The document outlines an agenda for a graph database prototyping meetup, covering topics such as graph modeling concepts, prototyping tools, and querying with Cypher using Neo4j. It discusses the fundamental components of a property graph, including nodes, relationships, properties, and labels, along with guidance on designing a graph model for specific application goals. Additionally, it provides examples of Cypher queries and tips for effective graph modeling and querying strategies.

1. Graph
Database
Prototyping
@
AMS
GraphDB
meetup

2. Agenda
for
Tonight
• Building
a
Graph
Database
Prototype
• 3
parts
– Graph
database
&
modeling
concepts
– Prototyping
tools
&
import
– Graph
querying
with
Cypher

3. Data
Modeling
With
Neo4j

4. Topics
• Graph
model
building
blocks
• Quick
intro
to
Cypher
• Example
modeling
process
• Modeling
Eps
• Recipes
for
common
modeling
scenarios
• Refactoring
• Test-­‐driven
data
modeling

5. Graph
Model
Building
Blocks

6. Property
Graph
Data
Model

7. Four
Building
Blocks
• Nodes
• RelaEonships
• ProperEes
• Labels

8. Nodes

9. Nodes
• Used
to
represent
en##es
and
complex
value
types
in
your
domain
• Can
contain
properEes
– Used
to
represent
enEty
a1ributes
and/or
metadata
(e.g.
Emestamps,
version)
– Key-­‐value
pairs
• Java
primiEves
• Arrays
• null
is
not
a
valid
value
– Every
node
can
have
different
properEes

10. EnEEes
and
Value
Types
• EnEEes
– Have
unique
conceptual
idenEty
– Change
aWribute
values,
but
idenEty
remains
the
same
• Value
types
– No
conceptual
idenEty
– Can
subsEtute
for
each
other
if
they
have
the
same
value
• Simple:
single
value
(e.g.
colour,
category)
• Complex:
mulEple
aWributes
(e.g.
address)

11. RelaEonships

12. RelaEonships
• Every
relaEonship
has
a
name
and
a
direc#on
– Add
structure
to
the
graph
– Provide
semanEc
context
for
nodes
• Can
contain
properEes
– Used
to
represent
quality
or
weight
of
relaEonship,
or
metadata
• Every
relaEonship
must
have
a
start
node
and
end
node
– No
dangling
relaEonships

13. RelaEonships
(conEnued)
Nodes
can
have
more
than
one
relaEonship
Nodes
can
be
connected
by
more
than
one
relaEonship
Self
relaEonships
are
allowed

14. Variable
Structure
• RelaEonships
are
defined
with
regard
to
node
instances,
not
classes
of
nodes
– Two
nodes
represenEng
the
same
kind
of
“thing”
can
be
connected
in
very
different
ways
• Allows
for
structural
variaEon
in
the
domain
– Contrast
with
relaEonal
schemas,
where
foreign
key
relaEonships
apply
to
all
rows
in
a
table
• No
need
to
use
null
to
represent
the
absence
of
a
connecEon

15. Labels

16. Labels
• Every
node
can
have
zero
or
more
labels
• Used
to
represent
roles
(e.g.
user,
product,
company)
– Group
nodes
– Allow
us
to
associate
indexes
and
constraints
with
groups
of
nodes

17. Four
Building
Blocks
• Nodes
– EnEEes
• RelaEonships
– Connect
enEEes
and
structure
domain
• ProperEes
– EnEty
aWributes,
relaEonship
qualiEes,
and
metadata
• Labels
– Group
nodes
by
role

18. Designing
a
Graph
Model

19. Models
Purposeful
abstracEon
of
a
domain
designed
to
saEsfy
parEcular
applicaEon/end-­‐user
goals
Images:
en.wikipedia.org

20. Design
for
Queryability
MQuoedreyl

21. Method
1. IdenEfy
applicaEon/end-­‐user
goals
2. Figure
out
what
quesEons
to
ask
of
the
domain
3. IdenEfy
enEEes
in
each
quesEon
4. IdenEfy
relaEonships
between
enEEes
in
each
quesEon
5. Convert
enEEes
and
relaEonships
to
paths
– These
become
the
basis
of
the
data
model
6. Express
quesEons
as
graph
paWerns
– These
become
the
basis
for
queries

22. ApplicaEon/End-­‐User
Goals
As
an
employee
I
want
to
know
who
in
the
company
has
similar
skills
to
me
So
that
we
can
exchange
knowledge

23. QuesEons
To
Ask
of
the
Domain
As
an
employee
I
want
to
know
who
in
the
company
has
similar
skills
to
me
So
that
we
can
exchange
knowledge
Which
people,
who
work
for
the
same
company
as
me,
have
similar
skills
to
me?

24. IdenEfy
EnEEes
Which
people,
who
work
for
the
same
company
as
me,
have
similar
skills
to
me?
Person
Company
Skill

25. IdenEfy
RelaEonships
Between
EnEEes
Which
people,
who
work
for
the
same
company
as
me,
have
similar
skills
to
me?
Person
WORKS_FOR
Company
Person
HAS_SKILL
Skill

26. Convert
to
Cypher
Paths
RelaEonship
Person
WORKS_FOR
Company
Person
HAS_SKILL
Skill
Label
(:Person)-[:WORKS_FOR]->(:Company),
(:Person)-[:HAS_SKILL]->(:Skill)

27. Consolidate
Paths
(:Person)-[:WORKS_FOR]->(:Company),
(:Person)-[:HAS_SKILL]->(:Skill)
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)

28. Create
Person
Subgraph
MERGE (c:Company{name:'Acme'})
MERGE (p:Person{name:'Ian'})
MERGE (s1:Skill{name:'Java'})
MERGE (s2:Skill{name:'C#'})
MERGE (s3:Skill{name:'Neo4j'})
CREATE UNIQUE (c)<-[:WORKS_FOR]-(p),
(p)-[:HAS_SKILL]->(s1),
(p)-[:HAS_SKILL]->(s2),
(p)-[:HAS_SKILL]->(s3)
RETURN c, p, s1, s2, s3

29. Candidate
Data
Model
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)

30. Express
QuesEon
as
Graph
PaWern
Which
people,
who
work
for
the
same
company
as
me,
have
similar
skills
to
me?

31. 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

32. Graph
PaWern
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

33. Anchor
PaWern
in
Graph
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
If
an
index
for
Person.name
exists,
Cypher
will
use
it

34. Create
ProjecEon
of
Results
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

35. First
Match

36. Second
Match

37. Third
Match

38. Running
the
Query
+-----------------------------------+
| name | score | skills |
+-----------------------------------+
| "Lucy" | 2 | ["Java","Neo4j"] |
| "Bill" | 1 | ["Neo4j"] |
+-----------------------------------+
2 rows

39. 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
the
company
has
similar
skills
to
me
So
that
we
can
exchange
knowledge
Person
WORKS_FOR
Company
Person
HAS_SKILL
Skill
(:Company)<-[:WORKS_FOR]-(:Person)-[:HAS_SKILL]->(:Skill)
? Which
people,
who
work
for
the
same
company
as
me,
have
similar
skills
to
me?

40. Modeling
Tips

41. ProperEes
Versus
RelaEonships

42. Use
RelaEonships
When…
• You
need
to
specify
the
weight,
strength,
or
some
other
quality
of
the
rela#onship
• AND/OR
the
aWribute
value
comprises
a
complex
value
type
(e.g.
address)
• Examples:
– Find
all
my
colleagues
who
are
expert
(relaEonship
quality)
at
a
skill
(aWribute
value)
we
have
in
common
– Find
all
recent
orders
delivered
to
the
same
delivery
address
(complex
value
type)

43. Use
ProperEes
When…
• There’s
no
need
to
qualify
the
relaEonship
• AND
the
aWribute
value
comprises
a
simple
value
type
(e.g.
colour)
• Examples:
– Find
those
projects
wriWen
by
contributors
to
my
projects
that
use
the
same
language
(aWribute
value)
as
my
projects

44. If
Performance
is
CriEcal…
• Small
property
lookup
on
a
node
will
be
quicker
than
traversing
a
relaEonship
– But
traversing
a
relaEonship
is
sEll
faster
than
a
SQL
join…
• However,
many
small
proper#es
on
a
node,
or
a
lookup
on
a
large
string
or
large
array
property
will
impact
performance
– Always
performance
test
against
a
representaEve
dataset

45. RelaEonship
Granularity

46. Align
With
Use
Cases
• RelaEonships
are
the
“royal
road”
into
the
graph
• When
querying,
well-­‐named
relaEonships
help
discover
only
what
is
absolutely
necessary
– And
eliminate
unnecessary
porEons
of
the
graph
from
consideraEon

47. General
RelaEonships
• Qualified
by
property

48. Specific
RelaEonships

49. Best
of
Both
Worlds

50. Model
and
Query
Recipes

51. Events
and
AcEons
• Oken
involve
mulEple
parEes
• Can
include
other
circumstanEal
detail,
which
may
be
common
to
mulEple
events
• Examples
– Patrick
worked
for
Acme
from
2001
to
2005
as
a
Sokware
Developer
– Sarah
sent
an
email
to
Lucy,
copying
in
David
and
Claire

52. Timeline
Trees
• Discrete
events
– No
natural
relaEonships
to
other
events
• You
need
to
find
events
at
differing
levels
of
granularity
– Between
two
days
– Between
two
months
– Between
two
minutes

53. Example
Timeline
Tree

54. Pimalls
and
AnE-­‐PaWerns

55. Modeling
EnEEes
as
RelaEonships
• Limits
data
model
evoluEon
– A
relaEonship
connects
two
things
– Modeling
an
enEty
as
a
relaEonship
prevents
it
from
being
related
to
more
than
two
things
• Smells:
– Lots
of
aWribute-­‐like
properEes
– Heavy
use
of
relaEonship
indexes
• EnEEes
hidden
in
verbs:
– E.g.
emailed,
reviewed

56. Example:
Movie
Reviews
• IniEal
requirements:
– People
review
films
– ApplicaEon
aggregates
reviews
from
mulEple
sites

57. IniEal
Model

58. New
Requirements
• Allow
user
to
comment
on
each
other’s
reviews
– Can’t
connect
a
review
to
a
third
enEty

59. Revised
model

60. Model
AcEons
in
Terms
of
Products

61. Now
for
Some
Prototyping!

62. Draw
a
Model!
Eg.
Using
Visio,
www.apcjones.com/arrows,
hWp://graphjson.io,
Omnigraffle

63. CreaEng
a
prototype
DB
out
of
our
model?

64. Now
for
Some
Queries!

65. Next
meetup!
• January
22nd
:
how
to
create
an
APPLICATION
on
top
of
our
newly
created
database

66. BACKUP
slides:
Cypher
Query
Language

67. Nodes
and
RelaEonships
()-->()

68. Labels
and
RelaEonship
Types
(:Person)-[:FRIEND]->(:Person)

69. ProperEes
(:Person{name:'Peter'})-[:FRIEND]->(:Person{name:'Lucy'})

70. IdenEfiers
(p1:Person{name:'Peter'})-[r:FRIEND]->(p2:Person{name:'Lucy'})

71. Cypher
MATCH graph_pattern
WHERE binding_and_filter_criteria
RETURN results

72. Cypher
MATCH (p:Person)-[:FRIEND]->(friends)
WHERE p.name = 'Peter'
RETURN friends

73. Lookup
Using
IdenEfier
+
Label
MATCH (p:Person)-[:FRIEND]->(friends)
WHERE p.name = 'Peter'
RETURN friends