DataStax | Graph Computing with Apache TinkerPop (Marko Rodriguez) | Cassandra Summit 2016

Gremlin’s Graph Traversal Machinery
Dr. Marko A. Rodriguez
Director of Engineering at DataStax, Inc.
Project Management Committee, Apache TinkerPop
http://tinkerpop.apache.org

f : X ! X
The function f is a process that maps a structure of type X to a structure of type X.

f(x1) = x2
The function f maps the object x1 (from the set of X) to the object x2 (from the set of X).
x1 2 X x2 2 X

f(x)
Assume that this step rotates an X by 90°.
90°

90°
The algorithm of the step is a “black box.”

90°
A traverser wraps a value of type V.
class Traverser<V> {
V value;
}
V value;
}

90°
The step maps an integer traverser to an integer traverser.
V value;
}
V value;
}
Traverser<Integer> Traverser<Integer>

90°
A traverser of with a rotation of 0° becomes a traverser with a rotation of 90°.
Traverser(0) Traverser(90)
V value;
}
V value;
}

Both the input and output traversers are of type Traverser<Integer>.
90°
T[N] ! T[N]
2 T[N]
2 T[N]

A stream of input traversers…
90°

…yields a stream of output traversers.
90°

A traverser can have a bulk which denotes how many V values it represents.
90°
V value;
long bulk;
}
V value;
long bulk;
}

4
4
Bulking groups identical traversers to reduce the number of evaluations of a step.
90°
V value;
long bulk;
}
V value;
long bulk;
}

A variegated stream of input traversers yields a variegated stream of output traversers.
90°

1
2
1 1
1
2
Bulking can reduce the size of the stream.
90°
V value;
long bulk;
}
V value;
long bulk;
}

If the order of the stream does not matter…
90°

…then reordering can increase the likelihood of bulking.
90°
1
21
total bulk = 4
total count = 4
total bulk = 4
total count = 3

90°
180°
270°
360°
A traversal is a list of one or more steps.
90° 90° 90°
90°

f : X ! Y
Different functions can yield different mappings between different domains and ranges.
h : Y ! Z

The output of f can be the input to h because the range of f is the domain of h (i.e. Y).
Y y = f(x)
Z z = h(y)

x · f · h = z
h(f(x)) = z
readable
unreadable
≣

z = x.f().h().next()
z = x · f · h
stream/iterator/traversalhead/start

90° 225°
315°
Diﬀerent types of steps exist and their various compositions yield query expressivity.

90° 225°
315°
Steps process traverser streams/ﬂows and their composition is a traversal/iterator.
= . (). ().next()90° 225°

repeat( ).times(2)
180°
Anonymous traversals can serve as step arguments.
90°
traversal with a single step
90°

repeat( ).times(2)
180°
Some functions serve as step-modulators instead of steps.
90°
…groupCount().by(out().count())
…select(“a”,”b”).by(“name”)
…addE(“knows”).from(“a”).to(“b”)
…order().by(“age”,decr)

repeat( ).times(2)
≣
90° 90°
180°
During optimization, traversal strategies may rewrite a traversal to a more eﬃcient,
semantically equivalent form.
90°
RepeatUnrollStrategy
interface TraversalStrategy {
void apply(Traversal traversal);
Set<TraversalStrategy> applyPrior();
Set<TraversalStrategy> applyPost();
}

repeat( ).until(0°)90°
Continuously process a traverser until some condition is met.

repeat( ).until(0°)
4 loops
2 loops
1 loop
90° 180° 360°
90°do while(x != )
repeat().until() provides do/while-semantics.
90°
≣

until(0°).repeat( )
0 loops
2 loops
1 loop
90° 180° 0°
90°dowhile(x != )
90°
≣
until().repeat() provides while/do-semantics.

until(0°).repeat( )
3
Even if the traversers in the input stream can not be bulked, the output traversers may be able to be.
90°

filter(x)map(x) sideEffect(x)flatMap(x)
one-to-one one-to-many one-to-(one-or-none) one-to-same
m

out(“knows”)
has(“name”,”gremlin”)
groupCount(“m”)
where(“a”,eq(“b”))
select(“a”,”b”)
path()
mean()
sum()
count()
groupCount()
many-to-one(reducers)
order()
values(“age”)
values(“name”)
and(x,y)
or(x,y)
coin(0.5)
sample(10)
simplePath()
dedup()
store(“m”)
tree(“m”)
subgraph(“m”)
in(“created”)
group(“m”)
m
label()
id()
* A collection of examples. Not the full step library.
match(x,y,z)
properties()
outE(“knows”)
V()

values(“age”)
out(“knows”)
path()
groupCount()
simplePath()
m
label()
outE(“knows”)
group(“m”)
V()

T[V [ E] ! T[N+
]
values(“age”)
out(“knows”)
path()
groupCount()
simplePath()
m
label()
outE(“knows”)
T[?] ! T[path]
T[V [ E] ! T[string]
T[?] ! T[?]
T[V [ E] ! ; [ T[V [ E]
T[?] ! ; [ T[?]
T[V ] ! T[V ]⇤
T[V ] ! T[E]⇤
group(“m”)
V()T[?] ! T[map[?, N+
]]
T[G] ! T[V ]⇤

T[V [ E] ! T[N+
]
values(“age”)
out(“knows”)
groupCount()
T[V [ E] ! ; [ T[V [ E]
T[V ] ! T[V ]⇤
V()T[?] ! T[map[?, N+
]]
T[G] ! T[V ]⇤
g.V().has(“name”,”gremlin”).
out(“knows”).values(“age”).
groupCount()

T[V [ E] ! T[N+
]values(“age”)
out(“knows”)
groupCount()
T[V ] ! T[V ]⇤
V()
T[?] ! T[map[?, N+
]]
T[G] ! T[V ]⇤
groupCount()
Steps can be composed if their respective domains and ranges match.
T[V [ E] ! ; [ T[V [ E]

values(“age”)
out(“knows”)
groupCount()
T[V ] ! T[V ]⇤
V() T[G] ! T[V ]⇤
groupCount()
T[V ] ! ; [ T[V ]
T[V ] ! T[N+
]
T[N+
] ! T[map[N+
, N+
]]

groupCount()
What is the distribution of ages of the people that Gremlin knows?

groupCount()
one graph to many vertices
(ﬂatMap)
…

groupCount()
(ﬂatMap)
one vertex
to that vertex or no vertex
(ﬁlter)
?
…

groupCount()
(flatMap)
one vertex
(filter)
one vertex
to many friend vertices
(flatMap)
?
…
name=gremlin

groupCount()
(flatMap)
one vertex
(filter)
one vertex
(flatMap)
one vertex to
one age value
(map)
?
…
37
name=gremlin

groupCount()
(flatMap)
one vertex
(filter)
one vertex
(flatMap)
one vertex to
one age value
(map)
many age values
to an age distribution
(map — reducer)
?
…
37 [37:2, 41:1,
24:1, 35:4]37
37
24
35
35
35
35 41
name=gremlin

The Gremlin Traversal Language
The Gremlin Traversal Machine

a b c
a b c
Traversal creation via
step composition
Step parameterization via
traversal and constant nesting
a().b().c()
a(b().c()).d(x)d(x)
function
com
position
function
nesting
ﬂuent m
ethods
m
ethod
argum
ents
Any language that supports function composition and function nesting can host Gremlin.
Gremlin Traversal Language

V value;
long bulk;
}
class Step<S,E> {
Traverser<E> processNextStart();
}
f(x)
class Traversal<S,E> implements Iterator<E> {
E next();
Traverser<E> nextTraverser();
}
The fundamental constructs of Gremlin’s machinery.
Gremlin Traversal Machine
interface TraversalStrategy {
void apply(Traversal traversal);
Set<TraversalStrategy> applyPrior();
Set<TraversalStrategy> applyPost();
}
a db c
a de
≣

1
Bytecode
Gremlin-Java
g.V(1).
repeat(out(“knows”)).times(2).
groupCount().by(“age”)
[[V, 1]
[repeat, [[out, knows]]]
[times, 2]
[groupCount]
[by, age]]
Traversal GraphStep GroupCountStep
RepeatStep
VertexStep
GraphStep GroupCountStepVertexStep VertexStep
Traversal
Strategies
translates
compiles
optimizes
executes
[29:2, 30:1,
31:1, 35:10]

1
Bytecode
Gremlin-Python
g.V(1).
repeat(out(‘knows’)).times(2).
groupCount().by(‘age’)
[[V, 1]
[times, 2]
[groupCount]
[by, age]]
RepeatStep
VertexStep
Traversal
Strategies
translates
compiles
optimizes
executes
[29:2, 30:1,
31:1, 35:10]

1
Bytecode
Gremlin-Python
g.V(1).
repeat(out(‘knows’)).times(2).
groupCount().by(‘age’)
[[V, 1]
[times, 2]
[groupCount]
[by, age]]
RepeatStep
VertexStep
Traversal
Strategies
translates
compiles
optimizes
executes
[29:2, 30:1,
31:1, 35:10]
Gremlin language variant
Language agnostic bytecode
Execution engine assembly
Execution engine optimization
Execution engine evaluation
LanguageMachine

LanguageMachine
Gremlin-Python
Gremlin-Groovy
Gremlin-Java
Gremlin-JavaScriptGremlin-Ruby
Gremlin-Scala
Gremlin-Clojure
Bytecode
Java-based Implementation
?
?-based Implementation
?
?

Python 2.7.2 (default, Oct 11 2012, 20:14:37)
[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from gremlin_python.structure.graph import Graph
>>> from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
Gremlin-Python
CPython

>>> graph = Graph()
>>> g = graph.traversal().withRemote(DriverRemoteConnection('ws://localhost:8182','g'))
Gremlin-Python
DriverRemoteConnection
CPython

>>> graph = Graph()
# nested traversal with Python slicing and attribute interception extensions
>>> g.V().hasLabel("person").repeat(both()).times(2).name[0:2].toList()
[u'marko', u'marko']
Gremlin-Python
Bytecode
CPython JVM

>>> graph = Graph()
# nested traversal with Python slicing and attribute interception extensions
>>> g.V().hasLabel("person").repeat(both()).times(2).name[0:2].toList()
[u'marko', u'marko']
# a complex, nested multi-line traversal
>>> g.V().match(
... as_(“a”).out("created").as_(“b”),
... as_(“b”).in_(“created").as_(“c”),
... as_(“a”).out("knows").as_(“c”)).
... select("c").
... union(in_(“knows"),out("created")).
... name.toList()
[u'ripple', u'marko', u'lop']
>>>
Gremlin-Python
Bytecode
CPython JVM

Cypher
Bytecode
Distinct query languages (not only Gremlin language variants) can generate bytecode for
evaluation by any OLTP/OLAP TinkerPop-enabled graph system.

SELECT DISTINCT ?c
WHERE {
?a v:label "person" .
?a e:created ?b .
?b v:name ?c .
?a v:age ?d .
FILTER (?d > 30)
}
[
[V],
[match,
[[as, a], [hasLabel, person]],
[[as, a], [out, created], [as, b]],
[[as, a], [has, age, gt(30)]]],
[select, b],
[by, name],
[dedup]
]
Bytecode

Graph
Database
OLTP
Graph
Processor
OLAP
Bytecode
Bytecode
Bytecode
TinkerGraph
DSE Graph
Titan
Neo4j
OrientDB
IBM Graph
…
TinkerComputer
Spark
Giraph
Hadoop
Fulgora
…
Traversal
Traversal
Traversal
Gremlin traversals can be executed against OLTP graph databases and OLAP graph processors.
That means that if, e.g., SPARQL compiles to bytecode, it can execute both OLTP and OLAP.

Graph
Database
OLTP
Graph
Processor
OLAP
Traversal
Traversal
Traversal
TraversalStrategies TraversalStrategies
optimizes
Graph system providers (OLTP/OLAP) typically have custom strategies registered
with the Gremlin traversal machine that take advantage of unique, provider-speciﬁc features.

Most OLTP graph systems have a traversal strategy that combines
[V,has*]-sequences into a single global index-based flatMap-step.
groupCount()
one graph to many vertices using index lookup
(flatMap)
GraphStepStrategy
(flatMap)
one vertex to that vertex or no vertex
(filter)
?
…
compiles
optimizes
name=gremlin
DataStax
Enterprise Graph

Most OLAP graph systems have a traversal strategy that bypasses Traversal semantics
and implements reducers using the native API of the system.
g.V().count()
one graph to long
(map — reducer)
rdd.count() 12,146,934
compiles
(ﬂatMap)
many vertices to long
(map — reducer)
… 12,146,934
optimizes
SparkInterceptorStrategy
…

Physical Machine
DataProgram Traversal
Heap/DiskMemory Memory
Memory/Graph System
Physical Machine
Java
Virtual Machine
bytecode
steps
DataProgram
Memory/DiskMemory
Physical Machine
instructions
Java
Virtual Machine
Gremlin
Traversal Machine
From the physical computing machine to the Gremlin traversal machine.

Stakeholders
Language Providers
Gremlin Language Variant
Distinct Query Language
Application Developers Graph System Providers
OLAP Provider
OLTP Provider

Stakeholders
Application Developers
One query language for
all OLTP/OLAP systems.
GremlinG = (V, E)
Real-time and analytic queries are represented in Gremlin.
Graph
Database
OLTP
Graph
Processor
OLAP

Stakeholders
No vendor lock-in.
Apache TinkerPop as the JDBC for graphs.
DataStax
Enterprise Graph

Stakeholders
No vendor lock-in.
Gremlin is embedded in
the developer’s language.
Iterator<String> result =
g.V().hasLabel(“person”).
order().by(“age”).
limit(10).values(“name”)
vs.
ResultSet result = statement.executeQuery(
“SELECT name FROM People n” +
“ ORDER BY age n” +
“ LIMIT 10”)
Grem
lin-Java
SQL
in
Java
No “fat strings.” The developer writes their graph database/processor
queries in their native programming language.

Stakeholders
Language Providers
Easy to generate bytecode.
GraphTraversal.getMethods()
.findAll { GraphTraversal.class == it.returnType }
.collect { it.name }
.unique()
.each {
pythonClass.append(
""" def ${it}(self, *args):
self.bytecode.add_step(“${it}”, *args)
return self
“””)}
Gremlin-Python’s source code is
programmatically generated using Java reﬂection.

Stakeholders
Language Providers
Bytecode executes against
TinkerPop-enabled systems.
Language providers write a translator for the Gremlin traversal machine,
not a particular graph database/processor.
DataStax
Enterprise Graph

Graph
Database
OLTP
Graph
Processor
OLAP
Stakeholders
Language Providers
Provider can focus on design,
not evaluation.
The language designer does not have to concern themselves with
OLTP or OLAP execution. They simply generate bytecode and the
Gremlin traversal machine handles the rest.

Easy to implement core
interfaces.
Graph System Providers
Stakeholders
OLAP Provider
OLTP Provider
Vertex
Edge
Graph
Transaction
TraversalStrategy
Property
key=value
?
? ?

Provider supports all
provided languages.
interfaces.
Stakeholders
OLAP Provider
OLTP Provider
The provider automatically supports all query languages
that have compilers that generate Gremlin bytecode.

OLTP providers can leverage
existing OLAP systems.
provided languages.
interfaces.
Stakeholders
OLAP Provider
OLTP Provider
DSE Graph leverages SparkGraphComputer for OLAP processing.
DataStax
Enterprise Graph

Stakeholders
Language Providers
Application Developers Graph System Providers
OLAP Provider
OLTP Provider
No vendor lock-in.
Gremlin is embedded in
the developer’s language.
Provider can focus on design,
not evaluation.
interfaces.
provided languages.
OLTP providers can leverage
existing OLAP systems.

Thank you.
http://tinkerpop.apache.org
http://www.datastax.com/products/datastax-enterprise-graph

DataStax | Graph Computing with Apache TinkerPop (Marko Rodriguez) | Cassandra Summit 2016

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DataStax | Graph Computing with Apache TinkerPop (Marko Rodriguez) | Cassandra Summit 2016