Gremlin's Anatomy

Gremlin's Anatomy
Stephen Mallette
@spmallette
© 2018. All Rights Reserved.

What is a graph?

● A property graph is a collection of vertices and
edges
○ A vertex represents entities/domain objects
○ An edge represents a directional relationship between
two vertices
○ Vertices and edges have labels and properties
label: person
name: Stephen
label: company
name: DataStax
label: person
name: Paras
label: employs
since: 2015
label: employs
since: 2016
label: knows

How does Apache
TinkerPop support
graph processing?

Gremlin's Anatomy refers to the
study of the Gremlin graph
traversal language at a level
beyond just reference to the set
of steps that define the language
itself.

● Reasons to study Gremlin's Anatomy
○ Write better Gremlin
○ Improve ability to read more
complex Gremlin
○ Ask more concise questions if you
get stuck
○ Develop more robust and
expressive Domain Specific
Languages

g.V()
GraphTraversalSource
● Spawns GraphTraversal
instances with start
steps
● Holds configurations that
Gremlin will use in the
traversal

g.V().
has('movie', 'title',
'Young Guns').
outE().
groupCount()
GraphTraversal
● The steps that make up
the Gremlin language
● Each step returns a
GraphTraversal so that
steps can be chained
together
● The output of one step
becomes the input to the
next

g.V().
'Young Guns').
outE().
groupCount().
by()
Step Modulators
● Step modulators look
back on the previous
step and modifies their
behavior

g.V().
'Young Guns').
outE().
groupCount().
by(label())
Anonymous Traversal
● A traversal not bound to
a GraphTraversalSource
● Spawned from the
double underscore class
(e.g __.label())
● Usually exposed as
standalone functions for
better readability

g.V().
within('Young Guns',
'Hot Shots!')).
outE().
groupCount().
by(label())
Expressions
● Typically refers to string
tokens, enums or
Predicate values (i.e.
anything that makes
Gremlin more readable)
● Be aware of naming
collisions with steps and
other expressions (e.g.
P.not() and not()
step).

g.V().
within('Young Guns',
'Hot Shots!')).
outE().
groupCount().
by(label()).next()
Terminal Steps
● Steps that do not return a
traversal and instead
return the traversal result.
● Commonly used terminal
steps include hasNext(),
next(), iterate(), toList().
● If you don't have a terminal
step, you don't have a
result. You just have a
GraphTraversal.

The Gremlin Debugging Cycle
presents methods and
techniques that provide insight
into Gremlin statements of any
complexity for purposes of
learning, debugging, and crafting
traversals.

https://stackoverflow.com/a/47680050/1831717
g.V().has('name','alice').as('v').
repeat(outE().as('e').inV().as('v')).
until(has('name','alice')).
store('a').
by('name').
store('a').
by(select(all, 'v').unfold().values('name').fold()).
store('a').
by(select(all, 'e').unfold().
store('x').
by(union(values('value'),
select('x').count(local)).fold()).
cap('x').
store('a').
by(unfold().limit(local, 1).fold()).
unfold().
sack(assign).
by(constant(1d)).
sack(div).
by(union(constant(1d),
tail(local, 1)).sum()).
sack(mult).
by(limit(local, 1)).
sack().sum()).
cap('a')

label: rates
tag: ruby
value: 8
label: rates
tag: ruby
value: 9
Sample Graph
name: alice name: bobby name: cindy
label: rates
tag: ruby
value: 7
name: david
label: rates
tag: ruby
value: 6
name: eliza
label: rates
tag: java
value: 10

,,,/
(o o)
-----oOOo-(3)-oOOo-----
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
plugin activated: tinkerpop.tinkergraph
gremlin>

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
gremlin> g.addV().property('name','alice').as('a').
......1> addV().property('name','bobby').as('b').
......2> addV().property('name','cindy').as('c').
......3> addV().property('name','david').as('d').
......4> addV().property('name','eliza').as('e').
......5> addE('rates').from('a').to('b').property('tag','ruby').property('value',9).
......6> addE('rates').from('b').to('c').property('tag','ruby').property('value',8).
......7> addE('rates').from('c').to('d').property('tag','ruby').property('value',7).
......8> addE('rates').from('d').to('e').property('tag','ruby').property('value',6).
......9> addE('rates').from('e').to('a').property('tag','java').property('value',10).
.....10> iterate()
gremlin> graph
==>tinkergraph[vertices:5 edges:5]

gremlin> g.V().has('name','alice').as('v').
......1> repeat(outE().as('e').inV().as('v')).
......2> until(has('name','alice')).
......3> store('a').
......4> by('name').
......5> store('a').
......6> by(select(all, 'v').unfold().values('name').fold()).
......7> store('a').
......8> by(select(all, 'e').unfold().
......9> store('x').
.....10> by(union(values('value'),
.....11> select('x').count(local)).fold()).
.....12> cap('x').
.....13> store('a').
.....14> by(unfold().limit(local, 1).fold()).
.....15> unfold().
.....16> sack(assign).
.....17> by(constant(1d)).
.....18> sack(div).
.....19> by(union(constant(1d),
.....20> tail(local, 1)).sum()).
.....21> sack(mult).
.....22> by(limit(local, 1)).
.....23> sack().sum()).
.....24> cap('a')
==>[alice,[alice,bobby,cindy,david,eliza,alice],[9,8,7,6,10],18.833333333333332]

Isolate for
Analysis
Clarify
Results
Validate
Assumptions
1
23
Gremlin Debugging Cycle

Isolate a portion of the larger Gremlin statement
for analysis. The isolated portion should be only
as long as you are capable of following by just
reading the code.
Isolate for Analysis 1

store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
......2> until(has('name','alice'))
==>v[0] Isolate a portion of the larger Gremlin
statement for analysis. The isolated portion
should be only as long as you are capable of
following by just reading the code.

Be clear on the result being returned from the
current portion of Gremlin under review as it will
feed into the following steps that will later be
analyzed.
Clarify Results 2

......3> valueMap()
==>[name:[alice]]
Be clear on the result being returned from the
current portion of Gremlin under review as it
will feed into the following steps that will later
be analyzed.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
Clarify Results 2

If using DataStax Studio, consider converting
results containing vertices to incident edges,
using inE(), outE() or bothE(), which will allow
for a graph visualization of the result.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
Clarify Results 2

Validate any assumptions regarding the nature
of the traversal. Consider the traversal path,
side-effects, the graph schema or anything that
might be hidden by a naive view of the result.
Validate Assumptions 3

......3> path().next()
==>v[0]
==>e[10][0-rates->2]
==>v[2]
==>e[11][2-rates->4]
==>v[4]
==>e[12][4-rates->6]
==>v[6]
==>e[13][6-rates->8]
==>v[8]
==>e[14][8-rates->0]
==>v[0]
Validate any assumptions regarding the nature
of the traversal. Consider the traversal path,
the graph schema or anything that might be
hidden by a naive view of the result.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

......3> select('v')
==>[v[0],v[2],v[4],v[6],v[8],v[0]]
Step labels - created with as()-step - are a
type of side-effect. Have clarity in terms of
what they contain. Use select()-step to extract
their contents.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

Isolate for
Analysis
1
Validate
Assumptions
3
● Use values() or valueMap()
for vertex/edge results
● Visualize results
● Use path() to inspect what
has been traversed
● Inspect step labels with
select()
Clarify
Results
2

Slowly expand the portion of Gremlin being
analyzed to the next point of isolation and repeat
the debugging cycle.

......3> store('a').
......4> by('name')
==>v[0]
analyzed to the next point of isolation and
repeat the debugging cycle.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

Isolate for
Analysis
1
Validate
Assumptions
3
has been traversed
select()
● Inspect contents of side-
effects steps with cap()
Clarify
Results
2

......3> store('a').
......4> by('name').
......5> cap('a')
==>[alice]
As with step labels, verify the contents of side-
effects. In this case, determine what is being
stored in the "a" side-effect by using cap()-
step.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
Examine Side-effects 3

......3> store('a').
......4> by('name').
......5> store('a').
......6> by(select(all, 'v').unfold().values('name').fold())
==>v[0]
analyzed to the next point of isolation and
repeat the debugging cycle.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

......3> store('a').
......4> by('name').
......5> store('a').
......7> cap('a')
==>[alice,[alice,bobby,cindy,david,eliza,alice]]
As with step labels, verify the contents of side-
effects. In this case, determine what is being
stored in the "a" side-effect by using cap()-
step.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
Examine Side-effects 3

Isolate for
Analysis
1
Validate
Assumptions
3
has been traversed
select()
Clarify
Results
2
● Large or complex
anonymous traversals may
require their own isolated
analysis

......3> store('a').
......4> by('name').
......5> store('a').
......6> by(select(all, 'v')).
......7> cap('a')
==>[alice,[v[0],v[2],v[4],v[6],v[8],v[0]]]
To gain clarity on anonymous traversals,
isolate them for review as part of the
debugging cycle.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

Isolate for
Analysis
1
Validate
Assumptions
3
● Consider temporarily
promoting an anonymous
traversal to its parent if it
yields better clarity into the
result
has been traversed
select()
Clarify
Results
2
● Large or complex anonymous
traversals may require their
own isolated analysis

......3> select(all, 'v')
==>[v[0],v[2],v[4],v[6],v[8],v[0]]
......3> select(all, 'v').
......4> unfold().
......5> values('name')
==>alice
==>bobby
==>cindy
==>david
==>eliza
==>alice
......3> select(all, 'v').
......4> unfold().
......5> values('name').
......6> fold()
==>[alice,bobby,cindy,david,eliza,alice]
For analysis consider temporarily promoting
the anonymous traversal to the parent
traversal as it might allow better focus on the
actual result. In this case, the store() side-
effects are not relevant to the specific analysis
of the contents of the by() modulator.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
Clarify Results 2A
B
C

......3> store('a').
......4> by('name').
......5> store('a').
......7> store('a').
......9> store('x').
.....12> cap('x').
.....13> store('a').
.....14> by(unfold().limit(local, 1).fold())).
.....15> cap('a').next()
==>alice
==>[9,8,7,6,10]
==>[[9,0],[8,1],[7,2],[6,3],[10,4]]
It may be necessary to isolate anonymous
traversals if they are especially complex.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

Isolate for
Analysis
1
Validate
Assumptions
3
result
has been traversed
select()
Clarify
Results
2
● Isolation within an
anonymous traversal can
produce misleading
intermediate results -
including the end portion of
a traversal may be helpful

......3> store('a').
......4> by('name').
......5> store('a').
......7> store('a').
......9> store('x').
.....12> cap('x').
.....13> store('a').
.....15> unfold()).
.....16> cap('a').next()
==>alice
==>[9,8,7,6,10]
==>[9,0]
Analyzing portions of a traversal can produce
misleading intermediate results that might
leave an incorrect impression as to the overall
intention of the traversal when executed as a
whole.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

......3> store('a').
......4> by('name').
......5> store('a').
......7> store('a').
......9> store('x').
.....12> cap('x').
.....13> store('a').
.....15> unfold().
.....17> by(constant(1d))).
.....18> cap('a').next()
==>alice
==>[9,8,7,6,10]
==>[9,0]
Analyzing portions of a traversal can produce
misleading intermediate results that might
leave an incorrect impression as to the overall
intention of the traversal when executed as a
whole.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

......3> store('a').
......4> by('name').
......5> store('a').
......7> store('a').
......9> store('x').
.....12> cap('x').
.....13> store('a').
.....15> unfold().
.....18> sack().sum()).
.....19> cap('a').next()
==>alice
==>[9,8,7,6,10]
==>5.0
Include context from the end of a portion of a
traversal to help ensure the intent of the
overall traversal is consistent.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')

Isolate for
Analysis
1
Validate
Assumptions
3
result
● fold() can be a good
temporary replacement for
other reducing steps like
sum(), max(), min(), etc. as
it provides visibility to the
values of that computation
has been traversed
select()
Clarify
Results
2
● Isolation within an anonymous
traversal can produce
misleading intermediate
results - including the end
portion of a traversal may be
helpful

......3> store('a').
......4> by('name').
......5> store('a').
......7> store('a').
......9> store('x').
.....12> cap('x').
.....13> store('a').
.....15> unfold().
.....18> sack().fold()).
.....19> cap('a').next()
==>alice
==>[9,8,7,6,10]
==>[1.0,1.0,1.0,1.0,1.0]
Do not allow the actual steps in the traversal to
prevent visibility into the nature of the
computations that Gremlin is performing.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
Clarify Results 2

Isolate for
Analysis
1
Validate
Assumptions
3
result
sum(), max(), min(), etc. as it
provides visibility to the values
of that computation
● Isolate results of interest
has been traversed
select()
Clarify
Results
2
helpful

......3> store('a').
......4> by('name').
......5> store('a').
......7> store('a').
......9> store('x').
.....12> cap('x').
.....13> store('a').
.....15> unfold().
.....18> sack(div).
.....20> tail(local, 1)).sum()).
.....21> sack().fold()).
.....22> cap('a').unfold().skip(3).fold().next()
==>[1.0,0.5,0.3333333333333333,0.25,0.2]
Limit output as necessary to focus on the
aspects of the results that are most under
consideration.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
Clarify Results 2

Isolate for
Analysis
1
Validate
Assumptions
3
result
of that computation
has been traversed
select()
Clarify
Results
2
helpful
● Simulate a traversal from a
specific known input with
the inject() start step

gremlin> g.inject([9,0],[8,1],[7,2],[6,3],[10,4]).
......1> sack(assign).
......2> by(constant(1d)).
......3> sack(div).
......4> by(union(constant(1d),tail(local,1)).sum()).
......5> sack()
==>1.0
==>0.5
==>0.3333333333333333
==>0.25
==>0.2
gremlin> g.inject([10,4]).
......1> union(constant(1d),tail(local,1))
==>1.0
==>4
gremlin> g.inject([10,4]).
......1> union(constant(1d),tail(local,1)).
......2> sum()
==>5.0
gremlin> 1.0d / g.inject([10,4]).
......1> union(constant(1d),tail(local,1)).
......2> sum().next()
==>0.2
Lost? Reclaim focus by simulating just the
portion of the traversal that is not yet
understood.
store('a').
by('name').
store('a').
store('a').
store('x').
cap('x').
store('a').
unfold().
sack(assign).
by(constant(1d)).
sack(div).
sack(mult).
sack().sum()).
cap('a')
B
A
C
D

......3> store('a').
......4> by('name').
......5> store('a').
......7> store('a').
......9> store('x').
.....12> cap('x').
.....13> store('a').
.....15> unfold().
.....18> sack(div).
.....20> tail(local, 1)).sum()).
.....21> sack(mult).
.....22> by(limit(local, 1)).
.....23> sack().sum()).
.....24> cap('a')
==>[alice,[alice,bobby,cindy,david,eliza,alice],[9,8,7,6,10],18.833333333333332]
Overriding traversers with by()-
modulators
Creating indexed values
Using sack() for computations
Result construction with side-effects
Methods of cycle-detection
Traversal patterns identified as a result
of Gremlin Debugging Cycle
Step label access and collection
manipulation

Isolate for
Analysis
1
Validate
Assumptions
3
result
of that computation
has been traversed
select()
Clarify
Results
2
helpful
● Simulate a traversal from a
specific known input with the
inject() start step

What's next?

● Apache TinkerPop Website
○ http://tinkerpop.apache.org/
● Gremlin Recipes
○ http://tinkerpop.apache.org/docs/current/recipes/
● The Gremlin Compendium
○ http://www.doanduyhai.com/blog/?p=13460
● Practical Gremlin
○ http://kelvinlawrence.net/book/Gremlin-Graph-Guide.html
● Gremlin DSLs - Part I
○ https://www.datastax.com/dev/blog/gremlin-dsls-in-java-with-dse-graph
● Gremlin DSLs - Part II
○ https://academy.datastax.com/content/gremlin-dsls-python-dse-graph
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