1. Gremlin 101.3
on your
FM Dial
Marko A. Rodriguez

2. Who are person 1’s friends?

3. SELECT p2.*
FROM person p1
INNER JOIN knows k
ON k.out = p1.id
INNER JOIN person p2
ON p2.id = k.in
WHERE p1.id = 1
person
1
knows
1 2
1 4
person
2
4
p2kp1

4. g.V(1).out(‘knows’)

5. g.V(1).out(‘knows’)

6. g.V(1).out(‘knows’)
1

7. g.V(1).out(‘knows’)
4
2

8. g.V(1).out(‘knows’)

9. g.V(1).out(‘knows’)

10. g.V(1).out(‘knows’)

11. g.V(1).out(‘knows’)

12. G ⟵μ t∈T ψ⟶Ψ
Rodriguez, M.A., “The Gremlin Graph Traversal Machine and Language,” ACM
Proceedings of the 15th Symposium on Database Programming Languages, 2015.
http://arxiv.org/abs/1508.03843

13. G ⟵μ t∈T ψ⟶Ψ
Graph
Traversers
Traversal

14. G ⟵μ t∈T ψ⟶Ψ
Graph
Traversers
Traversalobject steptraverser

15. g.V(1).out(‘knows’)Ψ
G
μ
ψ
tT
Graph
Traversers
Traversal
Vertices,Edges,Properties
Locations,Bulk,Sack,Path,Loops
Steps,SideEffects

16. Ψ
The Traversal

17. g.V(1).out(‘knows’)

18. g.V(1).out(‘knows’)
GraphStep VertexStep
compiles to

19. g.V(1).out(‘knows’)
GraphStep VertexStep
GraphStep VertexStep1 2 4
compiles to
executes as

20. g.V(1).out(‘knows’)
GraphStep VertexStep
GraphStep VertexStep1 2 4
GraphStep VertexStep1 2 4
compiles to
executes as
executes as

21. g.V(1).out(‘knows’)
ψ
GraphStep VertexStep1 2 4
→Graph

22. g.V(1).out(‘knows’)
ψ
GraphStep VertexStep1 2 4
Graph→Vertex

23. g.V(1).out(‘knows’)
ψ ψ
GraphStep VertexStep1 2 4
Vertex→Vertex

24. out : V→V
g.V(1).out(‘knows’)
knows
V : G→V1

25. out : V→V*
g.V(1).out(‘knows’)
knows
V : G→V*1

26. out : V→V*
g.V(1).out(‘knows’)
knows
V : G→V*1

27. out : V→V*
V ( )1
g.V(1).out(‘knows’)
knows
V : G→V*1

28. out : V→V*
g.V(1).out(‘knows’)
knows
V : G→V*1
1

29. out : V→V*
g.V(1).out(‘knows’)
knows
V : G→V*1
out ( )1knows

30. out : V→V*
g.V(1).out(‘knows’)
knows
V : G→V*1
2 4

31. g.V(1).out(‘knows’)
V ( )1out ( ) =knows 2 4

32. g.V(1).out(‘knows’).where(out(‘created’).has(‘name’,’lop’)).values(‘name’)
What are the names of the people that person
1 knows who created LinkedProcess (lop)?

33. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
What are the names of the people that person
1 knows who created LinkedProcess (lop)?

34. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
global scope
local scope
locally-global scope

35. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
global scope

36. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
global scope

37. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
i’m waiting…
I hope the vertex I
reference created lop!
local scope

38. i’m still waiting…
g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
33%
com
plete.
locally-global scope

39. i’m still waiting some more…
g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
uh
oh! bummer!
locally-global scope

40. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
damn!
my turn yet?
local scope

41. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)my turn finally!
local scope

42. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
prospermy child.
locally-global scope

43. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)one successful
offspring is all I need.
locally-global scope

44. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
woohoo!
live and learn :(
booyea!
locally-global scope

45. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
I reference a vertex
that created lop!
local scope

46. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
global scope

47. g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
I
reference
a
result.
global scope

48. Gremlin Language Traversal
g.V(1).out(‘knows’).
where(out(‘created’).has(‘name’,’lop’))
values(‘name’)
traversal = op((object | traversal)*)*

49. [[V,1][out,knows]
[where,[[out,created],[has,name,lop]]]
[values,name]]
Gremlin Bytecode Traversal
traversal = [[op,(object | traversal)*]*]

50. Gremlin Machine Traversal
GraphStep VertexStep
WhereStep VertexStep HasStep
PropertiesStep
traversal = step[(object | traversal)*]*

51. machine
code
machine
code
g.V().has('name','marko').
repeat(outE().identity().inV()).times(2).
name.groupCount()
[[V],[has,name,eq(marko)],
[repeat,[[outE],[identity],[inV]]],[times,2],
[values,name],[groupCount]]
[GraphStep,HasStep(name,eq(marko)),
RepeatStep([VertexStep(out,edges),IdentityStep,
EdgeVertexStep(in)],2),
PropertiesStep(values,name),GroupCountStep]
translate
compile
optimize
execute
languagebytecode
[ProviderGraphStep(name,eq(marko)),
VertexStep(out,vertices),NoOpBarrierStep,
VertexStep(out,vertices),NoOpBarrierStep,
PropertiesStep(values,name),GroupCountStep]

52. “FilterStep”
Rodriguez, M.A., “A Gremlin Implementation of the Gremlin Traversal Machine,”
DataStax Engineering Blog, October 2016.
https://www.datastax.com/dev/blog/a-gremlin-implementation-of-the-gremlin-traversal-machine

53. “FilterStep”

54. “FilterStep”

55. “FilterStep”

56. “FilterStep”

57. “FilterStep”

58. “FilterStep”

59. “FilterStep”

60. “MapStep”

61. “MapStep”

62. “MapStep”

63. “MapStep”

64. “MapStep”

65. “MapStep”

66. “MapStep”

67. “MapStep”

68. Chained and Nested Steps

69. Map: one-to-one
FlatMap: one-to-many
Filter: one-to-one.or.none
SideEffect: one-to[?]-one
Reducer: many-to-one
Barrier: many-to-many
Step Types

70. 1
label()
person
Map

71. 1
out(‘knows’)
label()
1 2 4
person
MapFlatMap

72. 1
out(‘knows’)
label()
1 2 4
person
MapFlatMap
Filter
1 hasLabel(‘android’)

73. 1
out(‘knows’)
label()
1 2 4
person
MapFlatMap
Filter
1 hasLabel(‘android’)
SideEffect
1 store(‘x’) 1
x=[ ]1

74. 1
out(‘knows’)
label()
1 2 4
person
MapFlatMap
Filter
1 hasLabel(‘android’)
SideEffect
1 store(‘x’) 1
x=[ ]1
Barrier
1 1
barrier()
1
2

75. 1
out(‘knows’)
label()
1 2 4
person
MapFlatMap
Filter
1 hasLabel(‘android’)
SideEffect
1 store(‘x’) 1
x=[ ]1
Barrier
1 1
barrier()
1
2
Reducer
2 4
count()
2

76. g.V().has(‘name’,’gremlin’)

77. g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’)
stash

78. g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’).
in(‘bought’)
stash

79. g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’).
in(‘bought’).has(‘name’,neq(‘gremlin’))
stash

80. g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’).
in(‘bought’).has(‘name’,neq(‘gremlin’)).
out(‘bought’)
stash
Failure
Is
An
Option

81. g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’).
in(‘bought’).has(‘name’,neq(‘gremlin’)).
out(‘bought’).not(within(‘stash’))
stash
Failure
Is
An
Option

82. Failure
Is
An
Option
g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’).
in(‘bought’).has(‘name’,neq(‘gremlin’)).
out(‘bought’).not(within(‘stash’)).
groupCount()
stash
Failure
Is
An
Option
3
1
1

83. Failure
Is
An
Option
g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’).
in(‘bought’).has(‘name’,neq(‘gremlin’)).
out(‘bought’).not(within(‘stash’)).
groupCount().
order(local).by(values,decr)
stash
Failure
Is
An
Option
3
1
1

84. Failure
Is
An
Option
g.V().has(‘name’,’gremlin’).
out(‘bought’).aggregate(‘stash’).
in(‘bought’).has(‘name’,neq(‘gremlin’)).
out(‘bought’).not(within(‘stash’)).
groupCount().
order(local).by(values,decr).
select(keys).unfold().limit(1)
stash
Failure
Is
An
Option
3
1
1

85. repeat().until()
until().repeat()
choose().option()
choose()
as()
select()
do-while
while-do
if-then-else
switch
var set
var get
Turing Completeness

86. Every modern programming language
supports function chaining and nesting.
g.V(1).out(“knows”)
g.V(1).out(‘knows’)
g.V(1).out(‘knows’)

87. Quiz

88. id()
out(‘created’)
values(‘name’)
count()
where(‘a’,eq(‘b’))
property(‘name’,’marko’)
label()
has(‘age’)
groupCount()
groupCount(’m’)
barrier()
repeat(out()).times(2)

89. id()
out(‘created’)
values(‘name’)
count()
where(‘a’,eq(‘b’))
property(‘name’,’marko’)
label()
has(‘age’)
groupCount()
groupCount(’m’)
barrier()
repeat(out()).times(2)
flatmap: vertex vertex*

90. id()
out(‘created’)
values(‘name’)
count()
where(‘a’,eq(‘b’))
property(‘name’,’marko’)
label()
has(‘age’)
groupCount()
groupCount(’m’)
barrier()
repeat(out()).times(2)
flatmap: vertex vertex*
reducer: object* long
map: element object
flatmap: vertex vertex*

91. id()
out(‘created’)
values(‘name’)
count()
where(‘a’,eq(‘b’))
property(‘name’,’marko’)
label()
has(‘age’)
groupCount()
groupCount(’m’)
barrier()
repeat(out()).times(2)
flatmap: vertex vertex*
reducer: object* long
map: element object
map: element string
reducer: object* map<object,long>
filter: object object
side-effect: object , ( ), object
flatmap: vertex vertex*

92. id()
out(‘created’)
values(‘name’)
count()
where(‘a’,eq(‘b’))
property(‘name’,’marko’)
label()
has(‘age’)
groupCount()
groupCount(’m’)
barrier()
repeat(out()).times(2)
flatmap: vertex vertex*
reducer: object* long
map: element object
map: element string
reducer: object* map<object,long>
filter: object object
filter: element element
side-effect: object , ( ), object
flatmap: vertex vertex*
side-effect: element ) element
barrier: object* object* flatmap: element string*

93. The traversal guides the traverser down the
long and winding graph.
Ψ
G
T

94. T
The Traversers

95. G ⟵μ t∈T ψ⟶Ψ
Graph
Traversers
Traversal

96. PathBulk
G ⟵μ t∈T ψ⟶Ψ
Traversers
β,Δ,ς,ι
Sack Loops

97. g.V().both().id()
1
2
4
knows
3
5
6
created
created
knows created
created
β

98. g.V().both().id()
1
2
4
knows
3
5
6
created
created
knows created
created
ψ

99. g.V().both().id()
1
2
4
knows
3
5
6
created
created
knows created
created
ψ

100. 1
2
4
knows
3
5
6
created
created
knows created
created
g.V().both().id()
ψ

101. 1
2
4
knows
3
5
6
created
created
knows created
created
g.V().both().id()
ψ
2
1
11
5
4
4
4
3
33 6

102. 1
2
4
knows
3
5
6
created
created
knows created
created
g.V().both().id()
ψ
2
1
11
5
4
4
4
3
33 6
==>2
==>5
==>1
==>1
==>1
==>4
==>4
==>4
==>3
==>3
==>3
==>6

103. 1
2
4
knows
3
5
6
created
created
knows created
created
g.V().both().id()
ψ
2
1
11
5
4
4
4
3
33 6
id( )5id( )2
id( )1
id( )4 id( )4
id( )1
id( )1 id( )4
id( )3 id( )3
id( )3
id( )6

104. Its time to bulk up.
Rodriguez, M.A., “The Beauty of Bulking,” Gremlin-Users Mailing List, June 2015.
https://twitter.com/twarko/status/606513003090092035

105. 1
2
4
knows
3
5
6
created
created
knows created
created
g.V().both().id()
ψ

106. 1
2
4
knows
3
5
6
created
created
knows created
created
1
3
1
3
3 1
g.V().both().id()
ψ

107. 1
2
4
knows
3
5
6
created
created
knows created
created
1
3
1
3
3 1
g.V().both().id()
ψ
2
1
5
4
3 6

108. 1
2
4
knows
3
5
6
created
created
knows created
created
1
3
1
3
3 1
g.V().both().id()
ψ ==>2
==>5
==>1
==>1
==>1
==>4
==>4
==>4
==>3
==>3
==>3
==>61
4
3 6
2 5

109. 1
2
4
knows
3
5
6
created
created
knows created
created
1
3
1
3
3 1
g.V().both().id()
ψ
1
4
3 6
id( )5id( )2
id( )1 id( )4
id( )3 id( )61/2 as many function
calls with bulking!
ballin’.
2 5

110. gremlin> g.V().both().id().explain()
==>Traversal Explanation
=======================================================================================================================
Original Traversal [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
ConnectiveStrategy [D] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
MatchPredicateStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
FilterRankingStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
InlineFilterStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
RepeatUnrollStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
PathRetractionStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
IncidentToAdjacentStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
AdjacentToIncidentStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
RangeByIsCountStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), IdStep]
LazyBarrierStrategy [O] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), IdStep]
TinkerGraphCountStrategy [P] [GraphStep(vertex,[]), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), IdStep]
TinkerGraphStepStrategy [P] [TinkerGraphStep(vertex,[]), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), IdStep]
ProfileStrategy [F] [TinkerGraphStep(vertex,[]), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), IdStep]
StandardVerificationStrategy [V] [TinkerGraphStep(vertex,[]), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), IdStep]
Final Traversal [TinkerGraphStep(vertex,[]), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), IdStep]
g.V().both().id()
g.V().both().barrier().id()
optimizes to

111. g.V(1).out(‘knows’).out(‘created’).path()
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1Δ=

112. g.V(1).out(‘knows’).out(‘created’).path()
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 2
[ ]1 4

113. g.V(1).out(‘knows’).out(‘created’).path()
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5
[ ]1 4 3

114. g.V(1).out(‘knows’).out(‘created’).path()
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5
[ ]1 4 3

115. Its time to get crazy with
locally scoped traversers.

116. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5
[ ]1 4 3

117. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5

118. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5

119. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5

120. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5
g.V(4).inE().count()
g.V(1).inE().count()
g.V(5).inE().count()

121. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5

122. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5
0
1
1

123. g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
[ ]1 4 5
0
1
1

124. [ ]
g.V(1).out(‘knows’).out(‘created’).limit(1).
path().by(inE().count())
1
2
4
knows
3
5
6
created
created
knows created
created
ψ
0 1 1

125. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ς

126. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ψ

127. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ψ
1

128. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ψ
1

129. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ψ
1
ι=0

130. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ψ
1ι=0
1
ι=0

131. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
1.0ι=0
0.5
ι=0
ψ

132. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ψ
1.0ι=0
0.5
ι=0

133. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
ψ
1.0ι=1
0.5
ι=1

134. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
1.0ι=1
0.5
ι=1
ψ
?
?

135. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
1.0ι=1
0.5
ι=1
ψ

136. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
1.0
ι=1
ψ
1.0
ι=1

137. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
0.4
ι=1
ψ
1.0
ι=1

138. ψ
g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
0.4
ι=11.0
ι=1

139. ψ
g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
0.4
ι=21.0
ι=2

140. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
0.4
ι=21.0
ι=2
ψ
!
!

141. g.withSack(1).V(1).
repeat(outE().sack(mult).by(‘weight’).inV()).
times(2).sack()
1
2
4
3
5
6
0.5
0.4
1.0 0.4
1.0
0.2
0.4
1.0
ψ
1.0
0.4

142. machine 2 machine 3machine 1
μ
ψ
The Gremlin traversal machine is a distributed virtual
machine — traversers can independently move around the
graph (across machines) and through the traversal.
ψt
ψt+1
μt+1
μt

143. Quiz

144. 2 2 Barrier
2 3
To Bulk or Not To Bulk

145. 2 2 Barrier
2 3
2
5
2 3 Barrier
2 3
To Bulk or Not To Bulk

146. 5
2 2 Barrier
2 3
2
5
2 3 Barrier
2 3
2 3
2 3
[ ]1 4 5
5
3
[ ]1 2 5
1
Barrier
To Bulk or Not To Bulk

147. 5
2 2 Barrier
2 3
2
5
2 3 Barrier
2 3
2 3
2 3
[ ]1 4 5
5
3
[ ]1 2 5
1
Barrier 5
[ ]1 4 5
5
3
[ ]1 2 5
1
4
1
4
3 72
Barrier
withSack(?,sum)
To Bulk or Not To Bulk

148. 5
2 2 Barrier
2 3
2
5
2 3 Barrier
2 3
2 3
2 3
[ ]1 4 5
5
3
[ ]1 2 5
1
Barrier 5
[ ]1 4 5
5
3
[ ]1 2 5
1
4
1
4
3 72
Barrier
4
4 9
withSack(?,sum)
2 2 Barrier
2 3
ι=1 ι=2
To Bulk or Not To Bulk

149. 5
2 2 Barrier
2 3
2
5
2 3 Barrier
2 3
2 3
2 3
[ ]1 4 5
5
3
[ ]1 2 5
1
Barrier 5
[ ]1 4 5
5
3
[ ]1 2 5
1
4
1
4
3 72
Barrier
4
4 9
withSack(?,sum)
2 2 Barrier
2 3
ι=1 ι=2
2 2
2 3
ι=1 ι=2
To Bulk or Not To Bulk
3 Barrier
3

150. 5
2 2 Barrier
2 3
2
5
2 3 Barrier
2 3
2 3
2 3
[ ]1 4 5
5
3
[ ]1 2 5
1
Barrier 5
[ ]1 4 5
5
3
[ ]1 2 5
1
4
1
4
3 72
Barrier
4
4 9
withSack(?,sum)
2 2 Barrier
2 3
ι=1 ι=2
2 2
2 3
ι=1 ι=2
To Bulk or Not To Bulk
3 Barrier
3
3
3

151. The traverser is an atomic unit of computing —
moving through both the traversal and the graph.

152. G
The Graph

153. Lattice Scale-Free Random
Rodriguez, M.A., “Graphoendodonticology,” DataStax Blog, March 2017.
https://www.datastax.com/2017/03/graphoendodonticology

154. Lattice Scale-Free Random
Diamond Jackson Pollock TV Fuzz

155. g.V().groupCount().by(both().count())
Lattice Scale-Free Random0.00.20.40.6
degree
frequency
0 1 2 3 4 0 5 10 15 20 25 30
0.00.20.4
degree
frequency
many vertices have
few edges
few vertices have
many edges
2 4 6 8 10
0.050.100.15
degree
frequency
gaussian
distribution

156. Lattice Scale-Free Random
g.V().repeat(both()).times(x).count()
2 4 6 8 10
0.0e+001.0e+102.0e+103.0e+10
path length
pathcount
scale-free
random
lattice

157. Scale-Free
!
"I’m cool.

158. Scale-Free
!
"

159. Scale-Free
!
"

160. Scale-Free
!
"
Why am I
holding a bomb?

161. Scale-Free
hub!
!
"

162. Scale-Free
explosion!
!

163. Scale-Free

164. Scale-Free
!
!
!
!
!

165. Scale-Free
!
! !
!
!
diiiiiiiamn.

166. Facebook Subgraph
|V| = 4039
|E| = 88234
Aliev, A., Rodriguez, M.A., “Reducing
Computational Complexity with Correlate
Traversals,” DataStax Blog, April 2017.
https://www.datastax.com/2017/04/reducing-computational-complexity-with-correlate-traversals

167. !
Yo.
Facebook Subgraph
|V| = 4039
|E| = 88234

168. !
Facebook Subgraph
|V| = 4039
|E| = 88234

169. So you want to be a graph surgeon?
* The same traversal on different graphs can have
wildly different performance characteristics.
* Traversals on scale-free/natural
graphs can easily explode due to
“hubs” (super nodes).
* Use Gremlin to study the
graph’s statistics before
using Gremlin to query the
graph.
* Build your traversals up
piecewise and use profile()
to find bottlenecks.

170. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐

171. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐
"
g.V(1).count()
1
Sup.

172. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐
g.V(1).out().count()
9

173. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐
g.V(1).out(‘watched’).count()
7

174. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐
g.V(1).outE(‘watched’)

175. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐
g.V(1).outE(‘watched’).has(‘stars’,gte(3))

176. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐
g.V(1).outE(‘watched’).has(‘stars’,gte(3))

177. knows knows
watchedwatched
watched
watched
watched
watched
watched
⭐⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐
⭐⭐⭐
g.V(1).outE(‘watched’).has(‘stars’,gte(3)).inV().count()
3

178. !
I’ll be more
selective this time.
Facebook Subgraph
|V| = 4039
|E| = 88234

179. Facebook Subgraph
|V| = 4039
|E| = 88234

180. Quiz

181. Controlling Traverser Populations
out(‘label’)
outE().has(‘key’,value).inV()
local(out().coin(0.5))
local(outE().sample(10).by(‘weight’).inV())
out()
local(out().limit(10))

182. Controlling Traverser Populations
out(‘label’)
outE().has(‘key’,value).inV()
local(out().coin(0.5))
local(outE().sample(10).by(‘weight’).inV())
out()
only edges in one direction
local(out().limit(10))

183. Controlling Traverser Populations
out(‘label’)
outE().has(‘key’,value).inV()
local(out().coin(0.5))
local(outE().sample(10).by(‘weight’).inV())
only edges w/ label
out()
only edges in one direction
local(out().limit(10))

184. Controlling Traverser Populations
out(‘label’)
outE().has(‘key’,value).inV()
local(out().coin(0.5))
local(outE().sample(10).by(‘weight’).inV())
only edges w/ label
only edges w/ property condition
out()
only edges in one direction
local(out().limit(10))

185. Controlling Traverser Populations
out(‘label’)
outE().has(‘key’,value).inV()
local(out().coin(0.5))
local(outE().sample(10).by(‘weight’).inV())
only edges w/ label
only edges w/ property condition
out()
only edges in one direction
local(out().limit(10))
only the first 10 edges

186. Controlling Traverser Populations
out(‘label’)
outE().has(‘key’,value).inV()
local(out().coin(0.5))
local(outE().sample(10).by(‘weight’).inV())
only edges w/ label
only edges w/ property condition
only 50% of edges
out()
only edges in one direction
local(out().limit(10))
only the first 10 edges

187. Controlling Traverser Populations
out(‘label’)
outE().has(‘key’,value).inV()
local(out().coin(0.5))
local(outE().sample(10).by(‘weight’).inV())
only edges w/ label
only edges w/ property condition
only 50% of edges
only 10 edges biased by weight
out()
only edges in one direction
local(out().limit(10))
only the first 10 edges

188. The computational complexity of the traversal is a
function of the structural complexity of the graph.

189. G ⟵μ t∈T ψ⟶Ψ
Graph
Traversers
Traversal

190. G ⟵μ t∈T ψ⟶Ψ
Graph
Traversers
Traversal
CPUs
ProgramData

191. The show is over.
Please tip your waitress and drive home safe!
traverse