Gremlin (query language)

• 2009-10-30 the project is born, and immediately named "TinkerPop"
• 2009-12-25 v0.1 is the first release
• 2011-05-21 v1.0 is released
• 2012-05-24 v2.0 is released
• 2015-01-16 TinkerPop becomes an Apache Incubator project
• 2015-07-09 v3.0.0-incubating is released
• 2016-05-23 Apache TinkerPop becomes a top-level project
• 2016-07-18 v3.1.3 and v3.2.1 are first releases as Apache TinkerPop
• 2017-12-17 v3.3.1 is released
• 2018-05-08 v3.3.3 is released
• 2019-08-05 v3.4.3 is released
• 2020-02-20 v3.4.6 is released

Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors. The table below outlines those graph vendors that support Gremlin.

The following examples of Gremlin queries and responses in a Gremlin-Groovy environment are relative to a graph representation of the [http://grouplens.org/datasets/movielens/ MovieLens] dataset.{{cite web|url=http://www.slideshare.net/slidarko/the-gremlin-traversal-language|title=The Gremlin Graph Traversal Language|accessdate=August 22, 2015|date=2015-08-19}} The dataset includes users who rate movies. Users each have one occupation, and each movie has one or more categories associated with it. The MovieLens graph schema is detailed below.

user--rated[stars:0-5]-->movie

user--occupation-->occupation

movie--category-->category

{{pull quote|For each vertex in the graph, emit its label, then group and count each distinct label.}}

gremlin> g.V().label().groupCount()

==>[occupation:21, movie:3883, category:18, user:6040]

{{pull quote|What year was the oldest movie made?}}

gremlin> g.V().hasLabel('movie').values('year').min()

==>1919

{{pull quote|What is Die Hard's average rating?}}

gremlin> g.V().has('movie','name','Die Hard').inE('rated').values('stars').mean()

==>4.121848739495798

{{pull quote|For each category, emit a map of its name and the number of movies it represents.}}

gremlin> g.V().hasLabel('category').as('a','b').

select('a','b').

by('name').

by(inE('category').count())

==>[a:Animation, b:105]

==>[a:Children's, b:251]

==>[a:Comedy, b:1200]

==>[a:Adventure, b:283]

==>[a:Fantasy, b:68]

==>[a:Romance, b:471]

==>[a:Drama, b:1603]

==>[a:Action, b:503]

==>[a:Crime, b:211]

==>[a:Thriller, b:492]

==>[a:Horror, b:343]

==>[a:Sci-Fi, b:276]

==>[a:Documentary, b:127]

==>[a:War, b:143]

==>[a:Musical, b:114]

==>[a:Mystery, b:106]

==>[a:Film-Noir, b:44]

==>[a:Western, b:68]

{{pull quote|For each movie with at least 11 ratings, emit a map of its name and average rating. Sort the maps in decreasing order by their average rating. Emit the first 10 maps (i.e. top 10).}}

gremlin> g.V().hasLabel('movie').as('a','b').

where(inE('rated').count().is(gt(10))).

select('a','b').

by('name').

by(inE('rated').values('stars').mean()).

order().by(select('b'),decr).

limit(10)

==>[a:Sanjuro, b:4.608695652173913]

==>[a:Seven Samurai (The Magnificent Seven), b:4.560509554140127]

==>[a:Shawshank Redemption, The, b:4.554557700942973]

==>[a:Godfather, The, b:4.524966261808367]

==>[a:Close Shave, A, b:4.52054794520548]

==>[a:Usual Suspects, The, b:4.517106001121705]

==>[a:Schindler's List, b:4.510416666666667]

==>[a:Wrong Trousers, The, b:4.507936507936508]

==>[a:Sunset Blvd. (a.k.a. Sunset Boulevard), b:4.491489361702127]

==>[a:Raiders of the Lost Ark, b:4.47772]

Gremlin supports declarative graph pattern matching similar to SPARQL. For instance, the following query below uses Gremlin's match()-step.

{{pull quote|What 80's action movies do 30-something programmers like? Group count the movies by their name and sort the group count map in decreasing order by value. Clip the map to the top 10 and emit the map entries.}}

gremlin> g.V().

match(

__.as('a').hasLabel('movie'),

__.as('a').out('category').has('name','Action'),

__.as('a').has('year',between(1980,1990)),

__.as('a').inE('rated').as('b'),

__.as('b').has('stars',5),

__.as('b').outV().as('c'),

__.as('c').out('occupation').has('name','programmer'),

__.as('c').has('age',between(30,40))).

select('a').groupCount().by('name').

order(local).by(valueDecr).

limit(local,10)

==>Raiders of the Lost Ark=26

==>Star Wars Episode V - The Empire Strikes Back=26

==>Terminator, The=23

==>Star Wars Episode VI - Return of the Jedi=22

==>Princess Bride, The=19

==>Aliens=18

==>Boat, The (Das Boot)=11

==>Indiana Jones and the Last Crusade=11

==>Star Trek The Wrath of Khan=10

==>Abyss, The=9

{{pull quote|Which movies are most central in the implicit 5-stars graph?}}

gremlin> g = graph.traversal(computer(SparkGraphComputer))

==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], sparkgraphcomputer]

gremlin> g.V().repeat(outE('rated').has('stars', 5).inV().

groupCount('m').by('name').

inE('rated').has('stars', 5).outV()).

times(4).cap('m')

==>Star Wars Episode IV - A New Hope 35405394353105332

==>American Beauty 31943228282020585

==>Raiders of the Lost Ark 31224779793238499

==>Star Wars Episode V - The Empire Strikes Back 30434677119726223

==>Godfather, The 30258518523013057

==>Shawshank Redemption, The 28297717387901031

==>Schindler's List 27539336654199309

==>Silence of the Lambs, The 26736276376806173

==>Fargo 26531050311325270

==>Matrix, The 26395118239203191

Gremlin is a virtual machine composed of an instruction set as well as an execution engine. An analogy is drawn between Gremlin and Java.

The following traversal is a Gremlin traversal in the Gremlin-Java8 dialect.

g.V().as("a").out("knows").as("b").

select("a","b").

by("name").

by("age")

The Gremlin language (i.e. the fluent-style of expressing a graph traversal) can be represented in any host language that supports function composition and function nesting. Due to this simple requirement, there exists various Gremlin dialects including Gremlin-Groovy, Gremlin-Scala, Gremlin-Clojure, etc. The above Gremlin-Java8 traversal is ultimately compiled down to a step sequence called a traversal. A string representation of the traversal above provided below.

[GraphStep([],vertex)@[a], VertexStep(OUT,[knows],vertex)@[b], SelectStep([a, b],[value(name), value(age)])]

The steps are the primitives of the Gremlin graph traversal machine. They are the parameterized instructions that the machine ultimately executes. The Gremlin instruction set is approximately 30 steps. These steps are sufficient to provide general purpose computing and what is typically required to express the common motifs of any graph traversal query.

Given that Gremlin is a language, an instruction set, and a virtual machine, it is possible to design another traversal language that compiles to the Gremlin traversal machine (analogous to how Scala compiles to the JVM). For instance, the popular SPARQL graph pattern match language can be compiled to execute on the Gremlin machine. The following SPARQL query

SELECT ?a ?b ?c

WHERE {

?a a Person .

?a ex:knows ?b .

?a ex:created ?c .

?b ex:created ?c .

?b ex:age ? d .

FILTER(?d < 30)

}

would compile to

[GraphStep([],vertex), MatchStep(AND,MatchStartStep(a), LabelStep, IsStep(eq(Person)), MatchEndStep], [MatchStartStep(a), VertexStep(OUT,[knows],vertex), MatchEndStep(b)], [MatchStartStep(a), VertexStep(OUT,[created],vertex), MatchEndStep(c)], [MatchStartStep(b), VertexStep(OUT,[created],vertex), MatchEndStep(c)], [MatchStartStep(b), PropertiesStep([age],value), MatchEndStep(d)], [MatchStartStep(d), IsStep(gt(30)), MatchEndStep), SelectStep([a, b, c])].

In Gremlin-Java8, the SPARQL query above would be represented as below and compile to the identical Gremlin step sequence (i.e. traversal).

g.V().match(

as("a").label().is("person"),

as("a").out("knows").as("b"),

as("a").out("created").as("c"),

as("b").out("created").as("c"),

as("b").values("age").as("d"),

as("d").is(gt(30))).

select("a","b","c")

The Gremlin graph traversal machine can execute on a single machine or across a multi-machine compute cluster. Execution agnosticism allows Gremlin to run over both graph databases (OLTP) and graph processors (OLAP).

• Cypher Query Language, another query language on graph data
• SPARQL, another query language on graph data
• Regular path query, a theoretical query language on graph data
• Graph query language, a proposed standardization of a query language on graph data

{{reflist}}

1. [http://tinkerpop.apache.org/ Apache TinkerPop Homepage]
2. [http://sql2gremlin.com/ sql2gremlin.com (TinkerPop2)]
3. Rodriguez, M.A., "[https://arxiv.org/abs/1508.03843 The Gremlin Graph Traversal Machine and Language]," Proceedings of the ACM Database Programming Languages Conference, October, 2015.

{{Apache Software Foundation}}

{{Query languages}}

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