See how graph helps solves complex problems with real world example

1. DSE Graph Fundamentals
Jon Haddad, DataStax
Technical Evangelist
@rustyrazorblade

2. © DataStax, All Rights Reserved.
A Quick Look at Relational
2

3. © DataStax, All Rights Reserved.
Tables
• Very structured data
• Relational calculus
• Various normal forms
3
id name title country
1 jon evangelist usa
2 patrick chief evangelist usa
3 rebecca jr evangelist canada
4 billy ceo usa

4. © DataStax, All Rights Reserved.
Selecting and Filtering
4
id name title country
1 jon evangelist usa
2 patrick chief evangelist usa
3 rebecca jr evangelist canada
4 billy ceo usa

5. © DataStax, All Rights Reserved.
Selecting and Filtering
4
id name title country
1 jon evangelist usa
2 patrick chief evangelist usa
3 rebecca jr evangelist canada
4 billy ceo usa
select * from people where country = 'canada';

6. © DataStax, All Rights Reserved.
Selecting and Filtering
4
id name title country
1 jon evangelist usa
2 patrick chief evangelist usa
3 rebecca jr evangelist canada
4 billy ceo usa
select * from people where country = 'canada';

7. © DataStax, All Rights Reserved.
Foreign Keys
5
id name title country
1 jon evangelist 1
2 patrick chief evangelist 1
3 rebecca jr evangelist 2
4 billy ceo 1
id name
1 usa
2 canada
3 germany
4 japan

8. © DataStax, All Rights Reserved.
Foreign Keys
5
id name title country
1 jon evangelist 1
2 patrick chief evangelist 1
3 rebecca jr evangelist 2
4 billy ceo 1
id name
1 usa
2 canada
3 germany
4 japan

9. © DataStax, All Rights Reserved.
Foreign Keys
5
id name title country
1 jon evangelist 1
2 patrick chief evangelist 1
3 rebecca jr evangelist 2
4 billy ceo 1
id name
1 usa
2 canada
3 germany
4 japan

10. © DataStax, All Rights Reserved.
Joins
6
select * from people join country on
people.country = country.id;
id name title country name
1 jon evangelist 1 usa
2 patrick chief evangelist 1 usa
3 rebecca jr evangelist 2 canada
4 billy ceo 1 usa

11. © DataStax, All Rights Reserved.
Many to Many Relationships
7
id name title
1 jon evangelist
2 patrick chief evangelist
3 rebecca jr evangelist
4 billy ceo
id name
1 usa
2 canada
3 germany
4 japan
user country
1 1
1 2
2 1
3 2
4 1

12. © DataStax, All Rights Reserved.
Many to Many Relationships
7
id name title
1 jon evangelist
2 patrick chief evangelist
3 rebecca jr evangelist
4 billy ceo
id name
1 usa
2 canada
3 germany
4 japan
user country
1 1
1 2
2 1
3 2
4 1

13. © DataStax, All Rights Reserved.
Many to Many Relationships
7
id name title
1 jon evangelist
2 patrick chief evangelist
3 rebecca jr evangelist
4 billy ceo
id name
1 usa
2 canada
3 germany
4 japan
user country
1 1
1 2
2 1
3 2
4 1

14. © DataStax, All Rights Reserved.
Many to Many Relationships
7
id name title
1 jon evangelist
2 patrick chief evangelist
3 rebecca jr evangelist
4 billy ceo
id name
1 usa
2 canada
3 germany
4 japan
user country
1 1
1 2
2 1
3 2
4 1

15. © DataStax, All Rights Reserved.
Many to Many Relationships
7
id name title
1 jon evangelist
2 patrick chief evangelist
3 rebecca jr evangelist
4 billy ceo
id name
1 usa
2 canada
3 germany
4 japan
user country
1 1
1 2
2 1
3 2
4 1

16. © DataStax, All Rights Reserved.
Joining
8
select * from people
join people_country on people.id = people_country.user
join country on people_country.country = country.id;
id name title country name
1 jon evangelist 1 usa
1 jon evangelist 2 canada
2 patrick chief evangelist 1 usa
3 rebecca jr evangelist 2 canada
4 billy ceo 1 usa

17. © DataStax, All Rights Reserved.
Joining
8
select * from people
join people_country on people.id = people_country.user
join country on people_country.country = country.id;
id name title country name
1 jon evangelist 1 usa
1 jon evangelist 2 canada
2 patrick chief evangelist 1 usa
3 rebecca jr evangelist 2 canada
4 billy ceo 1 usa

18. © DataStax, All Rights Reserved.
A Fun, Real World Example
9

19. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10

20. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
person
id
name
dob

21. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
person
id
name
dob

22. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob

23. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role

24. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob

25. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob

26. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob

27. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob
episode
tv_show
season
episode

28. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob
episode
tv_show
season
episode
actor_episode
episode
actor

29. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob
episode
tv_show
season
episode
actor_episode
episode
actor

30. © DataStax, All Rights Reserved.
Media Database
1. People - actors, directories, assistants
2. Movies, TV, episodes
10
movie
id
name
year
tv_show
id
name
person
id
name
dob
actor_movie
person
movie
role
actor_tv
person
tv_show
dob
episode
tv_show
season
episode
actor_episode
episode
actor

31. © DataStax, All Rights Reserved.
Problems
• Every entity gets a table
• Lots of many to many tables
• Very rigid structure
• Going from one to many
requires a migration and new
data model
11

32. © DataStax, All Rights Reserved.
Problems
• Every entity gets a table
• Lots of many to many tables
• Very rigid structure
• Going from one to many
requires a migration and new
data model
11

33. © DataStax, All Rights Reserved.
How is this Solved with a Graph?
12

34. © DataStax, All Rights Reserved.
Vertex
• A vertex represents a "thing"
• For example: Movie, Person
13

35. © DataStax, All Rights Reserved.
Vertex
• A vertex represents a "thing"
• For example: Movie, Person
13
JCVD

36. © DataStax, All Rights Reserved.
Vertex
• A vertex represents a "thing"
• For example: Movie, Person
13
JCVD
Time
cop

37. © DataStax, All Rights Reserved.
JCVD
Time
cop
Edge
• Edges are labeled relationships
• Edges have direction
14

38. © DataStax, All Rights Reserved.
JCVD
Time
cop
Edge
• Edges are labeled relationships
• Edges have direction
14
ActedIn

39. © DataStax, All Rights Reserved.
Edges are always many to many
15
JCVD
Time
cop
Blood
sport

40. © DataStax, All Rights Reserved.
Edges are always many to many
15
JCVD
Time
copActedIn
Blood
sport

41. © DataStax, All Rights Reserved.
Edges are always many to many
15
JCVD
Time
copActedIn
Blood
sport
ActedIn

42. © DataStax, All Rights Reserved.
Edges are always many to many
15
JCVD
Time
copActedIn
Blood
sport
ActedIn
Directed

43. © DataStax, All Rights Reserved.
The power of graph?
16

44. © DataStax, All Rights Reserved.
Relationships
17

45. © DataStax, All Rights Reserved.
Let's build on that
18

46. © DataStax, All Rights Reserved.
Properties
• Similar to fields in a table
• Much more flexible
• Meta properties - properties on properties
• Can be on an edge or a vertex
• Special property - a label
19
JCVD
status amazing
charm infinite
odds of rendezvous 99.6%
enemies decapitated 108747

47. © DataStax, All Rights Reserved.
Summary
1. We don't define tables
2. We create vertices to represent real
world objects
3. Relationships don't need to be
explicitly modeled
4. Expressing complex relationships is
not hard
5. Adding new edge types is easy
20

48. © DataStax, All Rights Reserved.
Tinkerpop 3 & Gremlin
• Cross database graph query framework
• API for graph
21

49. © DataStax, All Rights Reserved.
TinkerPop Basics
22

50. © DataStax, All Rights Reserved.
Create a Vertex
23
graph.createVertex(label, "person",
"name", "Jean Claude Van Damme")
graph.createVertex(label, "movie",
"name", "Bloodsport")

51. © DataStax, All Rights Reserved.
Traversals
24
g = graph.traversal()
g.V()

52. © DataStax, All Rights Reserved.
Finding Vertices
25
g.V().has("person", "name", "Jean Claude van Damme")

53. © DataStax, All Rights Reserved.
Finding Vertices
25
g.V().has("person", "name", "Jean Claude van Damme")

54. © DataStax, All Rights Reserved.
Finding Vertices
25
g.V().has("person", "name", "Jean Claude van Damme")

55. © DataStax, All Rights Reserved.
Finding Vertices
25
g.V().has("person", "name", "Jean Claude van Damme")

56. © DataStax, All Rights Reserved.
Finding Vertices
25
g.V().has("person", "name", "Jean Claude van Damme")

57. © DataStax, All Rights Reserved.
Create an Edge
26
g.V().has('person', 'name', "Jean Claude Van Damme").as('a').
V().has('movie', 'name', "Bloodsport").as('b').
addE("acted_in").property('year', 1988).
from("a").to("b")

58. © DataStax, All Rights Reserved.
Traversals
27
jcvd = g.V().has('person','name', "Jean Claude Van Damme")

59. © DataStax, All Rights Reserved.
Traversals to neighbors
28
Bloodsport (1988)
Kickboxer (1989)
Timecop (1994)jcvd.out()

60. © DataStax, All Rights Reserved.
Traversals to neighbors
28
Bloodsport (1988)
Kickboxer (1989)
Timecop (1994)jcvd.out()

61. © DataStax, All Rights Reserved.
Traversals to neighbors
28
Bloodsport (1988)
Kickboxer (1989)
Timecop (1994)jcvd.out()

62. © DataStax, All Rights Reserved.
Traversals to edges
29
Bloodsport (1988)
Kickboxer (1989)
Timecop (1994)
jcvd.outE()

63. © DataStax, All Rights Reserved.
Traversals to edges
29
Bloodsport (1988)
Kickboxer (1989)
Timecop (1994)
jcvd.outE()

64. © DataStax, All Rights Reserved.
Who cares?
30

65. © DataStax, All Rights Reserved. 31
SELECT movie.name from person
JOIN roles on person.id = roles.person
JOIN movies on roles.movie = movies.id
WHERE person.name = "Jean Claude Van Damme"

66. © DataStax, All Rights Reserved. 32
jcvd.out("acted_in")

67. © DataStax, All Rights Reserved. 33

68. © DataStax, All Rights Reserved. 33
NO

69. © DataStax, All Rights Reserved. 34
jcvd.out("acted_in", "directed")

70. © DataStax, All Rights Reserved. 35
SELECT movies.name from person
JOIN roles on person.id = roles.person
JOIN movies on roles.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN directed on person.id = directed.person
JOIN movies on directed.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"

71. © DataStax, All Rights Reserved.
SELECT movies.name from person
JOIN roles on person.id = roles.person
JOIN movies on roles.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN directed on person.id = directed.person
JOIN movies on directed.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN produced on person.id = produced.person
JOIN movies on produced.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN stage_hand on person.id = stage_hand.person
JOIN movies on stage_hand.movie = movies.id

72. © DataStax, All Rights Reserved.
SELECT movies.name from person
JOIN directed on person.id = directed.person
JOIN movies on directed.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN produced on person.id = produced.person
JOIN movies on produced.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN stage_hand on person.id = stage_hand.person
JOIN movies on stage_hand.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN stage_hand on person.id = stage_hand.person
JOIN movies on stage_hand.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"

73. © DataStax, All Rights Reserved.
SELECT movies.name from person
JOIN directed on person.id = directed.person
JOIN movies on directed.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN produced on person.id = produced.person
JOIN movies on produced.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN stage_hand on person.id = stage_hand.person
JOIN movies on stage_hand.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"
UNION
SELECT movies.name from person
JOIN stage_hand on person.id = stage_hand.person
JOIN movies on stage_hand.movie = movies.id
WHERE actor.name = "Jean Claude Van Damme"

74. © DataStax, All Rights Reserved. 37
jcvd.out()

75. © DataStax, All Rights Reserved.
Who acted with Van Damme
and was born in Texas?
38

76. © DataStax, All Rights Reserved.
Traversals
39
jcvd.out("acted_in")
jcvd
acted_in
acted_in
time cop
bloodsport

77. © DataStax, All Rights Reserved.
Traversals
39
jcvd.out("acted_in")
jcvd
acted_in
acted_in
.in("acted_in")
time cop
bloodsport

78. © DataStax, All Rights Reserved.
Traversals
39
jcvd.out("acted_in")
jcvd
acted_in
acted_in
.in("acted_in")
ron
silveracted_in
Bruce
McGill
acted_in
Forest
Whitaker
acted_in
New York
Texas
Texas
time cop
bloodsport

79. © DataStax, All Rights Reserved.
Traversals
39
jcvd.out("acted_in")
jcvd
acted_in
acted_in
.in("acted_in")
ron
silveracted_in
Bruce
McGill
acted_in
Forest
Whitaker
acted_in
New York
Texas
Texas
time cop
bloodsport

80. © DataStax, All Rights Reserved.
Traversals
39
jcvd.out("acted_in")
jcvd
acted_in
acted_in
.has("state_of_birth", "Texas")
.in("acted_in")
ron
silveracted_in
Bruce
McGill
acted_in
Forest
Whitaker
acted_in
New York
Texas
Texas
time cop
bloodsport

81. © DataStax, All Rights Reserved.
Traversals
39
jcvd.out("acted_in")
jcvd
acted_in
acted_in
.has("state_of_birth", "Texas")
.in("acted_in")
ron
silveracted_in
Bruce
McGill
acted_in
Forest
Whitaker
acted_in
New York
Texas
Texas
time cop
bloodsport

82. © DataStax, All Rights Reserved.
Creating Indexes
40
schema.vertexLabel('person').index('by_year_born')
.materialized().by('year_born').add()
schema.vertexLabel('person').index('search').
search().by('biography').asText().add()

83. © DataStax, All Rights Reserved.
Creating Indexes
40
schema.vertexLabel('person').index('by_year_born')
.materialized().by('year_born').add()
schema.vertexLabel('person').index('search').
search().by('biography').asText().add()

84. © DataStax, All Rights Reserved.
Creating Indexes
40
schema.vertexLabel('person').index('by_year_born')
.materialized().by('year_born').add()
schema.vertexLabel('person').index('search').
search().by('biography').asText().add()

85. © DataStax, All Rights Reserved.
Who has ANY relationship with any actor born in 1920?
41
g.V().has("person", "year_born", 1920).
out("acted_in").in().hasLabel("person")

86. © DataStax, All Rights Reserved.
index selection
Who has ANY relationship with any actor born in 1920?
41
g.V().has("person", "year_born", 1920).
out("acted_in").in().hasLabel("person")

87. © DataStax, All Rights Reserved.
Who has ANY relationship with any actor born in 1920?
41
g.V().has("person", "year_born", 1920).
out("acted_in").in().hasLabel("person")
unconstrained relationships with no foreign keys

88. © DataStax, All Rights Reserved.
How do I know Patrick?
42

89. © DataStax, All Rights Reserved.
Path Finding!
43

90. © DataStax, All Rights Reserved. 44
jon = g.V().has("nickname", "rustyrazorblade")
jon.until(has("nickname", "PatrickMcFadin")).
repeat(both())

91. © DataStax, All Rights Reserved.
My cousin went to school with Patrick
45
@rustyrazorblade
@PatrickMcfadin

92. © DataStax, All Rights Reserved.
My cousin went to school with Patrick
45
@rustyrazorblade
@PatrickMcfadin
cousin
@oscar_the_grouch

93. © DataStax, All Rights Reserved.
My cousin went to school with Patrick
45
@rustyrazorblade
@PatrickMcfadin
cousin
@oscar_the_grouch
attended
Cal Poly

94. © DataStax, All Rights Reserved.
My cousin went to school with Patrick
45
attended
@rustyrazorblade
@PatrickMcfadin
cousin
@oscar_the_grouch
attended
Cal Poly

95. © DataStax, All Rights Reserved.
We have a mutual friend in the same company
46

96. © DataStax, All Rights Reserved.
We have a mutual friend in the same company
46
friend

97. © DataStax, All Rights Reserved.
We have a mutual friend in the same company
46
friend
works_at

98. © DataStax, All Rights Reserved.
We have a mutual friend in the same company
46
friend
works_at
works_at

99. © DataStax, All Rights Reserved.
When do you use graph?
47

100. © DataStax, All Rights Reserved.
Graph helps solves complex
problems by utilizing of the
power of relationships
48

101. © DataStax, All Rights Reserved. 49

102. © DataStax, All Rights Reserved. 49
Thank you!!!