesn’t
exist
Foreign
keys
between
tables
Rela%onships
-­‐
joins
calculated
at
run
Jme
-­‐
stored
as
a
‘Pre-­‐computed
-­‐
the
more
tables
you
join
to
a
index’
at
write
Jme
query
the
slower
the
query
gets
-­‐
very
easy
to
do
lots
of
‘hops’
between
relaJonships
Graph
vs
RelaJonal

69. Neo4j
Server
ApplicaJon
H
T
T
P
REST
Client
.NET
and
Neo4j

70. Neo4j
Server
ApplicaJon
Neo4jClient
REST
Client
H
T
T
P
.NET
and
Neo4j

71. .NET
and
Neo4j

72. .NET
and
Neo4j

73. .NET
and
Neo4j

74. .NET
and
Neo4j

75. .NET
and
Neo4j

76. Thinking
in
graphs

77. Graphs
should
be
fun!

78. Last
Wednesday
of
the
month
Ask
for
help
if
you
get
stuck

79. ts gy
en lo
i m no
pl h
m ec
Co eo T
N
of
Graph
h
Databases
Ian Robinson,
Jim Webber Emil Eifrem
www.graphdatabases.com
Come
take
a
copy,
it’s
free!

80. Mark
Needham
@markhneedham
mark.needham@neotechnology.com
QuesJons?