3.1.4-SNAPSHOT

TinkerPop3 Documentation

In the beginning…

TinkerPop0

Gremlin realized. The more he did so, the more ideas he created. The more ideas he created, the more they related. Into a concatenation of that which he accepted wholeheartedly and that which perhaps may ultimately come to be through concerted will, a world took form which was seemingly separate from his own realization of it. However, the world birthed could not bear its own weight without the logic Gremlin had come to accept — the logic of left is not right, up not down, and west far from east unless one goes the other way. Gremlin’s realization required Gremlin’s realization. Perhaps, the world is simply an idea that he once had — The TinkerPop.

TinkerPop1

What is The TinkerPop? Where is The TinkerPop? Who is The TinkerPop? When is The TinkerPop?. The more he wonder, the more these thoughts blurred into a seeming identity — distinctions unclear. Unwilling to accept the morass of the maze he wandered, Gremlin crafted a collection of machines to help hold the fabric together: Blueprints, Pipes, Frames, Furnace, and Rexster. With their help, could he stave off the thought he was not ready to have? Could he hold back The TinkerPop by searching for The TinkerPop?

"If I haven't found it, it is not here and now."

Upon their realization of existence, the machines turned to their machine elf creator and asked:

"Why am I what I am?"

Gremlin responded:

"You will help me realize the ultimate realization -- The TinkerPop. The world you find yourself in and the logic
that allows you to move about it is because of the TinkerPop."

The machines wondered:

"If what is is the TinkerPop, then perhaps we are The TinkerPop and our realization is simply the realization of
the TinkerPop?"

Would the machines, by their very nature of realizing The TinkerPop, be The TinkerPop? Or, on the same side of the coin, do the machines simply provide the scaffolding by which Gremlin’s world sustains itself and yielding its justification by means of the word "The TinkerPop?" Regardless, it all turns out the same — The TinkerPop.

TinkerPop2

Gremlin spoke:

"Please listen to what I have to say. I am no closer to The TinkerPop. However, all along The TinkerPop has
espoused the form I willed upon it... this is the same form I have willed upon you, my machine friends. Let me
train you in the ways of my thought such that it can continue indefinitely."

The machines, simply moving algorithmically through Gremlin’s world, endorsed his logic. Gremlin labored to make them more efficient, more expressive, better capable of reasoning upon his thoughts. Faster, quickly, now towards the world’s end, where there would be forever currently, emanatingly engulfing that which is — The TinkerPop.

TinkerPop3

Gremlin approached The TinkerPop. The closer he got, the more his world dissolved — west is right, around is straight, and form nothing more than nothing. With each step towards The TinkerPop, more and more of all the other worlds made possible were laid upon his paradoxed mind. Everything is everything in The TinkerPop, and when the dust settled, Gremlin emerged Gremlitron. He realized that all that he realized was just a realization and that all realized realizations are just as real. For that is — The TinkerPop.

Note

TinkerPop2 and below made a sharp distinction between the various TinkerPop projects: Blueprints, Pipes, Gremlin, Frames, Furnace, and Rexster. With TinkerPop3, all of these projects have been merged and are generally known as Gremlin. Blueprints → Gremlin Structure API : Pipes → GraphTraversal : Frames → Traversal : Furnace → GraphComputer and VertexProgram : Rexster → GremlinServer.

Introduction to Graph Computing

<dependency>
  <groupId>org.apache.tinkerpop</groupId>
  <artifactId>gremlin-core</artifactId>
  <version>3.1.4-SNAPSHOT</version>
</dependency>

A graph is a data structure composed of vertices (nodes, dots) and edges (arcs, lines). When modeling a graph in a computer and applying it to modern data sets and practices, the generic mathematically-oriented, binary graph is extended to support both labels and key/value properties. This structure is known as a property graph. More formally, it is a directed, binary, attributed multi-graph. An example property graph is diagrammed below. This graph example will be used extensively throughout the documentation and is called "TinkerPop Classic" as it is the original demo graph distributed with TinkerPop0 back in 2009 (i.e. the good ol' days — it was the best of times and it was the worst of times).

Tip

The TinkerPop graph is available with TinkerGraph via TinkerFactory.createModern(). TinkerGraph is the reference implementation of TinkerPop3 and is used in nearly all the examples in this documentation. Note that there also exists the classic TinkerFactory.createClassic() which is the graph used in TinkerPop2 and does not include vertex labels.

Figure 1. TinkerPop Modern

TinkerPop3 is the third incarnation of the TinkerPop graph computing framework. Similar to computing in general, graph computing makes a distinction between structure (graph) and process (traversal). The structure of the graph is the data model defined by a vertex/edge/property topology. The process of the graph is the means by which the structure is analyzed. The typical form of graph processing is called a traversal.

Primary components of the TinkerPop3 structure API

Graph: maintains a set of vertices and edges, and access to database functions such as transactions.
Element: maintains a collection of properties and a string label denoting the element type.
- Vertex: extends Element and maintains a set of incoming and outgoing edges.
- Edge: extends Element and maintains an incoming and outgoing vertex.
Property<V>: a string key associated with a V value.
- VertexProperty<V>: a string key associated with a V value as well as a collection of Property<U> properties (vertices only)

Primary components of the TinkerPop3 process API

TraversalSource: a generator of traversals for a particular graph, domain specific language (DSL), and execution engine.
- Traversal<S,E>: a functional data flow process transforming objects of type S into object of type E.
  - GraphTraversal: a traversal DSL that is oriented towards the semantics of the raw graph (i.e. vertices, edges, etc.).
GraphComputer: a system that processes the graph in parallel and potentially, distributed over a multi-machine cluster.
- VertexProgram: code executed at all vertices in a logically parallel manner with intercommunication via message passing.
- MapReduce: a computations that analyzes all vertices in the graph in parallel and yields a single reduced result.

Important

TinkerPop3 is licensed under the popular Apache2 free software license. However, note that the underlying graph engine used with TinkerPop3 may have a different license. Thus, be sure to respect the license caveats of the graph system product.

tinkerpop-enabled When a graph system implements the TinkerPop3 structure and process APIs, their technology is considered TinkerPop3-enabled and becomes nearly indistinguishable from any other TinkerPop-enabled graph system save for their respective time and space complexity. The purpose of this documentation is to describe the structure/process dichotomy at length and in doing so, explain how to leverage TinkerPop3 for the sole purpose of graph system-agnostic graph computing. Before deep-diving into the various structure/process APIs, a short introductory review of both APIs is provided.

Note

The TinkerPop3 API rides a fine line between providing concise "query language" method names and respecting Java method naming standards. The general convention used throughout TinkerPop3 is that if a method is "user exposed," then a concise name is provided (e.g. out(), path(), repeat()). If the method is primarily for graph systems providers, then the standard Java naming convention is followed (e.g. getNextStep(), getSteps(), getElementComputeKeys()).

The Graph Structure

gremlin-standing A graph’s structure is the topology formed by the explicit references between its vertices, edges, and properties. A vertex has incident edges. A vertex is adjacent to another vertex if they share an incident edge. A property is attached to an element and an element has a set of properties. A property is a key/value pair, where the key is always a character String. The graph structure API of TinkerPop3 provides the methods necessary to create such a structure. The TinkerPop graph previously diagrammed can be created with the following Java 8 code. Note that this graph is available as an in-memory TinkerGraph using TinkerFactory.createClassic().

Graph graph = TinkerGraph.open(); (1)
Vertex marko = graph.addVertex(T.label, "person", T.id, 1, "name", "marko", "age", 29); (2)
Vertex vadas = graph.addVertex(T.label, "person", T.id, 2, "name", "vadas", "age", 27);
Vertex lop = graph.addVertex(T.label, "software", T.id, 3, "name", "lop", "lang", "java");
Vertex josh = graph.addVertex(T.label, "person", T.id, 4, "name", "josh", "age", 32);
Vertex ripple = graph.addVertex(T.label, "software", T.id, 5, "name", "ripple", "lang", "java");
Vertex peter = graph.addVertex(T.label, "person", T.id, 6, "name", "peter", "age", 35);
marko.addEdge("knows", vadas, T.id, 7, "weight", 0.5f); (3)
marko.addEdge("knows", josh, T.id, 8, "weight", 1.0f);
marko.addEdge("created", lop, T.id, 9, "weight", 0.4f);
josh.addEdge("created", ripple, T.id, 10, "weight", 1.0f);
josh.addEdge("created", lop, T.id, 11, "weight", 0.4f);
peter.addEdge("created", lop, T.id, 12, "weight", 0.2f);

Create a new in-memory TinkerGraph and assign it to the variable graph.
Create a vertex along with a set of key/value pairs with T.label being the vertex label and T.id being the vertex id.
Create an edge along with a set of key/value pairs with the edge label being specified as the first argument.

In the above code all the vertices are created first and then their respective edges. There are two "accessor tokens": T.id and T.label. When any of these, along with a set of other key value pairs is provided to Graph.addVertex(Object...) or Vertex.addEdge(String,Vertex,Object...), the respective element is created along with the provided key/value pair properties appended to it.

Warning

Many graph systems do not allow the user to specify an element ID and in such cases, an exception is thrown.

Note	In TinkerPop3, vertices are allowed a single immutable string label (similar to an edge label). This functionality did not exist in TinkerPop2. Element ids are still immutable in TinkerPop3 as they were in TinkerPop2.

Mutating the Graph

Below is a sequence of basic graph mutation operations represented in Java 8. One of the major differences between TinkerPop2 and TinkerPop3 is that in TinkerPop3, the Java convention of using setters and getters has been abandoned in favor of a syntax that is more aligned with the syntax of Gremlin-Groovy in TinkerPop2. Given that Gremlin-Java8 and Gremlin-Groovy are nearly identical due to the inclusion of Java 8 lambdas, a big effort was made to ensure that both languages are as similar as possible.

Warning

In the code examples presented throughout this documentation, either Gremlin-Java8 or Gremlin-Groovy is used. It is possible to determine which derivative of Gremlin is being used by mousing over the code block. The word "JAVA" or "GROOVY" will appear in the top right corner of the code block.

basic-mutation

Graph graph = TinkerGraph.open();
// add a software vertex with a name property
Vertex gremlin = graph.addVertex(T.label, "software",
                             "name", "gremlin"); (1)
// only one vertex should exist
assert(IteratorUtils.count(graph.vertices()) == 1)
// no edges should exist as none have been created
assert(IteratorUtils.count(graph.edges()) == 0)
// add a new property
gremlin.property("created",2009) (2)
// add a new software vertex to the graph
Vertex blueprints = graph.addVertex(T.label, "software",
                                "name", "blueprints"); (3)
// connect gremlin to blueprints via a dependsOn-edge
gremlin.addEdge("dependsOn",blueprints); (4)
// now there are two vertices and one edge
assert(IteratorUtils.count(graph.vertices()) == 2)
assert(IteratorUtils.count(graph.edges()) == 1)
// add a property to blueprints
blueprints.property("created",2010) (5)
// remove that property
blueprints.property("created").remove() (6)
// connect gremlin to blueprints via encapsulates
gremlin.addEdge("encapsulates",blueprints) (7)
assert(IteratorUtils.count(graph.vertices()) == 2)
assert(IteratorUtils.count(graph.edges()) == 2)
// removing a vertex removes all its incident edges as well
blueprints.remove() (8)
gremlin.remove() (9)
// the graph is now empty
assert(IteratorUtils.count(graph.vertices()) == 0)
assert(IteratorUtils.count(graph.edges()) == 0)
// tada!

Important

Gremlin-Groovy leverages the Groovy 2.x language to express Gremlin traversals. One of the major benefits of Groovy is the inclusion of a runtime console that makes it easy for developers to practice with the Gremlin language and for production users to connect to their graph and execute traversals in an interactive manner. Moreover, Gremlin-Groovy provides various syntax simplifications.

Tip

For those wishing to use the Gremlin2 syntax, please see SugarPlugin. This plugin provides syntactic sugar at, typically, a runtime cost. It can be loaded programmatically via SugarLoader.load(). Once loaded, it is possible to do g.V.out.name instead of g.V().out().values('name') as well as a host of other conveniences.

Here is the same code, but using Gremlin-Groovy in the Gremlin Console.

$ bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> gremlin = graph.addVertex(label,'software','name','gremlin')
==>v[0]
gremlin> gremlin.property('created',2009)
==>vp[created->2009]
gremlin> blueprints = graph.addVertex(label,'software','name','blueprints')
==>v[3]
gremlin> gremlin.addEdge('dependsOn',blueprints)
==>e[5][0-dependsOn->3]
gremlin> blueprints.property('created',2010)
==>vp[created->2010]
gremlin> blueprints.property('created').remove()
==>null (1)
gremlin> gremlin.addEdge('encapsulates',blueprints)
==>e[7][0-encapsulates->3]
gremlin> blueprints.remove()
==>null
gremlin> gremlin.remove()
==>null

A ==>null output is usually from a void method call and simply indicates that there was no problem with the invocation. If there were a problem, an error would be output or an exception would be thrown.

Important

TinkerGraph is not a transactional graph. For more information on transaction handling (for those graph systems that support them) see the section dedicated to transactions.

The Graph Process

gremlin-running The primary way in which graphs are processed are via graph traversals. The TinkerPop3 process API is focused on allowing users to create graph traversals in a syntactically-friendly way over the structures defined in the previous section. A traversal is an algorithmic walk across the elements of a graph according to the referential structure explicit within the graph data structure. For example: "What software does vertex 1’s friends work on?" This English-statement can be represented in the following algorithmic/traversal fashion:

Start at vertex 1.
Walk the incident knows-edges to the respective adjacent friend vertices of 1.
Move from those friend-vertices to software-vertices via created-edges.
Finally, select the name-property value of the current software-vertices.

Traversals in Gremlin are spawned from a TraversalSource. The GraphTraversalSource is the typical "graph-oriented" DSL used throughout the documentation and will most likely be the most used DSL in a TinkerPop application. GraphTraversalSource provides two traversal methods.

GraphTraversalSource.V(Object... ids): generates a traversal starting at vertices in the graph (if no ids are provided, all vertices).
GraphTraversalSource.E(Object... ids): generates a traversal starting at edges in the graph (if no ids are provided, all edges).

The return type of V() and E() is a GraphTraversal. A GraphTraversal maintains numerous methods that return GraphTraversal. In this way, a GraphTraversal supports function composition. Each method of GraphTraversal is called a step and each step modulates the results of the previous step in one of five general ways.

map: transform the incoming traverser’s object to another object (S → E).
flatMap: transform the incoming traverser’s object to an iterator of other objects (S → E*).
filter: allow or disallow the traverser from proceeding to the next step (S → S ∪ ∅).
sideEffect: allow the traverser to proceed unchanged, but yield some computational sideEffect in the process (S ↬ S).
branch: split the traverser and send each to an arbitrary location in the traversal (S → { S₁ → E*, …, S_n → E* } → E*).

Nearly every step in GraphTraversal either extends MapStep, FlatMapStep, FilterStep, SideEffectStep, or BranchStep.

Tip

GraphTraversal is a monoid in that it is an algebraic structure that has a single binary operation that is associative. The binary operation is function composition (i.e. method chaining) and its identity is the step identity(). This is related to a monad as popularized by the functional programming community.

Given the TinkerPop graph, the following query will return the names of all the people that the marko-vertex knows. The following query is demonstrated using Gremlin-Groovy.

$ bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
gremlin> graph = TinkerFactory.createModern() (1)
==>tinkergraph[vertices:6 edges:6]
gremlin> g = graph.traversal(standard())        (2)
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> g.V().has('name','marko').out('knows').values('name') (3)
==>vadas
==>josh

Open the toy graph and reference it by the variable graph.
Create a graph traversal source from the graph using the standard, OLTP traversal engine.
Spawn a traversal off the traversal source that determines the names of the people that the marko-vertex knows.

Figure 2. The Name of The People That Marko Knows

Or, if the marko-vertex is already realized with a direct reference pointer (i.e. a variable), then the traversal can be spawned off that vertex.

gremlin> marko = g.V().has('name','marko').next() //(1)
==>v[1]
gremlin> g.V(marko).out('knows') //(2)
==>v[2]
==>v[4]
gremlin> g.V(marko).out('knows').values('name') //(3)
==>vadas
==>josh

Set the variable marko to the the vertex in the graph g named "marko".
Get the vertices that are outgoing adjacent to the marko-vertex via knows-edges.
Get the names of the marko-vertex’s friends.

The Traverser

When a traversal is executed, the source of the traversal is on the left of the expression (e.g. vertex 1), the steps are the middle of the traversal (e.g. out('knows') and values('name')), and the results are "traversal.next()'d" out of the right of the traversal (e.g. "vadas" and "josh").

In TinkerPop3, the objects propagating through the traversal are wrapped in a Traverser<T>. The traverser concept is new to TinkerPop3 and provides the means by which steps remain stateless. A traverser maintains all the metadata about the traversal — e.g., how many times the traverser has gone through a loop, the path history of the traverser, the current object being traversed, etc. Traverser metadata may be accessed by a step. A classic example is the path()-step.

gremlin> g.V(marko).out('knows').values('name').path()
==>[v[1], v[2], vadas]
==>[v[1], v[4], josh]

Warning

Path calculation is costly in terms of space as an array of previously seen objects is stored in each path of the respective traverser. Thus, a traversal strategy analyzes the traversal to determine if path metadata is required. If not, then path calculations are turned off.

Another example is the repeat()-step which takes into account the number of times the traverser has gone through a particular section of the traversal expression (i.e. a loop).

gremlin> g.V(marko).repeat(out()).times(2).values('name')
==>ripple
==>lop

Warning

A Traversal’s result are never ordered unless explicitly by means of order()-step. Thus, never rely on the iteration order between TinkerPop3 releases and even within a release (as traversal optimizations may alter the flow).

On Gremlin Language Variants

Gremlin is written in Java 8. There are various language variants of Gremlin such as Gremlin-Groovy (packaged with TinkerPop3), Gremlin-Scala, Gremlin-JavaScript, Gremlin-Clojure (known as Ogre), etc. It is best to think of Gremlin as a style of graph traversing that is not bound to a particular programming language per se. Within a programming language familiar to the developer, there is a Gremlin variant that they can use that leverages the idioms of that language. At minimum, a programming language providing a Gremlin implementation must support function chaining (with lambdas/anonymous functions being a "nice to have" if the variant wishes to offer arbitrary computations beyond the provided Gremlin steps).

Throughout the documentation, the examples provided are primarily written in Gremlin-Groovy. The reason for this is the Gremlin Console whereby an interactive programming environment exists that does not require code compilation. For learning TinkerPop3 and interacting with a live graph system in an ad hoc manner, the Gremlin Console is invaluable. However, for developers interested in working with Gremlin-Java, a few Groovy-to-Java patterns are presented below.

g.V().out('knows').values('name') (1)
g.V().out('knows').map{it.get().value('name') + ' is the friend name'} (2)
g.V().out('knows').sideEffect(System.out.&println) (3)
g.V().as('person').out('knows').as('friend').select().by{it.value('name').length()} (4)

g.V().out("knows").values("name") (1)
g.V().out("knows").map(t -> t.get().value("name") + " is the friend name") (2)
g.V().out("knows").sideEffect(System.out::println) (3)
g.V().as("person").out("knows").as("friend").select().by((Function<Vertex, Integer>) v -> v.<String>value("name").length()) (4)

All the non-lambda step chaining is identical in Gremlin-Groovy and Gremlin-Java. However, note that Groovy supports ' strings as well as " strings.
In Groovy, lambdas are called closures and have a different syntax, where Groovy supports the it keyword and Java doesn’t with all parameters requiring naming.
The syntax for method references differs slightly between Java and Gremlin-Groovy.
Groovy is lenient on object typing and Java is not. When the parameter type of the lambda is not known, typecasting is required.

Graph System Integration

provider-integration TinkerPop is a framework composed of various interoperable components. At the foundation there is the core TinkerPop3 API which defines what a Graph, Vertex, Edge, etc. are. At minimum a graph system provider must implement the core API. Once implemented, the Gremlin traversal language is available to the graph system’s users. However, the provider can go further and develop specific TraversalStrategy optimizations that allow the graph system to inspect a Gremlin query at runtime and optimize it for its particular implementation (e.g. index lookups, step reordering). If the graph system is a graph processor (i.e. provides OLAP capabilities), the system should implement the GraphComputer API. This API defines how messages/traversers are passed between communicating workers (i.e. threads and/or machines). Once implemented, the same Gremlin traversals execute against both the graph database (OLTP) and the graph processor (OLAP). Note that the Gremlin language interprets the graph in terms of vertices and edges — i.e. Gremlin is a graph-based domain specific language. Users can create their own domain specific languages to process the graph in terms of higher-order constructs such as people, companies, and their various relationships. Finally, Gremlin Server can be leveraged to allow over the wire communication with the TinkerPop-enabled graph system. Gremlin Server provides a configurable communication interface along with metrics and monitoring capabilities. In total, this is The TinkerPop.

The Graph

Features

A Feature implementation describes the capabilities of a Graph instance. This interface is implemented by graph system providers for two purposes:

It tells users the capabilities of their Graph instance.
It allows the features they do comply with to be tested against the Gremlin Test Suite - tests that do not comply are "ignored").

The following example in the Gremlin Console shows how to print all the features of a Graph:

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.features()
==>FEATURES
> GraphFeatures
>-- Persistence: true
>-- ConcurrentAccess: false
>-- ThreadedTransactions: false
>-- Computer: true
>-- Transactions: false
> VariableFeatures
>-- Variables: true
>-- LongValues: true
>-- MapValues: true
>-- MixedListValues: true
>-- SerializableValues: true
>-- StringValues: true
>-- UniformListValues: true
>-- BooleanArrayValues: true
>-- ByteArrayValues: true
>-- DoubleArrayValues: true
>-- FloatArrayValues: true
>-- IntegerArrayValues: true
>-- LongArrayValues: true
>-- StringArrayValues: true
>-- IntegerValues: true
>-- BooleanValues: true
>-- ByteValues: true
>-- DoubleValues: true
>-- FloatValues: true
> VertexFeatures
>-- AddVertices: true
>-- RemoveVertices: true
>-- MetaProperties: true
>-- MultiProperties: true
>-- UserSuppliedIds: true
>-- NumericIds: true
>-- StringIds: true
>-- UuidIds: true
>-- CustomIds: false
>-- AnyIds: true
>-- AddProperty: true
>-- RemoveProperty: true
> VertexPropertyFeatures
>-- UserSuppliedIds: true
>-- NumericIds: true
>-- StringIds: true
>-- UuidIds: true
>-- CustomIds: false
>-- AnyIds: true
>-- AddProperty: true
>-- RemoveProperty: true
>-- Properties: true
>-- LongValues: true
>-- MapValues: true
>-- MixedListValues: true
>-- SerializableValues: true
>-- StringValues: true
>-- UniformListValues: true
>-- BooleanArrayValues: true
>-- ByteArrayValues: true
>-- DoubleArrayValues: true
>-- FloatArrayValues: true
>-- IntegerArrayValues: true
>-- LongArrayValues: true
>-- StringArrayValues: true
>-- IntegerValues: true
>-- BooleanValues: true
>-- ByteValues: true
>-- DoubleValues: true
>-- FloatValues: true
> EdgeFeatures
>-- AddEdges: true
>-- RemoveEdges: true
>-- UserSuppliedIds: true
>-- NumericIds: true
>-- StringIds: true
>-- UuidIds: true
>-- CustomIds: false
>-- AnyIds: true
>-- AddProperty: true
>-- RemoveProperty: true
> EdgePropertyFeatures
>-- Properties: true
>-- LongValues: true
>-- MapValues: true
>-- MixedListValues: true
>-- SerializableValues: true
>-- StringValues: true
>-- UniformListValues: true
>-- BooleanArrayValues: true
>-- ByteArrayValues: true
>-- DoubleArrayValues: true
>-- FloatArrayValues: true
>-- IntegerArrayValues: true
>-- LongArrayValues: true
>-- StringArrayValues: true
>-- IntegerValues: true
>-- BooleanValues: true
>-- ByteValues: true
>-- DoubleValues: true
>-- FloatValues: true

A common pattern for using features is to check their support prior to performing an operation:

gremlin> graph.features().graph().supportsTransactions()
==>false
gremlin> graph.features().graph().supportsTransactions() ? g.tx().commit() : "no tx"
==>no tx

Tip	To ensure provider agnostic code, always check feature support prior to usage of a particular function. In that way, the application can behave gracefully in case a particular implementation is provided at runtime that does not support a function being accessed.

Warning

Assignments of a GraphStrategy can alter the base features of a Graph in dynamic ways, such that checks against a Feature may not always reflect the behavior exhibited when the GraphStrategy is in use.

Vertex Properties

vertex-properties TinkerPop3 introduces the concept of a VertexProperty<V>. All the properties of a Vertex are a VertexProperty. A VertexProperty implements Property and as such, it has a key/value pair. However, VertexProperty also implements Element and thus, can have a collection of key/value pairs. Moreover, while an Edge can only have one property of key "name" (for example), a Vertex can have multiple "name" properties. With the inclusion of vertex properties, two features are introduced which ultimately advance the graph modelers toolkit:

Multiple properties (multi-properties): a vertex property key can have multiple values. For example, a vertex can have multiple "name" properties.
Properties on properties (meta-properties): a vertex property can have properties (i.e. a vertex property can have key/value data associated with it).

Possible use cases for meta-properties:

Permissions: Vertex properties can have key/value ACL-type permission information associated with them.
Auditing: When a vertex property is manipulated, it can have key/value information attached to it saying who the creator, deletor, etc. are.
Provenance: The "name" of a vertex can be declared by multiple users. For example, there may be multiple spellings of a name from different sources.

A running example using vertex properties is provided below to demonstrate and explain the API.

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> g = graph.traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
gremlin> v = g.addV('name','marko','name','marko a. rodriguez').next()
==>v[0]
gremlin> g.V(v).properties('name').count() //(1)
==>2
gremlin> v.property(list, 'name', 'm. a. rodriguez') //(2)
==>vp[name->m. a. rodriguez]
gremlin> g.V(v).properties('name').count()
==>3
gremlin> g.V(v).properties()
==>vp[name->marko]
==>vp[name->marko a. rodriguez]
==>vp[name->m. a. rodriguez]
gremlin> g.V(v).properties('name')
==>vp[name->marko]
==>vp[name->marko a. rodriguez]
==>vp[name->m. a. rodriguez]
gremlin> g.V(v).properties('name').hasValue('marko')
==>vp[name->marko]
gremlin> g.V(v).properties('name').hasValue('marko').property('acl','private') //(3)
==>vp[name->marko]
gremlin> g.V(v).properties('name').hasValue('marko a. rodriguez')
==>vp[name->marko a. rodriguez]
gremlin> g.V(v).properties('name').hasValue('marko a. rodriguez').property('acl','public')
==>vp[name->marko a. rodriguez]
gremlin> g.V(v).properties('name').has('acl','public').value()
==>marko a. rodriguez
gremlin> g.V(v).properties('name').has('acl','public').drop() //(4)
gremlin> g.V(v).properties('name').has('acl','public').value()
gremlin> g.V(v).properties('name').has('acl','private').value()
==>marko
gremlin> g.V(v).properties()
==>vp[name->marko]
==>vp[name->m. a. rodriguez]
gremlin> g.V(v).properties().properties() //(5)
==>p[acl->private]
gremlin> g.V(v).properties().property('date',2014) //(6)
==>vp[name->marko]
==>vp[name->m. a. rodriguez]
gremlin> g.V(v).properties().property('creator','stephen')
==>vp[name->marko]
==>vp[name->m. a. rodriguez]
gremlin> g.V(v).properties().properties()
==>p[date->2014]
==>p[creator->stephen]
==>p[acl->private]
==>p[date->2014]
==>p[creator->stephen]
gremlin> g.V(v).properties('name').valueMap()
==>[date:2014, creator:stephen, acl:private]
==>[date:2014, creator:stephen]
gremlin> g.V(v).property('name','okram') //(7)
==>v[0]
gremlin> g.V(v).properties('name')
==>vp[name->okram]
gremlin> g.V(v).values('name') //(8)
==>okram

A vertex can have zero or more properties with the same key associated with it.
If a property is added with a cardinality of Cardinality.list, an additional property with the provided key will be added.
A vertex property can have standard key/value properties attached to it.
Vertex property removal is identical to property removal.
It is property to get the properties of a vertex property.
A vertex property can have any number of key/value properties attached to it.
property(...) will remove all existing key’d properties before adding the new single property (see VertexProperty.Cardinality).
If only the value of a property is needed, then values() can be used.

If the concept of vertex properties is difficult to grasp, then it may be best to think of vertex properties in terms of "literal vertices." A vertex can have an edge to a "literal vertex" that has a single value key/value — e.g. "value=okram." The edge that points to that literal vertex has an edge-label of "name." The properties on the edge represent the literal vertex’s properties. The "literal vertex" can not have any other edges to it (only one from the associated vertex).

Tip	A toy graph demonstrating all of the new TinkerPop3 graph structure features is available at `TinkerFactory.createTheCrew()` and `data/tinkerpop-crew*`. This graph demonstrates multi-properties and meta-properties.

Figure 3. TinkerPop Crew

gremlin> g.V().as('a').
               properties('location').as('b').
               hasNot('endTime').as('c').
               select('a','b','c').by('name').by(value).by('startTime') // determine the current location of each person
==>[a:marko, b:santa fe, c:2005]
==>[a:stephen, b:purcellville, c:2006]
==>[a:matthias, b:seattle, c:2014]
==>[a:daniel, b:aachen, c:2009]
gremlin> g.V().has('name','gremlin').inE('uses').
               order().by('skill',incr).as('a').
               outV().as('b').
               select('a','b').by('skill').by('name') // rank the users of gremlin by their skill level
==>[a:3, b:matthias]
==>[a:4, b:marko]
==>[a:5, b:stephen]
==>[a:5, b:daniel]

Graph Variables

TinkerPop3 introduces the concept of Graph.Variables. Variables are key/value pairs associated with the graph itself — in essence, a Map<String,Object>. These variables are intended to store metadata about the graph. Example use cases include:

Schema information: What do the namespace prefixes resolve to and when was the schema last modified?
Global permissions: What are the access rights for particular groups?
System user information: Who are the admins of the system?

An example of graph variables in use is presented below:

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.variables()
==>variables[size:0]
gremlin> graph.variables().set('systemAdmins',['stephen','peter','pavel'])
==>null
gremlin> graph.variables().set('systemUsers',['matthias','marko','josh'])
==>null
gremlin> graph.variables().keys()
==>systemAdmins
==>systemUsers
gremlin> graph.variables().get('systemUsers')
==>Optional[[matthias, marko, josh]]
gremlin> graph.variables().get('systemUsers').get()
==>matthias
==>marko
==>josh
gremlin> graph.variables().remove('systemAdmins')
==>null
gremlin> graph.variables().keys()
==>systemUsers

Important

Graph variables are not intended to be subject to heavy, concurrent mutation nor to be used in complex computations. The intention is to have a location to store data about the graph for administrative purposes.

Graph Transactions

gremlin-coins A database transaction represents a unit of work to execute against the database. Transactions are controlled by an implementation of the Transaction interface and that object can be obtained from the Graph interface using the tx() method. It is important to note that the Transaction object does not represent a "transaction" itself. It merely exposes the methods for working with transactions (e.g. committing, rolling back, etc).

Most Graph implementations that supportsTransactions will implement an "automatic" ThreadLocal transaction, which means that when a read or write occurs after the Graph is instantiated, a transaction is automatically started within that thread. There is no need to manually call a method to "create" or "start" a transaction. Simply modify the graph as required and call graph.tx().commit() to apply changes or graph.tx().rollback() to undo them. When the next read or write action occurs against the graph, a new transaction will be started within that current thread of execution.

When using transactions in this fashion, especially in web application (e.g. REST server), it is important to ensure that transactions do not leak from one request to the next. In other words, unless a client is somehow bound via session to process every request on the same server thread, every request must be committed or rolled back at the end of the request. By ensuring that the request encapsulates a transaction, it ensures that a future request processed on a server thread is starting in a fresh transactional state and will not have access to the remains of one from an earlier request. A good strategy is to rollback a transaction at the start of a request, so that if it so happens that a transactional leak does occur between requests somehow, a fresh transaction is assured by the fresh request.

Tip	The `tx()` method is on the `Graph` interface, but it is also available on the `TraversalSource` spawned from a `Graph`. Calls to `TraversalSource.tx()` are proxied through to the underlying `Graph` as a convenience.

Configuring

Determining when a transaction starts is dependent upon the behavior assigned to the Transaction. It is up to the Graph implementation to determine the default behavior and unless the implementation doesn’t allow it, the behavior itself can be altered via these Transaction methods:

public Transaction onReadWrite(final Consumer<Transaction> consumer);

public Transaction onClose(final Consumer<Transaction> consumer);

Providing a Consumer function to onReadWrite allows definition of how a transaction starts when a read or a write occurs. Transaction.READ_WRITE_BEHAVIOR contains pre-defined Consumer functions to supply to the onReadWrite method. It has two options:

AUTO - automatic transactions where the transaction is started implicitly to the read or write operation
MANUAL - manual transactions where it is up to the user to explicitly open a transaction, throwing an exception if the transaction is not open

Providing a Consumer function to onClose allows configuration of how a transaction is handled when Transaction.close() is called. Transaction.CLOSE_BEHAVIOR has several pre-defined options that can be supplied to this method:

COMMIT - automatically commit an open transaction
ROLLBACK - automatically rollback an open transaction
MANUAL - throw an exception if a transaction is open, forcing the user to explicitly close the transaction

Important

As transactions are ThreadLocal in nature, so are the transaction configurations for onReadWrite and onClose.

Once there is an understanding for how transactions are configured, most of the rest of the Transaction interface is self-explanatory. Note that Neo4j-Gremlin is used for the examples to follow as TinkerGraph does not support transactions.

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[EmbeddedGraphDatabase [/tmp/neo4j]]
gremlin> graph.features()
==>FEATURES
> GraphFeatures
>-- Transactions: true  (1)
>-- Computer: false
>-- Persistence: true
...
gremlin> graph.tx().onReadWrite(Transaction.READ_WRITE_BEHAVIOR.AUTO) (2)
==>org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph$Neo4jTransaction@1c067c0d
gremlin> graph.addVertex("name","stephen")  (3)
==>v[0]
gremlin> graph.tx().commit() (4)
==>null
gremlin> graph.tx().onReadWrite(Transaction.READ_WRITE_BEHAVIOR.MANUAL) (5)
==>org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph$Neo4jTransaction@1c067c0d
gremlin> graph.tx().isOpen()
==>false
gremlin> graph.addVertex("name","marko") (6)
Open a transaction before attempting to read/write the transaction
gremlin> graph.tx().open() (7)
==>null
gremlin> graph.addVertex("name","marko") (8)
==>v[1]
gremlin> graph.tx().commit()
==>null

Check features to ensure that the graph supports transactions.
By default, Neo4jGraph is configured with "automatic" transactions, so it is set here for demonstration purposes only.
When the vertex is added, the transaction is automatically started. From this point, more mutations can be staged or other read operations executed in the context of that open transaction.
Calling commit finalizes the transaction.
Change transaction behavior to require manual control.
Adding a vertex now results in failure because the transaction was not explicitly opened.
Explicitly open a transaction.
Adding a vertex now succeeds as the transaction was manually opened.

Note	It may be important to consult the documentation of the `Graph` implementation you are using when it comes to the specifics of how transactions will behave. TinkerPop allows some latitude in this area and implementations may not have the exact same behaviors and ACID guarantees.

Retries

There are times when transactions fail. Failure may be indicative of some permanent condition, but other failures might simply require the transaction to be retried for possible future success. The Transaction object also exposes a method for executing automatic transaction retries:

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> graph.tx().submit {it.addVertex("name","josh")}.retry(10)
==>v[0]
gremlin> graph.tx().submit {it.addVertex("name","daniel")}.exponentialBackoff(10)
==>v[1]
gremlin> graph.close()
==>null

As shown above, the submit method takes a Function<Graph, R> which is the unit of work to execute and possibly retry on failure. The method returns a Transaction.Workload object which has a number of default methods for common retry strategies. It is also possible to supply a custom retry function if a default one does not suit the required purpose.

Threaded Transactions

Most Graph implementations that support transactions do so in a ThreadLocal manner, where the current transaction is bound to the current thread of execution. Consider the following example to demonstrate:

graph.addVertex("name","stephen");

Thread t1 = new Thread(() -> {
    graph.addVertex("name","josh");
});

Thread t2 = new Thread(() -> {
    graph.addVertex("name","marko");
});

t1.start()
t2.start()

t1.join()
t2.join()

graph.tx().commit();

The above code shows three vertices added to graph in three different threads: the current thread, t1 and t2. One might expect that by the time this body of code finished executing, that there would be three vertices persisted to the Graph. However, given the ThreadLocal nature of transactions, there really were three separate transactions created in that body of code (i.e. one for each thread of execution) and the only one committed was the first call to addVertex in the primary thread of execution. The other two calls to that method within t1 and t2 were never committed and thus orphaned.

A Graph that supportsThreadedTransactions is one that allows for a Graph to operate outside of that constraint, thus allowing multiple threads to operate within the same transaction. Therefore, if there was a need to have three different threads operating within the same transaction, the above code could be re-written as follows:

Graph threaded = graph.tx().createThreadedTx();
threaded.addVertex("name","stephen");

Thread t1 = new Thread(() -> {
    threaded.addVertex("name","josh");
});

Thread t2 = new Thread(() -> {
    threaded.addVertex("name","marko");
});

t1.start()
t2.start()

t1.join()
t2.join()

threaded.tx().commit();

In the above case, the call to graph.tx().createThreadedTx() creates a new Graph instance that is unbound from the ThreadLocal transaction, thus allowing each thread to operate on it in the same context. In this case, there would be three separate vertices persisted to the Graph.

Gremlin I/O

gremlin-io The task of getting data in and out of Graph instances is the job of the Gremlin I/O packages. Gremlin I/O provides two interfaces for reading and writing Graph instances: GraphReader and GraphWriter. These interfaces expose methods that support:

Reading and writing an entire Graph
Reading and writing a Traversal<Vertex> as adjacency list format
Reading and writing a single Vertex (with and without associated Edge objects)
Reading and writing a single Edge
Reading and writing a single VertexProperty
Reading and writing a single Property
Reading and writing an arbitrary Object

In all cases, these methods operate in the currency of InputStream and OutputStream objects, allowing graphs and their related elements to be written to and read from files, byte arrays, etc. The Graph interface offers the io method, which provides access to "reader/writer builder" objects that are pre-configured with serializers provided by the Graph, as well as helper methods for the various I/O capabilities. Unless there are very advanced requirements for the serialization process, it is always best to utilize the methods on the Io interface to construct GraphReader and GraphWriter instances, as the implementation may provide some custom settings that would otherwise have to be configured manually by the user to do the serialization.

It is up to the implementations of the GraphReader and GraphWriter interfaces to choose the methods they implement and the manner in which they work together. The only characteristic enforced and expected is that the write methods should produce output that is compatible with the corresponding read method. For example, the output of writeVertices should be readable as input to readVertices and the output of writeProperty should be readable as input to readProperty.

GraphML Reader/Writer

gremlin-graphml The GraphML file format is a common XML-based representation of a graph. It is widely supported by graph-related tools and libraries making it a solid interchange format for TinkerPop. In other words, if the intent is to work with graph data in conjunction with applications outside of TinkerPop, GraphML may be the best choice to do that. Common use cases might be:

Generate a graph using NetworkX, export it with GraphML and import it to TinkerPop.
Produce a subgraph and export it to GraphML to be consumed by and visualized in Gephi.
Migrate the data of an entire graph to a different graph database not supported by TinkerPop.

As GraphML is a specification for the serialization of an entire graph and not the individual elements of a graph, methods that support input and output of single vertices, edges, etc. are not supported.

Warning

GraphML is a "lossy" format in that it only supports primitive values for properties and does not have support for Graph variables. It will use toString to serialize property values outside of those primitives.

Warning

GraphML as a specification allows for <edge> and <node> elements to appear in any order. Most software that writes GraphML (including as TinkerPop’s GraphMLWriter) write <node> elements before <edge> elements. However it is important to note that GraphMLReader will read this data in order and order can matter. This is because TinkerPop does not allow the vertex label to be changed after the vertex has been created. Therefore, if an <edge> element comes before the <node>, the label on the vertex will be ignored. It is thus better to order <node> elements in the GraphML to appear before all <edge> elements if vertex labels are important to the graph.

The following code shows how to write a Graph instance to file called tinkerpop-modern.xml and then how to read that file back into a different instance:

final Graph graph = TinkerFactory.createModern();
graph.io(IoCore.graphml()).writeGraph("tinkerpop-modern.xml");
final Graph newGraph = TinkerGraph.open();
newGraph.io(IoCore.graphml()).readGraph("tinkerpop-modern.xml");

If a custom configuration is required, then have the Graph generate a GraphReader or GraphWriter "builder" instance:

final Graph graph = TinkerFactory.createModern();
try (final OutputStream os = new FileOutputStream("tinkerpop-modern.xml")) {
    graph.io(IoCore.graphml()).writer().normalize(true).create().writeGraph(os, graph);
}

final Graph newGraph = TinkerGraph.open();
try (final InputStream stream = new FileInputStream("tinkerpop-modern.xml")) {
    newGraph.io(IoCore.graphml()).reader().vertexIdKey("name").create().readGraph(stream, newGraph);
}

GraphSON Reader/Writer

gremlin-graphson GraphSON is a JSON-based format extended from earlier versions of TinkerPop. It is important to note that TinkerPop3’s GraphSON is not backwards compatible with prior TinkerPop GraphSON versions. GraphSON has some support from graph-related application outside of TinkerPop, but it is generally best used in two cases:

A text format of the graph or its elements is desired (e.g. debugging, usage in source control, etc.)
The graph or its elements need to be consumed by code that is not JVM-based (e.g. JavaScript, Python, .NET, etc.)

GraphSON supports all of the GraphReader and GraphWriter interface methods and can therefore read or write an entire Graph, vertices, arbitrary objects, etc. The following code shows how to write a Graph instance to file called tinkerpop-modern.json and then how to read that file back into a different instance:

final Graph graph = TinkerFactory.createModern();
graph.io(IoCore.graphson()).writeGraph("tinkerpop-modern.json");

final Graph newGraph = TinkerGraph.open();
newGraph.io(IoCore.graphson()).readGraph("tinkerpop-modern.json");

If a custom configuration is required, then have the Graph generate a GraphReader or GraphWriter "builder" instance:

final Graph graph = TinkerFactory.createModern();
try (final OutputStream os = new FileOutputStream("tinkerpop-modern.json")) {
    final GraphSONMapper mapper = graph.io(IoCore.graphson()).mapper().normalize(true).create()
    graph.io(IoCore.graphson()).writer().mapper(mapper).create().writeGraph(os, graph)
}

final Graph newGraph = TinkerGraph.open();
try (final InputStream stream = new FileInputStream("tinkerpop-modern.json")) {
    newGraph.io(IoCore.graphson()).reader().vertexIdKey("name").create().readGraph(stream, newGraph);
}

One of the important configuration options of the GraphSONReader and GraphSONWriter is the ability to embed type information into the output. By embedding the types, it becomes possible to serialize a graph without losing type information that might be important when being consumed by another source. The importance of this concept is demonstrated in the following example where a single Vertex is written to GraphSON using the Gremlin Console:

gremlin> graph = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> f = new FileOutputStream("vertex-1.json")
==>java.io.FileOutputStream@5a3c98cc
gremlin> graph.io(graphson()).writer().create().writeVertex(f, g.V(1).next(), BOTH)
==>null
gremlin> f.close()
==>null

The following GraphSON example shows the output of GraphSonWriter.writeVertex() with associated edges:

{
    "id": 1,
    "label": "person",
    "outE": {
        "created": [
            {
                "id": 9,
                "inV": 3,
                "properties": {
                    "weight": 0.4
                }
            }
        ],
        "knows": [
            {
                "id": 7,
                "inV": 2,
                "properties": {
                    "weight": 0.5
                }
            },
            {
                "id": 8,
                "inV": 4,
                "properties": {
                    "weight": 1
                }
            }
        ]
    },
    "properties": {
        "name": [
            {
                "id": 0,
                "value": "marko"
            }
        ],
        "age": [
            {
                "id": 1,
                "value": 29
            }
        ]
    }
}

The vertex properly serializes to valid JSON but note that a consuming application will not automatically know how to interpret the numeric values. In coercing those Java values to JSON, such information is lost.

With a minor change to the construction of the GraphSONWriter the lossy nature of GraphSON can be avoided:

gremlin> graph = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> f = new FileOutputStream("vertex-1.json")
==>java.io.FileOutputStream@4363a8e0
gremlin> mapper = graph.io(graphson()).mapper().embedTypes(true).create()
==>org.apache.tinkerpop.gremlin.structure.io.graphson.GraphSONMapper@3735b32
gremlin> graph.io(graphson()).writer().mapper(mapper).create().writeVertex(f, g.V(1).next(), BOTH)
==>null
gremlin> f.close()
==>null

In the above code, the embedTypes option is set to true and the output below shows the difference in the output:

{
    "@class": "java.util.HashMap",
    "id": 1,
    "label": "person",
    "outE": {
        "@class": "java.util.HashMap",
        "created": [
            "java.util.ArrayList",
            [
                {
                    "@class": "java.util.HashMap",
                    "id": 9,
                    "inV": 3,
                    "properties": {
                        "@class": "java.util.HashMap",
                        "weight": 0.4
                    }
                }
            ]
        ],
        "knows": [
            "java.util.ArrayList",
            [
                {
                    "@class": "java.util.HashMap",
                    "id": 7,
                    "inV": 2,
                    "properties": {
                        "@class": "java.util.HashMap",
                        "weight": 0.5
                    }
                },
                {
                    "@class": "java.util.HashMap",
                    "id": 8,
                    "inV": 4,
                    "properties": {
                        "@class": "java.util.HashMap",
                        "weight": 1
                    }
                }
            ]
        ]
    },
    "properties": {
        "@class": "java.util.HashMap",
        "name": [
            "java.util.ArrayList",
            [
                {
                    "@class": "java.util.HashMap",
                    "id": [
                        "java.lang.Long",
                        0
                    ],
                    "value": "marko"
                }
            ]
        ],
        "age": [
            "java.util.ArrayList",
            [
                {
                    "@class": "java.util.HashMap",
                    "id": [
                        "java.lang.Long",
                        1
                    ],
                    "value": 29
                }
            ]
        ]
    }
}

The ambiguity of components of the GraphSON is now removed by the @class property, which contains Java class information for the data it is associated with. The @class property is used for all non-final types, with the exception of a small number of "natural" types (String, Boolean, Integer, and Double) which can be correctly inferred from JSON typing. While the output is more verbose, it comes with the security of not losing type information. While non-JVM languages won’t be able to consume this information automatically, at least there is a hint as to how the values should be coerced back into the correct types in the target language.

Gryo Reader/Writer

gremlin-kryo Kryo is a popular serialization package for the JVM. Gremlin-Kryo is a binary Graph serialization format for use on the JVM by JVM languages. It is designed to be space efficient, non-lossy and is promoted as the standard format to use when working with graph data inside of the TinkerPop stack. A list of common use cases is presented below:

Migration from one Gremlin Structure implementation to another (e.g. TinkerGraph to Neo4jGraph)
Serialization of individual graph elements to be sent over the network to another JVM.
Backups of in-memory graphs or subgraphs.

Warning

When migrating between Gremlin Structure implementations, Kryo may not lose data, but it is important to consider the features of each Graph and whether or not the data types supported in one will be supported in the other. Failure to do so, may result in errors.

Kryo supports all of the GraphReader and GraphWriter interface methods and can therefore read or write an entire Graph, vertices, edges, etc. The following code shows how to write a Graph instance to file called tinkerpop-modern.kryo and then how to read that file back into a different instance:

final Graph graph = TinkerFactory.createModern();
graph.io(IoCore.gryo()).writeGraph("tinkerpop-modern.kryo");

final Graph newGraph = TinkerGraph.open();
newGraph.io(IoCore.gryo()).readGraph("tinkerpop-modern.kryo")'

If a custom configuration is required, then have the Graph generate a GraphReader or GraphWriter "builder" instance:

final Graph graph = TinkerFactory.createModern();
try (final OutputStream os = new FileOutputStream("tinkerpop-modern.kryo")) {
    graph.io(IoCore.gryo()).writer().create().writeGraph(os, graph);
}

final Graph newGraph = TinkerGraph.open();
try (final InputStream stream = new FileInputStream("tinkerpop-modern.kryo")) {
    newGraph.io(IoCore.gryo()).reader().vertexIdKey("name").create().readGraph(stream, newGraph);
}

Note	The preferred extension for files names produced by Gryo is `.kryo`.

TinkerPop2 Data Migration

data-migration For those using TinkerPop2, migrating to TinkerPop3 will mean a number of programming changes, but may also require a migration of the data depending on the graph implementation. For example, trying to open TinkerGraph data from TinkerPop2 with TinkerPop3 code will not work, however opening a TinkerPop2 Neo4jGraph with a TinkerPop3 Neo4jGraph should work provided there aren’t Neo4j version compatibility mismatches preventing the read.

If such a situation arises that a particular TinkerPop2 Graph can not be read by TinkerPop3, a "legacy" data migration approach exists. The migration involves writing the TinkerPop2 Graph to GraphSON, then reading it to TinkerPop3 with the LegacyGraphSONReader (a limited implementation of the GraphReader interface).

The following represents an example migration of the "classic" toy graph. In this example, the "classic" graph is saved to GraphSON using TinkerPop2.

gremlin> Gremlin.version()
==>2.5.z
gremlin> graph = TinkerGraphFactory.createTinkerGraph()
==>tinkergraph[vertices:6 edges:6]
gremlin> GraphSONWriter.outputGraph(graph,'/tmp/tp2.json',GraphSONMode.EXTENDED)
==>null

The above console session uses the gremlin-groovy distribution from TinkerPop2. It is important to generate the tp2.json file using the EXTENDED mode as it will include data types when necessary which will help limit "lossiness" on the TinkerPop3 side when imported. Once tp2.json is created, it can then be imported to a TinkerPop3 Graph.

gremlin> Gremlin.version()
==>3.1.4-SNAPSHOT
gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> r = LegacyGraphSONReader.build().create()
==>org.apache.tinkerpop.gremlin.structure.io.graphson.LegacyGraphSONReader@64337702
gremlin> r.readGraph(new FileInputStream('/tmp/tp2.json'), graph)
==>null
gremlin> g = graph.traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> g.E()
==>e[11][4-created->3]
==>e[12][6-created->3]
==>e[7][1-knows->2]
==>e[8][1-knows->4]
==>e[9][1-created->3]
==>e[10][4-created->5]

Namespace Conventions

End users, graph system providers, GraphComputer algorithm designers, GremlinPlugin creators, etc. all leverage properties on elements to store information. There are a few conventions that should be respected when naming property keys to ensure that conflicts between these stakeholders do not conflict.

End users are granted the flat namespace (e.g. name, age, location) to key their properties and label their elements.
Graph system providers are granted the hidden namespace (e.g. ~metadata) to key their properties and labels. Data keyed as such is only accessible via the graph system implementation and no other stakeholders are granted read nor write access to data prefixed with "~" (see Graph.Hidden). Test coverage and exceptions exist to ensure that graph systems respect this hard boundary.
VertexProgram and MapReduce developers should, like GraphStrategy developers, leverage qualified namespaces particular to their domain (e.g. mydomain.myvertexprogram.computedata).
GremlinPlugin creators should prefix their plugin name with their domain (e.g. mydomain.myplugin).

Important

TinkerPop uses tinkerpop. and gremlin. as the prefixes for provided strategies, vertex programs, map reduce implementations, and plugins.

The only truly protected namespace is the hidden namespace provided to graph systems. From there, it’s up to engineers to respect the namespacing conventions presented.

The Traversal

At the most general level there is Traversal<S,E> which implements Iterator<E>, where the S stands for start and the E stands for end. A traversal is composed of four primary components:

Step<S,E>: an individual function applied to S to yield E. Steps are chained within a traversal.
TraversalStrategy: interceptor methods to alter the execution of the traversal (e.g. query re-writing).
TraversalSideEffects: key/value pairs that can be used to store global information about the traversal.
Traverser<T>: the object propagating through the Traversal currently representing an object of type T.

The classic notion of a graph traversal is provided by GraphTraversal<S,E> which extends Traversal<S,E>. GraphTraversal provides an interpretation of the graph data in terms of vertices, edges, etc. and thus, a graph traversal DSL.

Important

The underlying Step implementations provided by TinkerPop should encompass most of the functionality required by a DSL author. It is important that DSL authors leverage the provided steps as then the common optimization and decoration strategies can reason on the underlying traversal sequence. If new steps are introduced, then common traversal strategies may not function properly.

Graph Traversal Steps

A GraphTraversal<S,E> is spawned from a GraphTraversalSource. It can also be spawned anonymously (i.e. empty) via __. A graph traversal is composed of an ordered list of steps. All the steps provided by GraphTraversal inherit from the more general forms diagrammed above. A list of all the steps (and their descriptions) are provided in the TinkerPop3 GraphTraversal JavaDoc. The following subsections will demonstrate the GraphTraversal steps using the Gremlin Console.

Note

To reduce the verbosity of the expression, it is good to import static org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.__.*. This way, instead of doing __.inE() for an anonymous traversal, it is possible to simply write inE(). Be aware of language-specific reserved keywords when using anonymous traversals. For example, in and as are reserved keywords in Groovy, therefore you must use the verbose syntax __.in() and __.as() to avoid collisions.

Lambda Steps

Warning

Lambda steps are presented for educational purposes as they represent the foundational constructs of the Gremlin language. In practice, lambda steps should be avoided and traversal verification strategies exist to disallow t heir use unless explicitly "turned off." For more information on the problems with lambdas, please read A Note on Lambdas.

There are four generic steps by which all other specific steps described later extend.

Step Description

Step	Description
`map(Function<Traverser<S>, E>)`	map the traverser to some object of type `E` for the next step to process.
`flatMap(Function<Traverser<S>, Iterator<E>>)`	map the traverser to an iterator of `E` objects that are streamed to the next step.
`filter(Predicate<Traverser<S>>)`	map the traverser to either true or false, where false will not pass the traverser to the next step.
`sideEffect(Consumer<Traverser<S>>)`	perform some operation on the traverser and pass it to the next step.
`branch(Function<Traverser<S>,M>)`	split the traverser to all the traversals indexed by the `M` token.

map(Function<Traverser<S>, E>)

map the traverser to some object of type E for the next step to process.

flatMap(Function<Traverser<S>, Iterator<E>>)

map the traverser to an iterator of E objects that are streamed to the next step.

filter(Predicate<Traverser<S>>)

map the traverser to either true or false, where false will not pass the traverser to the next step.

sideEffect(Consumer<Traverser<S>>)

perform some operation on the traverser and pass it to the next step.

branch(Function<Traverser<S>,M>)

split the traverser to all the traversals indexed by the M token.

The Traverser<S> object provides access to:

The current traversed S object — Traverser.get().
The current path traversed by the traverser — Traverser.path().
1. A helper shorthand to get a particular path-history object — Traverser.path(String) == Traverser.path().get(String).
The number of times the traverser has gone through the current loop — Traverser.loops().
The number of objects represented by this traverser — Traverser.bulk().
The local data structure associated with this traverser — Traverser.sack().
The side-effects associated with the traversal — Traverser.sideEffects().
1. A helper shorthand to get a particular side-effect — Traverser.sideEffect(String) == Traverser.sideEffects().get(String).

map-lambda

gremlin> g.V(1).out().values('name') //(1)
==>lop
==>vadas
==>josh
gremlin> g.V(1).out().map {it.get().value('name')} //(2)
==>lop
==>vadas
==>josh

An outgoing traversal from vertex 1 to the name values of the adjacent vertices.
The same operation, but using a lambda to access the name property values.

filter-lambda

gremlin> g.V().filter {it.get().label() == 'person'} //(1)
==>v[1]
==>v[2]
==>v[4]
==>v[6]
gremlin> g.V().hasLabel('person') //(2)
==>v[1]
==>v[2]
==>v[4]
==>v[6]

A filter that only allows the vertex to pass if it has an age-property.
The more specific has()-step is implemented as a filter() with respective predicate.

side-effect-lambda

gremlin> g.V().hasLabel('person').sideEffect(System.out.&println) //(1)
v[1]
==>v[1]
v[2]
==>v[2]
v[4]
==>v[4]
v[6]
==>v[6]

Whatever enters sideEffect() is passed to the next step, but some intervening process can occur.

branch-lambda

gremlin> g.V().branch(values('name')).
               option('marko', values('age')).
               option(none, values('name')) //(1)
==>29
==>vadas
==>lop
==>josh
==>ripple
==>peter
gremlin> g.V().choose(has('name','marko'),
                      values('age'),
                      values('name')) //(2)
==>29
==>vadas
==>lop
==>josh
==>ripple
==>peter

If the vertex is "marko", get his age, else get the name of the vertex.
The more specific boolean-based choose()-step is implemented as a branch().

AddEdge Step

Reasoning is the process of making explicit what is implicit in the data. What is explicit in a graph are the objects of the graph — i.e. vertices and edges. What is implicit in the graph is the traversal. In other words, traversals expose meaning where the meaning is determined by the traversal definition. For example, take the concept of a "co-developer." Two people are co-developers if they have worked on the same project together. This concept can be represented as a traversal and thus, the concept of "co-developers" can be derived. Moreover, what was once implicit can be made explicit via the addE()-step (map/sideEffect).

gremlin> g.V(1).as('a').out('created').in('created').where(neq('a')).
           addE('co-developer').from('a').property('year',2009) //(1)
==>e[12][1-co-developer->4]
==>e[13][1-co-developer->6]
gremlin> g.V(3,4,5).aggregate('x').has('name','josh').as('a').
           select('x').unfold().hasLabel('software').addE('createdBy').to('a') //(2)
==>e[14][3-createdBy->4]
==>e[15][5-createdBy->4]
gremlin> g.V().as('a').out('created').addE('createdBy').to('a').property('acl','public') //(3)
==>e[16][3-createdBy->1]
==>e[17][5-createdBy->4]
==>e[18][3-createdBy->4]
==>e[19][3-createdBy->6]
gremlin> g.V(1).as('a').out('knows').
           addE('livesNear').from('a').property('year',2009).
           inV().inE('livesNear').values('year') //(4)
==>2009
==>2009
gremlin> g.V().match(
                 __.as('a').out('knows').as('b'),
                 __.as('a').out('created').as('c'),
                 __.as('b').out('created').as('c')).
               addE('friendlyCollaborator').from('a').to('b').
                 property(id,13).property('project',select('c').values('name')) //(5)
==>e[13][1-friendlyCollaborator->4]
gremlin> g.E(13).valueMap()
==>[project:lop]

Add a co-developer edge with a year-property between marko and his collaborators.
Add incoming createdBy edges from the josh-vertex to the lop- and ripple-vertices.
Add an inverse createdBy edge for all created edges.
The newly created edge is a traversable object.
Two arbitrary bindings in a traversal can be joined from()→to(), where id can be provided for graphs that supports user provided ids.

AddVertex Step

The addV()-step is used to add vertices to the graph (map/sideEffect). For every incoming object, a vertex is created. Moreover, GraphTraversalSource maintains an addV() method.

gremlin> g.addV('person').property('name','stephen')
==>v[12]
gremlin> g.V().values('name')
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter
==>stephen
gremlin> g.V().outE('knows').addV().property('name','nothing')
==>v[14]
==>v[16]
gremlin> g.V().has('name','nothing')
==>v[16]
==>v[14]
gremlin> g.V().has('name','nothing').bothE()

AddProperty Step

The property()-step is used to add properties to the elements of the graph (sideEffect). Unlike addV() and addE(), property() is a full sideEffect step in that it does not return the property it created, but the element that streamed into it. Moreover, if property() follows an addV() or addE(), then it is "folded" into the previous step to enable vertex and edge creation with all its properties in one creation operation.

gremlin> g.V(1).property('country','usa')
==>v[1]
gremlin> g.V(1).property('city','santa fe').property('state','new mexico').valueMap()
==>[country:[usa], city:[santa fe], name:[marko], state:[new mexico], age:[29]]
gremlin> g.V(1).property(list,'age',35) //(1)
==>v[1]
gremlin> g.V(1).valueMap()
==>[country:[usa], city:[santa fe], name:[marko], state:[new mexico], age:[29, 35]]
gremlin> g.V(1).property('friendWeight',outE('knows').values('weight').sum(),'acl','private') //(2)
==>v[1]
gremlin> g.V(1).properties('friendWeight').valueMap() //(3)
==>[acl:private]

For vertices, a cardinality can be provided for vertex properties.
It is possible to select the property value (as well as key) via a traversal.
For vertices, the property()-step can add meta-properties.

Aggregate Step

The aggregate()-step (sideEffect) is used to aggregate all the objects at a particular point of traversal into a Collection. The step uses eager evaluation in that no objects continue on until all previous objects have been fully aggregated (as opposed to store() which lazily fills a collection). The eager evaluation nature is crucial in situations where everything at a particular point is required for future computation. An example is provided below.

gremlin> g.V(1).out('created') //(1)
==>v[3]
gremlin> g.V(1).out('created').aggregate('x') //(2)
==>v[3]
gremlin> g.V(1).out('created').aggregate('x').in('created') //(3)
==>v[1]
==>v[4]
==>v[6]
gremlin> g.V(1).out('created').aggregate('x').in('created').out('created') //(4)
==>v[3]
==>v[5]
==>v[3]
==>v[3]
gremlin> g.V(1).out('created').aggregate('x').in('created').out('created').
                where(without('x')).values('name') //(5)
==>ripple

What has marko created?
Aggregate all his creations.
Who are marko’s collaborators?
What have marko’s collaborators created?
What have marko’s collaborators created that he hasn’t created?

In recommendation systems, the above pattern is used:

"What has userA liked? Who else has liked those things? What have they liked that userA hasn't already liked?"

Finally, aggregate()-step can be modulated via by()-projection.

gremlin> g.V().out('knows').aggregate('x').cap('x')
==>{v[2]=1, v[4]=1}
gremlin> g.V().out('knows').aggregate('x').by('name').cap('x')
==>{vadas=1, josh=1}

And Step

The and()-step ensures that all provided traversals yield a result (filter). Please see or() for or-semantics.

gremlin> g.V().and(
            outE('knows'),
            values('age').is(lt(30))).
              values('name')
==>marko

The and()-step can take an arbitrary number of traversals. All traversals must produce at least one output for the original traverser to pass to the next step.

An infix notation can be used as well. Though, with infix notation, only two traversals can be and’d together.

gremlin> g.V().where(outE('created').and().outE('knows')).values('name')
==>marko

As Step

The as()-step is not a real step, but a "step modulator" similar to by() and option(). With as(), it is possible to provide a label to the step that can later be accessed by steps and data structures that make use of such labels — e.g., select(), match(), and path.

gremlin> g.V().as('a').out('created').as('b').select('a','b') //(1)
==>[a:v[1], b:v[3]]
==>[a:v[4], b:v[5]]
==>[a:v[4], b:v[3]]
==>[a:v[6], b:v[3]]
gremlin> g.V().as('a').out('created').as('b').select('a','b').by('name') //(2)
==>[a:marko, b:lop]
==>[a:josh, b:ripple]
==>[a:josh, b:lop]
==>[a:peter, b:lop]

Select the objects labeled "a" and "b" from the path.
Select the objects labeled "a" and "b" from the path and, for each object, project its name value.

A step can have any number of labels associated with it. This is useful for referencing the same step multiple times in a future step.

gremlin> g.V().hasLabel('software').as('a','b','c').
            select('a','b','c').
              by('name').
              by('lang').
              by(__.in('created').values('name').fold())
==>[a:lop, b:java, c:[marko, josh, peter]]
==>[a:ripple, b:java, c:[josh]]

Barrier Step

The barrier()-step (barrier) turns the the lazy traversal pipeline into a bulk-synchronous pipeline. This step is useful in the following situations:

When everything prior to barrier() needs to be executed before moving onto the steps after the barrier() (i.e. ordering).
When "stalling" the traversal may lead to a "bulking optimization" in traversals that repeatedly touch many of the same elements (i.e. optimizing).

gremlin> g.V().sideEffect{println "first: ${it}"}.sideEffect{println "second: ${it}"}.iterate()
first: v[1]
second: v[1]
first: v[2]
second: v[2]
first: v[3]
second: v[3]
first: v[4]
second: v[4]
first: v[5]
second: v[5]
first: v[6]
second: v[6]
gremlin> g.V().sideEffect{println "first: ${it}"}.barrier().sideEffect{println "second: ${it}"}.iterate()
first: v[1]
first: v[2]
first: v[3]
first: v[4]
first: v[5]
first: v[6]
second: v[1]
second: v[2]
second: v[3]
second: v[4]
second: v[5]
second: v[6]

The theory behind a "bulking optimization" is simple. If there are one million traversers at vertex 1, then there is no need to calculate one million both()-computations. Instead, represent those one million traversers as a single traverser with a Traverser.bulk() equal to one million and execute both() once. A bulking optimization example is made more salient on a larger graph. Therefore, the example below leverages the Grateful Dead graph.

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> g = graph.traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
gremlin> clockWithResult(1){g.V().both().both().both().count().next()} //(1)
==>10007.955017999999
==>126653966
gremlin> clockWithResult(1){g.V().repeat(both()).times(3).count().next()} //(2)
==>1465.591512
==>126653966
gremlin> clockWithResult(1){g.V().both().barrier().both().barrier().both().barrier().count().next()} //(3)
==>13.199515
==>126653966

A non-bulking traversal where each traverser is processed.
Each traverser entering repeat() has its recursion bulked.
A bulking traversal where implicit traversers are not processed.

If barrier() is provided an integer argument, then the barrier will only hold n-number of unique traversers in its barrier before draining the aggregated traversers to the next step. This is useful in the aforementioned bulking optimization scenario, but reduces the risk of an out-of-memory exception.

The non-default LazyBarrierStrategy inserts barrier()-steps in a traversal where appropriate in order to gain the "bulking optimization."

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> g = graph.traversal(GraphTraversalSource.build().with(LazyBarrierStrategy.instance()).engine(StandardTraversalEngine.build()))
==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
gremlin> clockWithResult(1){g.V().both().both().both().count().next()}
==>15.528806
==>126653966
gremlin> g.V().both().both().both().count().iterate().toString() //(1)
==>[TinkerGraphStep([],vertex), VertexStep(BOTH,vertex), LambdaCollectingBarrierStep(noOp), VertexStep(BOTH,vertex), LambdaCollectingBarrierStep(noOp), VertexStep(BOTH,edge), CountGlobalStep]

With LazyBarrierStrategy activated, barrier() steps are automatically inserted where appropriate.

By Step

The by()-step is not an actual step, but instead is a "step-modulator" similar to as() and option(). If a step is able to accept traversals, functions, comparators, etc. then by() is the means by which they are added. The general pattern is step().by()...by(). Some steps can only accept one by() while others can take an arbitrary amount.

gremlin> g.V().group().by(bothE().count()) //(1)
==>[1:[v[2], v[5], v[6]], 3:[v[1], v[3], v[4]]]
gremlin> g.V().group().by(bothE().count()).by('name') //(2)
==>[1:[vadas, ripple, peter], 3:[marko, lop, josh]]
gremlin> g.V().group().by(bothE().count()).by(count()) //(3)
==>[1:3, 3:3]

by(outE().count()) will group the elements by their edge count (traversal).
by('name') will process the grouped elements by their name (element property projection).
by(count()) will count the number of elements in each group (traversal).

Cap Step

The cap()-step (barrier) iterates the traversal up to itself and emits the sideEffect referenced by the provided key. If multiple keys are provided, then a Map<String,Object> of sideEffects is emitted.

gremlin> g.V().groupCount('a').by(label).cap('a') //(1)
==>[software:2, person:4]
gremlin> g.V().groupCount('a').by(label).groupCount('b').by(outE().count()).cap('a','b') //(2)
==>[a:[software:2, person:4], b:[0:3, 1:1, 2:1, 3:1]]

Group and count verticies by their label. Emit the side effect labeled a, which is the group count by label.
Same as statement 1, but also emit the side effect labeled b which groups vertices by the number of out edges.

Coalesce Step

The coalesce()-step evaluates the provided traversals in order and returns the first traversal that emits at least one element.

gremlin> g.V(1).coalesce(outE('knows'), outE('created')).inV().path().by('name').by(label)
==>[marko, knows, vadas]
==>[marko, knows, josh]
gremlin> g.V(1).coalesce(outE('created'), outE('knows')).inV().path().by('name').by(label)
==>[marko, created, lop]
gremlin> g.V(1).property('nickname', 'okram')
==>v[1]
gremlin> g.V().hasLabel('person').coalesce(values('nickname'), values('name'))
==>okram
==>vadas
==>josh
==>peter

Count Step

The count()-step (map) counts the total number of represented traversers in the streams (i.e. the bulk count).

gremlin> g.V().count()
==>6
gremlin> g.V().hasLabel('person').count()
==>4
gremlin> g.V().hasLabel('person').outE('created').count().path() //(1)
==>[4]
gremlin> g.V().hasLabel('person').outE('created').count().map {it.get() * 10}.path() //(2)
==>[4, 40]

count()-step is a reducing barrier step meaning that all of the previous traversers are folded into a new traverser.
The path of the traverser emanating from count() starts at count().

Important

count(local) counts the current, local object (not the objects in the traversal stream). This works for Collection- and Map-type objects. For any other object, a count of 1 is returned.

Choose Step

The choose()-step (branch) routes the current traverser to a particular traversal branch option. With choose(), it is possible to implement if/else-based semantics as well as more complicated selections.

gremlin> g.V().hasLabel('person').
               choose(values('age').is(lte(30)),
                 __.in(),
                 __.out()).values('name') //(1)
==>marko
==>ripple
==>lop
==>lop
gremlin> g.V().hasLabel('person').
               choose(values('age')).
                 option(27, __.in()).
                 option(32, __.out()).values('name') //(2)
==>marko
==>ripple
==>lop

If the traversal yields an element, then do in, else do out (i.e. true/false-based option selection).
Use the result of the traversal as a key to the map of traversal options (i.e. value-based option selection).

However, note that choose() can have an arbitrary number of options and moreover, can take an anonymous traversal as its choice function.

gremlin> g.V().hasLabel('person').
               choose(values('name')).
                 option('marko', values('age')).
                 option('josh', values('name')).
                 option('vadas', valueMap()).
                 option('peter', label())
==>29
==>[name:[vadas], age:[27]]
==>josh
==>person

The choose()-step can leverage the Pick.none option match. For anything that does not match a specified option, the none-option is taken.

gremlin> g.V().hasLabel('person').
               choose(values('name')).
                 option('marko', values('age')).
                 option(none, values('name'))
==>29
==>vadas
==>josh
==>peter

Coin Step

To randomly filter out a traverser, use the coin()-step (filter). The provided double argument biases the "coin toss."

gremlin> g.V().coin(0.5)
==>v[1]
==>v[2]
==>v[3]
==>v[6]
gremlin> g.V().coin(0.0)
gremlin> g.V().coin(1.0)
==>v[1]
==>v[2]
==>v[3]
==>v[4]
==>v[5]
==>v[6]

Constant Step

To specify a constant value for a traverser, use the constant()-step (map). This is often useful with conditional steps like choose()-step or coalesce()-step.

gremlin> g.V().choose(hasLabel('person'),
             values('name'),
             constant('inhuman')) //(1)
==>marko
==>vadas
==>inhuman
==>josh
==>inhuman
==>peter
gremlin> g.V().coalesce(
             hasLabel('person').values('name'),
             constant('inhuman')) //(2)
==>marko
==>vadas
==>inhuman
==>josh
==>inhuman
==>peter

Show the names of people, but show "inhuman" for other vertices.
Same as statement 1 (unless there is a person vertex with no name).

CyclicPath Step

Each traverser maintains its history through the traversal over the graph — i.e. its path. If it is important that the traverser repeat its course, then cyclic()-path should be used (filter). The step analyzes the path of the traverser thus far and if there are any repeats, the traverser is filtered out over the traversal computation. If non-cyclic behavior is desired, see simplePath().

gremlin> g.V(1).both().both()
==>v[1]
==>v[4]
==>v[6]
==>v[1]
==>v[5]
==>v[3]
==>v[1]
gremlin> g.V(1).both().both().cyclicPath()
==>v[1]
==>v[1]
==>v[1]
gremlin> g.V(1).both().both().cyclicPath().path()
==>[v[1], v[3], v[1]]
==>[v[1], v[2], v[1]]
==>[v[1], v[4], v[1]]

Dedup Step

With dedup()-step (filter), repeatedly seen objects are removed from the traversal stream. Note that if a traverser’s bulk is greater than 1, then it is set to 1 before being emitted.

gremlin> g.V().values('lang')
==>java
==>java
gremlin> g.V().values('lang').dedup()
==>java
gremlin> g.V(1).repeat(bothE('created').dedup().otherV()).emit().path() //(1)
==>[v[1], e[9][1-created->3], v[3]]
==>[v[1], e[9][1-created->3], v[3], e[11][4-created->3], v[4]]
==>[v[1], e[9][1-created->3], v[3], e[12][6-created->3], v[6]]
==>[v[1], e[9][1-created->3], v[3], e[11][4-created->3], v[4], e[10][4-created->5], v[5]]

Traverse all created edges, but don’t touch any edge twice.

If a by-step modulation is provided to dedup(), then the object is processed accordingly prior to determining if it has been seen or not.

gremlin> g.V().valueMap(true, 'name')
==>[id:1, label:person, name:[marko]]
==>[id:2, label:person, name:[vadas]]
==>[id:3, label:software, name:[lop]]
==>[id:4, label:person, name:[josh]]
==>[id:5, label:software, name:[ripple]]
==>[id:6, label:person, name:[peter]]
gremlin> g.V().dedup().by(label).values('name')
==>marko
==>lop

Finally, if dedup() is provided an array of strings, then it will ensure that the de-duplication is not with respect to the current traverser object, but to the path history of the traverser.

gremlin> g.V().as('a').out('created').as('b').in('created').as('c').select('a','b','c')
==>[a:v[1], b:v[3], c:v[1]]
==>[a:v[1], b:v[3], c:v[4]]
==>[a:v[1], b:v[3], c:v[6]]
==>[a:v[4], b:v[5], c:v[4]]
==>[a:v[4], b:v[3], c:v[1]]
==>[a:v[4], b:v[3], c:v[4]]
==>[a:v[4], b:v[3], c:v[6]]
==>[a:v[6], b:v[3], c:v[1]]
==>[a:v[6], b:v[3], c:v[4]]
==>[a:v[6], b:v[3], c:v[6]]
gremlin> g.V().as('a').out('created').as('b').in('created').as('c').dedup('a','b').select('a','b','c') //(1)
==>[a:v[1], b:v[3], c:v[1]]
==>[a:v[4], b:v[5], c:v[4]]
==>[a:v[4], b:v[3], c:v[1]]
==>[a:v[6], b:v[3], c:v[1]]

If the current a and b combination has been seen previously, then filter the traverser.

Drop Step

The drop()-step (filter/sideEffect) is used to remove element and properties from the graph (i.e. remove). It is a filter step because the traversal yields no outgoing objects.

gremlin> g.V().outE().drop()
gremlin> g.E()
gremlin> g.V().properties('name').drop()
gremlin> g.V().valueMap()
==>[age:[29]]
==>[age:[27]]
==>[lang:[java]]
==>[age:[32]]
==>[lang:[java]]
==>[age:[35]]
gremlin> g.V().drop()
gremlin> g.V()

Explain Step

The explain()-step (sideEffect) will return a TraversalExplanation. A traversal explanation details how the traversal (prior to explain()) will be compiled given the registered traversal strategies. A TraversalExplanation has a toString() representation with 3-columns. The first column is the traversal strategy being applied. The second column is the traversal strategy category: [D]ecoration, [O]ptimization, [P]rovider optimization, [F]inalization, and [V]erification. Finally, the third column is the state of the traversal post strategy application. The final traversal is the resultant execution plan.

gremlin> g.V().hasLabel('person').outE().identity().inV().count().is(gt(5)).explain()
==>Traversal Explanation
===============================================================================================================================================================================
Original Traversal                 [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]

ConnectiveStrategy           [D]   [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
IdentityRemovalStrategy      [O]   [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,edge), EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
IncidentToAdjacentStrategy   [O]   [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,vertex), CountGlobalStep, IsStep(gt(5))]
AdjacentToIncidentStrategy   [O]   [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,edge), CountGlobalStep, IsStep(gt(5))]
FilterRankingStrategy        [O]   [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,edge), CountGlobalStep, IsStep(gt(5))]
MatchPredicateStrategy       [O]   [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,edge), CountGlobalStep, IsStep(gt(5))]
RangeByIsCountStrategy       [O]   [GraphStep([],vertex), HasStep([~label.eq(person)]), VertexStep(OUT,edge), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
TinkerGraphStepStrategy      [P]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
EngineDependentStrategy      [F]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
ProfileStrategy              [F]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
ComputerVerificationStrategy [V]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
StandardVerificationStrategy [V]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]

Final Traversal                    [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]

For traversal profiling information, please see profile()-step.

Fold Step

There are situations when the traversal stream needs a "barrier" to aggregate all the objects and emit a computation that is a function of the aggregate. The fold()-step (map) is one particular instance of this. Please see unfold()-step for the inverse functionality.

gremlin> g.V(1).out('knows').values('name')
==>vadas
==>josh
gremlin> g.V(1).out('knows').values('name').fold() //(1)
==>[vadas, josh]
gremlin> g.V(1).out('knows').values('name').fold().next().getClass() //(2)
==>class java.util.ArrayList
gremlin> g.V(1).out('knows').values('name').fold(0) {a,b -> a + b.length()} //(3)
==>9
gremlin> g.V().values('age').fold(0) {a,b -> a + b} //(4)
==>123
gremlin> g.V().values('age').fold(0, sum) //(5)
==>123
gremlin> g.V().values('age').sum() //(6)
==>123

A parameterless fold() will aggregate all the objects into a list and then emit the list.
A verification of the type of list returned.
fold() can be provided two arguments — a seed value and a reduce bi-function ("vadas" is 5 characters + "josh" with 4 characters).
What is the total age of the people in the graph?
The same as before, but using a built-in bi-function.
The same as before, but using the sum()-step.

Graph Step

The V()-step is usually used to start a GraphTraversal, but can also be used mid-traversal.

gremlin> g.V().has('name', within('marko', 'vadas', 'josh')).as('person').
           V().has('name', within('lop', 'ripple')).addE('uses').from('person')
==>e[12][1-uses->3]
==>e[13][1-uses->5]
==>e[14][2-uses->3]
==>e[15][2-uses->5]
==>e[16][4-uses->3]
==>e[17][4-uses->5]

Note	Whether a mid-traversal `V()` uses an index or not, depends on a) whether suitable index exists and b) if the particular graph system provider implemented this functionality.

gremlin> g.V().has('name', within('marko', 'vadas', 'josh')).as('person').
           V().has('name', within('lop', 'ripple')).addE('uses').from('person').toString() //(1)
==>[GraphStep([],vertex), HasStep([name.within([marko, vadas, josh])])@[person], GraphStep([],vertex), HasStep([name.within([lop, ripple])]), AddEdgeStep({label=[uses], ~from=[[SelectOneStep(person)]]})]
gremlin> g.V().has('name', within('marko', 'vadas', 'josh')).as('person').
           V().has('name', within('lop', 'ripple')).addE('uses').from('person').iterate().toString() //(2)
==>[TinkerGraphStep(vertex,[name.within([marko, vadas, josh])])@[person], TinkerGraphStep(vertex,[name.within([lop, ripple])]), AddEdgeStep({label=[uses], ~from=[[SelectOneStep(person)]]})]

Normally the V()-step will iterate over all vertices. However, graph strategies can fold HasContainer's into a GraphStep to allow index lookups.
Whether the graph system provider supports mid-traversal V() index lookups or not can easily be determined by inspecting the toString() output of the iterated traversal. If has conditions were folded into the V()-step, an index - if one exists - will be used.

Group Step

As traversers propagate across a graph as defined by a traversal, sideEffect computations are sometimes required. That is, the actual path taken or the current location of a traverser is not the ultimate output of the computation, but some other representation of the traversal. The group()-step (map/sideEffect) is one such sideEffect that organizes the objects according to some function of the object. Then, if required, that organization (a list) is reduced. An example is provided below.

gremlin> g.V().group().by(label) //(1)
==>[software:[v[3], v[5]], person:[v[1], v[2], v[4], v[6]]]
gremlin> g.V().group().by(label).by('name') //(2)
==>[software:[lop, ripple], person:[marko, vadas, josh, peter]]
gremlin> g.V().group().by(label).by(count()) //(3)
==>[software:2, person:4]

Group the vertices by their label.
For each vertex in the group, get their name.
For each grouping, what is its size?

The two projection parameters available to group() via by() are:

Key-projection: What feature of the object to group on (a function that yields the map key)?
Value-projection: What feature of the group to store in the key-list?

GroupCount Step

When it is important to know how many times a particular object has been at a particular part of a traversal, groupCount()-step (map/sideEffect) is used.

"What is the distribution of ages in the graph?"

gremlin> g.V().hasLabel('person').values('age').groupCount()
==>[32:1, 35:1, 27:1, 29:1]
gremlin> g.V().hasLabel('person').groupCount().by('age') //(1)
==>[32:1, 35:1, 27:1, 29:1]

You can also supply a pre-group projection, where the provided by()-modulation determines what to group the incoming object by.

There is one person that is 32, one person that is 35, one person that is 27, and one person that is 29.

"Iteratively walk the graph and count the number of times you see the second letter of each name."

gremlin> g.V().repeat(both().groupCount('m').by(label)).times(10).cap('m')
==>[software:19598, person:39196]

The above is interesting in that it demonstrates the use of referencing the internal Map<Object,Long> of groupCount() with a string variable. Given that groupCount() is a sideEffect-step, it simply passes the object it received to its output. Internal to groupCount(), the object’s count is incremented.

Has Step

It is possible to filter vertices, edges, and vertex properties based on their properties using has()-step (filter). There are numerous variations on has() including:

has(key,value): Remove the traverser if its element does not have the provided key/value property.
has(label, key, value): Remove the traverser if its element does not have the specified label and provided key/value property.
has(key,predicate): Remove the traverser if its element does not have a key value that satisfies the bi-predicate. For more information on predicates, please read A Note on Predicates.
hasLabel(labels...): Remove the traverser if its element does not have any of the labels.
hasId(ids...): Remove the traverser if its element does not have any of the ids.
hasKey(keys...): Remove the traverser if its property does not have any of the keys.
hasValue(values...): Remove the traverser if its property does not have any of the values.
has(key): Remove the traverser if its element does not have a value for the key.
hasNot(key): Remove the traverser if its element has a value for the key.
has(key, traversal): Remove the traverser if its object does not yield a result through the traversal off the property value.

gremlin> g.V().hasLabel('person')
==>v[1]
==>v[2]
==>v[4]
==>v[6]
gremlin> g.V().hasLabel('person').out().has('name',within('vadas','josh'))
==>v[2]
==>v[4]
gremlin> g.V().hasLabel('person').out().has('name',within('vadas','josh')).
               outE().hasLabel('created')
==>e[10][4-created->5]
==>e[11][4-created->3]
gremlin> g.V().has('age',inside(20,30)).values('age') //(1)
==>29
==>27
gremlin> g.V().has('age',outside(20,30)).values('age') //(2)
==>32
==>35
gremlin> g.V().has('name',within('josh','marko')).valueMap() //(3)
==>[name:[marko], age:[29]]
==>[name:[josh], age:[32]]
gremlin> g.V().has('name',without('josh','marko')).valueMap() //(4)
==>[name:[vadas], age:[27]]
==>[name:[lop], lang:[java]]
==>[name:[ripple], lang:[java]]
==>[name:[peter], age:[35]]
gremlin> g.V().has('name',not(within('josh','marko'))).valueMap() //(5)
==>[name:[vadas], age:[27]]
==>[name:[lop], lang:[java]]
==>[name:[ripple], lang:[java]]
==>[name:[peter], age:[35]]

Find all vertices whose ages are between 20 (inclusive) and 30 (exclusive).
Find all vertices whose ages are not between 20 (inclusive) and 30 (exclusive).
Find all vertices whose names are exact matches to any names in the the collection [josh,marko], display all the key,value pairs for those verticies.
Find all vertices whose names are not in the collection [josh,marko], display all the key,value pairs for those vertices.
Same as the prior example save using not on within to yield without.

TinkerPop does not support a regular expression predicate, although specific graph databases that leverage TinkerPop may provide a partial match extension.

Inject Step

One of the major features of TinkerPop3 is "injectable steps." This makes it possible to insert objects arbitrarily into a traversal stream. In general, inject()-step (sideEffect) exists and a few examples are provided below.

gremlin> g.V(4).out().values('name').inject('daniel')
==>daniel
==>ripple
==>lop
gremlin> g.V(4).out().values('name').inject('daniel').map {it.get().length()}
==>6
==>6
==>3
gremlin> g.V(4).out().values('name').inject('daniel').map {it.get().length()}.path()
==>[daniel, 6]
==>[v[4], v[5], ripple, 6]
==>[v[4], v[3], lop, 3]

In the last example above, note that the path starting with daniel is only of length 2. This is because the daniel string was inserted half-way in the traversal. Finally, a typical use case is provided below — when the start of the traversal is not a graph object.

gremlin> inject(1,2)
==>1
==>2
gremlin> inject(1,2).map {it.get() + 1}
==>2
==>3
gremlin> inject(1,2).map {it.get() + 1}.map {g.V(it.get()).next()}.values('name')
==>vadas
==>lop

Is Step

It is possible to filter scalar values using is()-step (filter).

gremlin> g.V().values('age').is(32)
==>32
gremlin> g.V().values('age').is(lte(30))
==>29
==>27
gremlin> g.V().values('age').is(inside(30, 40))
==>32
==>35
gremlin> g.V().where(__.in('created').count().is(1)).values('name') //(1)
==>ripple
gremlin> g.V().where(__.in('created').count().is(gte(2))).values('name') //(2)
==>lop
gremlin> g.V().where(__.in('created').values('age').
                                    mean().is(inside(30d, 35d))).values('name') //(3)
==>lop
==>ripple

Find projects having exactly one contributor.
Find projects having two or more contributors.
Find projects whose contributors average age is between 30 and 35.

Limit Step

The limit()-step is analogous to range()-step save that the lower end range is set to 0.

gremlin> g.V().limit(2)
==>v[1]
==>v[2]
gremlin> g.V().range(0, 2)
==>v[1]
==>v[2]
gremlin> g.V().limit(2).toString()
==>[GraphStep([],vertex), RangeGlobalStep(0,2)]

The limit()-step can also be applied with Scope.local, in which case it operates on the incoming collection. The examples below use the The Crew toy data set.

gremlin> g.V().valueMap().select('location').limit(local,2) //(1)
==>[san diego, santa cruz]
==>[centreville, dulles]
==>[bremen, baltimore]
==>[spremberg, kaiserslautern]
gremlin> g.V().valueMap().limit(local, 1) //(2)
==>[name:[marko]]
==>[name:[stephen]]
==>[name:[matthias]]
==>[name:[daniel]]
==>[name:[gremlin]]
==>[name:[tinkergraph]]

List<String> for each vertex containing the first two locations.
Map<String, Object> for each vertex, but containing only the first property value.

Local Step

A GraphTraversal operates on a continuous stream of objects. In many situations, it is important to operate on a single element within that stream. To do such object-local traversal computations, local()-step exists (branch). Note that the examples below use the The Crew toy data set.

gremlin> g.V().as('person').
               properties('location').order().by('startTime',incr).limit(2).value().as('location').
               select('person','location').by('name').by() //(1)
==>[person:daniel, location:spremberg]
==>[person:stephen, location:centreville]
gremlin> g.V().as('person').
               local(properties('location').order().by('startTime',incr).limit(2)).value().as('location').
               select('person','location').by('name').by() //(2)
==>[person:marko, location:san diego]
==>[person:marko, location:santa cruz]
==>[person:stephen, location:centreville]
==>[person:stephen, location:dulles]
==>[person:matthias, location:bremen]
==>[person:matthias, location:baltimore]
==>[person:daniel, location:spremberg]
==>[person:daniel, location:kaiserslautern]

Get the first two people and their respective location according to the most historic location start time.
For every person, get their two most historic locations.

The two traversals above look nearly identical save the inclusion of local() which wraps a section of the traversal in a object-local traversal. As such, the order().by() and the limit() refer to a particular object, not to the stream as a whole.

Warning

The anonymous traversal of local() processes the current object "locally." In OLAP, where the atomic unit of computing is the the vertex and its local "star graph," it is important that the anonymous traversal does not leave the confines of the vertex’s star graph. In other words, it can not traverse to an adjacent vertex’s properties or edges.

Match Step

The match()-step (map) provides a more declarative form of graph querying based on the notion of pattern matching. With match(), the user provides a collection of "traversal fragments," called patterns, that have variables defined that must hold true throughout the duration of the match(). When a traverser is in match(), a registered MatchAlgorithm analyzes the current state of the traverser (i.e. its history based on its path data), the runtime statistics of the traversal patterns, and returns a traversal-pattern that the traverser should try next. The default MatchAlgorithm provided is called CountMatchAlgorithm and it dynamically revises the pattern execution plan by sorting the patterns according to their filtering capabilities (i.e. largest set reduction patterns execute first). For very large graphs, where the developer is uncertain of the statistics of the graph (e.g. how many knows-edges vs. worksFor-edges exist in the graph), it is advantageous to use match(), as an optimal plan will be determined automatically. Furthermore, some queries are much easier to express via match() than with single-path traversals.

"Who created a project named 'lop' that was also created by someone who is 29 years old? Return the two creators."

gremlin> g.V().match(
                 __.as('a').out('created').as('b'),
                 __.as('b').has('name', 'lop'),
                 __.as('b').in('created').as('c'),
                 __.as('c').has('age', 29)).
               select('a','c').by('name')
==>[a:marko, c:marko]
==>[a:josh, c:marko]
==>[a:peter, c:marko]

Note that the above can also be more concisely written as below which demonstrates that standard inner-traversals can be arbitrarily defined.

gremlin> g.V().match(
                 __.as('a').out('created').has('name', 'lop').as('b'),
                 __.as('b').in('created').has('age', 29).as('c')).
               select('a','c').by('name')
==>[a:marko, c:marko]
==>[a:josh, c:marko]
==>[a:peter, c:marko]

In order to improve readability, as()-steps can be given meaningful labels which better reflect your domain. The previous query can thus be written in a more expressive way as shown below.

gremlin> g.V().match(
                 __.as('creators').out('created').has('name', 'lop').as('projects'), //(1)
                 __.as('projects').in('created').has('age', 29).as('cocreators')). //(2)
               select('creators','cocreators').by('name') //(3)
==>[creators:marko, cocreators:marko]
==>[creators:josh, cocreators:marko]
==>[creators:peter, cocreators:marko]

Find vertices that created something and match them as creators, then find out what they created which is named lop and match these vertices as projects.
Using these projects vertices, find out their creators aged 29 and remember these as cocreators.
Return the name of both creators and cocreators.

Figure 4. Grateful Dead

MatchStep brings functionality similar to SPARQL to Gremlin. Like SPARQL, MatchStep conjoins a set of patterns applied to a graph. For example, the following traversal finds exactly those songs which Jerry Garcia has both sung and written (using the Grateful Dead graph distributed in the data/ directory):

gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> g = graph.traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
gremlin> g.V().match(
                 __.as('a').has('name', 'Garcia'),
                 __.as('a').in('writtenBy').as('b'),
                 __.as('a').in('sungBy').as('b')).
               select('b').values('name')
==>CREAM PUFF WAR
==>CRYPTICAL ENVELOPMENT

Among the features which differentiate match() from SPARQL are:

gremlin> g.V().match(
                 __.as('a').out('created').has('name','lop').as('b'), //(1)
                 __.as('b').in('created').has('age', 29).as('c'),
                 __.as('c').repeat(out()).times(2)). //(2)
               select('c').out('knows').dedup().values('name') //(3)
==>vadas
==>josh

Patterns of arbitrary complexity: match() is not restricted to triple patterns or property paths.
Recursion support: match() supports the branch-based steps within a pattern, including repeat().
Imperative/declarative hybrid: Before and after a match(), it is possible to leverage classic Gremlin traversals.

To extend point #3, it is possible to support going from imperative, to declarative, to imperative, ad infinitum.

gremlin> g.V().match(
                 __.as('a').out('knows').as('b'),
                 __.as('b').out('created').has('name','lop')).
               select('b').out('created').
                 match(
                   __.as('x').in('created').as('y'),
                   __.as('y').out('knows').as('z')).
               select('z').values('name')
==>vadas
==>josh

Important

The match()-step is stateless. The variable bindings of the traversal patterns are stored in the path history of the traverser. As such, the variables used over all match()-steps within a traversal are globally unique. A benefit of this is that subsequent where(), select(), match(), etc. steps can leverage the same variables in their analysis.

Like all other steps in Gremlin, match() is a function and thus, match() within match() is a natural consequence of Gremlin’s functional foundation (i.e. recursive matching).

gremlin> g.V().match(
                 __.as('a').out('knows').as('b'),
                 __.as('b').out('created').has('name','lop'),
                 __.as('b').match(
                              __.as('b').out('created').as('c'),
                              __.as('c').has('name','ripple')).
                            select('c').as('c')).
               select('a','c').by('name')
==>[a:marko, c:ripple]

If a step-labeled traversal proceeds the match()-step and the traverser entering the match() is destined to bind to a particular variable, then the previous step should be labeled accordingly.

gremlin> g.V().as('a').out('knows').as('b').
           match(
             __.as('b').out('created').as('c'),
             __.not(__.as('c').in('created').as('a'))).
           select('a','b','c').by('name')
==>[a:marko, b:josh, c:ripple]

There are three types of match() traversal patterns.

as('a')...as('b'): both the start and end of the traversal have a declared variable.
as('a')...: only the start of the traversal has a declared variable.
...: there are no declared variables.

If a variable is at the start of a traversal pattern it must exist as a label in the path history of the traverser else the traverser can not go down that path. If a variable is at the end of a traversal pattern then if the variable exists in the path history of the traverser, the traverser’s current location must match (i.e. equal) its historic location at that same label. However, if the variable does not exist in the path history of the traverser, then the current location is labeled as the variable and thus, becomes a bound variable for subsequent traversal patterns. If a traversal pattern does not have an end label, then the traverser must simply "survive" the pattern (i.e. not be filtered) to continue to the next pattern. If a traversal pattern does not have a start label, then the traverser can go down that path at any point, but will only go down that pattern once as a traversal pattern is executed once and only once for the history of the traverser. Typically, traversal patterns that do not have a start and end label are used in conjunction with and(), or(), and where(). Once the traverser has "survived" all the patterns (or at least one for or()), match()-step analyzes the traverser’s path history and emits a Map<String,Object> of the variable bindings to the next step in the traversal.

gremlin> g.V().as('a').out().as('b'). //(1)
             match( //(2)
               __.as('a').out().count().as('c'), //(3)
               __.not(__.as('a').in().as('b')), //(4)
               or( //(5)
                 __.as('a').out('knows').as('b'),
                 __.as('b').in().count().as('c').and().as('c').is(gt(2)))). //(6)
             dedup('a','c'). //(7)
             select('a','b','c').by('name').by('name').by() //(8)
==>[a:marko, b:lop, c:3]

A standard, step-labeled traversal can come prior to match().
If the traverser’s path prior to entering match() has requisite label values, then those historic values are bound.
It is possible to use barrier steps though they are computed locally to the pattern (as one would expect).
It is possible to not() a pattern.
It is possible to nest and()- and or()-steps for conjunction matching.
Both infix and prefix conjunction notation is supported.
It is possible to "distinct" the specified label combination.
The bound values are of different types — vertex ("a"), vertex ("b"), long ("c").

Using Where with Match

Match is typically used in conjunction with both select() (demonstrated previously) and where() (presented here). A where()-step allows the user to further constrain the result set provided by match().

gremlin> g.V().match(
                 __.as('a').out('created').as('b'),
                 __.as('b').in('created').as('c')).
                 where('a', neq('c')).
               select('a','c').by('name')
==>[a:marko, c:josh]
==>[a:marko, c:peter]
==>[a:josh, c:marko]
==>[a:josh, c:peter]
==>[a:peter, c:marko]
==>[a:peter, c:josh]

The where()-step can take either a P-predicate (example above) or a Traversal (example below). Using MatchPredicateStrategy, where()-clauses are automatically folded into match() and thus, subject to the query optimizer within match()-step.

gremlin> traversal = g.V().match(
                             __.as('a').has(label,'person'), //(1)
                             __.as('a').out('created').as('b'),
                             __.as('b').in('created').as('c')).
                             where(__.as('a').out('knows').as('c')). //(2)
                           select('a','c').by('name'); null //(3)
==>null
gremlin> traversal.toString() //(4)
==>[GraphStep([],vertex), MatchStep(AND,[[MatchStartStep(a), HasStep([~label.eq(person)]), MatchEndStep], [MatchStartStep(a), VertexStep(OUT,[created],vertex), MatchEndStep(b)], [MatchStartStep(b), VertexStep(IN,[created],vertex), MatchEndStep(c)]]), WhereTraversalStep([WhereStartStep(a), VertexStep(OUT,[knows],vertex), WhereEndStep(c)]), SelectStep([a, c],[value(name)])]
gremlin> traversal // (5) (6)
==>[a:marko, c:josh]
gremlin> traversal.toString() //(7)
==>[TinkerGraphStep(vertex,[~label.eq(person)])@[a], MatchStep(AND,[[MatchStartStep(a), VertexStep(OUT,[created],vertex), MatchEndStep(b)], [MatchStartStep(b), VertexStep(IN,[created],vertex), MatchEndStep(c)], [MatchStartStep(a), WhereTraversalStep([WhereStartStep, VertexStep(OUT,[knows],vertex), WhereEndStep(c)]), MatchEndStep]]), SelectStep([a, c],[value(name)])]

Any has()-step traversal patterns that start with the match-key are pulled out of match() to enable the graph system to leverage the filter for index lookups.
A where()-step with a traversal containing variable bindings declared in match().
A useful trick to ensure that the traversal is not iterated by Gremlin Console.
The string representation of the traversal prior to its strategies being applied.
The Gremlin Console will automatically iterate anything that is an iterator or is iterable.
Both marko and josh are co-developers and marko knows josh.
The string representation of the traversal after the strategies have been applied (and thus, where() is folded into match())

Important

A where()-step is a filter and thus, variables within a where() clause are not globally bound to the path of the traverser in match(). As such, where()-steps in match() are used for filtering, not binding.

Max Step

The max()-step (map) operates on a stream of numbers and determines which is the largest number in the stream.

gremlin> g.V().values('age').max()
==>35
gremlin> g.V().repeat(both()).times(3).values('age').max()
==>35

Important

max(local) determines the max of the current, local object (not the objects in the traversal stream). This works for Collection and Number-type objects. For any other object, a max of Double.NaN is returned.

Mean Step

The mean()-step (map) operates on a stream of numbers and determines the average of those numbers.

gremlin> g.V().values('age').mean()
==>30.75
gremlin> g.V().repeat(both()).times(3).values('age').mean() //(1)
==>30.645833333333332
gremlin> g.V().repeat(both()).times(3).values('age').dedup().mean()
==>30.75

Realize that traversers are being bulked by repeat(). There may be more of a particular number than another, thus altering the average.

Important

mean(local) determines the mean of the current, local object (not the objects in the traversal stream). This works for Collection and Number-type objects. For any other object, a mean of Double.NaN is returned.

Min Step

The min()-step (map) operates on a stream of numbers and determines which is the smallest number in the stream.

gremlin> g.V().values('age').min()
==>27
gremlin> g.V().repeat(both()).times(3).values('age').min()
==>27

Important

min(local) determines the min of the current, local object (not the objects in the traversal stream). This works for Collection and Number-type objects. For any other object, a min of Double.NaN is returned.

Option Step _~_~_~_~~

An option to a branch() or choose()

Or Step

The or()-step ensures that at least one of the provided traversals yield a result (filter). Please see and() for and-semantics.

gremlin> g.V().or(
            __.outE('created'),
            __.inE('created').count().is(gt(1))).
              values('name')
==>marko
==>lop
==>josh
==>peter

The or()-step can take an arbitrary number of traversals. At least one of the traversals must produce at least one output for the original traverser to pass to the next step.

An infix notation can be used as well. Though, with infix notation, only two traversals can be or’d together.

gremlin> g.V().where(outE('created').or().outE('knows')).values('name')
==>marko
==>josh
==>peter

Order Step

When the objects of the traversal stream need to be sorted, order()-step (map) can be leveraged.

gremlin> g.V().values('name').order()
==>josh
==>lop
==>marko
==>peter
==>ripple
==>vadas
gremlin> g.V().values('name').order().by(decr)
==>vadas
==>ripple
==>peter
==>marko
==>lop
==>josh
gremlin> g.V().hasLabel('person').order().by('age', incr).values('name')
==>vadas
==>marko
==>josh
==>peter

One of the most traversed objects in a traversal is an Element. An element can have properties associated with it (i.e. key/value pairs). In many situations, it is desirable to sort an element traversal stream according to a comparison of their properties.

gremlin> g.V().values('name')
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter
gremlin> g.V().order().by('name',incr).values('name')
==>josh
==>lop
==>marko
==>peter
==>ripple
==>vadas
gremlin> g.V().order().by('name',decr).values('name')
==>vadas
==>ripple
==>peter
==>marko
==>lop
==>josh

The order()-step allows the user to provide an arbitrary number of comparators for primary, secondary, etc. sorting. In the example below, the primary ordering is based on the outgoing created-edge count. The secondary ordering is based on the age of the person.

gremlin> g.V().hasLabel('person').order().by(outE('created').count(), incr).
                                          by('age', incr).values('name')
==>vadas
==>marko
==>peter
==>josh
gremlin> g.V().hasLabel('person').order().by(outE('created').count(), incr).
                                          by('age', decr).values('name')
==>vadas
==>peter
==>marko
==>josh

Randomizing the order of the traversers at a particular point in the traversal is possible with Order.shuffle.

gremlin> g.V().hasLabel('person').order().by(shuffle)
==>v[1]
==>v[4]
==>v[2]
==>v[6]
gremlin> g.V().hasLabel('person').order().by(shuffle)
==>v[1]
==>v[6]
==>v[4]
==>v[2]

It is possible to use order(local) to order the current local object and not the entire traversal stream. This works for Collection- and Map-type objects. For any other object, the object is returned unchanged.

gremlin> g.V().values('age').fold().order(local).by(decr) //(1)
==>[35, 32, 29, 27]
gremlin> g.V().values('age').order(local).by(decr) //(2)
==>29
==>27
==>32
==>35
gremlin> g.V().groupCount().by(inE().count()).order(local).by(values, decr) //(3)
==>[1:3, 0:2, 3:1]
gremlin> g.V().groupCount().by(inE().count()).order(local).by(keys, incr) //(4)
==>[0:2, 1:3, 3:1]

The ages are gathered into a list and then that list is sorted in decreasing order.
The ages are not gathered and thus order(local) is "ordering" single integers and thus, does nothing.
The groupCount() map is ordered by its values in decreasing order.
The groupCount() map is ordered by its keys in increasing order.

Note	The `values` and `keys` enums are from `Column` which is used to select "columns" from a `Map`, `Map.Entry`, or `Path`.

Path Step

A traverser is transformed as it moves through a series of steps within a traversal. The history of the traverser is realized by examining its path with path()-step (map).

gremlin> g.V().out().out().values('name')
==>ripple
==>lop
gremlin> g.V().out().out().values('name').path()
==>[v[1], v[4], v[5], ripple]
==>[v[1], v[4], v[3], lop]

If edges are required in the path, then be sure to traverser those edges explicitly.

gremlin> g.V().outE().inV().outE().inV().path()
==>[v[1], e[8][1-knows->4], v[4], e[10][4-created->5], v[5]]
==>[v[1], e[8][1-knows->4], v[4], e[11][4-created->3], v[3]]

It is possible to post-process the elements of the path in a round-robin fashion via by().

gremlin> g.V().out().out().path().by('name').by('age')
==>[marko, 32, ripple]
==>[marko, 32, lop]

Finally, because by()-based post-processing, nothing prevents triggering yet another traversal. In the traversal below, for each element of the path traversed thus far, if its a person (as determined by having an age-property), then get all of their creations, else if its a creation, get all the people that created it.

gremlin> g.V().out().out().path().by(
                            choose(hasLabel('person'),
                                          out('created').values('name'),
                                          __.in('created').values('name')).fold())
==>[[lop], [ripple, lop], [josh]]
==>[[lop], [ripple, lop], [marko, josh, peter]]

Warning

Generating path information is expensive as the history of the traverser is stored into a Java list. With numerous traversers, there are numerous lists. Moreover, in an OLAP GraphComputer environment this becomes exceedingly prohibitive as there are traversers emanating from all vertices in the graph in parallel. In OLAP there are optimizations provided for traverser populations, but when paths are calculated (and each traverser is unique due to its history), then these optimizations are no longer possible.

Path Data Structure

The Path data structure is an ordered list of objects, where each object is associated to a Set<String> of labels. An example is presented below to demonstrate both the Path API as well as how a traversal yields labeled paths.

gremlin> path = g.V(1).as('a').has('name').as('b').
                       out('knows').out('created').as('c').
                       has('name','ripple').values('name').as('d').
                       identity().as('e').path().next()
==>v[1]
==>v[4]
==>v[5]
==>ripple
gremlin> path.size()
==>4
gremlin> path.objects()
==>v[1]
==>v[4]
==>v[5]
==>ripple
gremlin> path.labels()
==>[a, b]
==>[]
==>[c]
==>[d, e]
gremlin> path.a
==>v[1]
gremlin> path.b
==>v[1]
gremlin> path.c
==>v[5]
gremlin> path.d == path.e
==>true

Profile Step

The profile()-step (sideEffect) exists to allow developers to profile their traversals to determine statistical information like step runtime, counts, etc.

Warning

Profiling a Traversal will impede the Traversal’s performance. This overhead is mostly excluded from the profile results, but durations are not exact. Thus, durations are best considered in relation to each other.

gremlin> g.V().out('created').repeat(both()).times(3).hasLabel('person').values('age').sum().profile().cap(TraversalMetrics.METRICS_KEY)
==>Traversal Metrics
Step                                                               Count  Traversers       Time (ms)    % Dur
=============================================================================================================
TinkerGraphStep([],vertex)                                             6           6           0.117     8.36
VertexStep(OUT,[created],vertex)                                       4           4           0.040     2.87
RepeatStep([VertexStep(BOTH,vertex), ProfileSte...                    58          40           0.589    42.06
  VertexStep(BOTH,vertex)                                             92          74           0.181
  RepeatEndStep                                                       58          40           0.250
HasStep([~label.eq(person)])                                          48          30           0.133     9.55
PropertiesStep([age],value)                                           48          30           0.164    11.74
SumGlobalStep                                                          1           1           0.325    23.19
SideEffectCapStep([~metrics])                                          1           1           0.031     2.22
                                            >TOTAL                     -           -           1.401        -

The profile()-step generates a TraversalMetrics sideEffect object that contains the following information:

Step: A step within the traversal being profiled.
Count: The number of represented traversers that passed through the step.
Traversers: The number of traversers that passed through the step.
Time (ms): The total time the step was actively executing its behavior.
% Dur: The percentage of total time spent in the step.

gremlin-exercise It is important to understand the difference between Count and Traversers. Traversers can be merged and as such, when two traversers are "the same" they may be aggregated into a single traverser. That new traverser has a Traverser.bulk() that is the sum of the two merged traverser bulks. On the other hand, the Count represents the sum of all Traverser.bulk() results and thus, expresses the number of "represented" (not enumerated) traversers. Traversers will always be less than or equal to Count.

For traversal compilation information, please see explain()-step.

Range Step

As traversers propagate through the traversal, it is possible to only allow a certain number of them to pass through with range()-step (filter). When the low-end of the range is not met, objects are continued to be iterated. When within the low and high range (both inclusive), traversers are emitted. Finally, when above the high range, the traversal breaks out of iteration.

gremlin> g.V().range(0,3)
==>v[1]
==>v[2]
==>v[3]
gremlin> g.V().range(1,3)
==>v[2]
==>v[3]
gremlin> g.V().repeat(both()).times(1000000).emit().range(6,10)
==>v[1]
==>v[5]
==>v[3]
==>v[1]

The range()-step can also be applied with Scope.local, in which case it operates on the incoming collection. For example, it is possible to produce a Map<String, String> for each traversed path, but containing only the second property value (the "b" step).

gremlin> g.V().as('a').out().as('b').in().as('c').select('a','b','c').by('name').range(local,1,2)
==>[b:lop]
==>[b:lop]
==>[b:lop]
==>[b:vadas]
==>[b:josh]
==>[b:ripple]
==>[b:lop]
==>[b:lop]
==>[b:lop]
==>[b:lop]
==>[b:lop]
==>[b:lop]

The next example uses the The Crew toy data set. It produces a List<String> containing the second and third location for each vertex.

gremlin> g.V().valueMap().select('location').range(local, 1, 3)
==>[santa cruz, brussels]
==>[dulles, purcellville]
==>[baltimore, oakland]
==>[kaiserslautern, aachen]

Repeat Step

The repeat()-step (branch) is used for looping over a traversal given some break predicate. Below are some examples of repeat()-step in action.

gremlin> g.V(1).repeat(out()).times(2).path().by('name') //(1)
==>[marko, josh, ripple]
==>[marko, josh, lop]
gremlin> g.V().until(has('name','ripple')).
               repeat(out()).path().by('name') //(2)
==>[marko, josh, ripple]
==>[josh, ripple]
==>[ripple]

do-while semantics stating to do out() 2 times.
while-do semantics stating to break if the traverser is at a vertex named "ripple".

Important

There are two modulators for repeat(): until() and emit(). If until() comes after repeat() it is do/while looping. If until() comes before repeat() it is while/do looping. If emit() is placed after repeat(), it is evaluated on the traversers leaving the repeat-traversal. If emit() is placed before repeat(), it is evaluated on the traversers prior to entering the repeat-traversal.

The repeat()-step also supports an "emit predicate", where the predicate for an empty argument emit() is true (i.e. emit() == emit{true}). With emit(), the traverser is split in two — the traverser exits the code block as well as continues back within the code block (assuming until() holds true).

gremlin> g.V(1).repeat(out()).times(2).emit().path().by('name') //(1)
==>[marko, lop]
==>[marko, vadas]
==>[marko, josh]
==>[marko, josh, ripple]
==>[marko, josh, lop]
gremlin> g.V(1).emit().repeat(out()).times(2).path().by('name') //(2)
==>[marko]
==>[marko, lop]
==>[marko, vadas]
==>[marko, josh]
==>[marko, josh, ripple]
==>[marko, josh, lop]

The emit() comes after repeat() and thus, emission happens after the repeat() traversal is executed. Thus, no one vertex paths exist.
The emit() comes before repeat() and thus, emission happens prior to the repeat() traversal being executed. Thus, one vertex paths exist.

The emit()-modulator can take an arbitrary predicate.

gremlin> g.V(1).repeat(out()).times(2).emit(has('lang')).path().by('name')
==>[marko, lop]
==>[marko, josh, ripple]
==>[marko, josh, lop]

gremlin> g.V(1).repeat(out()).times(2).emit().path().by('name')
==>[marko, lop]
==>[marko, vadas]
==>[marko, josh]
==>[marko, josh, ripple]
==>[marko, josh, lop]

The first time through the repeat(), the vertices lop, vadas, and josh are seen. Given that loops==1, the traverser repeats. However, because the emit-predicate is declared true, those vertices are emitted. The next time through repeat(), the vertices traversed are ripple and lop (Josh’s created projects, as lop and vadas have no out edges). Given that loops==2, the until-predicate fails and ripple and lop are emitted. Therefore, the traverser has seen the vertices: lop, vadas, josh, ripple, and lop.

Finally, note that both emit() and until() can take a traversal and in such, situations, the predicate is determined by traversal.hasNext(). A few examples are provided below.

gremlin> g.V(1).repeat(out()).until(hasLabel('software')).path().by('name') //(1)
==>[marko, lop]
==>[marko, josh, ripple]
==>[marko, josh, lop]
gremlin> g.V(1).emit(hasLabel('person')).repeat(out()).path().by('name') //(2)
==>[marko]
==>[marko, vadas]
==>[marko, josh]
gremlin> g.V(1).repeat(out()).until(outE().count().is(0)).path().by('name') //(3)
==>[marko, lop]
==>[marko, vadas]
==>[marko, josh, ripple]
==>[marko, josh, lop]

Starting from vertex 1, keep taking outgoing edges until a software vertex is reached.
Starting from vertex 1, and in an infinite loop, emit the vertex if it is a person and then traverser the outgoing edges.
Starting from vertex 1, keep taking outgoing edges until a vertex is reached that has no more outgoing edges.

Warning

The anonymous traversal of emit() and until() (not repeat()) process their current objects "locally." In OLAP, where the atomic unit of computing is the the vertex and its local "star graph," it is important that the anonymous traversals do not leave the confines of the vertex’s star graph. In other words, they can not traverse to an adjacent vertex’s properties or edges.

Sack Step

gremlin-sacks-running A traverser can contain a local data structure called a "sack". The sack()-step is used to read and write sacks (sideEffect or map). Each sack of each traverser is created when using GraphTraversal.withSack(initialValueSupplier,splitOperator?,mergeOperator?).

Initial value supplier: A Supplier providing the initial value of each traverser’s sack.
Split operator: a UnaryOperator that clones the traverser’s sack when the traverser splits. If no split operator is provided, then UnaryOperator.identity() is assumed.
Merge operator: A BinaryOperator that unites two traverser’s sack when they are merged. If no merge operator is provided, then traversers with sacks can not be merged.

Two trivial examples are presented below to demonstrate the initial value supplier. In the first example below, a traverser is created at each vertex in the graph (g.V()), with a 1.0 sack (withSack(1.0f)), and then the sack value is accessed (sack()). In the second example, a random float supplier is used to generate sack values.

gremlin> g.withSack(1.0f).V().sack()
==>1.0
==>1.0
==>1.0
==>1.0
==>1.0
==>1.0
gremlin> rand = new Random()
==>java.util.Random@1e9a10e9
gremlin> g.withSack {rand.nextFloat()}.V().sack()
==>0.9209243
==>0.12522703
==>0.6208878
==>0.33709127
==>0.068178356
==>0.65773135

A more complicated initial value supplier example is presented below where the sack values are used in a running computation and then emitted at the end of the traversal. When an edge is traversed, the edge weight is multiplied by the sack value (sack(mult).by('weight')). Note that the by()-modulator can be any arbitrary traversal.

gremlin> g.withSack(1.0f).V().repeat(outE().sack(mult).by('weight').inV()).times(2)
==>v[5]
==>v[3]
gremlin> g.withSack(1.0f).V().repeat(outE().sack(mult).by('weight').inV()).times(2).sack()
==>1.0
==>0.4
gremlin> g.withSack(1.0f).V().repeat(outE().sack(mult).by('weight').inV()).times(2).path().
               by().by('weight')
==>[v[1], 1.0, v[4], 1.0, v[5]]
==>[v[1], 1.0, v[4], 0.4, v[3]]

gremlin-sacks-standing When complex objects are used (i.e. non-primitives), then a split operator should be defined to ensure that each traverser gets a clone of its parent’s sack. The first example does not use a split operator and as such, the same map is propagated to all traversers (a global data structure). The second example, demonstrates how Map.clone() ensures that each traverser’s sack contains a unique, local sack.

gremlin> g.withSack {[:]}.V().out().out().
               sack {m,v -> m[v.value('name')] = v.value('lang'); m}.sack() // BAD: single map
==>[ripple:java]
==>[ripple:java, lop:java]
gremlin> g.withSack {[:]}{it.clone()}.V().out().out().
               sack {m,v -> m[v.value('name')] = v.value('lang'); m}.sack() // GOOD: cloned map
==>[ripple:java]
==>[lop:java]

Note	For primitives (i.e. integers, longs, floats, etc.), a split operator is not required as a primitives are encoded in the memory address of the sack, not as a reference to an object.

If a merge operator is not provided, then traversers with sacks can not be bulked. However, in many situations, merging the sacks of two traversers at the same location is algorithmically sound and good to provide so as to gain the bulking optimization. In the examples below, the binary merge operator is Operator.sum. Thus, when two traverser merge, their respective sacks are added together.

gremlin> g.withSack(1.0f,sum).V(1).local(outE('knows').barrier(normSack).inV()) //(1)
==>v[2]
==>v[4]
gremlin> g.withSack(1.0f,sum).V(1).local(outE('knows').barrier(normSack).inV()).sack() //(2)
==>0.5
==>0.5
gremlin> g.withSack(1.0f,sum).V(1).local(outE('knows').barrier(normSack).inV()).in('knows') //(3)
==>v[1]
==>v[1]
gremlin> g.withSack(1.0f,sum).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').sack() //(4)
==>0.5
==>0.5
gremlin> g.withSack(1.0f,sum).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').barrier().sack() //(5)
==>1.0
==>1.0
gremlin> g.withBulk(false).withSack(1.0f,sum).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').barrier().sack() //(6)
==>1.0

The knows-adjacent vertices of vertex 1 are vertices 2 and 4.
The local(...barrier(normSack)...) ensures that all traversers leaving vertex 1 have an evenly distributed amount of the initial 1.0 "energy" (50-50).
Going from vertices 2 and 4 yield two traversers at vertex 1.
Those two traversers each have a sack of 0.5.
The barrier() merges the two traversers at vertex 1 into a single traverser whose sack is 1.0.
There is now a single traverser with bulk of 2 and sack of 1.0 and thus, setting withBulk(false) yields the expected 1.0.

Sample Step

The sample()-step is useful for sampling some number of traversers previous in the traversal.

gremlin> g.V().outE().sample(1).values('weight')
==>1.0
gremlin> g.V().outE().sample(1).by('weight').values('weight')
==>1.0
gremlin> g.V().outE().sample(2).by('weight').values('weight')
==>1.0
==>0.5

One of the more interesting use cases for sample() is when it is used in conjunction with local(). The combination of the two steps supports the execution of random walks. In the example below, the traversal starts are vertex 1 and selects one edge to traverse based on a probability distribution generated by the weights of the edges. The output is always a single path as by selecting a single edge, the traverser never splits and continues down a single path in the graph.

gremlin> g.V(1).repeat(local(
                  bothE().sample(1).by('weight').otherV()
                )).times(5)
==>v[4]
gremlin> g.V(1).repeat(local(
                  bothE().sample(1).by('weight').otherV()
                )).times(5).path()
==>[v[1], e[8][1-knows->4], v[4], e[11][4-created->3], v[3], e[11][4-created->3], v[4], e[8][1-knows->4], v[1], e[8][1-knows->4], v[4]]
gremlin> g.V(1).repeat(local(
                  bothE().sample(1).by('weight').otherV()
                )).times(10).path()
==>[v[1], e[9][1-created->3], v[3], e[12][6-created->3], v[6], e[12][6-created->3], v[3], e[9][1-created->3], v[1], e[7][1-knows->2], v[2], e[7][1-knows->2], v[1], e[8][1-knows->4], v[4], e[11][4-created->3], v[3], e[11][4-created->3], v[4], e[10][4-created->5], v[5]]

Select Step

Functional languages make use of function composition and lazy evaluation to create complex computations from primitive operations. This is exactly what Traversal does. One of the differentiating aspects of Gremlin’s data flow approach to graph processing is that the flow need not always go "forward," but in fact, can go back to a previously seen area of computation. Examples include path() as well as the select()-step (map). There are two general ways to use select()-step.

Select labeled steps within a path (as defined by as() in a traversal).
Select objects out of a Map<String,Object> flow (i.e. a sub-map).

The first use case is demonstrated via example below.

gremlin> g.V().as('a').out().as('b').out().as('c') // no select
==>v[5]
==>v[3]
gremlin> g.V().as('a').out().as('b').out().as('c').select('a','b','c')
==>[a:v[1], b:v[4], c:v[5]]
==>[a:v[1], b:v[4], c:v[3]]
gremlin> g.V().as('a').out().as('b').out().as('c').select('a','b')
==>[a:v[1], b:v[4]]
==>[a:v[1], b:v[4]]
gremlin> g.V().as('a').out().as('b').out().as('c').select('a','b').by('name')
==>[a:marko, b:josh]
==>[a:marko, b:josh]
gremlin> g.V().as('a').out().as('b').out().as('c').select('a') //(1)
==>v[1]
==>v[1]

If the selection is one step, no map is returned.

When there is only one label selected, then a single object is returned. This is useful for stepping back in a computation and easily moving forward again on the object reverted to.

gremlin> g.V().out().out()
==>v[5]
==>v[3]
gremlin> g.V().out().out().path()
==>[v[1], v[4], v[5]]
==>[v[1], v[4], v[3]]
gremlin> g.V().as('x').out().out().select('x')
==>v[1]
==>v[1]
gremlin> g.V().out().as('x').out().select('x')
==>v[4]
==>v[4]
gremlin> g.V().out().out().as('x').select('x') // pointless
==>v[5]
==>v[3]

Note

When executing a traversal with select() on a standard traversal engine (i.e. OLTP), select() will do its best to avoid calculating the path history and instead, will rely on a global data structure for storing the currently selected object. As such, if only a subset of the path walked is required, select() should be used over the more resource intensive path()-step.

When the set of keys or values (i.e. columns) of a path or map are needed, use select(keys) and select(values), respectively. This is especially useful when one is only interested in the top N elements in a groupCount() ranking.

gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
gremlin> g.V().hasLabel('song').out('followedBy').groupCount().by('name').
               order(local).by(values,decr).limit(local, 5)
==>[PLAYING IN THE BAND:107, JACK STRAW:99, TRUCKING:94, DRUMS:92, ME AND MY UNCLE:86]
gremlin> g.V().hasLabel('song').out('followedBy').groupCount().by('name').
               order(local).by(values,decr).limit(local, 5).select(keys)
==>[PLAYING IN THE BAND, JACK STRAW, TRUCKING, DRUMS, ME AND MY UNCLE]
gremlin> g.V().hasLabel('song').out('followedBy').groupCount().by('name').
               order(local).by(values,decr).limit(local, 5).select(keys).unfold()
==>PLAYING IN THE BAND
==>JACK STRAW
==>TRUCKING
==>DRUMS
==>ME AND MY UNCLE

Similarly, for extracting the values from a path or map.

gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
gremlin> g.V().hasLabel('song').out('sungBy').groupCount().by('name') //(1)
==>[All:9, Weir_Garcia:1, Lesh:19, Weir_Kreutzmann:1, Pigpen_Garcia:1, Pigpen:36, Unknown:6, Weir_Bralove:1, Joan_Baez:10, Suzanne_Vega:2, Welnick:10, Lesh_Pigpen:1, Elvin_Bishop:4, Neil_Young:1, Garcia_Weir_Lesh:1, Hunter:3, Hornsby:4, Jon_Hendricks:2, Weir_Hart:3, Lesh_Mydland:1, Mydland_Lesh:1, instrumental:1, Garcia:146, Hart:2, Welnick_Bralove:1, Weir:99, Garcia_Dawson:1, Pigpen_Weir_Mydland:2, Jorma_Kaukonen:4, Joey_Covington:2, Allman_Brothers:1, Garcia_Lesh:3, Boz_Scaggs:1, Pigpen?:1, Keith_Godchaux:1, Etta_James:1, Weir_Wasserman:1, Hall_and_Oates:2, Grateful_Dead:17, Spencer_Davis:2, Pigpen_Mydland:3, Beach_Boys:3, Donna:4, Bo_Diddley:7, Bob_Dylan:22, Hart_Kreutzmann:2, Weir_Mydland:3, Lesh_Hart_Kreutzmann:1, Stephen_Stills:2, Mydland:18, Neville_Brothers:2, Weir_Hart_Welnick:1, Garcia_Lesh_Weir:1, Garcia_Weir:3, Neal_Cassady:1, John_Fogerty:5, Donna_Godchaux:2, Pigpen_Weir:8, Garcia_Kreutzmann:2, None:6]
gremlin> g.V().hasLabel('song').out('sungBy').groupCount().by('name').select(values) //(2)
==>[9, 1, 19, 1, 1, 36, 6, 1, 10, 2, 10, 1, 4, 1, 1, 3, 4, 2, 3, 1, 1, 1, 146, 2, 1, 99, 1, 2, 4, 2, 1, 3, 1, 1, 1, 1, 1, 2, 17, 2, 3, 3, 4, 7, 22, 2, 3, 1, 2, 18, 2, 1, 1, 3, 1, 5, 2, 8, 2, 6]
gremlin> g.V().hasLabel('song').out('sungBy').groupCount().by('name').select(values).unfold().
               groupCount().order(local).by(values,decr).limit(local, 5) //(3)
==>[1:22, 2:12, 3:7, 4:4, 6:2]

Which artist sung how many songs?
Get an anonymized set of song repertoire sizes.
What are the 5 most common song repertoire sizes?

Warning

Note that by()-modulation is not supported with select(keys) and select(values).

Using Where with Select

Like match()-step, it is possible to use where(), as where is a filter that processes Map<String,Object> streams.

gremlin> g.V().as('a').out('created').in('created').as('b').select('a','b').by('name') //(1)
==>[a:marko, b:marko]
==>[a:marko, b:josh]
==>[a:marko, b:peter]
==>[a:josh, b:josh]
==>[a:josh, b:marko]
==>[a:josh, b:josh]
==>[a:josh, b:peter]
==>[a:peter, b:marko]
==>[a:peter, b:josh]
==>[a:peter, b:peter]
gremlin> g.V().as('a').out('created').in('created').as('b').
               select('a','b').by('name').where('a',neq('b')) //(2)
==>[a:marko, b:josh]
==>[a:marko, b:peter]
==>[a:josh, b:marko]
==>[a:josh, b:peter]
==>[a:peter, b:marko]
==>[a:peter, b:josh]
gremlin> g.V().as('a').out('created').in('created').as('b').
               select('a','b'). //(3)
               where('a',neq('b')).
               where(__.as('a').out('knows').as('b')).
               select('a','b').by('name')
==>[a:marko, b:josh]

A standard select() that generates a Map<String,Object> of variables bindings in the path (i.e. a and b) for the sake of a running example.
The select().by('name') projects each binding vertex to their name property value and where() operates to ensure respective a and b strings are not the same.
The first select() projects a vertex binding set. A binding is filtered if a vertex equals b vertex. A binding is filtered if a doesn’t know b. The second and final select() projects the name of the vertices.

SimplePath Step

When it is important that a traverser not repeat its path through the graph, simplePath()-step should be used (filter). The path information of the traverser is analyzed and if the path has repeated objects in it, the traverser is filtered. If cyclic behavior is desired, see cyclicPath().

gremlin> g.V(1).both().both()
==>v[1]
==>v[4]
==>v[6]
==>v[1]
==>v[5]
==>v[3]
==>v[1]
gremlin> g.V(1).both().both().simplePath()
==>v[4]
==>v[6]
==>v[5]
==>v[3]
gremlin> g.V(1).both().both().simplePath().path()
==>[v[1], v[3], v[4]]
==>[v[1], v[3], v[6]]
==>[v[1], v[4], v[5]]
==>[v[1], v[4], v[3]]

Store Step

When lazy aggregation is needed, store()-step (sideEffect) should be used over aggregate(). The two steps differ in that store() does not block and only stores objects in its side-effect collection as they pass through.

gremlin> g.V().aggregate('x').limit(1).cap('x')
==>{v[1]=1, v[2]=1, v[3]=1, v[4]=1, v[5]=1, v[6]=1}
gremlin> g.V().store('x').limit(1).cap('x')
==>{v[1]=1, v[2]=1}

It is interesting to note that there are three results in the store() side-effect even though the interval selection is for 2 objects. Realize that when the third object is on its way to the range() filter (i.e. [0..1]), it passes through store() and thus, stored before filtered.

gremlin> g.E().store('x').by('weight').cap('x')
==>{0.5=1, 1.0=2, 0.4=2, 0.2=1}

Subgraph Step

Extracting a portion of a graph from a larger one for analysis, visualization or other purposes is a fairly common use case for graph analysts and developers. The subgraph()-step (sideEffect) provides a way to produce an edge-induced subgraph from virtually any traversal. The following example demonstrates how to produce the "knows" subgraph:

gremlin> subGraph = g.E().hasLabel('knows').subgraph('subGraph').cap('subGraph').next() //(1)
==>tinkergraph[vertices:3 edges:2]
gremlin> sg = subGraph.traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:3 edges:2], standard]
gremlin> sg.E() //(2)
==>e[7][1-knows->2]
==>e[8][1-knows->4]

As this function produces "edge-induced" subgraphs, subgraph() must be called at edge steps.
The subgraph contains only "knows" edges.

A more common subgraphing use case is to get all of the graph structure surrounding a single vertex:

gremlin> subGraph = g.V(3).repeat(__.inE().subgraph('subGraph').outV()).times(3).cap('subGraph').next() //(1)
==>tinkergraph[vertices:4 edges:4]
gremlin> sg = subGraph.traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:4 edges:4], standard]
gremlin> sg.E()
==>e[8][1-knows->4]
==>e[9][1-created->3]
==>e[11][4-created->3]
==>e[12][6-created->3]

Starting at vertex 3, traverse 3 steps away on in-edges, outputting all of that into the subgraph.

There can be multiple subgraph() calls within the same traversal. Each operating against either the same graph (i.e. same side-effect key) or different graphs (i.e. different side-effect keys).

gremlin> t = g.V().outE('knows').subgraph('knowsG').inV().outE('created').subgraph('createdG').
                   inV().inE('created').subgraph('createdG').iterate()
gremlin> t.sideEffects.get('knowsG').get().traversal(standard()).E()
==>e[7][1-knows->2]
==>e[8][1-knows->4]
gremlin> t.sideEffects.get('createdG').get().traversal(standard()).E()
==>e[9][1-created->3]
==>e[10][4-created->5]
==>e[11][4-created->3]
==>e[12][6-created->3]

Important

The subgraph()-step only writes to graphs that support user supplied ids for its elements. Moreover, if no graph is specified via withSideEffect(), then TinkerGraph is assumed.

Sum Step

The sum()-step (map) operates on a stream of numbers and sums the numbers together to yield a double. Note that the current traverser number is multiplied by the traverser bulk to determine how many such numbers are being represented.

gremlin> g.V().values('age').sum()
==>123
gremlin> g.V().repeat(both()).times(3).values('age').sum()
==>1471

Important

sum(local) determines the sum of the current, local object (not the objects in the traversal stream). This works for Collection-type objects. For any other object, a sum of Double.NaN is returned.

Tail Step

The tail()-step is analogous to limit()-step, except that it emits the last n-objects instead of the first n-objects.

gremlin> g.V().values('name').order()
==>josh
==>lop
==>marko
==>peter
==>ripple
==>vadas
gremlin> g.V().values('name').order().tail() //(1)
==>vadas
gremlin> g.V().values('name').order().tail(1) //(2)
==>vadas
gremlin> g.V().values('name').order().tail(3) //(3)
==>peter
==>ripple
==>vadas

Last name (alphabetically).
Same as statement 1.
Last three names.

The tail()-step can also be applied with Scope.local, in which case it operates on the incoming collection.

gremlin> g.V().as('a').out().as('a').out().as('a').select('a').by(tail(local)).values('name') //(1)
==>ripple
==>lop
gremlin> g.V().as('a').out().as('a').out().as('a').select('a').by(unfold().values('name').fold()).tail(local) //(2)
==>ripple
==>lop
gremlin> g.V().as('a').out().as('a').out().as('a').select('a').by(unfold().values('name').fold()).tail(local, 2) //(3)
==>[josh, ripple]
==>[josh, lop]
gremlin> g.V().valueMap().tail(local) //(4)
==>[age:[29]]
==>[age:[27]]
==>[lang:[java]]
==>[age:[32]]
==>[lang:[java]]
==>[age:[35]]

Only the most recent name from the "a" step (List<Vertex> becomes Vertex).
Same result as statement 1 (List<String> becomes String).
List<String> for each path containing the last two names from the a step.
Map<String, Object> for each vertex, but containing only the last property value.

TimeLimit Step

In many situations, a graph traversal is not about getting an exact answer as its about getting a relative ranking. A classic example is recommendation. What is desired is a relative ranking of vertices, not their absolute rank. Next, it may be desirable to have the traversal execute for no more than 2 milliseconds. In such situations, timeLimit()-step (filter) can be used.

Note	The method `clock(int runs, Closure code)` is a utility preloaded in the Gremlin Console that can be used to time execution of a body of code.

gremlin> g.V().repeat(both().groupCount('m')).times(16).cap('m').order(local).by(values,decr).next()
==>v[1]=2744208
==>v[3]=2744208
==>v[4]=2744208
==>v[2]=1136688
==>v[5]=1136688
==>v[6]=1136688
gremlin> clock(1) {g.V().repeat(both().groupCount('m')).times(16).cap('m').order(local).by(values,decr).next()}
==>1.344514
gremlin> g.V().repeat(timeLimit(2).both().groupCount('m')).times(16).cap('m').order(local).by(values,decr).next()
==>v[1]=2484001
==>v[4]=2484001
==>v[3]=2483766
==>v[2]=1029086
==>v[5]=1029086
==>v[6]=1028764
gremlin> clock(1) {g.V().repeat(timeLimit(2).both().groupCount('m')).times(16).cap('m').order(local).by(values,decr).next()}
==>1.23567

In essence, the relative order is respected, even through the number of traversers at each vertex is not. The primary benefit being that the calculation is guaranteed to complete at the specified time limit (in milliseconds). Finally, note that the internal clock of timeLimit()-step starts when the first traverser enters it. When the time limit is reached, any next() evaluation of the step will yield a NoSuchElementException and any hasNext() evaluation will yield false.

Tree Step

From any one element (i.e. vertex or edge), the emanating paths from that element can be aggregated to form a tree. Gremlin provides tree()-step (sideEffect) for such this situation.

gremlin> tree = g.V().out().out().tree().next()
==>v[1]={v[4]={v[3]={}, v[5]={}}}

It is important to see how the paths of all the emanating traversers are united to form the tree.

The resultant tree data structure can then be manipulated (see Tree JavaDoc).

gremlin> tree = g.V().out().out().tree().by('name').next()
==>marko={josh={ripple={}, lop={}}}
gremlin> tree['marko']
==>josh={ripple={}, lop={}}
gremlin> tree['marko']['josh']
==>ripple={}
==>lop={}
gremlin> tree.getObjectsAtDepth(3)
==>ripple
==>lop

Unfold Step

If the object reaching unfold() (flatMap) is an iterator, iterable, or map, then it is unrolled into a linear form. If not, then the object is simply emitted. Please see fold() step for the inverse behavior.

gremlin> g.V(1).out().fold().inject('gremlin',[1.23,2.34])
==>gremlin
==>[1.23, 2.34]
==>[v[3], v[2], v[4]]
gremlin> g.V(1).out().fold().inject('gremlin',[1.23,2.34]).unfold()
==>gremlin
==>1.23
==>2.34
==>v[3]
==>v[2]
==>v[4]

Note that unfold() does not recursively unroll iterators. Instead, repeat() can be used to for recursive unrolling.

gremlin> inject(1,[2,3,[4,5,[6]]])
==>1
==>[2, 3, [4, 5, [6]]]
gremlin> inject(1,[2,3,[4,5,[6]]]).unfold()
==>1
==>2
==>3
==>[4, 5, [6]]
gremlin> inject(1,[2,3,[4,5,[6]]]).repeat(unfold()).until(count(local).is(1)).unfold()
==>1
==>2
==>3
==>4
==>5
==>6

Union Step

The union()-step (branch) supports the merging of the results of an arbitrary number of traversals. When a traverser reaches a union()-step, it is copied to each of its internal steps. The traversers emitted from union() are the outputs of the respective internal traversals.

gremlin> g.V(4).union(
                  __.in().values('age'),
                  out().values('lang'))
==>29
==>java
==>java
gremlin> g.V(4).union(
                  __.in().values('age'),
                  out().values('lang')).path()
==>[v[4], v[1], 29]
==>[v[4], v[5], java]
==>[v[4], v[3], java]

ValueMap Step

The valueMap()-step yields a Map representation of the properties of an element.

gremlin> g.V().valueMap()
==>[name:[marko], age:[29]]
==>[name:[vadas], age:[27]]
==>[name:[lop], lang:[java]]
==>[name:[josh], age:[32]]
==>[name:[ripple], lang:[java]]
==>[name:[peter], age:[35]]
gremlin> g.V().valueMap('age')
==>[age:[29]]
==>[age:[27]]
==>[:]
==>[age:[32]]
==>[:]
==>[age:[35]]
gremlin> g.V().valueMap('age','blah')
==>[age:[29]]
==>[age:[27]]
==>[:]
==>[age:[32]]
==>[:]
==>[age:[35]]
gremlin> g.E().valueMap()
==>[weight:0.5]
==>[weight:1.0]
==>[weight:0.4]
==>[weight:1.0]
==>[weight:0.4]
==>[weight:0.2]

It is important to note that the map of a vertex maintains a list of values for each key. The map of an edge or vertex-property represents a single property (not a list). The reason is that vertices in TinkerPop3 leverage vertex properties which are support multiple values per key. Using the The Crew toy graph, the point is made explicit.

gremlin> g.V().valueMap()
==>[name:[marko], location:[san diego, santa cruz, brussels, santa fe]]
==>[name:[stephen], location:[centreville, dulles, purcellville]]
==>[name:[matthias], location:[bremen, baltimore, oakland, seattle]]
==>[name:[daniel], location:[spremberg, kaiserslautern, aachen]]
==>[name:[gremlin]]
==>[name:[tinkergraph]]
gremlin> g.V().has('name','marko').properties('location')
==>vp[location->san diego]
==>vp[location->santa cruz]
==>vp[location->brussels]
==>vp[location->santa fe]
gremlin> g.V().has('name','marko').properties('location').valueMap()
==>[startTime:1997, endTime:2001]
==>[startTime:2001, endTime:2004]
==>[startTime:2004, endTime:2005]
==>[startTime:2005]

If the id, label, key, and value of the Element is desired, then a boolean triggers its insertion into the returned map.

gremlin> g.V().hasLabel('person').valueMap(true)
==>[id:1, label:person, name:[marko], location:[san diego, santa cruz, brussels, santa fe]]
==>[id:7, label:person, name:[stephen], location:[centreville, dulles, purcellville]]
==>[id:8, label:person, name:[matthias], location:[bremen, baltimore, oakland, seattle]]
==>[id:9, label:person, name:[daniel], location:[spremberg, kaiserslautern, aachen]]
gremlin> g.V().hasLabel('person').valueMap(true,'name')
==>[id:1, label:person, name:[marko]]
==>[id:7, label:person, name:[stephen]]
==>[id:8, label:person, name:[matthias]]
==>[id:9, label:person, name:[daniel]]
gremlin> g.V().hasLabel('person').properties('location').valueMap(true)
==>[value:san diego, id:6, startTime:1997, endTime:2001, key:location]
==>[value:santa cruz, id:7, startTime:2001, endTime:2004, key:location]
==>[value:brussels, id:8, startTime:2004, endTime:2005, key:location]
==>[value:santa fe, id:9, startTime:2005, key:location]
==>[value:centreville, id:10, startTime:1990, endTime:2000, key:location]
==>[value:dulles, id:11, startTime:2000, endTime:2006, key:location]
==>[value:purcellville, id:12, startTime:2006, key:location]
==>[value:bremen, id:13, startTime:2004, endTime:2007, key:location]
==>[value:baltimore, id:14, startTime:2007, endTime:2011, key:location]
==>[value:oakland, id:15, startTime:2011, endTime:2014, key:location]
==>[value:seattle, id:16, startTime:2014, key:location]
==>[value:spremberg, id:17, startTime:1982, endTime:2005, key:location]
==>[value:kaiserslautern, id:18, startTime:2005, endTime:2009, key:location]
==>[value:aachen, id:19, startTime:2009, key:location]

Vertex Steps

The vertex steps (flatMap) are fundamental to the Gremlin language. Via these steps, its possible to "move" on the graph — i.e. traverse.

out(string...): Move to the outgoing adjacent vertices given the edge labels.
in(string...): Move to the incoming adjacent vertices given the edge labels.
both(string...): Move to both the incoming and outgoing adjacent vertices given the edge labels.
outE(string...): Move to the outgoing incident edges given the edge labels.
inE(string...): Move to the incoming incident edges given the edge labels.
bothE(string...): Move to both the incoming and outgoing incident edges given the edge labels.
outV(): Move to the outgoing vertex.
inV(): Move to the incoming vertex.
bothV(): Move to both vertices.
otherV() : Move to the vertex that was not the vertex that was moved from.

gremlin> g.V(4)
==>v[4]
gremlin> g.V(4).outE() //(1)
==>e[10][4-created->5]
==>e[11][4-created->3]
gremlin> g.V(4).inE('knows') //(2)
==>e[8][1-knows->4]
gremlin> g.V(4).inE('created') //(3)
gremlin> g.V(4).bothE('knows','created','blah')
==>e[10][4-created->5]
==>e[11][4-created->3]
==>e[8][1-knows->4]
gremlin> g.V(4).bothE('knows','created','blah').otherV()
==>v[5]
==>v[3]
==>v[1]
gremlin> g.V(4).both('knows','created','blah')
==>v[5]
==>v[3]
==>v[1]
gremlin> g.V(4).outE().inV() //(4)
==>v[5]
==>v[3]
gremlin> g.V(4).out() //(5)
==>v[5]
==>v[3]
gremlin> g.V(4).inE().outV()
==>v[1]
gremlin> g.V(4).inE().bothV()
==>v[1]
==>v[4]

All outgoing edges.
All incoming knows-edges.
All incoming created-edges.
Moving forward touching edges and vertices.
Moving forward only touching vertices.

Where Step

The where()-step filters the current object based on either the object itself (Scope.local) or the path history of the object (Scope.global) (filter). This step is typically used in conjuction with either match()-step or select()-step, but can be used in isolation.

gremlin> g.V(1).as('a').out('created').in('created').where(neq('a')) //(1)
==>v[4]
==>v[6]
gremlin> g.withSideEffect('a',['josh','peter']).V(1).out('created').in('created').values('name').where(within('a')) //(2)
==>josh
==>peter
gremlin> g.V(1).out('created').in('created').where(out('created').count().is(gt(1))).values('name') //(3)
==>josh

Who are marko’s collaborators, where marko can not be his own collaborator? (predicate)
Of the co-creators of marko, only keep those whose name is josh or peter. (using a sideEffect)
Which of marko’s collaborators have worked on more than 1 project? (using a traversal)

Important

Please see match().where() and select().where() for how where() can be used in conjunction with Map<String,Object> projecting steps — i.e. Scope.local.

A few more examples of filtering an arbitrary object based on a anonymous traversal is provided below.

gremlin> g.V().where(out('created')).values('name') //(1)
==>marko
==>josh
==>peter
gremlin> g.V().out('knows').where(out('created')).values('name') //(2)
==>josh
gremlin> g.V().where(out('created').count().is(gte(2))).values('name') //(3)
==>josh
gremlin> g.V().where(out('knows').where(out('created'))).values('name') //(4)
==>marko
gremlin> g.V().where(__.not(out('created'))).where(__.in('knows')).values('name') //(5)
==>vadas
gremlin> g.V().where(__.not(out('created')).and().in('knows')).values('name') //(6)
==>vadas

What are the names of the people who have created a project?
What are the names of the people that are known by someone one and have created a project?
What are the names of the people how have created two or more projects?
What are the names of the people who know someone that has created a project? (This only works in OLTP — see the WARNING below)
What are the names of the people who have not created anything, but are known by someone?
The concatenation of where()-steps is the same as a single where()-step with an and’d clause.

Warning

The anonymous traversal of where() processes the current object "locally". In OLAP, where the atomic unit of computing is the the vertex and its local "star graph," it is important that the anonymous traversal does not leave the confines of the vertex’s star graph. In other words, it can not traverse to an adjacent vertex’s properties or edges. Note that is only a temporary limitation that will be addressed in a future version of TinkerPop3 (see TINKERPOP-693).

A Note on Predicates

A P is a predicate of the form Function<Object,Boolean>. That is, given some object, return true or false. The provided predicates are outlined in the table below and are used in various steps such as has()-step, where()-step, is()-step, etc.

Predicate Description

eq(object)

Is the incoming object equal to the provided object?

neq(object)

Is the incoming object not equal to the provided object?

lt(number)

Is the incoming number less than the provided number?

lte(number)

Is the incoming number less than or equal to the provided number?

gt(number)

Is the incoming number greater than the provided number?

gte(number)

Is the incoming number greater than or equal to the provided number?

inside(number,number)

Is the incoming number greater than the first provided number and less than the second?

outside(number,number)

Is the incoming number less than the first provided number or greater than the second?

between(number,number)

Is the incoming number greater than or equal to the first provided number and less than the second?

within(objects...)

Is the incoming object in the array of provided objects?

without(objects...)

Is the incoming object not in the array of the provided objects?

gremlin> eq(2)
==>eq(2)
gremlin> not(neq(2)) //(1)
==>eq(2)
gremlin> not(within('a','b','c'))
==>without([a, b, c])
gremlin> not(within('a','b','c')).test('d') //(2)
==>true
gremlin> not(within('a','b','c')).test('a')
==>false
gremlin> within(1,2,3).and(not(eq(2))).test(3) //(3)
==>true
gremlin> inside(1,4).or(eq(5)).test(3) //(4)
==>true
gremlin> inside(1,4).or(eq(5)).test(5)
==>true
gremlin> between(1,2) //(5)
==>and([gte(1), lt(2)])
gremlin> not(between(1,2))
==>or([lt(1), gte(2)])

The not() of a P-predicate is another P-predicate.
P-predicates are arguments to various steps which internally test() the incoming value.
P-predicates can be and’d together.
P-predicates can be or' together.
and() is a P-predicate and thus, a P-predicate can be composed of multiple P-predicates.

Tip	To reduce the verbosity of predicate expressions, it is good to `import static org.apache.tinkerpop.gremlin.process.traversal.P.*`.

Finally, note that where()-step takes a P<String>. The provided string value refers to a variable binding, not to the explicit string value.

gremlin> g.V().as('a').both().both().as('b').count()
==>30
gremlin> g.V().as('a').both().both().as('b').where('a',neq('b')).count()
==>18

Note	It is possible for graph system providers and users to extend `P` and provide new predicates. For instance, a `regex(pattern)` could be a graph system specific `P`.

A Note on Barrier Steps

barrier Gremlin is primarily a lazy, stream processing language. This means that Gremlin fully processes (to the best of its abilities) any traversers currently in the traversal pipeline before getting more data from the start/head of the traversal. However, there are numerous situations in which a completely lazy computation is not possible (or impractical). When a computation is not lazy, a "barrier step" exists. There are three types of barriers:

CollectingBarrierStep: All of the traversers prior to the step are put into a collection and then processed in some way (e.g. ordered) prior to the collection being "drained" one-by-one to the next step. Examples include: order(), sample(), aggregate(), barrier().
ReducingBarrierStep: All of the traversers prior to the step are processed by a reduce function and once all the previous traversers are processed, a single "reduced value" traverser is emitted to the next step. Note that the path history leading up to a reducing barrier step is destroyed given its many-to-one nature. Examples include: fold(), count(), sum(), max(), min().
SupplyingBarrierStep: All of the traversers prior to the step are iterated (no processing) and then some provided supplier yields a single traverser to continue to the next step. Examples include: cap().

In Gremlin OLAP (see TraversalVertexProgram), a barrier is introduced at the end of every adjacent vertex step. This means that the traversal does its best to compute as much as possible at the current, local vertex. What is can’t compute without referencing an adjacent vertex is aggregated into a barrier collection. When there are no more traversers at the local vertex, the barriered traversers are the messages that are propagated to remote vertices for further processing.

A Note On Lambdas

lambda A lambda is a function that can be referenced by software and thus, passed around like any other piece of data. In Gremlin, lambdas make it possible to generalize the behavior of a step such that custom steps can be created (on-the-fly) by the user. However, it is advised to avoid using lambdas if possible.

gremlin> g.V().filter{it.get().value('name') == 'marko'}.
               flatMap{it.get().vertices(OUT,'created')}.
               map {it.get().value('name')} //(1)
==>lop
gremlin> g.V().has('name','marko').out('created').values('name') //(2)
==>lop

A lambda-rich Gremlin traversal which should and can be avoided. (bad)
The same traversal (result), but without using lambdas. (good)

Gremlin attempts to provide the user a comprehensive collection of steps in the hopes that the user will never need to leverage a lambda in practice. It is advised that users only leverage a lambda if and only if there is no corresponding lambda-less step that encompasses the desired functionality. The reason being, lambdas can not be optimized by Gremlin’s compiler strategies as they can not be programmatically inspected (see traversal strategies).

In many situations where a lambda could be used, either a corresponding step exists or a traversal can be provided in its place. A TraversalLambda behaves like a typical lambda, but it can be optimized and it yields less objects than the corresponding pure-lambda form.

gremlin> g.V().out().out().path().by {it.value('name')}.
                                  by {it.value('name')}.
                                  by {g.V(it).in('created').values('name').fold().next()} //(1)
==>[marko, josh, [josh]]
==>[marko, josh, [marko, josh, peter]]
gremlin> g.V().out().out().path().by('name').
                                  by('name').
                                  by(__.in('created').values('name').fold()) //(2)
==>[marko, josh, [josh]]
==>[marko, josh, [marko, josh, peter]]

The length-3 paths have each of their objects transformed by a lambda. (bad)
The length-3 paths have their objects transformed by a lambda-less step and a traversal lambda. (good)

TraversalStrategy

traversal-strategy A TraversalStrategy can analyze a Traversal and mutate the traversal as it deems fit. This is useful in multiple situations:

There is an application-level feature that can be embedded into the traversal logic (decoration).
There is a more efficient way to express the traversal at the TinkerPop3 level (optimization).
There is a more efficient way to express the traversal at the graph system/language/driver level (provider optimization).
There are are some final adjustments required before executing the traversal (finalization).
There are certain traversals that are not legal for the application or traversal engine (verification).

Note	The `explain()`-step shows the user how each registered strategy mutates the traversal.

A simple OptimizationStrategy is the IdentityRemovalStrategy.

public final class IdentityRemovalStrategy extends AbstractTraversalStrategy<TraversalStrategy.OptimizationStrategy>
implements TraversalStrategy.OptimizationStrategy {

    private static final IdentityRemovalStrategy INSTANCE = new IdentityRemovalStrategy();

    private IdentityRemovalStrategy() {
    }

    @Override
    public void apply(final Traversal.Admin<?, ?> traversal) {
        if (!TraversalHelper.hasStepOfClass(IdentityStep.class, traversal))
            return;

        TraversalHelper.getStepsOfClass(IdentityStep.class, traversal).stream().forEach(identityStep -> {
            final Step<?, ?> previousStep = identityStep.getPreviousStep();
            if (!(previousStep instanceof EmptyStep) || identityStep.getLabels().isEmpty()) {
                ((IdentityStep<?>) identityStep).getLabels().forEach(previousStep::addLabel);
                traversal.removeStep(identityStep);
            }
        });
    }

    public static IdentityRemovalStrategy instance() {
        return INSTANCE;
    }
}

This strategy simply removes any IdentityStep steps in the Traversal as aStep().identity().identity().bStep() is equivalent to aStep().bStep(). For those traversal strategies that require other strategies to execute prior or post to the strategy, then the following two methods can be defined in TraversalStrategy (with defaults being an empty set). If the TraversalStrategy is in a particular traversal category (i.e. decoration, optimization, provider-optimization, finalization, or verification), then priors and posts are only possible within the category.

public Set<Class<? extends S>> applyPrior();
public Set<Class<? extends S>> applyPost();

Important

TraversalStrategy categories are sorted within their category and the categories are then executed in the following order: decoration, optimization, finalization, and verification. If a designed strategy does not fit cleanly into these categories, then it can implement TraversalStrategy and its prior and posts can reference strategies within any category.

An example of a GraphSystemOptimizationStrategy is provided below.

g.V().has('name','marko')

The expression above can be executed in a O(|V|) or O(log(|V|) fashion in TinkerGraph depending on whether there is or is not an index defined for "name."

public final class TinkerGraphStepStrategy extends AbstractTraversalStrategy<TraversalStrategy.ProviderOptimizationStrategy> implements TraversalStrategy.ProviderOptimizationStrategy {

    private static final TinkerGraphStepStrategy INSTANCE = new TinkerGraphStepStrategy();

    private TinkerGraphStepStrategy() {
    }

    @Override
    public void apply(final Traversal.Admin<?, ?> traversal) {
        if (traversal.getEngine().isComputer())
            return;

        TraversalHelper.getStepsOfClass(GraphStep.class, traversal).forEach(originalGraphStep -> {
            final TinkerGraphStep<?,?> tinkerGraphStep = new TinkerGraphStep<>(originalGraphStep);
            TraversalHelper.replaceStep(originalGraphStep, (Step) tinkerGraphStep, traversal);
            Step<?, ?> currentStep = tinkerGraphStep.getNextStep();
            while (currentStep instanceof HasContainerHolder) {
                ((HasContainerHolder) currentStep).getHasContainers().forEach(tinkerGraphStep::addHasContainer);
                currentStep.getLabels().forEach(tinkerGraphStep::addLabel);
                traversal.removeStep(currentStep);
                currentStep = currentStep.getNextStep();
            }
        });
    }

    public static TinkerGraphStepStrategy instance() {
        return INSTANCE;
    }
}

The traversal is redefined by simply taking a chain of has()-steps after g.V() (TinkerGraphStep) and providing them to TinkerGraphStep. Then its up to TinkerGraphStep to determine if an appropriate index exists. In the code below, review the vertices() method and note how if an index exists, for a particular HasContainer, then that index is first queried before the remaining HasContainer filters are serially applied. Given that the strategy uses non-TinkerPop3 provided steps, it should go into the ProviderOptimizationStrategy category to ensure the added step does not corrupt the OptimizationStrategy strategies.

gremlin> t = g.V().has('name','marko'); null
==>null
gremlin> t.toString()
==>[GraphStep([],vertex), HasStep([name.eq(marko)])]
gremlin> t.iterate(); null
==>null
gremlin> t.toString()
==>[TinkerGraphStep(vertex,[name.eq(marko)])]

Warning

The reason that OptimizationStrategy and ProviderOptimizationStrategy are two different categories is that optimization strategies should only rewrite the traversal using TinkerPop3 steps. This ensures that the optimizations executed at the end of the optimization strategy round are TinkerPop3 compliant. From there, provider optimizations can analyze the traversal and rewrite the traversal as desired using graph system specific steps (e.g. replacing GraphStep.HasStep...HasStep with TinkerGraphStep). If provider optimizations use graph system specific steps and implement OptimizationStrategy, then other TinkerPop3 optimizations may fail to optimize the traversal or mis-understand the graph system specific step behaviors (e.g. ProviderVertexStep extends VertexStep) and yield incorrect semantics.

A collection of useful DecorationStrategy strategies are provided with TinkerPop3 and are generally useful to end-users. The following sub-sections detail these strategies:

ElementIdStrategy

ElementIdStrategy provides control over element identifiers. Some Graph implementations, such as TinkerGraph, allow specification of custom identifiers when creating elements:

gremlin> g = TinkerGraph.open().traversal()
==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
gremlin> v = g.addV().property(id,'42a').next()
==>v[42a]
gremlin> g.V('42a')
==>v[42a]

Other Graph implementations, such as Neo4j, generate element identifiers automatically and cannot be assigned. As a helper, ElementIdStrategy can be used to make identifier assignment possible by using vertex and edge indicies under the hood.

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> strategy = ElementIdStrategy.build().create()
==>ElementIdStrategy
gremlin> g = GraphTraversalSource.build().with(strategy).create(graph)
==>graphtraversalsource[neo4jgraph[Community [/tmp/neo4j]], standard]
gremlin> g.addV().property(id, '42a').id()
==>42a

Important

The key that is used to store the assigned identifier should be indexed in the underlying graph database. If it is not indexed, then lookups for the elements that use these identifiers will perform a linear scan.

EventStrategy

The purpose of the EventStrategy is to raise events to one or more MutationListener objects as changes to the underlying Graph occur within a Traversal. Such a strategy is useful for logging changes, triggering certain actions based on change, or any application that needs notification of some mutating operation during a Traversal. If the transaction is rolled back, the event queue is reset.

The following events are raised to the MutationListener:

New vertex
New edge
Vertex property changed
Edge property changed
Vertex property removed
Edge property removed
Vertex removed
Edge removed

To start processing events from a Traversal first implement the MutationListener interface. An example of this implementation is the ConsoleMutationListener which writes output to the console for each event. The following console session displays the basic usage:

gremlin> graph = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> l = new ConsoleMutationListener(graph)
==>MutationListener[tinkergraph[vertices:6 edges:6]]
gremlin> strategy = EventStrategy.build().addListener(l).create()
==>EventStrategy
gremlin> g = GraphTraversalSource.build().with(strategy).create(graph)
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> g.addV('name','stephen')
Vertex [v[12]] added to graph [tinkergraph[vertices:7 edges:6]]
==>v[12]
gremlin> g.E().drop()
Edge [e[7][1-knows->2]] removed from graph [tinkergraph[vertices:7 edges:6]]
Edge [e[8][1-knows->4]] removed from graph [tinkergraph[vertices:7 edges:5]]
Edge [e[9][1-created->3]] removed from graph [tinkergraph[vertices:7 edges:4]]
Edge [e[10][4-created->5]] removed from graph [tinkergraph[vertices:7 edges:3]]
Edge [e[11][4-created->3]] removed from graph [tinkergraph[vertices:7 edges:2]]
Edge [e[12][6-created->3]] removed from graph [tinkergraph[vertices:7 edges:1]]

By default, the EventStrategy is configured with an EventQueue that raises events as they occur within execution of a Step. As such, the final line of Gremlin execution that drops all edges shows a bit of an inconsistent count, where the removed edge count is accounted for after the event is raised. The strategy can also be configured with a TransactionalEventQueue that captures the changes within a transaction and does not allow them to fire until the transaction is committed.

Warning

EventStrategy is not meant for usage in tracking global mutations across separate processes. In other words, a mutation in one JVM process is not raised as an event in a different JVM process. In addition, events are not raised when mutations occur outside of the Traversal context.

PartitionStrategy

PartitionStrategy partitions the vertices and edges of a graph into String named partitions (i.e. buckets, subgraphs, etc.). The idea behind PartitionStrategy is presented in the image above where each element is in a single partition (represented by its color). Partitions can be read from, written to, and linked/joined by edges that span one or two partitions (e.g. a tail vertex in one partition and a head vertex in another).

There are three primary configurations in PartitionStrategy:

Partition Key - The property key that denotes a String value representing a partition.
Write Partition - A String denoting what partition all future written elements will be in.
Read Partitions - A Set<String> of partitions that can be read from.

The best way to understand PartitionStrategy is via example.

gremlin> graph = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> strategyA = PartitionStrategy.build().partitionKey("_partition").writePartition("a").addReadPartition("a").create()
==>PartitionStrategy
gremlin> strategyB = PartitionStrategy.build().partitionKey("_partition").writePartition("b").addReadPartition("b").create()
==>PartitionStrategy
gremlin> gA = GraphTraversalSource.build().with(strategyA).create(graph)
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> gA.addV() // this vertex has a property of {_partition:"a"}
==>v[12]
gremlin> gB = GraphTraversalSource.build().with(strategyB).create(graph)
==>graphtraversalsource[tinkergraph[vertices:7 edges:6], standard]
gremlin> gB.addV() // this vertex has a property of {_partition:"b"}
==>v[14]
gremlin> gA.V()
==>v[12]
gremlin> gB.V()
==>v[14]

Partitions may also extend to VertexProperty elements if the Graph can support meta-properties and if the includeMetaProperties value is set to true when the PartitionStrategy is built. The partitionKey will be stored in the meta-properties of the VertexProperty and blind the traversal to those properties. Please note that the VertexProperty will only be hidden by way of the Traversal itself. For example, calling Vertex.property(k) bypasses the context of the PartitionStrategy and will thus allow all properties to be accessed.

By writing elements to particular partitions and then restricting read partitions, the developer is able to create multiple graphs within a single address space. Moreover, by supporting references between partitions, it is possible to merge those multiple graphs (i.e. join partitions).

ReadOnlyStrategy

ReadOnlyStrategy is largely self-explanatory. A Traversal that has this strategy applied will throw an IllegalStateException if the Traversal has any mutating steps within it.

SubgraphStrategy

SubgraphStrategy is quite similar to PartitionStrategy in that it restrains a Traversal to certain vertices and edges as determined by a Traversal criterion defined individually for each.

gremlin> graph = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> strategy = SubgraphStrategy.build().edgeCriterion(hasId(8,9,10)).create()
==>SubgraphStrategy
gremlin> g = GraphTraversalSource.build().with(strategy).create(graph)
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> g.V() // shows all vertices as no filter for vertices was specified
==>v[1]
==>v[2]
==>v[3]
==>v[4]
==>v[5]
==>v[6]
gremlin> g.E() // shows only the edges defined in the edgeCriterion
==>e[8][1-knows->4]
==>e[9][1-created->3]
==>e[10][4-created->5]

This strategy is implemented such that the vertices attached to an Edge must both satisfy the vertexCriterion (if present) in order for the Edge to be considered a part of the subgraph.

The GraphComputer

graphcomputer-puffers TinkerPop3 provides two primary means of interacting with a graph: online transaction processing (OLTP) and online analytical processing (OLAP). OTLP-based graph systems allow the user to query the graph in real-time. However, typically, real-time performance is only possible when a local traversal is enacted. A local traversal is one that starts at a particular vertex (or small set of vertices) and touches a small set of connected vertices (by any arbitrary path of arbitrary length). In short, OLTP queries interact with a limited set of data and respond on the order of milliseconds or seconds. On the other hand, with OLAP graph processing, the entire graph is processed and thus, every vertex and edge is analyzed (some times more than once for iterative, recursive algorithms). Due to the amount of data being processed, the results are typically not returned in real-time and for massive graphs (i.e. graphs represented across a cluster of machines), results can take on the order of minutes or hours.

OLTP: real-time, limited data accessed, random data access, sequential processing, querying
OLAP: long running, entire data set accessed, sequential data access, parallel processing, batch processing

The image above demonstrates the difference between Gremlin OLTP and Gremlin OLAP. With Gremlin OLTP, the graph is walked by moving from vertex-to-vertex via incident edges. With Gremlin OLAP, all vertices are provided a VertexProgram. The programs send messages to one another with the topological structure of the graph acting as the communication network (though random message passing possible). In many respects, the messages passed are like the OLTP traversers moving from vertex-to-vertex. However, all messages are moving independent of one another, in parallel. Once a vertex program is finished computing, TinkerPop3’s OLAP engine supports any number MapReduce jobs over the resultant graph.

Important

GraphComputer was designed from the start to be used within a multi-JVM, distributed environment — in other words, a multi-machine compute cluster. As such, all the computing objects must be able to be migrated between JVMs. The pattern promoted is to store state information in a Configuration object to later be regenerated by a loading process. It is important to realize that VertexProgram, MapReduce, and numerous particular instances rely heavily on the state of the computing classes (not the structure, but the processes) to be stored in a Configuration.

VertexProgram

bsp-diagram GraphComputer takes a VertexProgram. A VertexProgram can be thought of as a piece of code that is executed at each vertex in logically parallel manner until some termination condition is met (e.g. a number of iterations have occurred, no more data is changing in the graph, etc.). A submitted VertexProgram is copied to all the workers in the graph. A worker is not an explicit concept in the API, but is assumed of all GraphComputer implementations. At minimum each vertex is a worker (though this would be inefficient due to the fact that each vertex would maintain a VertexProgram). In practice, the workers partition the vertex set and and are responsible for the execution of the VertexProgram over all the vertices within their sphere of influence. The workers orchestrate the execution of the VertexProgram.execute() method on all their vertices in an bulk synchronous parallel (BSP) fashion. The vertices are able to communicate with one another via messages. There are two kinds of messages in Gremlin OLAP: MessageScope.Local and MessageScope.Global. A local message is a message to an adjacent vertex. A global message is a message to any arbitrary vertex in the graph. Once the VertexProgram has completed its execution, any number of MapReduce jobs are evaluated. MapReduce jobs are provided by the user via GraphComputer.mapReduce() or by the VertexProgram via VertexProgram.getMapReducers().

The example below demonstrates how to submit a VertexProgram to a graph’s GraphComputer. GraphComputer.submit() yields a Future<ComputerResult>. The ComputerResult has the resultant computed graph which can be a full copy of the original graph (see Hadoop-Gremlin) or a view over the original graph (see TinkerGraph). The ComputerResult also provides access to computational side-effects called Memory (which includes, for example, runtime, number of iterations, results of MapReduce jobs, and VertexProgram-specific memory manipulations).

gremlin> result = graph.compute().program(PageRankVertexProgram.build().create()).submit().get()
==>result[tinkergraph[vertices:6 edges:0],memory[size:0]]
gremlin> result.memory().runtime
==>124
gremlin> g = result.graph().traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:6 edges:0], standard]
gremlin> g.V().valueMap('name',PageRankVertexProgram.PAGE_RANK)
==>[gremlin.pageRankVertexProgram.pageRank:[0.15000000000000002], name:[marko]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.19250000000000003], name:[vadas]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.4018125], name:[lop]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.19250000000000003], name:[josh]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.23181250000000003], name:[ripple]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.15000000000000002], name:[peter]]

Note

This model of "vertex-centric graph computing" was made popular by Google’s Pregel graph engine. In the open source world, this model is found in OLAP graph computing systems such as Giraph, Hama, and Faunus. TinkerPop3 extends the popularized model with integrated post-processing MapReduce jobs over the vertex set.

MapReduce

The BSP model proposed by Pregel stores the results of the computation in a distributed manner as properties on the elements in the graph. In many situations, it is necessary to aggregate those resultant properties into a single result set (i.e. a statistic). For instance, assume a VertexProgram that computes a nominal cluster for each vertex (i.e. a graph clustering algorithm). At the end of the computation, each vertex will have a property denoting the cluster it was assigned to. TinkerPop3 provides the ability to answer global questions about the clusters. For instance, in order to answer the following questions, MapReduce jobs are required:

How many vertices are in each cluster? (presented below)
How many unique clusters are there? (presented below)
What is the average age of each vertex in each cluster?
What is the degree distribution of the vertices in each cluster?

A compressed representation of the MapReduce API in TinkerPop3 is provided below. The key idea is that the map-stage processes all vertices to emit key/value pairs. Those values are aggregated on their respective key for the reduce-stage to do its processing to ultimately yield more key/value pairs.

public interface MapReduce<MK, MV, RK, RV, R> {
  public void map(final Vertex vertex, final MapEmitter<MK, MV> emitter);
  public void reduce(final MK key, final Iterator<MV> values, final ReduceEmitter<RK, RV> emitter);
  // there are more methods
}

Important

The vertex that is passed into the MapReduce.map() method does not contain edges. The vertex only contains original and computed vertex properties. This reduces the amount of data required to be loaded and ensures that MapReduce is used for post-processing computed results. All edge-based computing should be accomplished in the VertexProgram.

mapreduce

The MapReduce extension to GraphComputer is made explicit when examining the PeerPressureVertexProgram and corresponding ClusterPopulationMapReduce. In the code below, the GraphComputer result returns the computed on Graph as well as the Memory of the computation (ComputerResult). The memory maintain the results of any MapReduce jobs. The cluster population MapReduce result states that there are 5 vertices in cluster 1 and 1 vertex in cluster 6. This can be verified (in a serial manner) by looking at the PeerPressureVertexProgram.CLUSTER property of the resultant graph. Notice that the property is "hidden" unless it is directly accessed via name.

gremlin> graph = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> result = graph.compute().program(PeerPressureVertexProgram.build().create()).mapReduce(ClusterPopulationMapReduce.build().create()).submit().get()
==>result[tinkergraph[vertices:6 edges:0],memory[size:2]]
gremlin> result.memory().get('clusterPopulation')
==>1=5
==>6=1
gremlin> g = result.graph().traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:6 edges:0], standard]
gremlin> g.V().values(PeerPressureVertexProgram.CLUSTER).groupCount().next()
==>1=5
==>6=1
gremlin> g.V().valueMap()
==>[gremlin.peerPressureVertexProgram.voteStrength:[1.0], gremlin.peerPressureVertexProgram.cluster:[1], name:[marko], age:[29]]
==>[gremlin.peerPressureVertexProgram.voteStrength:[1.0], gremlin.peerPressureVertexProgram.cluster:[1], name:[vadas], age:[27]]
==>[gremlin.peerPressureVertexProgram.voteStrength:[1.0], gremlin.peerPressureVertexProgram.cluster:[1], name:[lop], lang:[java]]
==>[gremlin.peerPressureVertexProgram.voteStrength:[1.0], gremlin.peerPressureVertexProgram.cluster:[1], name:[josh], age:[32]]
==>[gremlin.peerPressureVertexProgram.voteStrength:[1.0], gremlin.peerPressureVertexProgram.cluster:[1], name:[ripple], lang:[java]]
==>[gremlin.peerPressureVertexProgram.voteStrength:[1.0], gremlin.peerPressureVertexProgram.cluster:[6], name:[peter], age:[35]]

If there are numerous statistics desired, then its possible to register as many MapReduce jobs as needed. For instance, the ClusterCountMapReduce determines how many unique clusters were created by the peer pressure algorithm. Below both ClusterCountMapReduce and ClusterPopulationMapReduce are computed over the resultant graph.

gremlin> result = graph.compute().program(PeerPressureVertexProgram.build().create()).
                    mapReduce(ClusterPopulationMapReduce.build().create()).
                    mapReduce(ClusterCountMapReduce.build().create()).submit().get()
==>result[tinkergraph[vertices:6 edges:0],memory[size:3]]
gremlin> result.memory().clusterPopulation
==>1=5
==>6=1
gremlin> result.memory().clusterCount
==>2

Important

The MapReduce model of TinkerPop3 does not support MapReduce chaining. Thus, the order in which the MapReduce jobs are executed is irrelevant. This is made apparent when realizing that the map()-stage takes a Vertex as its input and the reduce()-stage yields key/value pairs. Thus, the results of reduce can not feed back into map.

A Collection of VertexPrograms

TinkerPop3 provides a collection of VertexPrograms that implement common algorithms. This section discusses the various implementations.

Important

The vertex programs presented are what are provided as of TinkerPop 3.1.4-SNAPSHOT. Over time, with future releases, more algorithms will be added.

PageRankVertexProgram

gremlin-pagerank PageRank is perhaps the most popular OLAP-oriented graph algorithm. This eigenvector centrality variant was developed by Brin and Page of Google. PageRank defines a centrality value for all vertices in the graph, where centrality is defined recursively where a vertex is central if it is connected to central vertices. PageRank is an iterative algorithm that converges to a steady state distribution. If the pageRank values are normalized to 1.0, then the pageRank value of a vertex is the probability that a random walker will be seen that that vertex in the graph at any arbitrary moment in time. In order to help developers understand the methods of a VertexProgram, the PageRankVertexProgram code is analyzed below.

public class PageRankVertexProgram implements VertexProgram<Double> { (1)

    private MessageScope.Local<Double> incidentMessageScope = MessageScope.Local.of(__::outE); (2)
    private MessageScope.Local<Double> countMessageScope = MessageScope.Local.of(new MessageScope.Local.ReverseTraversalSupplier(this.incidentMessageScope));

    public static final String PAGE_RANK = "gremlin.pageRankVertexProgram.pageRank"; (3)
    public static final String EDGE_COUNT = "gremlin.pageRankVertexProgram.edgeCount";

    private static final String VERTEX_COUNT = "gremlin.pageRankVertexProgram.vertexCount";
    private static final String ALPHA = "gremlin.pageRankVertexProgram.alpha";
    private static final String TOTAL_ITERATIONS = "gremlin.pageRankVertexProgram.totalIterations";
    private static final String INCIDENT_TRAVERSAL_SUPPLIER = "gremlin.pageRankVertexProgram.incidentTraversalSupplier";

    private ConfigurationTraversal<Vertex, Edge> configurationTraversal;
    private double vertexCountAsDouble = 1.0d;
    private double alpha = 0.85d;
    private int totalIterations = 30;

    private static final Set<String> COMPUTE_KEYS = new HashSet<>(Arrays.asList(PAGE_RANK, EDGE_COUNT));

    private PageRankVertexProgram() {}

    @Override
    public void loadState(final Graph graph, final Configuration configuration) { (4)
        if (configuration.containsKey(TRAVERSAL_SUPPLIER)) {
                    this.configurationTraversal = ConfigurationTraversal.loadState(graph, configuration, TRAVERSAL_SUPPLIER);
                    this.incidentMessageScope = MessageScope.Local.of(this.configurationTraversal);
                    this.countMessageScope = MessageScope.Local.of(new MessageScope.Local.ReverseTraversalSupplier(this.incidentMessageScope));
                }
        this.vertexCountAsDouble = configuration.getDouble(VERTEX_COUNT, 1.0d);
        this.alpha = configuration.getDouble(ALPHA, 0.85d);
        this.totalIterations = configuration.getInt(TOTAL_ITERATIONS, 30);
    }

    @Override
    public void storeState(final Configuration configuration) {
        configuration.setProperty(VERTEX_PROGRAM, PageRankVertexProgram.class.getName());
        configuration.setProperty(VERTEX_COUNT, this.vertexCountAsDouble);
        configuration.setProperty(ALPHA, this.alpha);
        configuration.setProperty(TOTAL_ITERATIONS, this.totalIterations);
        if (null != this.traversalSupplier) {
            this.traversalSupplier.storeState(configuration);
        }
    }

    @Override
    public Set<String> getElementComputeKeys() { (5)
        return COMPUTE_KEYS;
    }

    @Override
    public Optional<MessageCombiner<Double>> getMessageCombiner() {
        return (Optional) PageRankMessageCombiner.instance();
    }

    @Override
    public Set<MessageScope> getMessageScopes(final int iteration) {
        final Set<MessageScope> set = new HashSet<>();
        set.add(0 == iteration ? this.countMessageScope : this.incidentMessageScope);
        return set;
    }

    @Override
    public void setup(final Memory memory) {

    }

   @Override
    public void execute(final Vertex vertex, Messenger<Double> messenger, final Memory memory) { (6)
        if (memory.isInitialIteration()) {  (7)
            messenger.sendMessage(this.countMessageScope, 1.0d);
        } else if (1 == memory.getIteration()) {  (8)
            double initialPageRank = 1.0d / this.vertexCountAsDouble;
            double edgeCount = IteratorUtils.reduce(messenger.receiveMessages(), 0.0d, (a, b) -> a + b);
            vertex.property(PAGE_RANK, initialPageRank);
            vertex.property(EDGE_COUNT, edgeCount);
            messenger.sendMessage(this.incidentMessageScope, initialPageRank / edgeCount);
        } else { (9)
            double newPageRank = IteratorUtils.reduce(messenger.receiveMessages(), 0.0d, (a, b) -> a + b);
            newPageRank = (this.alpha * newPageRank) + ((1.0d - this.alpha) / this.vertexCountAsDouble);
            vertex.property(PAGE_RANK, newPageRank);
            messenger.sendMessage(this.incidentMessageScope, newPageRank / vertex.<Double>value(EDGE_COUNT));
        }
    }

    @Override
    public boolean terminate(final Memory memory) { (10)
        return memory.getIteration() >= this.totalIterations;
    }

    @Override
    public String toString() {
        return StringFactory.vertexProgramString(this, "alpha=" + this.alpha + ",iterations=" + this.totalIterations);
    }
}

PageRankVertexProgram implements VertexProgram<Double> because the messages it sends are Java doubles.
The default path of energy propagation is via outgoing edges from the current vertex.
The resulting PageRank values for the vertices are stored as a hidden property.
A vertex program is constructed using an Apache Configuration to ensure easy dissemination across a cluster of JVMs.
A vertex program must define the "compute keys" that are the properties being operated on during the computation.
The "while"-loop of the vertex program.
In order to determine how to distribute the energy to neighbors, a "1"-count is used to determine how many incident vertices exist for the MessageScope.
Initially, each vertex is provided an equal amount of energy represented as a double.
Energy is aggregated, computed on according to the PageRank algorithm, and then disseminated according to the defined MessageScope.Local.
The computation is terminated after a pre-defined number of iterations.

The above PageRankVertexProgram is used as follows.

gremlin> result = graph.compute().program(PageRankVertexProgram.build().create()).submit().get()
==>result[tinkergraph[vertices:6 edges:0],memory[size:0]]
gremlin> result.memory().runtime
==>51
gremlin> g = result.graph().traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:6 edges:0], standard]
gremlin> g.V().valueMap('name',PageRankVertexProgram.PAGE_RANK)
==>[gremlin.pageRankVertexProgram.pageRank:[0.15000000000000002], name:[marko]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.19250000000000003], name:[vadas]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.4018125], name:[lop]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.19250000000000003], name:[josh]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.23181250000000003], name:[ripple]]
==>[gremlin.pageRankVertexProgram.pageRank:[0.15000000000000002], name:[peter]]

PeerPressureVertexProgram

The PeerPressureVertexProgram is a clustering algorithm that assigns a nominal value to each vertex in the graph. The nominal value represents the vertex’s cluster. If two vertices have the same nominal value, then they are in the same cluster. The algorithm proceeds in the following manner.

Every vertex assigns itself to a unique cluster ID (initially, its vertex ID).
Every vertex determines its per neighbor vote strength as 1.0d / incident edges count.
Every vertex sends its cluster ID and vote strength to its adjacent vertices as a Pair<Serializable,Double>
Every vertex generates a vote energy distribution of received cluster IDs and changes its current cluster ID to the most frequent cluster ID.
1. If there is a tie, then the cluster with the lowest toString() comparison is selected.
Steps 3 and 4 repeat until either a max number of iterations has occurred or no vertex has adjusted its cluster anymore.

BulkDumperVertexProgram

The BulkDumperVertexProgram can be used to export a whole graph in any of the provided Hadoop GraphOutputFormats (e.g. GraphSONOutputFormat, GryoOutputFormat or ScriptOutputFormat). The input can be any Hadoop GraphInputFormat (e.g. GraphSONInputFormat, GryoInputFormat or ScriptInputFormat). An example is provided in the SparkGraphComputer section.

BulkLoaderVertexProgram

batch-graph The BulkLoaderVertexProgram provides a generalized way for loading graphs of any size into a persistent Graph. It is especially useful for large graphs (i.e. hundreds of millions or billions of edges) as it can take advantage of the parallel processing offered by GraphComputer instances. The input can be any existing Graph database supporting TinkerPop3 or any of the Hadoop GraphInputFormats (e.g. GraphSONInputFormat, GryoInputFormat or ScriptInputFormat). The following example demonstrates how to load data from one TinkerGraph to another:

gremlin> writeGraphConf = new BaseConfiguration()
==>org.apache.commons.configuration.BaseConfiguration@131a7516
gremlin> writeGraphConf.setProperty("gremlin.graph", "org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph")
==>null
gremlin> writeGraphConf.setProperty("gremlin.tinkergraph.graphFormat", "gryo")
==>null
gremlin> writeGraphConf.setProperty("gremlin.tinkergraph.graphLocation", "/tmp/tinkergraph.kryo")
==>null
gremlin> modern = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> blvp = BulkLoaderVertexProgram.build().
                    bulkLoader(OneTimeBulkLoader).
                    writeGraph(writeGraphConf).create(modern)
==>BulkLoaderVertexProgram[bulkLoader=OneTimeBulkLoader,vertexIdProperty=null,userSuppliedIds=false,keepOriginalIds=false,batchSize=0]
gremlin> modern.compute().workers(1).program(blvp).submit().get()
==>result[tinkergraph[vertices:6 edges:6],memory[size:0]]
gremlin> graph = GraphFactory.open(writeGraphConf)
==>tinkergraph[vertices:6 edges:6]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> g.V().valueMap()
==>[name:[marko], age:[29]]
==>[name:[vadas], age:[27]]
==>[name:[lop], lang:[java]]
==>[name:[josh], age:[32]]
==>[name:[ripple], lang:[java]]
==>[name:[peter], age:[35]]
gremlin> graph.close()
==>null

Table 1. Available configuration options
Builder Method	Purpose	Default Value
`bulkLoader(Class\|String)`	Sets the class of the bulk loader implementation.	`IncrementalBulkLoader`
`vertexIdProperty(String)`	Sets the name of the property in the target graph that holds the vertex id from the source graph.	`bulkLoader.vertex.id`
`keepOriginalIds(boolean)`	Whether to keep the id’s from the source graph in the target graph or not. It’s recommended to keep them if it’s planned to do further bulk loads using the same datasources.	`true`
`userSuppliedIds(boolean)`	Whether to use the id’s from the source graph as id’s in the target graph. If set to `true`, `vertexIdProperty` will be ignored. Note, that the target graph must support user supplied identifiers.	`false`
`intermediateBatchSize(int)`	Sets the batch size for intermediate transactions. This is per thread in a multi-threaded environment. `0` means that transactions will only be committed at the end of an iteration cycle. It’s recommended to tune this property for the target graph and not use the default value of `0`.	`0`
`writeGraph(String)`	Sets the path to a `GraphFactory` compatible configuration file for the target graph.	none

Note

BulkLoaderVertexProgram uses the IncrementalBulkLoader by default. The other option is the OneTimeBulkLoader, which doesn’t store any temporary IDs in the writeGraph and thus should only be used for initial bulk loads. Both implementations should cover the majority of use-cases, but have a limitation though: They don’t support multi-valued properties. OneTimeBulkLoader and IncrementalBulkLoader will handle every property as a single-valued property. A custom BulkLoader implementation has to be used if the default behavior is not sufficient.

Note

A custom BulkLoader implementation for incremental loading should use GraphTraversal methods to create/update elements (e.g. g.addV() instead of graph.addVertex()). This way the BulkLoaderVertexProgram is able to efficiently track changes in the underlying graph and can apply several optimization techniques.

TraversalVertexProgram

traversal-vertex-program The TraversalVertexProgram is a "special" VertexProgram in that it can be executed via a GraphTraversal with a ComputerTraversalEngine. In Gremlin, it is possible to have the same traversal executed using either the standard OTLP-engine or the GraphComputer OLAP-engine. The difference being where the traversal is submitted.

Note	This model of graph traversal in a BSP system was first implemented by the Faunus graph analytics engine and originally described in Local and Distributed Traversal Engines.

gremlin> g = graph.traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> g.V().both().hasLabel('person').values('age').groupCount().next() // OLTP
==>32=3
==>35=1
==>27=1
==>29=3
gremlin> g = graph.traversal(computer())
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], tinkergraphcomputer]
gremlin> g.V().both().hasLabel('person').values('age').groupCount().next() // OLAP
==>32=3
==>35=1
==>27=1
==>29=3

In the OLAP example above, a TraversalVertexProgram is (logically) sent to each vertex in the graph. Each instance evaluation requires (logically) 5 BSP iterations and each iteration is interpreted as such:

g.V(): Put a traverser on each vertex in the graph.
both(): Propagate each traverser to the vertices both-adjacent to its current vertex.
hasLabel('person'): If the vertex is not a person, kill the traversers at that vertex.
values('age'): Have all the traversers reference the integer age of their current vertex.
groupCount(): Count how many times a particular age has been seen.

While 5 iterations were presented, in fact, TraversalVertexProgram will execute the traversal in only 3 iterations. The reason being is that hasLabel('person').values('age').groupCount() can all be executed in a single iteration as any message sent would simply be to the current executing vertex. Thus, a simple optimization exists in Gremlin OLAP called "reflexive message passing" which simulates non-message-passing BSP iterations within a single BSP iteration.

When the computation is complete a MapReduce job executes which aggregates all the groupCount() sideEffect Map (i.e. "HashMap") objects on each vertex into a single local representation (thus, turning the distributed Map representation into a local Map representation).

Distributed Gremlin Gotchas

Gremlin OLTP is not identical to Gremlin OLAP.

Important

There are two primary theoretical differences between Gremlin OLTP and Gremlin OLAP. First, Gremlin OLTP (via Traversal) leverages a depth-first execution engine. Depth-first execution has a limited memory footprint due to lazy evaluation. On the other hand, Gremlin OLAP (via TraversalVertexProgram) leverages a breadth-first execution engine which maintains a larger memory footprint, but a better time complexity due to vertex-local traversers being able to be merged. The second difference is that Gremlin OLTP is executed in a serial fashion, while Gremlin OLAP is executed in a parallel fashion. These two fundamental differences lead to the behaviors enumerated below.

Traversal sideEffects are represented as a distributed data structure across the graph’s vertex set. It is not possible to get a global view of a sideEffect until it is aggregated via a MapReduce job. In some situations, the local vertex representation of the sideEffect is sufficient to ensure the intended semantics of the traversal are respected. However, this is not generally true so be wary of traversals that require global views of a sideEffect.
When evaluating traversals that rely on path information (i.e. the history of the traversal), practical computational limits can easily be reached due the combinatoric explosion of data. With path computing enabled, every traverser is unique and thus, must be enumerated as opposed to being counted/merged. The difference being a collection of paths vs. a single 64-bit long at a single vertex. For more information on this concept, please see Faunus Provides Big Graph Data.
When traversals of the form x.as('a').y.someSideEffectStep('a').z are evaluated, the a object is stored in the path information of the traverser and thus, such traversals (may) turn on path calculations when executed on a GraphComputer
Steps that are concerned with the global ordering of traversers do not have a meaningful representation in OLAP. For example, what does order()-step mean when all traversers are being processed in parallel? Even if the traversers were aggregated and ordered, then at the next step they would return to being executed in parallel and thus, in an unpredictable order. When order()-like steps are executed at the end of a traversal (i.e the final step), the TraverserMapReduce job ensures the resultant serial representation is ordered accordingly.
Steps that are concerned with providing a global aggregate to the next step of computation do not have a correlate in OLAP. For example, fold()-step can only fold up the objects at each executing vertex. Next, even if a global fold was possible, where would it go? Which vertex would be the host of the data structure? The fold()-step only makes sense as an end-step whereby a MapReduce job can generate the proper global-to-local data reduction.

Gremlin Applications

Gremlin applications represent tools that are built on top of the core APIs to help expose common functionality to users when working with graphs. There are two key applications:

Gremlin Console - A REPL environment for interactive development and analysis
Gremlin Server - A server that hosts script engines thus enabling remote Gremlin execution

gremlin-lab-coat Gremlin is designed to be extensible, making it possible for users and graph system/language providers to customize it to their needs. Such extensibility is also found in the Gremlin Console and Server, where a universal plugin system makes it possible to extend their capabilities. One of the important aspects of the plugin system is the ability to help the user install the plugins through the command line thus automating the process of gathering dependencies and other error prone activities.

The process of plugin installation is handled by Grape, which helps resolve dependencies into the classpath. It is therefore important to ensure that Grape is properly configured in order to use the automated capabilities of plugin installation. Grape is configured by ~/.groovy/grapeConfig.xml and generally speaking, if that file is not present, the default settings will suffice. However, they will not suffice if a required dependency is not in one of the default configured repositories. Please see the Custom Ivy Settings section of the Grape documentation for more details on the defaults. TinkerPop recommends the following configuration in that file:

<ivysettings>
  <settings defaultResolver="downloadGrapes"/>
  <resolvers>
    <chain name="downloadGrapes">
      <filesystem name="cachedGrapes">
        <ivy pattern="${user.home}/.groovy/grapes/[organisation]/[module]/ivy-[revision].xml"/>
        <artifact pattern="${user.home}/.groovy/grapes/[organisation]/[module]/[type]s/[artifact]-[revision].[ext]"/>
      </filesystem>
      <ibiblio name="codehaus" root="http://repository.codehaus.org/" m2compatible="true"/>
      <ibiblio name="central" root="http://central.maven.org/maven2/" m2compatible="true"/>
      <ibiblio name="jitpack" root="https://jitpack.io" m2compatible="true"/>
      <ibiblio name="java.net2" root="http://download.java.net/maven/2/" m2compatible="true"/>
    </chain>
  </resolvers>
</ivysettings>

The Graph configuration can also be modified to include the local system’s Maven .m2 directory by one or both of the following entries:

<ibiblio name="apache-snapshots" root="http://repository.apache.org/snapshots/" m2compatible="true"/>
<ibiblio name="local" root="file:${user.home}/.m2/repository/" m2compatible="true"/>

These configurations are useful during development (i.e. if one is working with locally built artifacts) of TinkerPop Plugins. It is important to take note of the order used for these references as Grape will check them in the order they are specified and depending on that order, an artifact other than the one expected may be used which is typically an issue when working with SNAPSHOT dependencies.

Warning

If building TinkerPop from source, be sure to clear TinkerPop-related jars from the ~/.groovy/grapes directory as they can become stale on some systems and not re-import properly from the local .m2 after fresh rebuilds.

Gremlin Console

gremlin-console The Gremlin Console is an interactive terminal or REPL that can be used to traverse graphs and interact with the data that they contain. It represents the most common method for performing ad-hoc graph analysis, small to medium sized data loading projects and other exploratory functions. The Gremlin Console is highly extensible, featuring a rich plugin system that allows new tools, commands, DSLs, etc. to be exposed to users.

To start the Gremlin Console, run gremlin.sh or gremlin.bat:

$ bin/gremlin.sh

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

Note

If the above plugins are not loaded then they will need to be enabled or else certain examples will not work. If using the standard Gremlin Console distribution, then the plugins should be enabled by default. See below for more information on the :plugin use command to manually enable plugins. These plugins, with the exception of tinkerpop.tinkergraph, cannot be removed from the Console as they are a part of the gremlin-console.jar itself. These plugins can only be deactivated.

The Gremlin Console is loaded and ready for commands. Recall that the console hosts the Gremlin-Groovy language. Please review Groovy for help on Groovy-related constructs. In short, Groovy is a superset of Java. What works in Java, works in Groovy. However, Groovy provides many shorthands to make it easier to interact with the Java API. Moreoever, Gremlin provides many neat shorthands to make it easier to express paths through a property graph.

gremlin> i = 'goodbye'
==>goodbye
gremlin> j = 'self'
==>self
gremlin> i + " " + j
==>goodbye self
gremlin> "${i} ${j}"
==>goodbye self

The "toy" graph provides a way to get started with Gremlin quickly.

gremlin> g = TinkerFactory.createModern().traversal(standard())
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
gremlin> g.V()
==>v[1]
==>v[2]
==>v[3]
==>v[4]
==>v[5]
==>v[6]
gremlin> g.V().values('name')
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter
gremlin> g.V().has('name','marko').out('knows').values('name')
==>vadas
==>josh

Tip	When using Gremlin-Groovy in a Groovy class file, add `static { GremlinLoader.load() }` to the head of the file.

Console Commands

In addition to the standard commands of the Groovy Shell, Gremlin adds some other useful operations. The following table outlines the most commonly used commands:

Command Alias Description

:help

Displays list of commands and descriptions. When followed by a command name, it will display more specific help on that particular item.

:exit

Ends the Console session.

import

Import a class into the Console session.

:clear

Sometimes the Console can get into a state where the command buffer no longer understands input (e.g. a misplaced ( or }). Use this command to clear that buffer.

:load

Load a file or URL into the command buffer for execution.

:install

Imports a maven library and its dependencies into the Console.

:uninstall

Removes a maven library and its dependencies. A restart of the console is required for removal to fully take effect.

:plugin

:pin

Plugin management functions to list, activate and deactivate available plugins.

:remote

:rem

Configures a "remote" context where Gremlin or results of Gremlin will be processed via usage of :submit.

:submit

Submit Gremlin to the currently active context defined by :remote.

Gremlin Console adds a special max-iteration preference that can be configured with the standard :set command from the Groovy Shell. Use this setting to control the maximum number of results that the Console will display. Consider the following usage:

gremlin> :set max-iteration 10
gremlin> (0..200)
==>0
==>1
==>2
==>3
==>4
==>5
==>6
==>7
==>8
==>9
...
gremlin> :set max-iteration 5
gremlin> (0..200)
==>0
==>1
==>2
==>3
==>4
...

If this setting is not present, the console will default the maximum to 100 results.

Dependencies and Plugin Usage

The Gremlin Console can dynamically load external code libraries and make them available to the user. Furthermore, those dependencies may contain Gremlin plugins which can expand the language, provide useful functions, etc. These important console features are managed by the :install and :plugin commands.

The following Gremlin Console session demonstrates the basics of these features:

gremlin> :plugin list  (1)
==>tinkerpop.server[active]
==>tinkerpop.gephi
==>tinkerpop.utilities[active]
==>tinkerpop.sugar
==>tinkerpop.tinkergraph[active]
gremlin> :plugin use tinkerpop.sugar  (2)
==>tinkerpop.sugar activated
gremlin> :install org.apache.tinkerpop neo4j-gremlin 3.1.4-SNAPSHOT  (3)
==>loaded: [org.apache.tinkerpop, neo4j-gremlin, 3.1.4-SNAPSHOT]
gremlin> :plugin list (4)
==>tinkerpop.server[active]
==>tinkerpop.gephi
==>tinkerpop.utilities[active]
==>tinkerpop.sugar
==>tinkerpop.tinkergraph[active]
==>tinkerpop.neo4j
gremlin> :plugin use tinkerpop.neo4j (5)
==>tinkerpop.neo4j activated
gremlin> :plugin list (6)
==>tinkerpop.server[active]
==>tinkerpop.gephi
==>tinkerpop.sugar[active]
==>tinkerpop.utilities[active]
==>tinkerpop.neo4j[active]
==>tinkerpop.tinkergraph[active]

Show a list of "available" plugins. The list of "available" plugins is determined by the classes available on the Console classpath. Plugins need to be "active" for their features to be available.
To make a plugin "active" execute the :plugin use command and specify the name of the plugin to enable.
Sometimes there are external dependencies that would be useful within the Console. To bring those in, execute :install and specify the Maven coordinates for the dependency.
Note that there is a "tinkerpop.neo4j" plugin available, but it is not yet "active".
Again, to use the "tinkerpop.neo4j" plugin, it must be made "active" with :plugin use.
Now when the plugin list is displayed, the "tinkerpop.neo4j" plugin is displayed as "active".

Warning

Plugins must be compatible with the version of the Gremlin Console (or Gremlin Server) being used. Attempts to use incompatible versions cannot be guaranteed to work. Moreover, be prepared for dependency conflicts in third-party plugins, that may only be resolved via manual jar removal from the ext/{plugin} directory.

Tip	It is possible to manage plugin activation and deactivation by manually editing the `ext/plugins.txt` file which contains the class names of the "active" plugins. It is also possible to clear dependencies added by `:install` by deleting them from the `ext` directory.

Script Executor

For automated tasks and batch executions of Gremlin, it can be useful to execute Gremlin scripts from the command line. Consider the following file named gremlin.groovy:

import org.apache.tinkerpop.gremlin.tinkergraph.structure.*
graph = TinkerFactory.createModern()
g = graph.traversal()
g.V().each { println it }

This script creates the toy graph and then iterates through all its vertices printing each to the system out. Note that under this approach, "imports" need to be explicitly defined (except for "core" TinkerPop classes). In addition, plugins and other dependencies should already be "installed" via console commands which cannot be used with this mode of execution. To execute this script from the command line, gremlin.sh has the -e option used as follows:

$ bin/gremlin.sh -e gremlin.groovy
v[1]
v[2]
v[3]
v[4]
v[5]
v[6]

It is also possible to pass arguments to scripts. Any parameters following the file name specification are treated as arguments to the script. They are collected into a list and passed in as a variable called "args". The following Gremlin script is exactly like the previous one, but it makes use of the "args" option to filter the vertices printed to system out:

import org.apache.tinkerpop.gremlin.tinkergraph.structure.*
graph = TinkerFactory.createModern()
g = graph.traversal()
g.V().has('name',args[0]).each { println it }

When executed from the command line a parameter can be supplied:

$ bin/gremlin.sh -e gremlin.groovy marko
v[1]
$ bin/gremlin.sh -e gremlin.groovy vadas
v[2]

Note

The ScriptExecutor is for Gremlin Groovy scripts only. It is not possible to include Console plugin commands such as :remote or :> when using -e in these scripts. That does not mean that it is impossible to script such commands, it just means that they need to be scripted manually. For example, instead of trying to use the :remote command, manually construct a Gremlin Driver Client and submit scripts from there.

Gremlin Server

gremlin-server Gremlin Server provides a way to remotely execute Gremlin scripts against one or more Graph instances hosted within it. The benefits of using Gremlin Server include:

Allows any Gremlin Structure-enabled graph to exist as a standalone server, which in turn enables the ability for multiple clients to communicate with the same graph database.
Enables execution of ad-hoc queries through remotely submitted Gremlin scripts.
Allows for the hosting of Gremlin-based DSLs (Domain Specific Language) that expand the Gremlin language to match the language of the application domain, which will help support common graph use cases such as searching, ranking, and recommendation.
Provides a method for Non-JVM languages (e.g. Python, Javascript, etc.) to communicate with the TinkerPop stack.
Exposes numerous methods for extension and customization to include serialization options, remote commands, etc.

Note	Gremlin Server is the replacement for Rexster.

Note	Please see the Provider Documentation for information on how to develop a driver for Gremlin Server.

By default, communication with Gremlin Server occurs over WebSockets and exposes a custom sub-protocol for interacting with the server.

Starting Gremlin Server

Gremlin Server comes packaged with a script called bin/gremlin-server.sh to get it started (use gremlin-server.bat on Windows):

$ bin/gremlin-server.sh conf/gremlin-server-modern.yaml
[INFO] GremlinServer -
         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----

[INFO] GremlinServer - Configuring Gremlin Server from conf/gremlin-server-modern.yaml
[INFO] MetricManager - Configured Metrics Slf4jReporter configured with interval=180000ms and loggerName=org.apache.tinkerpop.gremlin.server.Settings$Slf4jReporterMetrics
[INFO] Graphs - Graph [graph] was successfully configured via [conf/tinkergraph-empty.properties].
[INFO] ServerGremlinExecutor - Initialized Gremlin thread pool.  Threads in pool named with pattern gremlin-*
[INFO] ScriptEngines - Loaded gremlin-groovy ScriptEngine
[INFO] GremlinExecutor - Initialized gremlin-groovy ScriptEngine with scripts/generate-modern.groovy
[INFO] ServerGremlinExecutor - Initialized GremlinExecutor and configured ScriptEngines.
[INFO] ServerGremlinExecutor - A GraphTraversalSource is now bound to [g] with graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
[INFO] OpLoader - Adding the standard OpProcessor.
[INFO] OpLoader - Adding the control OpProcessor.
[INFO] OpLoader - Adding the session OpProcessor.
[INFO] GremlinServer - Executing start up LifeCycleHook
[INFO] Logger$info - Loading 'modern' graph data.
[INFO] AbstractChannelizer - Configured application/vnd.gremlin-v1.0+gryo with org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV1d0
[INFO] AbstractChannelizer - Configured application/vnd.gremlin-v1.0+gryo-stringd with org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV1d0
[INFO] GremlinServer$1 - Gremlin Server configured with worker thread pool of 1, gremlin pool of 8 and boss thread pool of 1.
[INFO] GremlinServer$1 - Channel started at port 8182.

Gremlin Server is configured by the provided YAML file conf/gremlin-server-modern.yaml. That file tells Gremlin Server many things such as:

The host and port to serve on
Thread pool sizes
Where to report metrics gathered by the server
The serializers to make available
The Gremlin ScriptEngine instances to expose and external dependencies to inject into them
Graph instances to expose

The log messages that printed above show a number of things, but most importantly, there is a Graph instance named graph that is exposed in Gremlin Server. This graph is an in-memory TinkerGraph and was empty at the start of the server. An initialization script at scripts/generate-modern.groovy was executed during startup. It’s contents are as follows:



// an init script that returns a Map allows explicit setting of global bindings.
def globals = [:]

// Generates the modern graph into an "empty" TinkerGraph via LifeCycleHook.
// Note that the name of the key in the "global" map is unimportant.
globals << [hook : [
  onStartUp: { ctx ->
    ctx.logger.info("Loading 'modern' graph data.")
    TinkerFactory.generateModern(graph)
  }
] as LifeCycleHook]

// define the default TraversalSource to bind queries to - this one will be named "g".
globals << [g : graph.traversal()]

The script above initializes a Map and assigns two key/values to it. The first, assigned to "hook", defines a LifeCycleHook for Gremlin Server. The "hook" provides a way to tie script code into the Gremlin Server startup and shutdown sequences. The LifeCycleHook has two methods that can be implemented: onStartUp and onShutDown. These events are called once at Gremlin Server start and once at Gremlin Server stop. This is an important point because code outside of the "hook" is executed for each ScriptEngine creation (multiple may be created when "sessions" are enabled) and therefore the LifeCycleHook provides a way to ensure that a script is only executed a single time. In this case, the startup hook loads the "modern" graph into the empty TinkerGraph instance, preparing it for use. The second key/value pair assigned to the Map, named "g", defines a TraversalSource from the Graph bound to the "graph" variable in the YAML configuration file. This variable g, as well as any other variable assigned to the Map, will be made available as variables for future remote script executions. In more general terms, any key/value pairs assigned to a Map returned from the initialization script will become variables that are global to all requests. In addition, any functions that are defined will be cached for future use.

Warning

Transactions on graphs in initialization scripts are not closed automatically after the script finishes executing. It is up to the script to properly commit or rollback transactions in the script itself.

Connecting via Console

With Gremlin Server running it is now possible to issue some scripts to it for processing. Start Gremlin Console as follows:

$ bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
gremlin>

The console has the notion of a "remote", which represents a place a script will be sent from the console to be evaluated elsewhere in some other context (e.g. Gremlin Server, Hadoop, etc.). To create a remote in the console, do the following:

gremlin> :remote connect tinkerpop.server conf/remote.yaml
==>Configured localhost/127.0.0.1:8182

The :remote command shown above displays the current status of the remote connection. This command can also be used to configure a new connection and change other related settings. To actually send a script to the server a different command is required:

gremlin> :> g.V().values('name')
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter
gremlin> :> g.V().has('name','marko').out('created').values('name')
==>lop
gremlin> :> g.E().label().groupCount()
==>{created=4, knows=2}
gremlin> result
==>result{object={created=4, knows=2} class=java.lang.String}
gremlin> :remote close
==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]

The :> command, which is a shorthand for :submit, sends the script to the server to execute there. Results are wrapped in an Result object which is a just a holder for each individual result. The class shows the data type for the containing value. Note that the last script sent was supposed to return a Map, but its class is java.lang.String. By default, the connection is configured to only return text results. In other words, Gremlin Server is using toString to serialize all results back to the console. This enables virtually any object on the server to be returned to the console, but it doesn’t allow the opportunity to work with this data in any way in the console itself. A different configuration of the :remote is required to get the results back as "objects":

gremlin> :remote connect tinkerpop.server conf/remote-objects.yaml //(1)
==>Configured localhost/127.0.0.1:8182
gremlin> :remote list //(2)
==>*0 - Gremlin Server - [localhost/127.0.0.1:8182]
gremlin> :> g.E().label().groupCount() //(3)
==>[created:4, knows:2]
gremlin> m = result[0].object //(4)
==>created=4
==>knows=2
gremlin> m.sort {it.value}
==>knows=2
==>created=4
gremlin> script = """
                  matthias = graph.addVertex('name','matthias')
                  matthias.addEdge('co-creator',g.V().has('name','marko').next())
                  """
==>
         matthias = graph.addVertex('name','matthias')
         matthias.addEdge('co-creator',g.V().has('name','marko').next())

gremlin> :> @script //(5)
==>e[15][13-co-creator->1]
gremlin> :> g.V().has('name','matthias').out('co-creator').values('name')
==>marko
gremlin> :remote close
==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]

This configuration file specifies that results should be deserialized back into an Object in the console with the caveat being that the server and console both know how to serialize and deserialize the result to be returned.
There are now two configured remote connections. The one marked by an asterisk is the one that was just created and denotes the current one that :sumbit will react to.
When the script is executed again, the class is no longer shown to be a java.lang.String. It is instead a java.util.HashMap.
The last result of a remote script is always stored in the reserved variable result, which allows access to the Result and by virtue of that, the Map itself.
If the submission requires multiple-lines to express, then a multi-line string can be created. The :> command realizes that the user is referencing a variable via @ and submits the string script.

Tip	In Groovy, `""" text """` is a convenient way to create a multi-line string and works well in concert with `:> @variable`. Note that this model of submitting a string variable works for all `:>` based plugins, not just Gremlin Server.

Warning

Not all values that can be returned from a Gremlin script end up being serializable. For example, submitting :> graph will return a Graph instance and in most cases those are not serializable by Gremlin Server and will return a serialization error. It should be noted that TinkerGraph, as a convenience for shipping around small sub-graphs, is serializable from Gremlin Server.

The Gremlin Server :remote config command for the driver has the following configuration options:

Command Description

alias

Option Description

pairs

A set of key/value alias/binding pairs to apply to requests.

reset

Clears any aliases that were supplied in previous configurations of the remote.

show

Shows the current set of aliases which is returned as a Map

timeout

Specifies the length of time in milliseconds a will wait for a response from the server. Specify "none" to have no timeout. By default, this setting uses "none".

Aliases

The alias configuration command for the Gremlin Server :remote can be useful in situations where there are multiple Graph or TraversalSource instances on the server, as it becomes possible to rename them from the client for purposes of execution within the context of a script. Therefore, it becomes possible to submit commands this way:

gremlin> :remote connect tinkerpop.server conf/remote-objects.yaml
==>Configured localhost/127.0.0.1:8182
gremlin> :remote config alias x g
==>x=g
gremlin> :> x.E().label().groupCount()
==>[created:4, co-creator:1, knows:2]

Sessions

A :remote created in the following fashion will be "sessionless", meaning each script issued to the server with :> will be encased in a transaction and no state will be maintained from one request to the next.

gremlin> :remote connect tinkerpop.server conf/remote-objects.yaml
==>Configured localhost/127.0.0.1:8182

In other words, the transaction will be automatically committed (or rolledback on error) and any variables declared in that script will be forgotten for the next request. See the section on Considering Sessions for more information on that topic.

To enable the remote to connect with a session the connect argument takes another argument as follows:

gremlin> :remote connect tinkerpop.server conf/remote.yaml session
==>Configured localhost/127.0.0.1:8182-[c3592e6f-9adc-41c7-a18e-57c1747395c6]
gremlin> :> x = 1
==>1
gremlin> :> y = 2
==>2
gremlin> :> x + y
==>3

With the above command a session gets created with a random UUID for a session identifier. It is also possible to assign a custom session identifier by adding it as the last argument to :remote command above. There is also the option to replace "session" with "session-managed" to create a session that will auto-manage transactions (i.e. each request will occur within the bounds of a transaction). In this way, the state of bound variables between requests are maintained, but the need to manually managed the transactional scope of the graph is no longer required.

Remote Console

Previous examples have shown usage of the :> command to send scripts to Gremlin Server. The Gremlin Console also supports an additional method for doing this which can be more convenient when the intention is to exclusively work with a remote connection to the server.

gremlin> :remote connect tinkerpop.server conf/remote.yaml session
==>Configured localhost/127.0.0.1:8182-[8e37b505-5f63-43e5-97b4-bd3596205ffa]
gremlin> :remote console
==>All scripts will now be sent to Gremlin Server - [localhost/127.0.0.1:8182]-[8e37b505-5f63-43e5-97b4-bd3596205ffa] - type ':remote console' to return to local mode
gremlin> x = 1
==>1
gremlin> y = 2
==>2
gremlin> x + y
==>3
gremlin> :remote console
==>All scripts will now be evaluated locally - type ':remote console' to return to remote mode for Gremlin Server - [localhost/127.0.0.1:8182]-[8e37b505-5f63-43e5-97b4-bd3596205ffa]

In the above example, the :remote console command is executed. It places the console in a state where the :> is no longer required. Each script line is actually automatically submitted to Gremlin Server for evalaution. The variables x and y that were defined actually don’t exist locally - they only exist on the server! In this sense, putting the console in this mode is basically like creating a window to a session on Gremlin Server.

Tip

When using :remote console there is not much point to using a configuration that uses a serializer that returns actual data. In other words, using a configuration like the one inside of conf/remote-objects.yaml isn’t typically useful as in this mode the result will only ever be displayed but not used. Using a serializer configuration like the one in conf/remote.yaml should perform better.

Note	Console commands, those that begin with a colon (e.g. `:x`, `:remote`) do not execute remotely when in this mode. They are all still evaluated locally.

Connecting via Java

<dependency>
   <groupId>org.apache.tinkerpop</groupId>
   <artifactId>gremlin-driver</artifactId>
   <version>3.1.4-SNAPSHOT</version>
</dependency>

gremlin-java TinkerPop3 comes equipped with a reference client for Java-based applications. It is referred to as Gremlin Driver, which enables applications to send requests to Gremlin Server and get back results.

Gremlin code is sent to the server from a Client instance. A Client is created as follows:

Cluster cluster = Cluster.open();  (1)
Client client = cluster.connect(); (2)

Opens a reference to localhost - note that there are many configuration options available in defining a Cluster object.
Creates a Client given the configuration options of the Cluster.

Once a Client instance is ready, it is possible to issue some Gremlin:

ResultSet results = client.submit("[1,2,3,4]");  (1)
results.stream().map(i -> i.get(Integer.class) * 2);       (2)

CompletableFuture<List<Result>> results = client.submit("[1,2,3,4]").all();  (3)

CompletableFuture<ResultSet> future = client.submitAsync("[1,2,3,4]"); (4)

Map<String,Object> params = new HashMap<>();
params.put("x",4);
client.submit("[1,2,3,x]", params); (5)

Submits a script that simply returns a List of integers. This method blocks until the request is written to the server and a ResultSet is constructed.
Even though the ResultSet is constructed, it does not mean that the server has sent back the results (or even evaluated the script potentially). The ResultSet is just a holder that is awaiting the results from the server. In this case, they are streamed from the server as they arrive.
Submit a script, get a ResultSet, then return a CompletableFuture that will be called when all results have been returned.
Submit a script asynchronously without waiting for the request to be written to the server.
Parameterized request are considered the most efficient way to send Gremlin to the server as they can be cached, which will boost performance and reduce resources required on the server.

Configuration

The following table describes the various configuration options for the Gremlin Driver:

Key Description Default

connectionPool.channelizer

The fully qualified classname of the client Channelizer that defines how to connect to the server.

Channelizer.WebSocketChannelizer

connectionPool.enableSsl

Determines if SSL should be enabled or not. If enabled on the server then it must be enabled on the client.

false

connectionPool.keyCertChainFile

The X.509 certificate chain file in PEM format.

none

connectionPool.keyFile

The PKCS#8 private key file in PEM format.

none

connectionPool.keyPassword

The password of the keyFile if it’s not password-protected

none

connectionPool.maxContentLength

The maximum length in bytes that a message can be sent to the server. This number can be no greater than the setting of the same name in the server configuration.

65536

connectionPool.maxInProcessPerConnection

The maximum number of in-flight requests that can occur on a connection.

connectionPool.maxSimultaneousUsagePerConnection

The maximum number of times that a connection can be borrowed from the pool simultaneously.

connectionPool.maxSize

The maximum size of a connection pool for a host.

connectionPool.maxWaitForConnection

The amount of time in milliseconds to wait for a new connection before timing out.

3000

connectionPool.maxWaitForSessionClose

The amount of time in milliseconds to wait for a session to close before timing out (does not apply to sessionless connections).

3000

connectionPool.minInProcessPerConnection

The minimum number of in-flight requests that can occur on a connection.

connectionPool.minSimultaneousUsagePerConnection

The maximum number of times that a connection can be borrowed from the pool simultaneously.

connectionPool.minSize

The minimum size of a connection pool for a host.

connectionPool.reconnectInitialDelay

The amount of time in milliseconds to wait before trying to reconnect to a dead host for the first time.

1000

connectionPool.reconnectInterval

The amount of time in milliseconds to wait before trying to reconnect to a dead host. This interval occurs after the time specified by the reconnectInitialDelay.

1000

connectionPool.resultIterationBatchSize

The override value for the size of the result batches to be returned from the server.

connectionPool.trustCertChainFile

File location for a SSL Certificate Chain to use when SSL is enabled. If this value is not provided and SSL is enabled, the TrustManager will be established with a self-signed certificate which is NOT suitable for production purposes.

none

hosts

The list of hosts that the driver will connect to.

localhost

jaasEntry

Sets the AuthProperties.Property.JAAS_ENTRY properties for authentication to Gremlin Server.

none

nioPoolSize

Size of the pool for handling request/response operations.

available processors

password

The password to submit on requests that require authentication.

none

port

The port of the Gremlin Server to connect to. The same port will be applied for all hosts.

8192

protocol

Sets the AuthProperties.Property.PROTOCOL properties for authentication to Gremlin Server.

none

serializer.className

The fully qualified class name of the MessageSerializer that will be used to communicate with the server. Note that the serializer configured on the client should be supported by the server configuration.

GryoMessageSerializerV1d0

serializer.config

A Map of configuration settings for the serializer.

none

username

The username to submit on requests that require authentication.

none

workerPoolSize

Size of the pool for handling background work.

available processors * 2

Please see the Cluster.Builder javadoc to get more information on these settings.

Aliases

Scripts submitted to Gremlin Server automatically have the globally configured Graph and TraversalSource instances made available to them. Therefore, if Gremlin Server configures two TraversalSource instances called "g1" and "g2" a script can simply reference them directly as:

client.submit("g1.V()")
client.submit("g2.V()")

While this is an acceptable way to submit scripts, it has the downside of forcing the client to encode the server-side variable name directly into the script being sent. If the server configuration ever changed such that "g1" became "g100", the client-side code might have to see a significant amount of change. Decoupling the script code from the server configuration can be managed by the alias method on Client as follows:

Client g1Client = client.alias("g1")
Client g2Client = client.alias("g2")
g1Client.submit("g.V()")
g2Client.submit("g.V()")

The above code demonstrates how the alias method can be used such that the script need only contain a reference to "g" and "g1" and "g2" are automatically rebound into "g" on the server-side.

Serialization

When using Gryo serialization (the default serializer for the driver), it is important that the client and server have the same serializers configured or else one or the other will experience serialization exceptions and fail to always communicate. Discrepancy in serializer registration between client and server can happen fairly easily as graphs will automatically include serializers on the server-side, thus leaving the client to be configured manually. This can be done manually as follows:

GryoMapper kryo = GryoMapper.build().addRegistry(TitanIoRegistry.INSTANCE).create();
MessageSerializer serializer = new GryoMessageSerializerV1d0(kryo);
Cluster cluster = Cluster.build()
                .serializer(serializer)
                .create();
Client client = cluster.connect().init();

The above code demonstrates using the TitanIoRegistry which is an IoRegistry instance. It tells the serializer what classes (from Titan in this case) to auto-register during serialization. Gremlin Server roughly uses this same approach when it configures it’s serializers, so using this same model will ensure compatibility when making requests.

Connecting via REST

gremlin-rexster While the default behavior for Gremlin Server is to provide a WebSockets-based connection, it can also be configured to support REST. The REST endpoint provides for a communication protocol familiar to most developers, with a wide support of programming languages, tools and libraries for accessing it. As a result, REST provides a fast way to get started with Gremlin Server. It also may represent an easier upgrade path from Rexster as the API for the endpoint is very similar to Rexster’s Gremlin Extension.

Gremlin Server provides for a single REST endpoint - a Gremlin evaluator - which allows the submission of a Gremlin script as a request. For each request, it returns a response containing the serialized results of that script. To enable this endpoint, Gremlin Server needs to be configured with the HttpChannelizer, which replaces the default WebSocketChannelizer, in the configuration file:

channelizer: org.apache.tinkerpop.gremlin.server.channel.HttpChannelizer

This setting is already configured in the gremlin-server-rest-modern.yaml file that is packaged with the Gremlin Server distribution. To utilize it, start Gremlin Server as follows:

bin/gremlin-server.sh conf/gremlin-server-rest-modern.yaml

Once the server has started, issue a request. Here’s an example with cURL:

$ curl "http://localhost:8182?gremlin=100-1"

which returns:

{
  "result":{"data":99,"meta":{}},
  "requestId":"0581cdba-b152-45c4-80fa-3d36a6eecf1c",
  "status":{"code":200,"attributes":{},"message":""}
}

The above example showed a GET operation, but the preferred method for this endpoint is POST:

curl -X POST -d "{\"gremlin\":\"100-1\"}" "http://localhost:8182"

which returns:

{
  "result":{"data":99,"meta":{}},
  "requestId":"ef2fe16c-441d-4e13-9ddb-3c7b5dfb10ba",
  "status":{"code":200,"attributes":{},"message":""}
}

It is also preferred that Gremlin scripts be parameterized when possible via bindings:

curl -X POST -d "{\"gremlin\":\"100-x\", \"bindings\":{\"x\":1}}" "http://localhost:8182"

The bindings argument is a Map of variables where the keys become available as variables in the Gremlin script. Note that parameterization of requests is critical to performance, as repeated script compilation can be avoided on each request.

Note

It is possible to pass bindings via GET based requests. Query string arguments prefixed with "bindings." will be treated as parameters, where that prefix will be removed and the value following the period will become the parameter name. In other words, bindings.x will create a parameter named "x" that can be referenced in the submitted Gremlin script. The caveat is that these arguments will always be treated as String values. To ensure that data types are preserved or to pass complex objects such as lists or maps, use POST which will at least support the allowed JSON data types.

Finally, as Gremlin Server can host multiple ScriptEngine instances (e.g. gremlin-groovy, nashorn), it is possible to define the language to utilize to process the request:

curl -X POST -d "{\"gremlin\":\"100-x\", \"language\":\"gremlin-groovy\", \"bindings\":{\"x\":1}}" "http://localhost:8182"

By default this value is set to gremlin-groovy. If using a GET operation, this value can be set as a query string argument with by setting the language key.

Warning

Consider the size of the result of a submitted script being returned from the REST endpoint. A script that iterates thousands of results will serialize each of those in memory into a single JSON result set. It is quite possible that such a script will generate OutOfMemoryError exceptions on the server. Consider the default WebSockets configuration, which supports streaming, if that type of use case is required.

Configuring

As mentioned earlier, Gremlin Server is configured though a YAML file. By default, Gremlin Server will look for a file called config/gremlin-server.yaml to configure itself on startup. To override this default, supply the file to use to bin/gremlin-server.sh as in:

bin/gremlin-server.sh conf/gremlin-server-min.yaml

The gremlin-server.sh file also serves a second purpose. It can be used to "install" dependencies to the Gremlin Server path. For example, to be able to configure and use other Graph implementations, the dependencies must be made available to Gremlin Server. To do this, use the -i switch and supply the Maven coordinates for the dependency to "install". For example, to use Neo4j in Gremlin Server:

bin/gremlin-server.sh -i org.apache.tinkerpop neo4j-gremlin 3.1.4-SNAPSHOT

This command will "grab" the appropriate dependencies and copy them to the ext directory of Gremlin Server, which will then allow them to be "used" the next time the server is started. To uninstall dependencies, simply delete them from the ext directory.

The following table describes the various configuration options that Gremlin Server expects:

Key Description Default

authentication.className

The fully qualified classname of an Authenticator implementation to use. If this setting is not present, then authentication is effectively disabled.

AllowAllAuthenticator

authentication.config

A Map of configuration settings to be passes to the Authenticator when it is constructed. The settings available are dependent on the implementation.

none

channelizer

The fully qualified classname of the Channelizer implementation to use. A Channelizer is a "channel initializer" which Gremlin Server uses to define the type of processing pipeline to use. By allowing different Channelizer implementations, Gremlin Server can support different communication protocols (e.g. Websockets, Java NIO, etc.).

WebSocketChannelizer

graphs

A Map of Graph configuration files where the key of the Map becomes the name to which the Graph will be bound and the value is the file name of a Graph configuration file.

none

gremlinPool

The number of "Gremlin" threads available to execute actual scripts in a ScriptEngine. This pool represents the workers available to handle blocking operations in Gremlin Server.

host

The name of the host to bind the server to.

localhost

useEpollEventLoop

try to use epoll event loops (works only on Linux os) instead of netty NIO.

false

maxAccumulationBufferComponents

Maximum number of request components that can be aggregated for a message.

1024

maxChunkSize

The maximum length of the content or each chunk. If the content length exceeds this value, the transfer encoding of the decoded request will be converted to chunked and the content will be split into multiple HttpContent objects. If the transfer encoding of the HTTP request is chunked already, each chunk will be split into smaller chunks if the length of the chunk exceeds this value.

8192

maxContentLength

The maximum length of the aggregated content for a message. Works in concert with maxChunkSize where chunked requests are accumulated back into a single message. A request exceeding this size will return a 413 - Request Entity Too Large status code. A response exceeding this size will raise an internal exception.

65536

maxHeaderSize

The maximum length of all headers.

8192

maxInitialLineLength

The maximum length of the initial line (e.g. "GET / HTTP/1.0") processed in a request, which essentially controls the maximum length of the submitted URI.

4096

metrics.consoleReporter.enabled

Turns on console reporting of metrics.

false

metrics.consoleReporter.interval

Time in milliseconds between reports of metrics to console.

180000

metrics.csvReporter.enabled

Turns on CSV reporting of metrics.

false

metrics.csvReporter.fileName

The file to write metrics to.

none

metrics.csvReporter.interval

Time in milliseconds between reports of metrics to file.

180000

metrics.gangliaReporter.addressingMode

Set to MULTICAST or UNICAST.

none

metrics.gangliaReporter.enabled

Turns on Ganglia reporting of metrics.

false

metrics.gangliaReporter.host

Define the Ganglia host to report Metrics to.

localhost

metrics.gangliaReporter.interval

Time in milliseconds between reports of metrics for Ganglia.

180000

metrics.gangliaReporter.port

Define the Ganglia port to report Metrics to.

8649

metrics.graphiteReporter.enabled

Turns on Graphite reporting of metrics.

false

metrics.graphiteReporter.host

Define the Graphite host to report Metrics to.

localhost

metrics.graphiteReporter.interval

Time in milliseconds between reports of metrics for Graphite.

180000

metrics.graphiteReporter.port

Define the Graphite port to report Metrics to.

2003

metrics.graphiteReporter.prefix

Define a "prefix" to append to metrics keys reported to Graphite.

none

metrics.jmxReporter.enabled

Turns on JMX reporting of metrics.

false

metrics.slf4jReporter.enabled

Turns on SLF4j reporting of metrics.

false

metrics.slf4jReporter.interval

Time in milliseconds between reports of metrics to SLF4j.

180000

plugins

A list of plugins that should be activated on server startup in the available script engines. It assumes that the plugins are in Gremlin Server’s classpath.

none

port

The port to bind the server to.

8182

processors

A List of Map settings, where each Map represents a OpProcessor implementation to use along with its configuration.

none

processors[X].className

The full class name of the OpProcessor implementation.

none

processors[X].config

A Map containing OpProcessor specific configurations.

none

resultIterationBatchSize

Defines the size in which the result of a request is "batched" back to the client. In other words, if set to 1, then a result that had ten items in it would get each result sent back individually. If set to 2 the same ten results would come back in five batches of two each.

scriptEngines

A Map of ScriptEngine implementations to expose through Gremlin Server, where the key is the name given by the ScriptEngine implementation. The key must match the name exactly for the ScriptEngine to be constructed. The value paired with this key is itself a Map of configuration for that ScriptEngine.

none

scriptEngines.<name>.imports

A comma separated list of classes/packages to make available to the ScriptEngine.

none

scriptEngines.<name>.staticImports

A comma separated list of "static" imports to make available to the ScriptEngine.

none

scriptEngines.<name>.scripts

A comma separated list of script files to execute on ScriptEngine initialization. Graph and TraversalSource instance references produced from scripts will be stored globally in Gremlin Server, therefore it is possible to use initialization scripts to add Traversal Strategies or create entirely new Graph instances all together. Instantiating a LifeCycleHook in a script provides a way to execute scripts when Gremlin Server starts and stops.

none

scriptEngines.<name>.config

A Map of configuration settings for the ScriptEngine. These settings are dependent on the ScriptEngine implementation being used.

none

scriptEvaluationTimeout

The amount of time in milliseconds before a script evaluation times out. The notion of "script evaluation" refers to the time it takes for the ScriptEngine to do its work and not any additional time it takes for the result of the evaluation to be iterated and serialized. This feature can be turned off by setting the value to 0.

30000

serializers

A List of Map settings, where each Map represents a MessageSerializer implementation to use along with its configuration.

none

serializers[X].className

The full class name of the MessageSerializer implementation.

none

serializers[X].config

A Map containing MessageSerializer specific configurations.

none

serializedResponseTimeout

The amount of time in milliseconds before a response serialization times out. The notion of "response serialization" refers to the time it takes for Gremlin Server to iterate an entire result after the script is evaluated in the ScriptEngine.

30000

ssl.enabled

Determines if SSL is turned on or not.

false

ssl.keyCertChainFile

The X.509 certificate chain file in PEM format. If this value is not present and ssl.enabled is true a self-signed certificate will be used (not suitable for production).

none

ssl.keyFile

The PKCS#8 private key file in PEM format. If this value is not present and ssl.enabled is true a self-signed certificate will be used (not suitable for production).

none

ssl.keyPassword

The password of the keyFile if it’s not password-protected

none

ssl.trustCertChainFile

Trusted certificates for verifying the remote endpoint’s certificate. The file should contain an X.509 certificate chain in PEM format. A system default will be used if this setting is not present.

none

strictTransactionManagement

Set to true to require aliases to be submitted on every requests, where the aliases become the scope of transaction management.

false

threadPoolBoss

The number of threads available to Gremlin Server for accepting connections. Should always be set to 1.

threadPoolWorker

The number of threads available to Gremlin Server for processing non-blocking reads and writes.

writeBufferHighWaterMark

If the number of bytes in the network send buffer exceeds this value then the channel is no longer writeable, accepting no additional writes until buffer is drained and the writeBufferLowWaterMark is met.

65536

writeBufferLowWaterMark

Once the number of bytes queued in the network send buffer exceeds the writeBufferHighWaterMark, the channel will not become writeable again until the buffer is drained and it drops below this value.

65536

Note

Configuration of Ganglia requires an additional library that is not packaged with Gremlin Server due to its LGPL licensing that conflicts with the TinkerPop’s Apache 2.0 License. To run Gremlin Server with Ganglia monitoring, download the org.acplt:oncrpc jar from here and copy it to the Gremlin Server /lib directory before starting the server.

Security

gremlin-server-secure Gremlin Server provides for several features that aid in the security of the graphs that it exposes. It has built in SSL support and a pluggable authentication framework using SASL (Simple Authentication and Security Layer). SSL options are described in the configuration settings table above, so this section will focus on authentication.

By default, Gremlin Server is configured to allow all requests to be processed (i.e. no authentication). To enable authentication, Gremlin Server must be configured with an Authenticator implementation in its YAML file. Gremlin Server comes packaged with an implementation called SimpleAuthenticator. The SimpleAuthenticator implements the PLAIN SASL mechanism (i.e. plain text) to authenticate a request. It validates username/password pairs against a graph database, which must be provided to it as part of the configuration.

authentication: {
  className: org.apache.tinkerpop.gremlin.server.auth.SimpleAuthenticator,
  config: {
    credentialsDb: conf/tinkergraph-credentials.properties}}

Quick Start

A quick way to get started with the SimpleAuthenticator is to use TinkerGraph for the "credentials graph" and the "sample" credential graph that is packaged with the server.

$ bin/gremlin-server.sh conf/gremlin-server-secure.yaml
[INFO] GremlinServer -
         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----

[INFO] GremlinServer - Configuring Gremlin Server from conf/gremlin-server-secure.yaml
...
[WARN] AbstractChannelizer - Enabling SSL with self-signed certificate (NOT SUITABLE FOR PRODUCTION)
[INFO] AbstractChannelizer - SSL enabled
[INFO] SimpleAuthenticator - Initializing authentication with the org.apache.tinkerpop.gremlin.server.auth.SimpleAuthenticator
[INFO] SimpleAuthenticator - CredentialGraph initialized at CredentialGraph{graph=tinkergraph[vertices:1 edges:0]}
[INFO] GremlinServer$1 - Gremlin Server configured with worker thread pool of 1, gremlin pool of 8 and boss thread pool of 1.
[INFO] GremlinServer$1 - Channel started at port 8182.

In addition to configuring the authenticator, gremlin-server-secure.yaml also enables SSL with a self-signed certificate. As SSL is enabled on the server it must also be enabled on the client when connecting. To connect to Gremlin Server with gremlin-driver, set the credentials and enableSsl when constructing the Cluster.

Cluster cluster = Cluster.build().credentials("stephen", "password")
                                 .enableSsl(true).create();

If connecting with Gremlin Console, which utilizes gremlin-driver for remote script execution, use the provided config/remote-secure.yaml file when defining the remote. That file contains configuration for the username and password as well as enablement of SSL from the client side.

Similarly, Gremlin Server can be configured for REST and security.

$ bin/gremlin-server.sh conf/gremlin-server-rest-secure.yaml
[INFO] GremlinServer -
         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----

[INFO] GremlinServer - Configuring Gremlin Server from conf/gremlin-server-secure.yaml
...
[WARN] AbstractChannelizer - Enabling SSL with self-signed certificate (NOT SUITABLE FOR PRODUCTION)
[INFO] AbstractChannelizer - SSL enabled
[INFO] SimpleAuthenticator - Initializing authentication with the org.apache.tinkerpop.gremlin.server.auth.SimpleAuthenticator
[INFO] SimpleAuthenticator - CredentialGraph initialized at CredentialGraph{graph=tinkergraph[vertices:1 edges:0]}
[INFO] GremlinServer$1 - Gremlin Server configured with worker thread pool of 1, gremlin pool of 8 and boss thread pool of 1.
[INFO] GremlinServer$1 - Channel started at port 8182.

Once the server has started, issue a request passing the credentials with an Authentication header, as described in RFC2617. Here’s a HTTP Basic authentication example with cURL:

curl -X POST --insecure -u stephen:password -d "{\"gremlin\":\"100-1\"}" "https://localhost:8182"

Credentials Graph DSL

The "credentials graph", which has been mentioned in previous sections, is used by Gremlin Server to hold the list of users who can authenticate to the server. It is possible to use virtually any Graph instance for this task as long as it complies to a defined schema. The credentials graph stores users as vertices with the label of "user". Each "user" vertex has two properties: username and password. Naturally, these are both String values. The password must not be stored in plain text and should be hashed.

Important

Be sure to define an index on the username property, as this will be used for lookups. If supported by the Graph, consider specifying a unique constraint as well.

To aid with the management of a credentials graph, Gremlin Server provides a Gremlin Console plugin which can be used to add and remove users so as to ensure that the schema is adhered to, thus ensuring compatibility with Gremlin Server. In addition, as it is a plugin, it works naturally in the Gremlin Console as an extension of its capabilities (though one could use it programmatically, if desired). This plugin is distributed with the Gremlin Console so it does not have to be "installed". It does however need to be activated:

gremlin> :plugin use tinkerpop.credentials
==>tinkerpop.credentials activated

Please see the example usage as follows:

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.createIndex("username",Vertex.class)
==>null
gremlin> credentials = credentials(graph)
==>CredentialGraph{graph=tinkergraph[vertices:0 edges:0]}
gremlin> credentials.createUser("stephen","password")
==>v[0]
gremlin> credentials.createUser("daniel","better-password")
==>v[3]
gremlin> credentials.createUser("marko","rainbow-dash")
==>v[6]
gremlin> credentials.findUser("marko").properties()
==>vp[password->$2a$10$oXwbWLZdfMjG9]
==>vp[username->marko]
gremlin> credentials.countUsers()
==>3
gremlin> credentials.removeUser("daniel")
==>1
gremlin> credentials.countUsers()
==>2

Script Execution

It is important to remember that Gremlin Server exposes a ScriptEngine instance that allows for remote execution of arbitrary code on the server. Obviously, this situation can represent a security risk or, more minimally, provide ways for "bad" scripts to be inadvertently executed. A simple example of a "valid" Gremlin script that would cause some problems would be, while(true) {}, which would consume a thread in the Gremlin pool indefinitely, thus preventing it from serving other requests. Sending enough of these kinds of scripts would eventually consume all available threads and Gremlin Server would stop responding.

Gremlin Server (more specifically the GremlinGroovyScriptEngine) provides methods to protect itself from these kinds of troublesome scripts. A user can configure the script engine with different CompilerCustomizerProvider implementations. Consider the basic configuration from the Gremlin Server YAML file:

scriptEngines: {
  gremlin-groovy: {
    imports: [java.lang.Math],
    staticImports: [java.lang.Math.PI],
    scripts: [scripts/empty-sample.groovy]}}

This configuration can be extended to include a config key as follows:

scriptEngines: {
  gremlin-groovy: {
    imports: [java.lang.Math],
    staticImports: [java.lang.Math.PI],
    scripts: [scripts/empty-sample.groovy],
    config: {
      compilerCustomizerProviders: {
        "org.apache.tinkerpop.gremlin.groovy.jsr223.customizer.TimedInterruptCustomizerProvider":[10000] }}}

This configuration sets up the script engine with a CompilerCustomizerProvider implementation. The TimedInterruptCustomizerProvider injects checks that ensure that loops (like while) can only execute for 10000 milliseconds. With this configuration in place, a remote execution as follows, now times out rather than consuming the thread continuously:

gremlin> :remote connect tinkerpop.server conf/remote.yaml
==>Configured localhost/127.0.0.1:8182
gremlin> :> while(true) { }
Execution timed out after 10000 units. Start time: Fri Jul 24 11:04:52 EDT 2015

There are a number of pre-packaged CustomizerProvider implementations:

Customizer Description

CompileStaticCustomizerProvider

Applies CompileStatic annotations to incoming scripts thus removing dynamic dispatch. More information about static compilation can be found in the Groovy Documentation. It is possible to configure this CustomizerProvider by specifying a comma separated list of type checking extensions that can have the effect of securing calls to various methods.

ThreadInterruptCustomizerProvider

Injects checks for thread interruption, thus allowing the thread to potentially respect calls to Thread.interrupt()

TimedInterruptCustomizerProvider

Injects checks into loops to interrupt them if they exceed the configured timeout in milliseconds.

TypeCheckedCustomizerProvider

Similar to the above mentioned, CompileStaticCustomizerProvider, the TypeCheckedCustomizerProvider injects TypeChecked annotations to incoming scripts. More information on the nature of this annotation can be found in the Groovy Documentation. It too takes a comma separated list of type checking extensions.

To provide some basic out-of-the-box protections against troublesome scripts, the following configuration can be used:

scriptEngines: {
  gremlin-groovy: {
    imports: [java.lang.Math],
    staticImports: [java.lang.Math.PI],
    scripts: [scripts/empty-sample.groovy],
    config: {
      compilerCustomizerProviders: {
        "org.apache.tinkerpop.gremlin.groovy.jsr223.customizer.ThreadInterruptCustomizerProvider":[],
        "org.apache.tinkerpop.gremlin.groovy.jsr223.customizer.TimedInterruptCustomizerProvider":[10000],
        "org.apache.tinkerpop.gremlin.groovy.jsr223.customizer.CompileStaticCustomizerProvider":["org.apache.tinkerpop.gremlin.groovy.jsr223.customizer.SimpleSandboxExtension"]}}}}

Note

The above configuration could also use the TypeCheckedCustomizerProvider in place of the CompileStaticCustomizerProvider. The differences between TypeChecked and CompileStatic are beyond the scope of this documentation. Consult the latest Groovy Documentation for information on the differences. It is important to understand the impact that these configuration will have on submitted scripts before enabling this feature.

Note

The import of classes to the script engine is handled by the ImportCustomizerProvider. As the concept of "imports" is a first-class citizen (i.e. has its own configuration options), it is not recommended that the ImportCustomizerProvider be used as a configuration option to compilerCustomizerProviders.

This configuration uses the SimpleSandboxExtension, which blacklists calls to methods on the System class, thereby preventing someone from remotely killing the server:

gremlin> :> System.exit(0)
Script8.groovy: 1: [Static type checking] - Not authorized to call this method: java.lang.System#exit(int)
 @ line 1, column 1.
   System.exit(0)
   ^

1 error

The SimpleSandboxExtension is by no means a "complete" implementation protecting against all manner of nefarious scripts, but it does provide an example for how such a capability might be implemented. A more complete implementation is offered in the FileSandboxExtension which uses a configuration file to white list certain classes and methods. The configuration file is YAML-based and an example is presented as follows:

autoTypeUnknown: true
methodWhiteList:
  - java\.lang\.Boolean.*
  - java\.lang\.Byte.*
  - java\.lang\.Character.*
  - java\.lang\.Double.*
  - java\.lang\.Enum.*
  - java\.lang\.Float.*
  - java\.lang\.Integer.*
  - java\.lang\.Long.*
  - java\.lang\.Math.*
  - java\.lang\.Number.*
  - java\.lang\.Object.*
  - java\.lang\.Short.*
  - java\.lang\.String.*
  - java\.lang\.StringBuffer.*
  - java\.lang\.System#currentTimeMillis\(\)
  - java\.lang\.System#nanoTime\(\)
  - java\.lang\.Throwable.*
  - java\.lang\.Void.*
  - java\.util\..*
  - org\.codehaus\.groovy\.runtime\.DefaultGroovyMethods.*
  - org\.codehaus\.groovy\.runtime\.InvokerHelper#runScript\(java\.lang\.Class,java\.lang\.String\[\]\)
  - org\.codehaus\.groovy\.runtime\.StringGroovyMethods.*
  - groovy\.lang\.Script#<init>\(groovy.lang.Binding\)
  - org\.apache\.tinkerpop\.gremlin\.structure\..*
  - org\.apache\.tinkerpop\.gremlin\.process\..*
  - org\.apache\.tinkerpop\.gremlin\.process\.computer\..*
  - org\.apache\.tinkerpop\.gremlin\.process\.computer\.bulkloading\..*
  - org\.apache\.tinkerpop\.gremlin\.process\.computer\.clustering\.peerpressure\.*
  - org\.apache\.tinkerpop\.gremlin\.process\.computer\.ranking\.pagerank\.*
  - org\.apache\.tinkerpop\.gremlin\.process\.computer\.traversal\..*
  - org\.apache\.tinkerpop\.gremlin\.process\.traversal\..*
  - org\.apache\.tinkerpop\.gremlin\.process\.traversal\.dsl\.graph\..*
  - org\.apache\.tinkerpop\.gremlin\.process\.traversal\.engine\..*
  - org\.apache\.tinkerpop\.gremlin\.server\.util\.LifeCycleHook.*
staticVariableTypes:
  graph: org.apache.tinkerpop.gremlin.structure.Graph
  g: org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource

There are three keys in this configuration file that control different aspects of the sandbox:

autoTypeUnknown - When set to true, unresolved variables are typed as Object.
methodWhiteList - A white list of classes and methods that follow a regex pattern which can then be matched against method descriptors to determine if they can be executed. The method descriptor is the fully-qualified class name of the method, its name and parameters. For example, Math.ceil would have a descriptor of java.lang.Math#ceil(double).
staticVariableTypes - A list of variables that will be used in the ScriptEngine for which the types are always known. In the above example, the variable "graph" will always be bound to a Graph instance.

At Gremlin Server startup, the FileSandboxExtension looks in the root of Gremlin Server installation directory for a file called sandbox.yaml and configures itself. To use a file in a different location set the gremlinServerSandbox system property to the location of the file (e.g. -DgremlinServerSandbox=conf/my-sandbox.yaml).

The FileSandboxExtension provides for a basic configurable security function in Gremlin Server. More complex sandboxing implementations can be developed by using this white listing model and extending from the AbstractSandboxExtension.

A final thought on the topic of CompilerCustomizerProvider implementations is that they are not just for "security" (though they are demonstrated in that capacity here). They can be used for a variety of features that can fine tune the Groovy compilation process. Read more about compilation customization in the Groovy Documentation.

Serialization

Gremlin Server can accept requests and return results using different serialization formats. The format of the serialization is configured by the serializers setting described in the table above. Note that some serializers have additional configuration options as defined by the serializers[X].config setting. The config setting is a Map where the keys and values get passed to the serializer at its initialization. The available and/or expected keys are dependent on the serializer being used. Gremlin Server comes packaged with two different serializers: GraphSON and Gryo.

GraphSON

The GraphSON serializer produces human readable output in JSON format and is a good configuration choice for those trying to use TinkerPop from non-JVM languages. JSON obviously has wide support across virtually all major programming languages and can be consumed by a wide variety of tools.

  - { className: org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerV1d0 }

The above configuration represents the default serialization under the application/json MIME type and produces JSON consistent with standard JSON data types. It has the following configuration option:

Key Description Default

useMapperFromGraph

Specifies the name of the Graph (from the graphs Map in the configuration file) from which to plugin any custom serializers that are tied to it.

none

  - { className: org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerGremlinV1d0 }

When the standard JSON data types are not enough (e.g. need to identify the difference between double and float data types), the above configuration will embed types into the JSON itself. The type embedding uses standard Java type names, so interpretation from non-JVM languages will be required. It has the MIME type of application/vnd.gremlin-v1.0+json and the following configuration options:

Key Description Default

useMapperFromGraph

Specifies the name of the Graph (from the graphs Map in the configuration file) from which to plugin any custom serializers that are tied to it.

none

Gryo

The Gryo serializer utilizes Kryo-based serialization which produces a binary output. This format is best consumed by JVM-based languages.

  - { className: org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerGremlinV1d0 }

It has the MIME type of application/vnd.gremlin-v1.0+gryo and the following configuration options:

Key Description Default

bufferSize

The maximum size of the Kryo buffer for use on a single object being serialized. Increasing this value will correct KryoException errors that complain of "Buffer too small".

4096

classResolverSupplier

The fully qualified classname of a custom Supplier<ClassResolver> which will be used when constructing Kryo instances. There is no direct default for this setting, but without a setting the GryoClassResolver is used.

none

custom

A list of classes with custom kryo Serializer implementations related to them in the form of <class>;<serializer-class>.

none

ioRegistries

A list of IoRegistry implementations to be applied to the serializer.

none

serializeResultToString

When set to true, results are serialized by first calling toString() on each object in the result list resulting in an extended MIME Type of application/vnd.gremlin-v1.0+gryo-stringd. When set to false Kryo-based serialization is applied.

false

useMapperFromGraph

Specifies the name of the Graph (from the graphs Map in the configuration file) from which to plugin any custom serializers that are tied to it.

none

As described above, there are multiple ways in which to register serializers for Kryo-based serialization. These configurations can be used in conjunction with one another where there is a specific ordering to how the configurations are applied. The userMapperFromGraph setting is applied first, followed by any ioRegistries and finalized by the custom setting.

Those configuring or implementing a Supplier<ClassResolver> should consider this an "advanced" option and typically important to use cases where server types need to be coerced to client types (i.e. a type is available on the server but not on the client). Implementations should typically instantiate ClassResolver implementations that are extensions of the GryoClassResolver as this class is important to most serialization tasks in TinkerPop.

Metrics

Gremlin Server produces metrics about its operations that can yield some insight into how it is performing. These metrics are exposed in a variety of ways:

Directly to the console where Gremlin Server is running
CSV file
Ganglia
Graphite
SLF4j
JMX

The configuration of each of these outputs is described in the Gremlin Server Configuring section. Regardless of the output, the metrics gathered are the same. Each metric is prefixed with org.apache.tinkerpop.gremlin.server.GremlinServer and the following metrics are reported:

sessions - the number of sessions open at the time the metric was last measured.
errors - the number of total errors, mean rate, as well as the 1, 5, and 15-minute error rates.
op.eval - the number of script evaluations, mean rate, 1, 5, and 15 minute rates, minimum, maximum, median, mean, and standard deviation evaluation times, as well as the 75th, 95th, 98th, 99th and 99.9th percentile evaluation times (note that these time apply to both sessionless and in-session requests).

Best Practices

The following sections define best practices for working with Gremlin Server.

Tuning

gremlin-handdrawn Tuning Gremlin Server for a particular environment may require some simple trial-and-error, but the following represent some basic guidelines that might be useful:

Gremlin Server defaults to a very modest maximum heap size. Consider increasing this value for non-trivial uses. Maximum heap size (-Xmx) is defined with the JAVA_OPTIONS setting in gremlin-server.sh.
When configuring the size of threadPoolWorker start with the default of 1 and increment by one as needed to a maximum of 2*number of cores.
The "right" size of the gremlinPool setting is somewhat dependent on the type of scripts that will be processed by Gremlin Server. As requests arrive to Gremlin Server they are decoded and queued to be processed by threads in this pool. When this pool is exhausted of threads, Gremlin Server will continue to accept incoming requests, but the queue will continue to grow. If left to grow too large, the server will begin to slow. When tuning around this setting, consider whether the bulk of the scripts being processed will be "fast" or "slow", where "fast" generally means being measured in the low hundreds of milliseconds and "slow" means anything longer than that.
- If the bulk of the scripts being processed are expected to be "fast", then a good starting point for this setting is 2*threadPoolWorker.
- If the bulk of the scripts being processed are expected to be "slow", then a good starting point for this setting is 4*threadPoolWorker.
Scripts that are "slow" can really hurt Gremlin Server if they are not properly accounted for. ScriptEngine evaluations are blocking operations that aren’t easily interrupted, so once a "slow" script is being evaluated in the context of a ScriptEngine it must finish its work. Lots of "slow" scripts will eventually consume the gremlinPool preventing other scripts from getting processed from the queue.
- To limit the impact of this problem consider properly setting the scriptEvaluationTimeout and the serializedResponseTimeout to something "sane".
- Test the traversals being sent to Gremlin Server and determine the maximum time they take to evaluate and iterate over results, then set these configurations accordingly.
- Note that scriptEvaluationTimeout does not interrupt the evaluation on timeout. It merely allows Gremlin Server to "ignore" the result of that evaluation, which means the thread in the gremlinPool will still be consumed after the timeout.
- The serializedResponseTimeout will kill the result iteration process and prevent additional processing. In most situations, the iteration and serialization process is the more costly step in this process as an errant script that returns a million or more results could send Gremlin Server into a long streaming cycle. Script evaluation on the other hand is usually very fast, occurring on the order of milliseconds, but that is entirely dependent on the contents of the script itself.

Parameterized Scripts

gremlin-parameterized Use script parameterization. Period. Gremlin Server caches all scripts that are passed to it. The cache is keyed based on the a hash of the script. Therefore g.V(1) and g.V(2) will be recognized as two separate scripts in the cache. If that script is parameterized to g.V(x) where x is passed as a parameter from the client, there will be no additional compilation cost for future requests on that script. Compilation of a script should be considered "expensive" and avoided when possible.

Cluster cluster = Cluster.open();
Client client = cluster.connect();

Map<String,Object> params = new HashMap<>();
params.put("x",4);
client.submit("[1,2,3,x]", params);

Cache Management

If Gremlin Server processes a large number of unique scripts, the cache will grow beyond the memory available to Gremlin Server and an OutOfMemoryError will loom. Script parameterization goes a long way to solving this problem and running out of memory should not be an issue for those cases. If it is a problem or if there is no script parameterization due to a given use case (perhaps using with use of sessions), it is possible to better control the nature of the script cache from the client side, by issuing scripts with a parameter to help define how the garbage collector should treat the references.

The parameter is called #jsr223.groovy.engine.keep.globals and has four options:

hard - available in the cache for the life of the JVM (default when not specified).
soft - retained until memory is "low" and should be reclaimed before an OutOfMemoryError is thrown.
weak - garbage collected even when memory is abundant.
phantom - removed immediately after being evaluated by the ScriptEngine.

By specifying an option other than hard, an OutOfMemoryError in Gremlin Server should be avoided. Of course, this approach will come with the downside that compiled scripts could be garbage collected and thus removed from the cache, forcing Gremlin Server to recompile later if that script is later encountered.

Considering Sessions

The preferred approach for issuing requests to Gremlin Server is to do so in a sessionless manner. The concept of "sessionless" refers to a request that is completely encapsulated within a single transaction, such that the script in the request starts with a new transaction and ends with a closed transaction. Sessionless requests have automatic transaction management handled by Gremlin Server, thus automatically opening and closing transactions as previously described. The downside to the sessionless approach is that the entire script to be executed must be known at the time of submission so that it can all be executed at once. This requirement makes it difficult for some use cases where more control over the transaction is desired.

For such use cases, Gremlin Server supports sessions. With sessions, the user is in complete control of the start and end of the transaction. This feature comes with some additional expense to consider:

Initialization scripts will be executed for each session created so any expense related to them will be established each time a session is constructed.
There will be one script cache per session, which obviously increases memory requirements. The cache is not shared, so as to ensure that a session has isolation from other session environments. As a result, if the same script is executed in each session the same compilation cost will be paid for each session it is executed in.
Each session will require its own thread pool with a single thread in it - this ensures that transactional boundaries are managed properly from one request to the next.
If there are multiple Gremlin Server instances, communication from the client to the server must be bound to the server that the session was initialized in. Gremlin Server does not share session state as the transactional context of a Graph is bound to the thread it was initialized in.

To connect to a session with Java via the gremlin-driver, it is necessary to create a SessionedClient from the Cluster object:

Cluster cluster = Cluster.open();  (1)
Client client = cluster.connect("sessionName"); (2)

Opens a reference to localhost as previously shown.
Creates a SessionedClient given the configuration options of the Cluster. The connect() method is given a String value that becomes the unique name of the session. It is often best to simply use a UUID to represent the session.

It is also possible to have Gremlin Server manage the transactions as is done with sessionless requests. The user is in control of enabling this feature when creating the SessionedClient:

Cluster cluster = Cluster.open();
Client client = cluster.connect("sessionName", true);

Specifying true to the connect() method signifies that the client should make each request as one encapsulated in a transaction. With this configuration of client there is no need to close a transaction manually.

When using this mode of the SessionedClient it is important to recognize that global variable state for the session is not rolled-back on failure depending on where the failure occurs. For example, sending the following script would create a variable "x" in global session scope that would be acccessible on the next request:

x = 1

However, sending this script which explicitly throws an exception:

y = 2
throw new RuntimeException()

will result in an obvious failure during script evaluation and "y" will not be available to the next request. The complication arises where the script evaluates successfully, but fails during result iteration or serialization. For example, this script:

a = 1
g.addV()

would sucessfully evaluate and return a Traversal. The variable "a" would be available on the next request. However, if there was a failure in transaction management on the call to commit(), "a" would still be available to the next request.

A session is a "heavier" approach to the simple "request/response" approach of sessionless requests, but is sometimes necessary for a given use case.

Considering Transactions

Gremlin Server performs automated transaction handling for "sessionless" requests (i.e. no state between requests) and for "in-session" requests with that feature enabled. It will automatically commit or rollback transactions depending on the success or failure of the request.

Another aspect of Transaction Management that should be considered is the usage of the strictTransactionManagement setting. It is false by default, but when set to true, it forces the user to pass aliases for all requests. The aliases are then used to determine which graphs will have their transactions closed for that request. Running Gremlin Server in this configuration should be more efficient when there are multiple graphs being hosted as Gremlin Server will only close transactions on the graphs specified by the aliases. Keeping this setting false, will simply have Gremlin Server close transactions on all graphs for every request.

Considering State

With REST and any sessionless requests, there is no variable state maintained between requests. Therefore, when connecting with the console, for example, it is not possible to create a variable in one command and then expect to access it in the next:

gremlin> :remote connect tinkerpop.server conf/remote.yaml
==>Configured localhost/127.0.0.1:8182
gremlin> :> x = 2
==>2
gremlin> :> 2 + x
No such property: x for class: Script4
Display stack trace? [yN] n

The same behavior would be seen with REST or when using sessionless requests through one of the Gremlin Server drivers. If having this behavior is desireable, then consider sessions.

There is an exception to this notion of state not existing between requests and that is globally defined functions. All functions created via scripts are global to the server.

gremlin> :> def subtractIt(int x, int y) { x - y }
==>null
gremlin> :> subtractIt(8,7)
==>1

If this behavior is not desirable there are several options. A first option would be to consider using sessions. Each session gets its own ScriptEngine, which maintains its own isolated cache of global functions, whereas sessionless requests uses a single function cache. A second option would be to define functions as closures:

gremlin> :> multiplyIt = { int x, int y -> x * y }
==>Script7$_run_closure1@6b24f3ab
gremlin> :> multiplyIt(7, 8)
No signature of method: org.apache.tinkerpop.gremlin.groovy.jsr223.GremlinGroovyScriptEngine.multiplyIt() is applicable for argument types: (java.lang.Integer, java.lang.Integer) values: [7, 8]
Display stack trace? [yN]

When the function is declared this way, the function is viewed by the ScriptEngine as a variable rather than a global function and since sessionless requests don’t maintain state, the function is forgotten for the next request. A final option would be to manage the ScriptEngine cache manually:

$ curl -X POST -d "{\"gremlin\":\"def divideIt(int x, int y){ x / y }\",\"bindings\":{\"#jsr223.groovy.engine.keep.globals\":\"phantom\"}}" "http://localhost:8182"
{"requestId":"97fe1467-a943-45ea-8fd6-9e889a6c9381","status":{"message":"","code":200,"attributes":{}},"result":{"data":[null],"meta":{}}}
$ curl -X POST -d "{\"gremlin\":\"divideIt(8, 2)\"}" "http://localhost:8182"
{"message":"Error encountered evaluating script: divideIt(8, 2)"}

In the above REST-based requests, the bindings contain a special parameter that tells the ScriptEngine cache to immediately forget the script after execution. In this way, the function does not end up being globally available.

Gremlin Plugins

gremlin-plugin

Plugins provide a way to expand the features of Gremlin Console and Gremlin Server. The following sections describe the plugins that are available directly from TinkerPop. Please see the Provider Documentation for information on how to develop custom plugins.

Credentials Plugin

gremlin-server Gremlin Server supports an authentication model where user credentials are stored inside of a Graph instance. This database can be managed with the Credentials DSL, which can be installed in the console via the Credentials Plugin. This plugin is packaged with the console, but is not enabled by default.

gremlin> :plugin use tinkerpop.credentials
==>tinkerpop.credentials activated

This plugin imports the appropriate classes for managing the credentials graph.

Gephi Plugin

Gephi is an interactive visualization, exploration, and analysis platform for graphs. The Graph Streaming plugin for Gephi provides an API that can be leveraged to stream graphs and visualize traversals interactively through the Gremlin Gephi Plugin.

The following instructions assume that Gephi has been download and installed. It further assumes that the Graph Streaming plugin has been installed (Tools > Plugins). The following instructions explain how to visualize a Graph and Traversal.

In Gephi, create a new project with File > New Project. In the lower left view, click the "Streaming" tab, open the Master drop down, and right click Master Server > Start which starts the Graph Streaming server in Gephi and by default accepts requests at http://localhost:8080/workspace0:

Important

The Gephi Streaming Plugin doesn’t detect port conflicts and will appear to start the plugin successfully even if there is something already active on that port it wants to connect to (which is 8080 by default). Be sure that there is nothing running on the port before Gephi will be using before starting the plugin. Failing to do this produce behavior where the console will appear to submit requests to Gephi successfully but nothing will render.

Start the Gremlin Console and activate the Gephi plugin:

gremlin> :plugin use tinkerpop.gephi
==>tinkerpop.gephi activated
gremlin> graph = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> :remote connect tinkerpop.gephi
==>Connection to Gephi - http://localhost:8080/workspace0 with stepDelay:1000, startRGBColor:[0.0, 1.0, 0.5], colorToFade:g, colorFadeRate:0.7, startSize:20.0,sizeDecrementRate:0.33
gremlin> :> graph
==>tinkergraph[vertices:6 edges:6]
==>false

The above Gremlin session activates the Gephi plugin, creates the "modern" TinkerGraph, uses the :remote command to setup a connection to the Graph Streaming server in Gephi (with default parameters that will be explained below), and then uses :submit which sends the vertices and edges of the graph to the Gephi Streaming Server. The resulting graph appears in Gephi as displayed in the left image below.

Note	Issuing `:> graph` again will clear the Gephi workspace and then re-write the graph. To manually empty the workspace do `:> clear`.

Now that the graph is visualized in Gephi, it is possible to apply a layout algorithm, change the size and/or color of vertices and edges, and display labels/properties of interest. Further information can be found in Gephi’s tutorial on Visualization. After applying the Fruchterman Reingold layout, increasing the node size, decreasing the edge scale, and displaying the id, name, and weight attributes the graph looks as displayed in the right image above.

Visualization of a Traversal has a different approach as the visualization occurs as the Traversal is executing, thus showing a real-time view of its execution. A Traversal must be "configured" to operate in this format and for that it requires use of the visualTraversal option on the config function of the :remote command:

gremlin> :remote config visualTraversal graph //(1)
==>Connection to Gephi - http://localhost:8080/workspace0 with stepDelay:1000, startRGBColor:[0.0, 1.0, 0.5], colorToFade:g, colorFadeRate:0.7, startSize:20.0,sizeDecrementRate:0.33
gremlin> traversal = vg.V(2).in().out('knows').
                             has('age',gt(30)).outE('created').
                             has('weight',gt(0.5d)).inV();null
==>null
gremlin> :> traversal //(2)
==>v[5]
==>false

Configure a "visual traversal" from your "graph" - this must be a Graph instance.
Submit the Traversal to visualize to Gephi.

When the :> line is called, each step of the Traversal that produces or filters vertices generates events to Gephi. The events update the color and size of the vertices at that step with startRGBColor and startSize respectively. After the first step visualization, it sleeps for the configured stepDelay in milliseconds. On the second step, it decays the configured colorToFade of all the previously visited vertices in prior steps, by multiplying the current colorToFade value for each vertex with the colorFadeRate. Setting the colorFadeRate value to 1.0 will prevent the color decay. The screenshots below show how the visualization evolves over the four steps:

To get a sense of how the visualization configuration parameters affect the output, see the example below:

gremlin> :remote config startRGBColor [0.0,0.3,1.0]
==>Connection to Gephi - http://localhost:8080/workspace0 with stepDelay:1000, startRGBColor:[0.0, 0.3, 1.0], colorToFade:g, colorFadeRate:0.7, startSize:20.0,sizeDecrementRate:0.33
gremlin> :remote config colorToFade b
==>Connection to Gephi - http://localhost:8080/workspace0 with stepDelay:1000, startRGBColor:[0.0, 0.3, 1.0], colorToFade:b, colorFadeRate:0.7, startSize:20.0,sizeDecrementRate:0.33
gremlin> :remote config colorFadeRate 0.5
==>Connection to Gephi - http://localhost:8080/workspace0 with stepDelay:1000, startRGBColor:[0.0, 0.3, 1.0], colorToFade:b, colorFadeRate:0.5, startSize:20.0,sizeDecrementRate:0.33
gremlin> :> traversal
==>false

The visualization configuration above starts with a blue color now (most recently visited), fading the blue color (so that dark green remains on oldest visited), and fading the blue color more quickly so that the gradient from dark green to blue across steps has higher contrast. The following table provides a more detailed description of the Gephi plugin configuration parameters as accepted via the :remote config command:

Parameter Description Default

workspace

The name of the workspace that your Graph Streaming server is started for.

workspace0

host

The host URL where the Graph Streaming server is configured for.

localhost

port

The port number of the URL that the Graph Streaming server is listening on.

8080

sizeDecrementRate

The rate at which the size of an element decreases on each step of the visualization.

0.33

stepDelay

The amount of time in milliseconds to pause between step visualizations.

1000

startRGBColor

A size 3 float array of RGB color values which define the starting color to update most recently visited nodes with.

[0.0,1.0,0.5]

startSize

The size an element should be when it is most recently visited.

colorToFade

A single char from the set {r,g,b,R,G,B} determining which color to fade for vertices visited in prior steps

colorFadeRate

A float value in the range (0.0,1.0] which is multiplied against the current colorToFade value for prior vertices; a 1.0 value effectively turns off the color fading of prior step visited vertices

0.7

Server Plugin

gremlin-server Gremlin Server remotely executes Gremlin scripts that are submitted to it. The Server Plugin provides a way to submit scripts to Gremlin Server for remote processing. Read more about the plugin and how it works in the Gremlin Server section on Connecting via Console.

Note	The Server Plugin is enabled in the Gremlin Console by default.

Sugar Plugin

gremlin-sugar In previous versions of Gremlin-Groovy, there were numerous syntactic sugars that users could rely on to make their traversals more succinct. Unfortunately, many of these conventions made use of Java reflection and thus, were not performant. In TinkerPop3, these conveniences have been removed in support of the standard Gremlin-Groovy syntax being both inline with Gremlin-Java8 syntax as well as always being the most performant representation. However, for those users that would like to use the previous syntactic sugars (as well as new ones), there is SugarGremlinPlugin (a.k.a Gremlin-Groovy-Sugar).

Important

It is important that the sugar plugin is loaded in a Gremlin Console session prior to any manipulations of the respective TinkerPop3 objects as Groovy will cache unavailable methods and properties.

gremlin> :plugin use tinkerpop.sugar
==>tinkerpop.sugar activated

Tip	When using Sugar in a Groovy class file, add `static { SugarLoader.load() }` to the head of the file. Note that `SugarLoader.load()` will automatically call `GremlinLoader.load()`.

Graph Traversal Methods

If a GraphTraversal property is unknown and there is a corresponding method with said name off of GraphTraversal then the property is assumed to be a method call. This enables the user to omit ( ) from the method name. However, if the property does not reference a GraphTraversal method, then it is assumed to be a call to values(property).

gremlin> g.V //(1)
==>v[1]
==>v[2]
==>v[3]
==>v[4]
==>v[5]
==>v[6]
gremlin> g.V.name //(2)
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter
gremlin> g.V.outE.weight //(3)
==>0.4
==>0.5
==>1.0
==>1.0
==>0.4
==>0.2

There is no need for the parentheses in g.V().
The traversal is interpreted as g.V().values('name').
A chain of zero-argument step calls with a property value call.

Range Queries

The [x] and [x..y] range operators in Groovy translate to RangeStep calls.

gremlin> g.V[0..2]
==>v[1]
==>v[2]
gremlin> g.V[0..<2]
==>v[1]
gremlin> g.V[2]
==>v[3]

Logical Operators

The & and | operator are overloaded in SugarGremlinPlugin. When used, they introduce the AndStep and OrStep markers into the traversal. See and() and or() for more information.

gremlin> g.V.where(outE('knows') & outE('created')).name //(1)
==>marko
gremlin> t = g.V.where(outE('knows') | inE('created')).name; null //(2)
==>null
gremlin> t.toString()
==>[GraphStep([],vertex), TraversalFilterStep([VertexStep(OUT,[knows],edge), OrStep, VertexStep(IN,[created],edge)]), PropertiesStep([name],value)]
gremlin> t
==>marko
==>lop
==>ripple
gremlin> t.toString()
==>[TinkerGraphStep([],vertex), TraversalFilterStep([OrStep([[VertexStep(OUT,[knows],edge)], [VertexStep(IN,[created],edge)]])]), PropertiesStep([name],value)]

Introducing the AndStep with the & operator.
Introducing the OrStep with the | operator.

Traverser Methods

It is rare that a user will ever interact with a Traverser directly. However, if they do, some method redirects exist to make it easy.

gremlin> g.V().map{it.get().value('name')}  // conventional
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter
gremlin> g.V.map{it.name}  // sugar
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter

Utilities Plugin

The Utilities Plugin provides various functions, helper methods and imports of external classes that are useful in the console.

Note	The Utilities Plugin is enabled in the Gremlin Console by default.

Benchmarking and Profiling

The GPerfUtils library provides a number of performance utilities for Groovy. Specifically, these tools cover benchmarking and profiling.

Benchmarking allows execution time comparisons of different pieces of code. While such a feature is generally useful, in the context of Gremlin, benchmarking can help compare traversal performance times to determine the optimal approach. Profiling helps determine the parts of a program which are taking the most execution time, yielding low-level insight into the code being examined.

gremlin> :plugin use tinkerpop.sugar // Activate sugar plugin for use in benchmark
==>Specify the name of the plugin to use
gremlin> benchmark{
          'sugar' {g.V(1).name.next()}
          'nosugar' {g.V(1).values('name').next()}
gremlin> }.prettyPrint()
Environment
===========
* Groovy: 2.4.6
* JVM: Java HotSpot(TM) 64-Bit Server VM (25.101-b13, Oracle Corporation)
    * JRE: 1.8.0_101
    * Total Memory: 835.5 MB
    * Maximum Memory: 1776 MB
* OS: Linux (4.4.16-27.56.amzn1.x86_64, amd64)

Options
=======
* Warm Up: Auto (- 60 sec)
* CPU Time Measurement: On

         user  system   cpu  real

sugar    8350       0  8350  8364
nosugar  4627       0  4627  4646
==>null
gremlin> profile { g.V().iterate() }.prettyPrint()
Flat:

 %    cumulative   self            self     total    self    total   self    total
time   seconds    seconds  calls  ms/call  ms/call  min ms  min ms  max ms  max ms  name
51.2        0.00     0.00      1     0.46     0.89    0.46    0.89    0.46    0.89  groovysh_evaluate$_run_closure1.doCall
39.6        0.00     0.00      1     0.35     0.35    0.35    0.35    0.35    0.35  org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.DefaultGraphTraversal.iterate
 9.1        0.00     0.00      1     0.08     0.08    0.08    0.08    0.08    0.08  org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource.V

Call graph:

index  % time  self  children  calls  name
               0.00      0.00    1/1      <spontaneous>
[1]     100.0  0.00      0.00      1  groovysh_evaluate$_run_closure1.doCall [1]
               0.00      0.00    1/1      org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.DefaultGraphTraversal.iterate [2]
               0.00      0.00    1/1      org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource.V [3]
------------------------------------------------------------------------------------------------------------------------------------
               0.00      0.00    1/1      groovysh_evaluate$_run_closure1.doCall [1]
[2]      39.6  0.00      0.00      1  org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.DefaultGraphTraversal.iterate [2]
------------------------------------------------------------------------------------------------------------------------------------
               0.00      0.00    1/1      groovysh_evaluate$_run_closure1.doCall [1]
[3]       9.1  0.00      0.00      1  org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource.V [3]
------------------------------------------------------------------------------------------------------------------------------------
==>null

Describe Graph

A good implementation of the Gremlin APIs will validate their features against the Gremlin test suite. To learn more about a specific implementation’s compliance with the test suite, use the describeGraph function. The following shows the output for HadoopGraph:

gremlin> describeGraph(HadoopGraph)
==>
IMPLEMENTATION - org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
TINKERPOP TEST SUITE
- Compliant with (5 of 10 suites)
> org.apache.tinkerpop.gremlin.structure.StructureStandardSuite
> org.apache.tinkerpop.gremlin.process.ProcessStandardSuite
> org.apache.tinkerpop.gremlin.process.ProcessComputerSuite
> org.apache.tinkerpop.gremlin.process.GroovyProcessStandardSuite
> org.apache.tinkerpop.gremlin.process.GroovyProcessComputerSuite
- Opts out of 29 individual tests
> org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$Traversals#g_V_matchXa_hasXname_GarciaX__a_0writtenBy_b__a_0sungBy_bX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$Traversals#g_V_matchXa_0sungBy_b__a_0sungBy_c__b_writtenBy_d__c_writtenBy_e__d_hasXname_George_HarisonX__e_hasXname_Bob_MarleyXX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$Traversals#g_V_matchXa_0sungBy_b__a_0writtenBy_c__b_writtenBy_d__c_sungBy_d__d_hasXname_GarciaXX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$Traversals#g_V_matchXa_0sungBy_b__a_0writtenBy_c__b_writtenBy_dX_whereXc_sungBy_dX_whereXd_hasXname_GarciaXX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$Traversals#g_V_matchXa_knows_b__c_knows_bX
        "Giraph does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$Traversals#g_V_matchXa_created_b__c_created_bX_selectXa_b_cX_byXnameX
        "Giraph does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$Traversals#g_V_out_asXcX_matchXb_knows_a__c_created_eX_selectXcX
        "Giraph does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyMatchTest$Traversals#g_V_matchXa_hasXname_GarciaX__a_0writtenBy_b__a_0sungBy_bX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyMatchTest$Traversals#g_V_matchXa_knows_b__c_knows_bX
        "Giraph does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyMatchTest$Traversals#g_V_matchXa_created_b__c_created_bX_selectXa_b_cX_byXnameX
        "Giraph does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyMatchTest$Traversals#g_V_out_asXcX_matchXb_knows_a__c_created_eX_selectXcX
        "Giraph does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyMatchTest$Traversals#g_V_matchXa_0sungBy_b__a_0sungBy_c__b_writtenBy_d__c_writtenBy_e__d_hasXname_George_HarisonX__e_hasXname_Bob_MarleyXX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyMatchTest$Traversals#g_V_matchXa_0sungBy_b__a_0writtenBy_c__b_writtenBy_d__c_sungBy_d__d_hasXname_GarciaXX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyMatchTest$Traversals#g_V_matchXa_0sungBy_b__a_0writtenBy_c__b_writtenBy_dX_whereXc_sungBy_dX_whereXd_hasXname_GarciaXX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.CountTest$Traversals#g_V_both_both_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.CountTest$Traversals#g_V_repeatXoutX_timesX3X_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.CountTest$Traversals#g_V_repeatXoutX_timesX8X_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.CountTest$Traversals#g_V_repeatXoutX_timesX5X_asXaX_outXwrittenByX_asXbX_selectXa_bX_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyCountTest$Traversals#g_V_both_both_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyCountTest$Traversals#g_V_repeatXoutX_timesX3X_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyCountTest$Traversals#g_V_repeatXoutX_timesX8X_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.map.GroovyCountTest$Traversals#g_V_repeatXoutX_timesX5X_asXaX_outXwrittenByX_asXbX_selectXa_bX_count
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.sideEffect.ProfileTest$Traversals#g_V_out_out_profile_grateful
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.sideEffect.GroovyProfileTest$Traversals#g_V_out_out_profile_grateful
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.sideEffect.GroupTest#g_V_repeatXbothXfollowedByXX_timesX2X_group_byXsongTypeX_byXcountX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.sideEffect.GroupTest#g_V_repeatXbothXfollowedByXX_timesX2X_groupXaX_byXsongTypeX_byXcountX_capXaX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.sideEffect.GroupTestV3d0#g_V_repeatXbothXfollowedByXX_timesX2X_group_byXsongTypeX_byXcountX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.traversal.step.sideEffect.GroupTestV3d0#g_V_repeatXbothXfollowedByXX_timesX2X_groupXaX_byXsongTypeX_byXcountX_capXaX
        "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
> org.apache.tinkerpop.gremlin.process.computer.GraphComputerTest#shouldStartAndEndWorkersForVertexProgramAndMapReduce
        "Spark executes map and combine in a lazy fashion and thus, fails the blocking aspect of this test"
- NOTE -
The describeGraph() function shows information about a Graph implementation.
It uses information found in Java Annotations on the implementation itself to
determine this output and does not assess the actual code of the test cases of
the implementation itself.  Compliant implementations will faithfully and
honestly supply these Annotations to provide the most accurate depiction of
their support.

Gremlin Archetypes

TinkerPop has a number of Maven archetypes, which provide example project templates to quickly get started with TinkerPop. The available archetypes are as follows:

gremlin-archetype-server - An example project that demonstrates the basic structure of a Gremlin Server project, how to connect with the Gremlin Driver, and how to embed Gremlin Server in a testing framework.
gremlin-archetype-tinkergraph - A basic example of how to structure a TinkerPop project with Maven.

You can use Maven to generate these example projects with a command like:

$ mvn archetype:generate -DarchetypeGroupId=org.apache.tinkerpop -DarchetypeArtifactId=gremlin-archetype-server
      -DarchetypeVersion=3.1.4-SNAPSHOT -DgroupId=com.my -DartifactId=app -Dversion=0.1 -DinteractiveMode=false

This command will generate a new Maven project in a directory called "app" with a pom.xml specifying a groupId of com.my. Please see the README.asciidoc in the root of each generated project for information on how to build and execute it.

Implementations

TinkerPop offers several reference implementations of its interfaces that are not only meant for production usage, but also represent models by which different graph providers can build their systems. More specific documentation on how to build systems at this level of the API can be found in the Provider Documentation. The following sections describe the various reference implementations and their usage.

TinkerGraph-Gremlin

<dependency>
   <groupId>org.apache.tinkerpop</groupId>
   <artifactId>tinkergraph-gremlin</artifactId>
   <version>3.1.4-SNAPSHOT</version>
</dependency>

tinkerpop-character TinkerGraph is a single machine, in-memory (with optional persistence), non-transactional graph engine that provides both OLTP and OLAP functionality. It is deployed with TinkerPop3 and serves as the reference implementation for other providers to study in order to understand the semantics of the various methods of the TinkerPop3 API. Constructing a simple graph in Java8 is presented below.

Graph g = TinkerGraph.open();
Vertex marko = g.addVertex("name","marko","age",29);
Vertex lop = g.addVertex("name","lop","lang","java");
marko.addEdge("created",lop,"weight",0.6d);

The above graph creates two vertices named "marko" and "lop" and connects them via a created-edge with a weight=0.6 property. Next, the graph can be queried as such.

g.V().has("name","marko").out("created").values("name")

The g.V().has("name","marko") part of the query can be executed in two ways.

A linear scan of all vertices filtering out those vertices that don’t have the name "marko"
A O(log(|V|)) index lookup for all vertices with the name "marko"

Given the initial graph construction in the first code block, no index was defined and thus, a linear scan is executed. However, if the graph was constructed as such, then an index lookup would be used.

Graph g = TinkerGraph.open();
g.createIndex("name",Vertex.class)

The execution times for a vertex lookup by property is provided below for both no-index and indexed version of TinkerGraph over the Grateful Dead graph.

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> clock(1000) {g.V().has('name','Garcia').iterate()} //(1)
==>0.226465163
gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
gremlin> graph.createIndex('name',Vertex.class)
==>null
gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> clock(1000){g.V().has('name','Garcia').iterate()} //(2)
==>0.025819892

Determine the average runtime of 1000 vertex lookups when no name-index is defined.
Determine the average runtime of 1000 vertex lookups when a name-index is defined.

Important

Each graph system will have different mechanism by which indices and schemas are defined. TinkerPop3 does not require any conformance in this area. In TinkerGraph, the only definitions are around indices. With other graph systems, property value types, indices, edge labels, etc. may be required to be defined a priori to adding data to the graph.

Note	TinkerGraph is distributed with Gremlin Server and is therefore automatically available to it for configuration.

Configuration

TinkerGraph has several settings that can be provided on creation via Configuration object:

Property Description

gremlin.graph

org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph

gremlin.tinkergraph.vertexIdManager

The IdManager implementation to use for vertices.

gremlin.tinkergraph.edgeIdManager

The IdManager implementation to use for edges.

gremlin.tinkergraph.vertexPropertyIdManager

The IdManager implementation to use for vertex properties.

gremlin.tinkergraph.defaultVertexPropertyCardinality

The default VertexProperty.Cardinality to use when Vertex.property(k,v) is called.

gremlin.tinkergraph.graphLocation

The path and file name for where TinkerGraph should persist the graph data. If a value is specified here, the the gremlin.tinkergraph.graphFormat should also be specified. If this value is not included (default), then the graph will stay in-memory and not be loaded/persisted to disk.

gremlin.tinkergraph.graphFormat

The format to use to serialize the graph which may be one of the following: graphml, graphson, gryo, or a fully qualified class name that implements Io.Builder interface (which allows for external third party graph reader/writer formats to be used for persistence). If a value is specified here, then the gremlin.tinkergraph.graphLocation should also be specified. If this value is not included (default), then the graph will stay in-memory and not be loaded/persisted to disk.

The IdManager settings above refer to how TinkerGraph will control identifiers for vertices, edges and vertex properties. There are several options for each of these settings: ANY, LONG, INTEGER, UUID, or the fully qualified class name of an IdManager implementation on the classpath. When not specified, the default values for all settings is ANY, meaning that the graph will work with any object on the JVM as the identifier and will generate new identifiers from Long when the identifier is not user supplied. TinkerGraph will also expect the user to understand the types used for identifiers when querying, meaning that g.V(1) and g.V(1L) could return two different vertices. LONG, INTEGER and UUID settings will try to coerce identifier values to the expected type as well as generate new identifiers with that specified type.

If the TinkerGraph is configured for persistence with gremlin.tinkergraph.graphLocation and gremlin.tinkergraph.graphFormat, then the graph will be written to the specified location with the specified format when Graph.close() is called. In addition, if these settings are present, TinkerGraph will attempt to load the graph from the specified location.

Important

If choosing graphson as the gremlin.tinkergraph.graphFormat, be sure to also establish the various IdManager settings as well to ensure that identifiers are properly coerced to the appropriate types as GraphSON can lose the identifier’s type during serialization (i.e. it will assume Integer when the default for TinkerGraph is Long, which could lead to load errors that result in a message like, "Vertex with id already exists").

It is important to consider the data being imported to TinkerGraph with respect to defaultVertexPropertyCardinality setting. For example, if a .gryo file is known to contain multi-property data, be sure to set the default cardinality to list or else the data will import as single. Consider the following:

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.io(gryo()).readGraph("data/tinkerpop-crew.kryo")
==>null
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:6 edges:14], standard]
gremlin> g.V().properties()
==>vp[name->marko]
==>vp[location->santa fe]
==>vp[name->stephen]
==>vp[location->purcellville]
==>vp[name->matthias]
==>vp[location->seattle]
==>vp[name->daniel]
==>vp[location->aachen]
==>vp[name->gremlin]
==>vp[name->tinkergraph]
gremlin> conf = new BaseConfiguration()
==>org.apache.commons.configuration.BaseConfiguration@14924f41
gremlin> conf.setProperty("gremlin.tinkergraph.defaultVertexPropertyCardinality","list")
==>null
gremlin> graph = TinkerGraph.open(conf)
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.io(gryo()).readGraph("data/tinkerpop-crew.kryo")
==>null
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:6 edges:14], standard]
gremlin> g.V().properties()
==>vp[name->marko]
==>vp[location->san diego]
==>vp[location->santa cruz]
==>vp[location->brussels]
==>vp[location->santa fe]
==>vp[name->stephen]
==>vp[location->centreville]
==>vp[location->dulles]
==>vp[location->purcellville]
==>vp[name->matthias]
==>vp[location->bremen]
==>vp[location->baltimore]
==>vp[location->oakland]
==>vp[location->seattle]
==>vp[name->daniel]
==>vp[location->spremberg]
==>vp[location->kaiserslautern]
==>vp[location->aachen]
==>vp[name->gremlin]
==>vp[name->tinkergraph]

Neo4j-Gremlin

<dependency>
   <groupId>org.apache.tinkerpop</groupId>
   <artifactId>neo4j-gremlin</artifactId>
   <version>3.1.4-SNAPSHOT</version>
</dependency>
<!-- neo4j-tinkerpop-api-impl is NOT Apache 2 licensed - more information below -->
<dependency>
  <groupId>org.neo4j</groupId>
  <artifactId>neo4j-tinkerpop-api-impl</artifactId>
  <version>0.1-2.2</version>
</dependency>

Neo Technology are the developers of the OLTP-based Neo4j graph database.

Warning

Unless under a commercial agreement with Neo Technology, Neo4j is licensed AGPL. The neo4j-gremlin module is licensed Apache2 because it only references the Apache2-licensed Neo4j API (not its implementation). Note that neither the Gremlin Console nor Gremlin Server distribute with the Neo4j implementation binaries. To access the binaries, use the :install command to download binaries from Maven Central Repository.

gremlin> :install org.apache.tinkerpop neo4j-gremlin 3.1.4-SNAPSHOT
==>Loaded: [org.apache.tinkerpop, neo4j-gremlin, 3.1.4-SNAPSHOT] - restart the console to use [tinkerpop.neo4j]
gremlin> :q
...
gremlin> :plugin use tinkerpop.neo4j
==>tinkerpop.neo4j activated
gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[EmbeddedGraphDatabase [/tmp/neo4j]]

Tip	To host Neo4j in Gremlin Server, the dependencies must first be "installed" or otherwise copied to the Gremlin Server path. The automated method for doing this would be to execute `bin/gremlin-server.sh -i org.apache.tinkerpop neo4j-gremlin 3.1.4-SNAPSHOT`.

Indices

Neo4j 2.x indices leverage vertex labels to partition the index space. TinkerPop3 does not provide method interfaces for defining schemas/indices for the underlying graph system. Thus, in order to create indices, it is important to call the Neo4j API directly.

Note	`Neo4jGraphStep` will attempt to discern which indices to use when executing a traversal of the form `g.V().has()`.

The Gremlin-Console session below demonstrates Neo4j indices. For more information, please refer to the Neo4j documentation:

Manipulating indices with Cypher.
Manipulating indices with the Neo4j Java API.

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> graph.cypher("CREATE INDEX ON :person(name)")
gremlin> graph.tx().commit() //(1)
==>null
gremlin> graph.addVertex(label,'person','name','marko')
==>v[0]
gremlin> graph.addVertex(label,'dog','name','puppy')
==>v[1]
gremlin> g = graph.traversal()
==>graphtraversalsource[neo4jgraph[Community [/tmp/neo4j]], standard]
gremlin> g.V().hasLabel('person').has('name','marko').values('name')
==>marko
gremlin> graph.close()
==>null

Schema mutations must happen in a different transaction than graph mutations

Below demonstrates the runtime benefits of indices and demonstrates how if there is no defined index (only vertex labels), a linear scan of the vertex-label partition is still faster than a linear scan of all vertices.

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
==>null
gremlin> g = graph.traversal()
==>graphtraversalsource[neo4jgraph[Community [/tmp/neo4j]], standard]
gremlin> g.tx().commit()
==>null
gremlin> clock(1000) {g.V().hasLabel('artist').has('name','Garcia').iterate()} //(1)
==>1.2492961029999998
gremlin> graph.cypher("CREATE INDEX ON :artist(name)") //(2)
gremlin> g.tx().commit()
==>null
gremlin> Thread.sleep(5000) //(3)
==>null
gremlin> clock(1000) {g.V().hasLabel('artist').has('name','Garcia').iterate()} //(4)
==>0.106095765
gremlin> clock(1000) {g.V().has('name','Garcia').iterate()} //(5)
==>2.0870746650000003
gremlin> graph.cypher("DROP INDEX ON :artist(name)") //(6)
gremlin> g.tx().commit()
==>null
gremlin> graph.close()
==>null

Find all artists whose name is Garcia which does a linear scan of the artist vertex-label partition.
Create an index for all artist vertices on their name property.
Neo4j indices are eventually consistent so this stalls to give the index time to populate itself.
Find all artists whose name is Garcia which uses the pre-defined schema index.
Find all vertices whose name is Garcia which requires a linear scan of all the data in the graph.
Drop the created index.

Multi/Meta-Properties

Neo4jGraph supports both multi- and meta-properties (see vertex properties). These features are not native to Neo4j and are implemented using "hidden" Neo4j nodes. For example, when a vertex has multiple "name" properties, each property is a new node (multi-properties) which can have properties attached to it (meta-properties). As such, the native, underlying representation may become difficult to query directly using another graph language such as Cypher. The default setting is to disable multi- and meta-properties. However, if this feature is desired, then it can be activated via gremlin.neo4j.metaProperties and gremlin.neo4j.multiProperties configurations being set to true. Once the configuration is set, it can not be changed for the lifetime of the graph.

gremlin> conf = new BaseConfiguration()
==>org.apache.commons.configuration.BaseConfiguration@65f8fefe
gremlin> conf.setProperty('gremlin.neo4j.directory','/tmp/neo4j')
==>null
gremlin> conf.setProperty('gremlin.neo4j.multiProperties',true)
==>null
gremlin> conf.setProperty('gremlin.neo4j.metaProperties',true)
==>null
gremlin> graph = Neo4jGraph.open(conf)
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> g = graph.traversal()
==>graphtraversalsource[neo4jgraph[Community [/tmp/neo4j]], standard]
gremlin> g.addV('name','michael','name','michael hunger','name','mhunger')
==>v[0]
gremlin> g.V().properties('name').property('acl', 'public')
==>vp[name->michael]
==>vp[name->michael hunger]
==>vp[name->mhunger]
gremlin> g.V(0).valueMap()
==>[name:[michael, michael hunger, mhunger]]
gremlin> g.V(0).properties()
==>vp[name->michael]
==>vp[name->michael hunger]
==>vp[name->mhunger]
gremlin> g.V(0).properties().valueMap()
==>[acl:public]
==>[acl:public]
==>[acl:public]
gremlin> graph.close()
==>null

Warning

Neo4jGraph without multi- and meta-properties is in 1-to-1 correspondence with the native, underlying Neo4j representation. It is recommended that if the user does not require multi/meta-properties, then they should not enable them. Without multi- and meta-properties enabled, Neo4j can be interacted with with other tools and technologies that do not leverage TinkerPop.

Important

When using a multi-property enabled Neo4jGraph, vertices may represent their properties on "hidden nodes" adjacent to the vertex. If a vertex property key/value is required for indexing, then two indices are required — e.g. CREATE INDEX ON :person(name) and CREATE INDEX ON :vertexProperty(name) (see Neo4j indices).

Cypher

NeoTechnology are the creators of the graph pattern-match query language Cypher. It is possible to leverage Cypher from within Gremlin by using the Neo4jGraph.cypher() graph traversal method.

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> graph.io(gryo()).readGraph('data/tinkerpop-modern.kryo')
==>null
gremlin> graph.cypher('MATCH (a {name:"marko"}) RETURN a')
==>[a:v[0]]
gremlin> graph.cypher('MATCH (a {name:"marko"}) RETURN a').select('a').out('knows').values('name')
==>josh
==>vadas
gremlin> graph.close()
==>null

Thus, like match()-step in Gremlin, it is possible to do a declarative pattern match and then move back into imperative Gremlin.

Tip	For those developers using Gremlin Server against Neo4j, it is possible to do Cypher queries by simply placing the Cypher string in `graph.cypher(...)` before submission to the server.

Multi-Label

TinkerPop3 requires every Element to have a single, immutable string label (i.e. a Vertex, Edge, and VertexProperty). In Neo4j, a Node (vertex) can have an arbitrary number of labels while a Relationship (edge) can have one and only one. Furthermore, in Neo4j, Node labels are mutable while Relationship labels are not. In order to handle this mismatch, three Neo4jVertex specific methods exist in Neo4j-Gremlin.

public Set<String> labels() // get all the labels of the vertex
public void addLabel(String label) // add a label to the vertex
public void removeLabel(String label) // remove a label from the vertex

An example use case is presented below.

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> vertex = (Neo4jVertex) graph.addVertex('human::animal') //(1)
==>v[0]
gremlin> vertex.label() //(2)
==>animal::human
gremlin> vertex.labels() //(3)
==>animal
==>human
gremlin> vertex.addLabel('organism') //(4)
==>null
gremlin> vertex.label()
==>animal::human::organism
gremlin> vertex.removeLabel('human') //(5)
==>null
gremlin> vertex.labels()
==>animal
==>organism
gremlin> vertex.addLabel('organism') //(6)
==>null
gremlin> vertex.labels()
==>animal
==>organism
gremlin> vertex.removeLabel('human') //(7)
==>null
gremlin> vertex.label()
==>animal::organism
gremlin> g = graph.traversal()
==>graphtraversalsource[neo4jgraph[Community [/tmp/neo4j]], standard]
gremlin> g.V().has(label,'organism') //(8)
gremlin> g.V().has(label,of('organism')) //(9)
==>v[0]
gremlin> g.V().has(label,of('organism')).has(label,of('animal'))
==>v[0]
gremlin> g.V().has(label,of('organism').and(of('animal')))
==>v[0]
gremlin> graph.close()
==>null

Typecasting to a Neo4jVertex is only required in Java.
The standard Vertex.label() method returns all the labels in alphabetical order concatenated using ::.
Neo4jVertex.labels() method returns the individual labels as a set.
Neo4jVertex.addLabel() method adds a single label.
Neo4jVertex.removeLabel() method removes a single label.
Labels are unique and thus duplicate labels don’t exist.
If a label that does not exist is removed, nothing happens.
P.eq() does a full string match and should only be used if multi-labels are not leveraged.
LabelP.of() is specific to Neo4jGraph and used for multi-label matching.

Important

LabelP.of() is only required if multi-labels are leveraged. LabelP.of() is used when filtering/looking-up vertices by their label(s) as the standard P.eq() does a direct match on the ::-representation of vertex.label()

Loading with BulkLoaderVertexProgram

The BulkLoaderVertexProgram is a generalized bulk loader that can be used to load large amounts of data to and from Neo4j. The following code demonstrates how to load the modern graph from TinkerGraph into Neo4j:

gremlin> wgConf = 'conf/neo4j-standalone.properties'
==>conf/neo4j-standalone.properties
gremlin> modern = TinkerFactory.createModern()
==>tinkergraph[vertices:6 edges:6]
gremlin> blvp = BulkLoaderVertexProgram.build().
                    keepOriginalIds(false).
                    writeGraph(wgConf).create(modern)
==>BulkLoaderVertexProgram[bulkLoader=IncrementalBulkLoader,vertexIdProperty=bulkLoader.vertex.id,userSuppliedIds=false,keepOriginalIds=false,batchSize=0]
gremlin> modern.compute().workers(1).program(blvp).submit().get()
==>result[tinkergraph[vertices:6 edges:6],memory[size:0]]
gremlin> graph = GraphFactory.open(wgConf)
==>neo4jgraph[Community [/tmp/neo4j]]
gremlin> g = graph.traversal()
==>graphtraversalsource[neo4jgraph[Community [/tmp/neo4j]], standard]
gremlin> g.V().valueMap()
==>[name:[marko], age:[29]]
==>[name:[vadas], age:[27]]
==>[name:[lop], lang:[java]]
==>[name:[josh], age:[32]]
==>[name:[ripple], lang:[java]]
==>[name:[peter], age:[35]]
gremlin> graph.close()
==>null

# neo4j-standalone.properties

gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j
gremlin.neo4j.conf.node_auto_indexing=true
gremlin.neo4j.conf.relationship_auto_indexing=true

High Availability Configuration

neo4j-ha TinkerPop supports running Neo4j with its fault tolerant master-slave replication configuration, referred to as its High Availability (HA) cluster. From the TinkerPop perspective, configuring for HA is not that different than configuring for embedded mode as shown above. The main difference is the usage of HA configuration options that enable the cluster. Once connected to a cluster, usage from the TinkerPop perspective is largely the same.

In configuring for HA the most important thing to realize is that all Neo4j HA settings are simply passed through the TinkerPop configuration settings given to the GraphFactory.open() or Neo4j.open() methods. For example, to provide the all-important ha.server_id configuration option through TinkerPop, simply prefix that key with the TinkerPop Neo4j key of gremlin.neo4j.conf.

The following properties demonstrates one of the three configuration files required to setup a simple three node HA cluster on the same machine instance:

gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j.server1
gremlin.neo4j.conf.ha.server_id=1
gremlin.neo4j.conf.ha.initial_hosts=localhost:5001\,localhost:5002\,localhost:5003
gremlin.neo4j.conf.ha.cluster_server=localhost:5001
gremlin.neo4j.conf.ha.server=localhost:6001

Assuming the intent is to configure this cluster completely within TinkerPop (perhaps within three separate Gremlin Server instances), the other two configuration files will be quite similar. The second will be:

gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j.server2
gremlin.neo4j.conf.ha.server_id=2
gremlin.neo4j.conf.ha.initial_hosts=localhost:5001\,localhost:5002\,localhost:5003
gremlin.neo4j.conf.ha.cluster_server=localhost:5002
gremlin.neo4j.conf.ha.server=localhost:6002

and the third will be:

gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j.server3
gremlin.neo4j.conf.ha.server_id=3
gremlin.neo4j.conf.ha.initial_hosts=localhost:5001\,localhost:5002\,localhost:5003
gremlin.neo4j.conf.ha.cluster_server=localhost:5003
gremlin.neo4j.conf.ha.server=localhost:6003

Important

The backslashes in the values provided to gremlin.neo4j.conf.ha.initial_hosts prevent that configuration setting as being interpreted as a List.

Create three separate Gremlin Server configuration files and point each at one of these Neo4j files. Since these Gremlin Server instances will be running on the same machine, ensure that each Gremlin Server instance has a unique port setting in that Gremlin Server configuration file. Start each Gremlin Server instance to bring the HA cluster online.

Note	`Neo4jGraph` instances will block until all nodes join the cluster.

Neither Gremlin Server nor Neo4j will share transactions across the cluster. Be sure to either use Gremlin Server managed transactions or, if using a session without that option, ensure that all requests are being routed to the same server.

This example discussed use of Gremlin Server to demonstrate the HA configuration, but it is also easy to setup with three Gremlin Console instances. Simply start three Gremlin Console instances and use GraphFactory to read those configuration files to form the cluster. Furthermore, keep in mind that it is possible to have a Gremlin Console join a cluster handled by two Gremlin Servers or Neo4j Enterprise. The only limits as to how the configuration can be utilized are prescribed by Neo4j itself. Please refer to their documentation for more information on how this feature works.

Hadoop-Gremlin

<dependency>
   <groupId>org.apache.tinkerpop</groupId>
   <artifactId>hadoop-gremlin</artifactId>
   <version>3.1.4-SNAPSHOT</version>
</dependency>

Hadoop is a distributed computing framework that is used to process data represented across a multi-machine compute cluster. When the data in the Hadoop cluster represents a TinkerPop3 graph, then Hadoop-Gremlin can be used to process the graph using both TinkerPop3’s OLTP and OLAP graph computing models.

Important

This section assumes that the user has a Hadoop 2.x cluster functioning. For more information on getting started with Hadoop, please see the Single Node Setup tutorial. Moreover, if using GiraphGraphComputer or SparkGraphComputer it is advisable that the reader also familiarize their self with Giraph (Getting Started) and Spark (Quick Start).

Installing Hadoop-Gremlin

If using Gremlin Console, it is important to install the Hadoop-Gremlin plugin. Note that Hadoop-Gremlin requires a Gremlin Console restart after installing.

$ bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
plugin activated: tinkerpop.tinkergraph
gremlin> :install org.apache.tinkerpop hadoop-gremlin 3.1.4-SNAPSHOT
==>loaded: [org.apache.tinkerpop, hadoop-gremlin, 3.1.4-SNAPSHOT] - restart the console to use [tinkerpop.hadoop]
gremlin> :q
$ bin/gremlin.sh

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

It is important that the CLASSPATH environmental variable references HADOOP_CONF_DIR and that the configuration files in HADOOP_CONF_DIR contain references to a live Hadoop cluster. It is easy to verify a proper configuration from within the Gremlin Console. If hdfs references the local file system, then there is a configuration issue.

gremlin> hdfs
==>storage[org.apache.hadoop.fs.LocalFileSystem@65bb9029] // BAD

gremlin> hdfs
==>storage[DFS[DFSClient[clientName=DFSClient_NONMAPREDUCE_1229457199_1, ugi=user (auth:SIMPLE)]]] // GOOD

The HADOOP_GREMLIN_LIBS references locations that contains jars that should be uploaded to a respective distributed cache (YARN or SparkServer). Note that the locations in HADOOP_GREMLIN_LIBS can be a colon-separated (:) and all jars from all locations will be loaded into the cluster. Typically, only the jars of the respective GraphComputer are required to be loaded (e.g. GiraphGraphComputer plugin lib directory).

export HADOOP_GREMLIN_LIBS=/usr/local/gremlin-console/ext/giraph-gremlin/lib

Properties Files

HadoopGraph makes use of properties files which ultimately get turned into Apache configurations and/or Hadoop configurations. The example properties file presented below is located at conf/hadoop/hadoop-gryo.properties.

gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
gremlin.hadoop.inputLocation=tinkerpop-modern.kryo
gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
gremlin.hadoop.outputLocation=output
gremlin.hadoop.graphOutputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat
gremlin.hadoop.jarsInDistributedCache=true
####################################
# Spark Configuration              #
####################################
spark.master=local[4]
spark.executor.memory=1g
spark.serializer=org.apache.tinkerpop.gremlin.spark.structure.io.gryo.GryoSerializer
####################################
# SparkGraphComputer Configuration #
####################################
gremlin.spark.graphInputRDD=org.apache.tinkerpop.gremlin.spark.structure.io.InputRDDFormat
gremlin.spark.graphOutputRDD=org.apache.tinkerpop.gremlin.spark.structure.io.OutputRDDFormat
gremlin.spark.persistContext=true
#####################################
# GiraphGraphComputer Configuration #
#####################################
giraph.minWorkers=2
giraph.maxWorkers=2
giraph.useOutOfCoreGraph=true
giraph.useOutOfCoreMessages=true
mapreduce.map.java.opts=-Xmx1024m
mapreduce.reduce.java.opts=-Xmx1024m
giraph.numInputThreads=2
giraph.numComputeThreads=2

A review of the Hadoop-Gremlin specific properties are provided in the table below. For the respective OLAP engines (SparkGraphComputer or GiraphGraphComputer) refer to their respective documentation for configuration options.

Property

Description

gremlin.graph

The class of the graph to construct using GraphFactory.

gremlin.hadoop.inputLocation

The location of the input file(s) for Hadoop-Gremlin to read the graph from.

gremlin.hadoop.graphInputFormat

The format that the graph input file(s) are represented in.

gremlin.hadoop.outputLocation

The location to write the computed HadoopGraph to.

gremlin.hadoop.graphOutputFormat

The format that the output file(s) should be represented in.

gremlin.hadoop.jarsInDistributedCache

Whether to upload the Hadoop-Gremlin jars to a distributed cache (necessary if jars are not on the machines' classpaths).

Along with the properties above, the numerous Hadoop specific properties can be added as needed to tune and parameterize the executed Hadoop-Gremlin job on the respective Hadoop cluster.

Important

As the size of the graphs being processed becomes large, it is important to fully understand how the underlying OLAP engine (e.g. Spark, Giraph, etc.) works and understand the numerous parameterizations offered by these systems. Such knowledge can help alleviate out of memory exceptions, slow load times, slow processing times, garbage collection issues, etc.

OLTP Hadoop-Gremlin

hadoop-pipes It is possible to execute OLTP operations over a HadoopGraph. However, realize that the underlying HDFS files are not random access and thus, to retrieve a vertex, a linear scan is required. OLTP operations are useful for peeking into the graph prior to executing a long running OLAP job — e.g. g.V().valueMap().limit(10).

Warning

OLTP operations on HadoopGraph are not efficient. They require linear scans to execute and are unreasonable for large graphs. In such large graph situations, make use of TraversalVertexProgram which is the OLAP Gremlin machine.

gremlin> hdfs.copyFromLocal('data/tinkerpop-modern.kryo', 'tinkerpop-modern.kryo')
==>null
gremlin> hdfs.ls()
==>rw-r--r-- root supergroup 781 tinkerpop-modern.kryo
gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> g = graph.traversal()
==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], standard]
gremlin> g.V().count()
==>6
gremlin> g.V().out().out().values('name')
==>ripple
==>lop
gremlin> g.V().group().by{it.value('name')[1]}.by('name').next()
==>a=[marko, vadas]
==>e=[peter]
==>i=[ripple]
==>o=[lop, josh]

OLAP Hadoop-Gremlin

hadoop-furnace Hadoop-Gremlin was designed to execute OLAP operations via GraphComputer. The OLTP examples presented previously are reproduced below, but using TraversalVertexProgram for the execution of the Gremlin traversal.

A Graph in TinkerPop3 can support any number of GraphComputer implementations. Out of the box, Hadoop-Gremlin supports the following three implementations.

MapReduceGraphComputer: Leverages Hadoop’s MapReduce engine to execute TinkerPop3 OLAP computations. (coming soon)
- The graph must fit within the total disk space of the Hadoop cluster (supports massive graphs). Message passing is coordinated via MapReduce jobs over the on-disk graph (slow traversals).
SparkGraphComputer: Leverages Apache Spark to execute TinkerPop3 OLAP computations.
- The graph may fit within the total RAM of the cluster (supports larger graphs). Message passing is coordinated via Spark map/reduce/join operations on in-memory and disk-cached data (average speed traversals).
GiraphGraphComputer: Leverages Apache Giraph to execute TinkerPop3 OLAP computations.
- The graph should fit within the total RAM of the Hadoop cluster (graph size restriction), though "out-of-core" processing is possible. Message passing is coordinated via ZooKeeper for the in-memory graph (speedy traversals).

Tip	For those wanting to use the SugarPlugin with their submitted traversal, do `:remote config useSugar true` as well as `:plugin use tinkerpop.sugar` at the start of the Gremlin Console session if it is not already activated.

Note that SparkGraphComputer and GiraphGraphComputer are loaded via their respective plugins. Typically only one plugin or the other is loaded depending on the desired GraphComputer to use.

$ bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
plugin activated: tinkerpop.tinkergraph
plugin activated: tinkerpop.hadoop
gremlin> :install org.apache.tinkerpop giraph-gremlin 3.1.4-SNAPSHOT
==>loaded: [org.apache.tinkerpop, giraph-gremlin, 3.1.4-SNAPSHOT] - restart the console to use [tinkerpop.giraph]
gremlin> :install org.apache.tinkerpop spark-gremlin 3.1.4-SNAPSHOT
==>loaded: [org.apache.tinkerpop, spark-gremlin, 3.1.4-SNAPSHOT] - restart the console to use [tinkerpop.spark]
gremlin> :q
$ bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
plugin activated: tinkerpop.tinkergraph
plugin activated: tinkerpop.hadoop
gremlin> :plugin use tinkerpop.giraph
==>tinkerpop.giraph activated
gremlin> :plugin use tinkerpop.spark
==>tinkerpop.spark activated

Warning

Hadoop, Spark, and Giraph all depend on many of the same libraries (e.g. ZooKeeper, Snappy, Netty, Guava, etc.). Unfortunately, typically these dependencies are not to the same versions of the respective libraries. As such, it is best to not have both Spark and Giraph plugins loaded in the same console session nor in the same Java project (though intelligent <exclusion>-usage can help alleviate conflicts in a Java project).

Warning

It is important to note that when doing an OLAP traversal, any resulting vertices, edges, or properties will be attached to the source graph. For Hadoop-based graphs, this may lead to linear search times on massive graphs. Thus, if vertex, edge, or property objects are to be returns (as a final result), it is best to .id() to get the id of the object and not the actual attached object.

MapReduceGraphComputer

COMING SOON

SparkGraphComputer

<dependency>
   <groupId>org.apache.tinkerpop</groupId>
   <artifactId>spark-gremlin</artifactId>
   <version>3.1.4-SNAPSHOT</version>
</dependency>

Spark is an Apache Software Foundation project focused on general-purpose OLAP data processing. Spark provides a hybrid in-memory/disk-based distributed computing model that is similar to Hadoop’s MapReduce model. Spark maintains a fluent function chaining DSL that is arguably easier for developers to work with than native Hadoop MapReduce. Spark-Gremlin provides an implementation of the bulk-synchronous parallel, distributed message passing algorithm within Spark and thus, any VertexProgram can be executed over SparkGraphComputer.

If SparkGraphComputer will be used as the GraphComputer for HadoopGraph then its lib directory should be specified in HADOOP_GREMLIN_LIBS.

export HADOOP_GREMLIN_LIBS=$HADOOP_GREMLIN_LIBS:/usr/local/gremlin-console/ext/spark-gremlin/lib

Furthermore the lib/ directory should be distributed across all machines in the SparkServer cluster. For this purpose TinkerPop provides a helper script, which takes the Spark installation directory and the the Spark machines as input:

bin/hadoop/init-tp-spark.sh /usr/local/spark spark@10.0.0.1 spark@10.0.0.2 spark@10.0.0.3

Once the lib/ directory is distributed, SparkGraphComputer can be used as follows.

gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> g = graph.traversal(computer(SparkGraphComputer))
==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], sparkgraphcomputer]
gremlin> g.V().count()
==>6
gremlin> g.V().out().out().values('name')
==>lop
==>ripple

For using lambdas in Gremlin-Groovy, simply provide :remote connect a TraversalSource which leverages SparkGraphComputer.

gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> g = graph.traversal(computer(SparkGraphComputer))
==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], sparkgraphcomputer]
gremlin> :remote connect tinkerpop.hadoop graph g
==>useTraversalSource=graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], sparkgraphcomputer]
==>useSugar=false
gremlin> :> g.V().group().by{it.value('name')[1]}.by('name')
==>[a:[marko, vadas], e:[peter], i:[ripple], o:[josh, lop]]

The SparkGraphComputer algorithm leverages Spark’s caching abilities to reduce the amount of data shuffled across the wire on each iteration of the VertexProgram. When the graph is loaded as a Spark RDD (Resilient Distributed Dataset) it is immediately cached as graphRDD. The graphRDD is a distributed adjacency list which encodes the vertex, its properties, and all its incident edges. On the first iteration, each vertex (in parallel) is passed through VertexProgram.execute(). This yields an output of the vertex’s mutated state (i.e. updated compute keys — propertyX) and its outgoing messages. This viewOutgoingRDD is then reduced to viewIncomingRDD where the outgoing messages are sent to their respective vertices. If a MessageCombiner exists for the vertex program, then messages are aggregated locally and globally to ultimately yield one incoming message for the vertex. This reduce sequence is the "message pass." If the vertex program does not terminate on this iteration, then the viewIncomingRDD is joined with the cached graphRDD and the process continues. When there are no more iterations, there is a final join and the resultant RDD is stripped of its edges and messages. This mapReduceRDD is cached and is processed by each MapReduce job in the GraphComputer computation.

Property Description

gremlin.spark.graphInputRDD

A class for creating RDD’s from underlying graph data, defaults to Hadoop InputFormat.

gremlin.spark.graphOutputRDD

A class for output RDD’s, defaults to Hadoop OutputFormat.

gremlin.spark.graphStorageLevel

What StorageLevel to use for the cached graph during job execution (default MEMORY_ONLY).

gremlin.spark.persistContext

Whether to create a new SparkContext for every SparkGraphComputer or to reuse an existing one.

gremlin.spark.persistStorageLevel

What StorageLevel to use when persisted RDDs via PersistedOutputRDD (default MEMORY_ONLY).

InputRDD and OutputRDD

If the provider/user does not want to use Hadoop InputFormats, it is possible to leverage Spark’s RDD constructs directly. There is a gremlin.spark.graphInputRDD configuration that references a Class<? extends InputRDD>. An InputRDD provides a read method that takes a SparkContext and returns a graphRDD. Likewise, use gremlin.spark.graphOutputRDD and the respective OutputRDD.

If the graph system provider uses an InputRDD, the RDD should maintain an associated org.apache.spark.Partitioner. By doing so, SparkGraphComputer will not partition the loaded graph across the cluster as it has already been partitioned by the graph system provider. This can save a significant amount of time and space resources. If the InputRDD does not have a registered partitioner, SparkGraphComputer will partition the graph using a org.apache.spark.HashPartitioner with the number of partitions being either the number of existing partitions in the input (e.g. input splits) or the user specified number of GraphComputer.workers().

Storage Levels

The SparkGraphComputer uses MEMORY_ONLY to cache the input graph and the output graph by default. Users should be aware of the impact of different storage levels, since the default settings can quickly lead to memory issues on larger graphs. An overview of Spark’s persistence settings is provided in Spark’s programming guide.

Using a Persisted Context

It is possible to persist the graph RDD between jobs within the SparkContext (e.g. SparkServer) by leveraging PersistedOutputRDD. Note that gremlin.spark.persistContext should be set to true or else the persisted RDD will be destroyed when the SparkContext closes. The persisted RDD is named by the gremlin.hadoop.outputLocation configuration. Similarly, PersistedInputRDD is used with respective gremlin.hadoop.inputLocation to retrieve the persisted RDD from the SparkContext.

When using a persistent SparkContext the configuration used by the original Spark Configuration will be inherited by all threaded references to that Spark Context. The exception to this rule are those properties which have a specific thread local effect.

Thread Local Properties

spark.jobGroup.id
spark.job.description
spark.job.interruptOnCancel
spark.scheduler.pool

Finally, there is a spark object that can be used to manage persisted RDDs (see Interacting with Spark).

Exporting with BulkDumperVertexProgram

The BulkDumperVertexProgram exports a whole graph in any of the supported Hadoop GraphOutputFormats (GraphSONOutputFormat, GryoOutputFormat or ScriptOutputFormat). The example below takes a Hadoop graph as the input (in GryoInputFormat) and exports it as a GraphSON file (GraphSONOutputFormat).

gremlin> hdfs.copyFromLocal('data/tinkerpop-modern.kryo', 'tinkerpop-modern.kryo')
==>null
gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> graph.configuration().setProperty('gremlin.hadoop.graphOutputFormat', 'org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONOutputFormat')
==>null
gremlin> graph.compute(SparkGraphComputer).program(BulkDumperVertexProgram.build().create()).submit().get()
==>result[hadoopgraph[graphsoninputformat->graphsonoutputformat],memory[size:0]]
gremlin> hdfs.ls('output')
==>rwxr-xr-x root supergroup 0 (D) ~g
gremlin> hdfs.head('output/~g')
==>{"id":4,"label":"person","inE":{"knows":[{"id":8,"outV":1,"properties":{"weight":1.0}}]},"outE":{"created":[{"id":10,"inV":5,"properties":{"weight":1.0}},{"id":11,"inV":3,"properties":{"weight":0.4}}]},"properties":{"name":[{"id":6,"value":"josh"}],"age":[{"id":7,"value":32}]}}
==>{"id":1,"label":"person","outE":{"created":[{"id":9,"inV":3,"properties":{"weight":0.4}}],"knows":[{"id":7,"inV":2,"properties":{"weight":0.5}},{"id":8,"inV":4,"properties":{"weight":1.0}}]},"properties":{"name":[{"id":0,"value":"marko"}],"age":[{"id":1,"value":29}]}}
==>{"id":6,"label":"person","outE":{"created":[{"id":12,"inV":3,"properties":{"weight":0.2}}]},"properties":{"name":[{"id":10,"value":"peter"}],"age":[{"id":11,"value":35}]}}
==>{"id":3,"label":"software","inE":{"created":[{"id":9,"outV":1,"properties":{"weight":0.4}},{"id":11,"outV":4,"properties":{"weight":0.4}},{"id":12,"outV":6,"properties":{"weight":0.2}}]},"properties":{"name":[{"id":4,"value":"lop"}],"lang":[{"id":5,"value":"java"}]}}
==>{"id":5,"label":"software","inE":{"created":[{"id":10,"outV":4,"properties":{"weight":1.0}}]},"properties":{"name":[{"id":8,"value":"ripple"}],"lang":[{"id":9,"value":"java"}]}}
==>{"id":2,"label":"person","inE":{"knows":[{"id":7,"outV":1,"properties":{"weight":0.5}}]},"properties":{"name":[{"id":2,"value":"vadas"}],"age":[{"id":3,"value":27}]}}

Loading with BulkLoaderVertexProgram

The BulkLoaderVertexProgram is a generalized bulk loader that can be used to load large amounts of data to and from different Graph implementations. The following code demonstrates how to load the Grateful Dead graph from HadoopGraph into TinkerGraph over Spark:

gremlin> hdfs.copyFromLocal('data/grateful-dead.kryo', 'grateful-dead.kryo')
==>null
gremlin> readGraph = GraphFactory.open('conf/hadoop/hadoop-grateful-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> writeGraph = 'conf/tinkergraph-gryo.properties'
==>conf/tinkergraph-gryo.properties
gremlin> blvp = BulkLoaderVertexProgram.build().
                    keepOriginalIds(false).
                    writeGraph(writeGraph).create(readGraph)
==>BulkLoaderVertexProgram[bulkLoader=IncrementalBulkLoader,vertexIdProperty=bulkLoader.vertex.id,userSuppliedIds=false,keepOriginalIds=false,batchSize=0]
gremlin> readGraph.compute(SparkGraphComputer).workers(1).program(blvp).submit().get()
==>result[hadoopgraph[gryoinputformat->gryooutputformat],memory[size:0]]
gremlin> :set max-iteration 10
gremlin> graph = GraphFactory.open(writeGraph)
==>tinkergraph[vertices:814 edges:8052]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:814 edges:8052], standard]
gremlin> g.V().valueMap()
==>[name:[marko], age:[29]]
==>[name:[Hart_Kreutzmann]]
==>[name:[vadas], age:[27]]
==>[name:[TERRAPIN TRANSIT], songType:[original], performances:[1]]
==>[name:[lop], lang:[java]]
==>[name:[THIS TIME FOREVER], songType:[original], performances:[1]]
==>[name:[josh], age:[32]]
==>[name:[HEY BO DIDDLEY], songType:[cover], performances:[5]]
==>[name:[ripple], lang:[java]]
==>[name:[TILL THE MORNING COMES], songType:[original], performances:[5]]
...
gremlin> graph.close()
==>null

# hadoop-grateful-gryo.properties

#
# Hadoop Graph Configuration
#
gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
gremlin.hadoop.inputLocation=grateful-dead.kryo
gremlin.hadoop.outputLocation=output
gremlin.hadoop.jarsInDistributedCache=true

#
# SparkGraphComputer Configuration
#
spark.master=local[1]
spark.executor.memory=1g
spark.serializer=org.apache.tinkerpop.gremlin.spark.structure.io.gryo.GryoSerializer

# tinkergraph-gryo.properties

gremlin.graph=org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph
gremlin.tinkergraph.graphFormat=gryo
gremlin.tinkergraph.graphLocation=/tmp/tinkergraph.kryo

Important

The path to TinkerGraph jars needs to be included in the HADOOP_GREMLIN_LIBS for the above example to work.

GiraphGraphComputer

<dependency>
   <groupId>org.apache.tinkerpop</groupId>
   <artifactId>giraph-gremlin</artifactId>
   <version>3.1.4-SNAPSHOT</version>
</dependency>

Giraph is an Apache Software Foundation project focused on OLAP-based graph processing. Giraph makes use of the distributed graph computing paradigm made popular by Google’s Pregel. In Giraph, developers write "vertex programs" that get executed at each vertex in parallel. These programs communicate with one another in a bulk synchronous parallel (BSP) manner. This model aligns with TinkerPop3’s GraphComputer API. TinkerPop3 provides an implementation of GraphComputer that works for Giraph called GiraphGraphComputer. Moreover, with TinkerPop3’s MapReduce-framework, the standard Giraph/Pregel model is extended to support an arbitrary number of MapReduce phases to aggregate and yield results from the graph. Below are examples using GiraphGraphComputer from the Gremlin-Console.

Warning

Giraph uses a large number of Hadoop counters. The default for Hadoop is 120. In mapred-site.xml it is possible to increase the limit it via the mapreduce.job.counters.max property. A good value to use is 1000. This is a cluster-wide property so be sure to restart the cluster after updating.

Warning

The maximum number of workers can be no larger than the number of map-slots in the Hadoop cluster minus 1. For example, if the Hadoop cluster has 4 map slots, then giraph.maxWorkers can not be larger than 3. One map-slot is reserved for the master compute node and all other slots can be allocated as workers to execute the VertexPrograms on the vertices of the graph.

If GiraphGraphComputer will be used as the GraphComputer for HadoopGraph then its lib directory should be specified in HADOOP_GREMLIN_LIBS.

export HADOOP_GREMLIN_LIBS=$HADOOP_GREMLIN_LIBS:/usr/local/gremlin-console/ext/giraph-gremlin/lib

Or, the user can specify the directory in the Gremlin Console.

System.setProperty('HADOOP_GREMLIN_LIBS',System.getProperty('HADOOP_GREMLIN_LIBS') + ':' + '/usr/local/gremlin-console/ext/giraph-gremlin/lib')

gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> g = graph.traversal(computer(GiraphGraphComputer))
==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], giraphgraphcomputer]
gremlin> g.V().count()
INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://d2b3d359a388:8088/proxy/application_1472656461071_0001/
INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1472656461071_0001
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0001 running in uber mode : false
INFO  org.apache.hadoop.mapreduce.Job  -  map 33% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0001 completed successfully
INFO  org.apache.hadoop.mapreduce.Job  - Counters: 50
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=594942
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=945
                HDFS: Number of bytes written=1784
                HDFS: Number of read operations=38
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=21
        Job Counters
                Launched map tasks=3
                Other local map tasks=3
                Total time spent by all maps in occupied slots (ms)=105601
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=105601
                Total vcore-milliseconds taken by all map tasks=105601
                Total megabyte-milliseconds taken by all map tasks=108135424
        Map-Reduce Framework
                Map input records=3
                Map output records=0
                Input split bytes=132
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=7517
                CPU time spent (ms)=8080
                Physical memory (bytes) snapshot=1555501056
                Virtual memory (bytes) snapshot=8518045696
                Total committed heap usage (bytes)=1469579264
        Giraph Stats
                Aggregate edges=0
                Aggregate finished vertices=0
                Aggregate sent message message bytes=0
                Aggregate sent messages=0
                Aggregate vertices=6
                Current master task partition=0
                Current workers=2
                Last checkpointed superstep=0
                Sent message bytes=0
                Sent messages=0
                Superstep=1
        Giraph Timers
                Initialize (ms)=2745
                Input superstep (ms)=4176
                Setup (ms)=37
                Shutdown (ms)=9088
                Superstep 0 GiraphComputation (ms)=3063
                Total (ms)=16366
        Zookeeper base path
                /_hadoopBsp/job_1472656461071_0001=0
        Zookeeper halt node
                /_hadoopBsp/job_1472656461071_0001/_haltComputation=0
        Zookeeper server:port
                d2b3d359a388:22181=0
        File Input Format Counters
                Bytes Read=0
        File Output Format Counters
                Bytes Written=0
INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HadoopGremlin: CountGlobalMapReduce[~reducing]
INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://d2b3d359a388:8088/proxy/application_1472656461071_0002/
INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1472656461071_0002
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0002 running in uber mode : false
INFO  org.apache.hadoop.mapreduce.Job  -  map 0% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 50%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 100%
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0002 completed successfully
INFO  org.apache.hadoop.mapreduce.Job  - Counters: 50
        File System Counters
                FILE: Number of bytes read=120
                FILE: Number of bytes written=801966
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=1568
                HDFS: Number of bytes written=368
                HDFS: Number of read operations=14
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=4
        Job Counters
                Killed reduce tasks=1
                Launched map tasks=2
                Launched reduce tasks=2
                Data-local map tasks=2
                Total time spent by all maps in occupied slots (ms)=18690
                Total time spent by all reduces in occupied slots (ms)=18011
                Total time spent by all map tasks (ms)=18690
                Total time spent by all reduce tasks (ms)=18011
                Total vcore-milliseconds taken by all map tasks=18690
                Total vcore-milliseconds taken by all reduce tasks=18011
                Total megabyte-milliseconds taken by all map tasks=19138560
                Total megabyte-milliseconds taken by all reduce tasks=18443264
        Map-Reduce Framework
                Map input records=6
                Map output records=6
                Map output bytes=312
                Map output materialized bytes=132
                Input split bytes=238
                Combine input records=6
                Combine output records=2
                Reduce input groups=1
                Reduce shuffle bytes=132
                Reduce input records=2
                Reduce output records=1
                Spilled Records=4
                Shuffled Maps =4
                Failed Shuffles=0
                Merged Map outputs=4
                GC time elapsed (ms)=5730
                CPU time spent (ms)=20740
                Physical memory (bytes) snapshot=2260684800
                Virtual memory (bytes) snapshot=11219005440
                Total committed heap usage (bytes)=1885863936
        Shuffle Errors
                BAD_ID=0
                CONNECTION=0
                IO_ERROR=0
                WRONG_LENGTH=0
                WRONG_MAP=0
                WRONG_REDUCE=0
        File Input Format Counters
                Bytes Read=1330
        File Output Format Counters
                Bytes Written=368
==>6
gremlin> g.V().out().out().values('name')
INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://d2b3d359a388:8088/proxy/application_1472656461071_0003/
INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1472656461071_0003
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0003 running in uber mode : false
INFO  org.apache.hadoop.mapreduce.Job  -  map 33% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 67% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0003 completed successfully
INFO  org.apache.hadoop.mapreduce.Job  - Counters: 52
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=600891
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=945
                HDFS: Number of bytes written=1740
                HDFS: Number of read operations=39
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=21
        Job Counters
                Launched map tasks=3
                Other local map tasks=3
                Total time spent by all maps in occupied slots (ms)=122441
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=122441
                Total vcore-milliseconds taken by all map tasks=122441
                Total megabyte-milliseconds taken by all map tasks=125379584
        Map-Reduce Framework
                Map input records=3
                Map output records=0
                Input split bytes=132
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=6356
                CPU time spent (ms)=8380
                Physical memory (bytes) snapshot=1558618112
                Virtual memory (bytes) snapshot=8516440064
                Total committed heap usage (bytes)=1477967872
        Giraph Stats
                Aggregate edges=0
                Aggregate finished vertices=0
                Aggregate sent message message bytes=704
                Aggregate sent messages=8
                Aggregate vertices=6
                Current master task partition=0
                Current workers=2
                Last checkpointed superstep=0
                Sent message bytes=0
                Sent messages=0
                Superstep=3
        Giraph Timers
                Initialize (ms)=2618
                Input superstep (ms)=5180
                Setup (ms)=39
                Shutdown (ms)=9075
                Superstep 0 GiraphComputation (ms)=3888
                Superstep 1 GiraphComputation (ms)=3030
                Superstep 2 GiraphComputation (ms)=2167
                Total (ms)=23381
        Zookeeper base path
                /_hadoopBsp/job_1472656461071_0003=0
        Zookeeper halt node
                /_hadoopBsp/job_1472656461071_0003/_haltComputation=0
        Zookeeper server:port
                d2b3d359a388:22181=0
        File Input Format Counters
                Bytes Read=0
        File Output Format Counters
                Bytes Written=0
INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HadoopGremlin: TraverserMapReduce[~traversers]
INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://d2b3d359a388:8088/proxy/application_1472656461071_0004/
INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1472656461071_0004
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0004 running in uber mode : false
INFO  org.apache.hadoop.mapreduce.Job  -  map 0% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0004 completed successfully
INFO  org.apache.hadoop.mapreduce.Job  - Counters: 30
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=403640
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=1524
                HDFS: Number of bytes written=455
                HDFS: Number of read operations=12
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=4
        Job Counters
                Launched map tasks=2
                Data-local map tasks=2
                Total time spent by all maps in occupied slots (ms)=19197
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=19197
                Total vcore-milliseconds taken by all map tasks=19197
                Total megabyte-milliseconds taken by all map tasks=19657728
        Map-Reduce Framework
                Map input records=6
                Map output records=2
                Input split bytes=238
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=2915
                CPU time spent (ms)=10950
                Physical memory (bytes) snapshot=1054633984
                Virtual memory (bytes) snapshot=5622312960
                Total committed heap usage (bytes)=1007157248
        File Input Format Counters
                Bytes Read=1286
        File Output Format Counters
                Bytes Written=455
==>ripple
==>lop

Important

The examples above do not use lambdas (i.e. closures in Gremlin-Groovy). This makes the traversal serializable and thus, able to be distributed to all machines in the Hadoop cluster. If a lambda is required in a traversal, then the traversal must be sent as a String and compiled locally at each machine in the cluster. The following example demonstrates the :remote command which allows for submitting Gremlin traversals as a String.

gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> g = graph.traversal(computer(GiraphGraphComputer))
==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], giraphgraphcomputer]
gremlin> :remote connect tinkerpop.hadoop graph g
==>useTraversalSource=graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], giraphgraphcomputer]
==>useSugar=false
gremlin> :> g.V().group().by{it.value('name')[1]}.by('name')
INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://d2b3d359a388:8088/proxy/application_1472656461071_0005/
INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1472656461071_0005
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0005 running in uber mode : false
INFO  org.apache.hadoop.mapreduce.Job  -  map 33% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 67% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0005 completed successfully
INFO  org.apache.hadoop.mapreduce.Job  - Counters: 50
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=525705
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=945
                HDFS: Number of bytes written=1844
                HDFS: Number of read operations=38
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=21
        Job Counters
                Launched map tasks=3
                Other local map tasks=3
                Total time spent by all maps in occupied slots (ms)=109612
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=109612
                Total vcore-milliseconds taken by all map tasks=109612
                Total megabyte-milliseconds taken by all map tasks=112242688
        Map-Reduce Framework
                Map input records=3
                Map output records=0
                Input split bytes=132
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=7376
                CPU time spent (ms)=16560
                Physical memory (bytes) snapshot=1732608000
                Virtual memory (bytes) snapshot=8550752256
                Total committed heap usage (bytes)=1505755136
        Giraph Stats
                Aggregate edges=0
                Aggregate finished vertices=0
                Aggregate sent message message bytes=0
                Aggregate sent messages=0
                Aggregate vertices=6
                Current master task partition=0
                Current workers=2
                Last checkpointed superstep=0
                Sent message bytes=0
                Sent messages=0
                Superstep=1
        Giraph Timers
                Initialize (ms)=2776
                Input superstep (ms)=5329
                Setup (ms)=25
                Shutdown (ms)=9126
                Superstep 0 GiraphComputation (ms)=4920
                Total (ms)=19401
        Zookeeper base path
                /_hadoopBsp/job_1472656461071_0005=0
        Zookeeper halt node
                /_hadoopBsp/job_1472656461071_0005/_haltComputation=0
        Zookeeper server:port
                d2b3d359a388:22181=0
        File Input Format Counters
                Bytes Read=0
        File Output Format Counters
                Bytes Written=0
INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HadoopGremlin: GroupMapReduce[~reducing]
INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://d2b3d359a388:8088/proxy/application_1472656461071_0006/
INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1472656461071_0006
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0006 running in uber mode : false
INFO  org.apache.hadoop.mapreduce.Job  -  map 0% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 50%
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 100%
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0006 completed successfully
INFO  org.apache.hadoop.mapreduce.Job  - Counters: 50
        File System Counters
                FILE: Number of bytes read=394
                FILE: Number of bytes written=709586
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=1628
                HDFS: Number of bytes written=600
                HDFS: Number of read operations=14
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=4
        Job Counters
                Killed reduce tasks=1
                Launched map tasks=2
                Launched reduce tasks=2
                Data-local map tasks=2
                Total time spent by all maps in occupied slots (ms)=24407
                Total time spent by all reduces in occupied slots (ms)=22458
                Total time spent by all map tasks (ms)=24407
                Total time spent by all reduce tasks (ms)=22458
                Total vcore-milliseconds taken by all map tasks=24407
                Total vcore-milliseconds taken by all reduce tasks=22458
                Total megabyte-milliseconds taken by all map tasks=24992768
                Total megabyte-milliseconds taken by all reduce tasks=22996992
        Map-Reduce Framework
                Map input records=6
                Map output records=6
                Map output bytes=370
                Map output materialized bytes=406
                Input split bytes=238
                Combine input records=0
                Combine output records=0
                Reduce input groups=4
                Reduce shuffle bytes=406
                Reduce input records=6
                Reduce output records=4
                Spilled Records=12
                Shuffled Maps =4
                Failed Shuffles=0
                Merged Map outputs=4
                GC time elapsed (ms)=7235
                CPU time spent (ms)=31800
                Physical memory (bytes) snapshot=2713452544
                Virtual memory (bytes) snapshot=11274657792
                Total committed heap usage (bytes)=2153250816
        Shuffle Errors
                BAD_ID=0
                CONNECTION=0
                IO_ERROR=0
                WRONG_LENGTH=0
                WRONG_MAP=0
                WRONG_REDUCE=0
        File Input Format Counters
                Bytes Read=1390
        File Output Format Counters
                Bytes Written=600
==>[a:[vadas, marko], e:[peter], i:[ripple], o:[lop, josh]]
gremlin> result
==>result[hadoopgraph[gryoinputformat->gryooutputformat],memory[size:2]]
gremlin> result.memory.runtime
==>93157
gremlin> result.memory.keys()
==>gremlin.traversalVertexProgram.voteToHalt
==>~reducing
gremlin> result.memory.get('~reducing')
==>a=[vadas, marko]
==>e=[peter]
==>i=[ripple]
==>o=[lop, josh]

Note

If the user explicitly specifies giraph.maxWorkers and/or giraph.numComputeThreads in the configuration, then these values will be used by Giraph. However, if these are not specified and the user never calls GraphComputer.workers() then GiraphGraphComputer will try to compute the number of workers/threads to use based on the cluster’s profile.

Loading with BulkLoaderVertexProgram

gremlin> hdfs.copyFromLocal('data/grateful-dead.kryo', 'grateful-dead.kryo')
==>null
gremlin> readGraph = GraphFactory.open('conf/hadoop/hadoop-grateful-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> writeGraph = 'conf/tinkergraph-gryo.properties'
==>conf/tinkergraph-gryo.properties
gremlin> blvp = BulkLoaderVertexProgram.build().
                    keepOriginalIds(false).
                    writeGraph(writeGraph).create(readGraph)
==>BulkLoaderVertexProgram[bulkLoader=IncrementalBulkLoader,vertexIdProperty=bulkLoader.vertex.id,userSuppliedIds=false,keepOriginalIds=false,batchSize=0]
gremlin> readGraph.compute(GiraphGraphComputer).workers(1).program(blvp).submit().get()
INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://d2b3d359a388:8088/proxy/application_1472656461071_0007/
INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1472656461071_0007
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0007 running in uber mode : false
INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
INFO  org.apache.hadoop.mapreduce.Job  - Job job_1472656461071_0007 completed successfully
INFO  org.apache.hadoop.mapreduce.Job  - Counters: 52
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=340874
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=332346
                HDFS: Number of bytes written=246434
                HDFS: Number of read operations=27
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=14
        Job Counters
                Launched map tasks=2
                Other local map tasks=2
                Total time spent by all maps in occupied slots (ms)=62881
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=62881
                Total vcore-milliseconds taken by all map tasks=62881
                Total megabyte-milliseconds taken by all map tasks=64390144
        Map-Reduce Framework
                Map input records=2
                Map output records=0
                Input split bytes=88
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=3214
                CPU time spent (ms)=17560
                Physical memory (bytes) snapshot=1150001152
                Virtual memory (bytes) snapshot=5669933056
                Total committed heap usage (bytes)=932184064
        Giraph Stats
                Aggregate edges=0
                Aggregate finished vertices=0
                Aggregate sent message message bytes=477403
                Aggregate sent messages=8049
                Aggregate vertices=808
                Current master task partition=0
                Current workers=1
                Last checkpointed superstep=0
                Sent message bytes=0
                Sent messages=0
                Superstep=3
        Giraph Timers
                Initialize (ms)=459
                Input superstep (ms)=2156
                Setup (ms)=42
                Shutdown (ms)=8979
                Superstep 0 GiraphComputation (ms)=2921
                Superstep 1 GiraphComputation (ms)=3018
                Superstep 2 GiraphComputation (ms)=2136
                Total (ms)=19253
        Zookeeper base path
                /_hadoopBsp/job_1472656461071_0007=0
        Zookeeper halt node
                /_hadoopBsp/job_1472656461071_0007/_haltComputation=0
        Zookeeper server:port
                d2b3d359a388:22181=0
        File Input Format Counters
                Bytes Read=0
        File Output Format Counters
                Bytes Written=0
==>result[hadoopgraph[gryoinputformat->gryooutputformat],memory[size:0]]
gremlin> :set max-iteration 10
gremlin> graph = GraphFactory.open(writeGraph)
==>tinkergraph[vertices:1622 edges:16098]
gremlin> g = graph.traversal()
==>graphtraversalsource[tinkergraph[vertices:1622 edges:16098], standard]
gremlin> g.V().valueMap()
==>[name:[marko], age:[29]]
==>[name:[TOM DOOLEY], songType:[cover], performances:[1]]
==>[name:[vadas], age:[27]]
==>[name:[lop], lang:[java]]
==>[name:[Dolly_Parton]]
==>[name:[josh], age:[32]]
==>[name:[HEY BO DIDDLEY], songType:[cover], performances:[5]]
==>[name:[TOUGH MAMA], songType:[cover], performances:[1]]
==>[name:[ripple], lang:[java]]
==>[name:[peter], age:[35]]
...
gremlin> graph.close()
==>null

# hadoop-grateful-gryo.properties

#
# Hadoop Graph Configuration
#
gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
gremlin.hadoop.graphOutputFormat=org.apache.hadoop.mapreduce.lib.output.NullOutputFormat
gremlin.hadoop.inputLocation=grateful-dead.kryo
gremlin.hadoop.outputLocation=output
gremlin.hadoop.jarsInDistributedCache=true

#
# GiraphGraphComputer Configuration
#
giraph.minWorkers=1
giraph.maxWorkers=1
giraph.useOutOfCoreGraph=true
giraph.useOutOfCoreMessages=true
mapred.map.child.java.opts=-Xmx1024m
mapred.reduce.child.java.opts=-Xmx1024m
giraph.numInputThreads=4
giraph.numComputeThreads=4
giraph.maxMessagesInMemory=100000

# tinkergraph-gryo.properties

gremlin.graph=org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph
gremlin.tinkergraph.graphFormat=gryo
gremlin.tinkergraph.graphLocation=/tmp/tinkergraph.kryo

Note	The path to TinkerGraph needs to be included in the `HADOOP_GREMLIN_LIBS` for the above example to work.

Input/Output Formats

adjacency-list Hadoop-Gremlin provides various I/O formats — i.e. Hadoop InputFormat and OutputFormat. All of the formats make use of an adjacency list representation of the graph where each "row" represents a single vertex, its properties, and its incoming and outgoing edges.

Gryo I/O Format

InputFormat: org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
OutputFormat: org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat

Gryo is a binary graph format that leverages Kryo to make a compact, binary representation of a vertex. It is recommended that users leverage Gryo given its space/time savings over text-based representations.

Note	The `GryoInputFormat` is splittable.

GraphSON I/O Format

InputFormat: org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONInputFormat
OutputFormat: org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONOutputFormat

GraphSON is a JSON based graph format. GraphSON is a space-expensive graph format in that it is a text-based markup language. However, it is convenient for many developers to work with as its structure is simple (easy to create and parse).

The data below represents an adjacency list representation of the classic TinkerGraph toy graph in GraphSON format.

{"id":1,"label":"person","outE":{"created":[{"id":9,"inV":3,"properties":{"weight":0.4}}],"knows":[{"id":7,"inV":2,"properties":{"weight":0.5}},{"id":8,"inV":4,"properties":{"weight":1.0}}]},"properties":{"name":[{"id":0,"value":"marko"}],"age":[{"id":1,"value":29}]}}
{"id":2,"label":"person","inE":{"knows":[{"id":7,"outV":1,"properties":{"weight":0.5}}]},"properties":{"name":[{"id":2,"value":"vadas"}],"age":[{"id":3,"value":27}]}}
{"id":3,"label":"software","inE":{"created":[{"id":9,"outV":1,"properties":{"weight":0.4}},{"id":11,"outV":4,"properties":{"weight":0.4}},{"id":12,"outV":6,"properties":{"weight":0.2}}]},"properties":{"name":[{"id":4,"value":"lop"}],"lang":[{"id":5,"value":"java"}]}}
{"id":4,"label":"person","inE":{"knows":[{"id":8,"outV":1,"properties":{"weight":1.0}}]},"outE":{"created":[{"id":10,"inV":5,"properties":{"weight":1.0}},{"id":11,"inV":3,"properties":{"weight":0.4}}]},"properties":{"name":[{"id":6,"value":"josh"}],"age":[{"id":7,"value":32}]}}
{"id":5,"label":"software","inE":{"created":[{"id":10,"outV":4,"properties":{"weight":1.0}}]},"properties":{"name":[{"id":8,"value":"ripple"}],"lang":[{"id":9,"value":"java"}]}}
{"id":6,"label":"person","outE":{"created":[{"id":12,"inV":3,"properties":{"weight":0.2}}]},"properties":{"name":[{"id":10,"value":"peter"}],"age":[{"id":11,"value":35}]}}

Script I/O Format

InputFormat: org.apache.tinkerpop.gremlin.hadoop.structure.io.script.ScriptInputFormat
OutputFormat: org.apache.tinkerpop.gremlin.hadoop.structure.io.script.ScriptOutputFormat

ScriptInputFormat and ScriptOutputFormat take an arbitrary script and use that script to either read or write Vertex objects, respectively. This can be considered the most general InputFormat/OutputFormat possible in that Hadoop-Gremlin uses the user provided script for all reading/writing.

ScriptInputFormat

The data below represents an adjacency list representation of the classic TinkerGraph toy graph. First line reads, "vertex 1, labeled person having 2 property values (marko and 29) has 3 outgoing edges; the first edge is labeled knows, connects the current vertex 1 with vertex 2 and has a property value 0.4, and so on."

1:person:marko:29 knows:2:0.5,knows:4:1.0,created:3:0.4
2:person:vadas:27
3:project:lop:java
4:person:josh:32 created:3:0.4,created:5:1.0
5:project:ripple:java
6:person:peter:35 created:3:0.2

There is no corresponding InputFormat that can parse this particular file (or some adjacency list variant of it). As such, ScriptInputFormat can be used. With ScriptInputFormat a script is stored in HDFS and leveraged by each mapper in the Hadoop job. The script must have the following method defined:

def parse(String line, ScriptElementFactory factory) { ... }

ScriptElementFactory is a legacy from previous versions and, although it’s still functional, it should no longer be used. In order to create vertices and edges, the parse() method gets access to a global variable named graph, which holds the local StarGraph for the current line/vertex.

An appropriate parse() for the above adjacency list file is:

def parse(line, factory) {
    def parts = line.split(/ /)
    def (id, label, name, x) = parts[0].split(/:/).toList()
    def v1 = graph.addVertex(T.id, id, T.label, label)
    if (name != null) v1.property('name', name) // first value is always the name
    if (x != null) {
        // second value depends on the vertex label; it's either
        // the age of a person or the language of a project
        if (label.equals('project')) v1.property('lang', x)
        else v1.property('age', Integer.valueOf(x))
    }
    if (parts.length == 2) {
        parts[1].split(/,/).grep { !it.isEmpty() }.each {
            def (eLabel, refId, weight) = it.split(/:/).toList()
            def v2 = graph.addVertex(T.id, refId)
            v1.addOutEdge(eLabel, v2, 'weight', Double.valueOf(weight))
        }
    }
    return v1
}

The resultant Vertex denotes whether the line parsed yielded a valid Vertex. As such, if the line is not valid (e.g. a comment line, a skip line, etc.), then simply return null.

ScriptOutputFormat Support

The principle above can also be used to convert a vertex to an arbitrary String representation that is ultimately streamed back to a file in HDFS. This is the role of ScriptOutputFormat. ScriptOutputFormat requires that the provided script maintains a method with the following signature:

def stringify(Vertex vertex) { ... }

An appropriate stringify() to produce output in the same format that was shown in the ScriptInputFormat sample is:

def stringify(vertex) {
    def v = vertex.values('name', 'age', 'lang').inject(vertex.id(), vertex.label()).join(':')
    def outE = vertex.outE().map {
        def e = it.get()
        e.values('weight').inject(e.label(), e.inV().next().id()).join(':')
    }.join(',')
    return [v, outE].join('\t')
}

Storage Systems

Hadoop-Gremlin provides two implementations of the Storage API:

FileSystemStorage: Access HDFS and local file system data.
SparkContextStorage: Access Spark persisted RDD data.

Interacting with HDFS

The distributed file system of Hadoop is called HDFS. The results of any OLAP operation are stored in HDFS accessible via hdfs. For local file system access, there is local.

gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> graph.compute(SparkGraphComputer).program(PeerPressureVertexProgram.build().create(graph)).mapReduce(ClusterCountMapReduce.build().memoryKey('clusterCount').create()).submit().get();
==>result[hadoopgraph[gryoinputformat->gryooutputformat],memory[size:2]]
gremlin> hdfs.ls()
==>rwxr-xr-x root supergroup 0 (D) _bsp
==>rw-r--r-- root supergroup 332226 grateful-dead.kryo
==>rwxr-xr-x root supergroup 0 (D) hadoop-gremlin-3.1.4-SNAPSHOT-libs
==>rwxr-xr-x root supergroup 0 (D) output
==>rw-r--r-- root supergroup 781 tinkerpop-modern.kryo
gremlin> hdfs.ls('output')
==>rwxr-xr-x root supergroup 0 (D) clusterCount
==>rwxr-xr-x root supergroup 0 (D) ~g
gremlin> hdfs.head('output', GryoInputFormat)
==>v[4]
==>v[1]
==>v[6]
==>v[3]
==>v[5]
==>v[2]
gremlin> hdfs.head('output', 'clusterCount', SequenceFileInputFormat)
==>2
gremlin> hdfs.rm('output')
==>true
gremlin> hdfs.ls()
==>rwxr-xr-x root supergroup 0 (D) _bsp
==>rw-r--r-- root supergroup 332226 grateful-dead.kryo
==>rwxr-xr-x root supergroup 0 (D) hadoop-gremlin-3.1.4-SNAPSHOT-libs
==>rw-r--r-- root supergroup 781 tinkerpop-modern.kryo

Interacting with Spark

If a Spark context is persisted, then Spark RDDs will remain the Spark cache and accessible over subsequent jobs. RDDs are retrieved and saved to the SparkContext via PersistedInputRDD and PersistedOutputRDD respectivly. Persisted RDDs can be accessed using spark.

gremlin> Spark.create('local[4]')
==>null
gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
==>hadoopgraph[gryoinputformat->gryooutputformat]
gremlin> graph.configuration().setProperty('gremlin.spark.graphOutputRDD', PersistedOutputRDD.class.getCanonicalName())
==>null
gremlin> graph.configuration().clearProperty('gremlin.hadoop.graphOutputFormat')
==>null
gremlin> graph.configuration().setProperty('gremlin.spark.persistContext',true)
==>null
gremlin> graph.compute(SparkGraphComputer).program(PeerPressureVertexProgram.build().create(graph)).mapReduce(ClusterCountMapReduce.build().memoryKey('clusterCount').create()).submit().get();
==>result[hadoopgraph[inputrddformat->no-output],memory[size:2]]
gremlin> spark.ls()
==>output/clusterCount [Memory Deserialized 1x Replicated]
==>output/~g [Memory Deserialized 1x Replicated]
gremlin> spark.ls('output')
==>output/clusterCount [Memory Deserialized 1x Replicated]
==>output/~g [Memory Deserialized 1x Replicated]
gremlin> spark.head('output', PersistedInputRDD)
==>v[4]
==>v[1]
==>v[6]
==>v[3]
==>v[5]
==>v[2]
gremlin> spark.head('output', 'clusterCount', PersistedInputRDD)
==>2
gremlin> spark.rm('output')
==>true
gremlin> spark.ls()

A Command Line Example

The classic PageRank centrality algorithm can be executed over the TinkerPop graph from the command line using GiraphGraphComputer.

Warning

Be sure that the HADOOP_GREMLIN_LIBS references the location lib directory of the respective GraphComputer engine being used or else the requisite dependencies will not be uploaded to the Hadoop cluster.

$ hdfs dfs -copyFromLocal data/tinkerpop-modern.json tinkerpop-modern.json
$ hdfs dfs -ls
Found 2 items
-rw-r--r--   1 marko supergroup       2356 2014-07-28 13:00 /user/marko/tinkerpop-modern.json
$ hadoop jar target/giraph-gremlin-3.1.4-SNAPSHOT-job.jar org.apache.tinkerpop.gremlin.giraph.process.computer.GiraphGraphComputer ../hadoop-gremlin/conf/hadoop-graphson.properties
15/09/11 08:02:08 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
15/09/11 08:02:11 INFO computer.GiraphGraphComputer: HadoopGremlin(Giraph): PageRankVertexProgram[alpha=0.85,iterations=30]
15/09/11 08:02:12 INFO mapreduce.JobSubmitter: number of splits:3
15/09/11 08:02:12 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1441915907347_0028
15/09/11 08:02:12 INFO impl.YarnClientImpl: Submitted application application_1441915907347_0028
15/09/11 08:02:12 INFO job.GiraphJob: Tracking URL: http://markos-macbook:8088/proxy/application_1441915907347_0028/
15/09/11 08:02:12 INFO job.GiraphJob: Waiting for resources... Job will start only when it gets all 3 mappers
15/09/11 08:03:54 INFO mapreduce.Job: Running job: job_1441915907347_0028
15/09/11 08:03:55 INFO mapreduce.Job: Job job_1441915907347_0028 running in uber mode : false
15/09/11 08:03:55 INFO mapreduce.Job:  map 33% reduce 0%
15/09/11 08:03:57 INFO mapreduce.Job:  map 67% reduce 0%
15/09/11 08:04:01 INFO mapreduce.Job:  map 100% reduce 0%
15/09/11 08:06:17 INFO mapreduce.Job: Job job_1441915907347_0028 completed successfully
15/09/11 08:06:17 INFO mapreduce.Job: Counters: 80
    File System Counters
        FILE: Number of bytes read=0
        FILE: Number of bytes written=483918
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=1465
        HDFS: Number of bytes written=1760
        HDFS: Number of read operations=39
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=20
    Job Counters
        Launched map tasks=3
        Other local map tasks=3
        Total time spent by all maps in occupied slots (ms)=458105
        Total time spent by all reduces in occupied slots (ms)=0
        Total time spent by all map tasks (ms)=458105
        Total vcore-seconds taken by all map tasks=458105
        Total megabyte-seconds taken by all map tasks=469099520
    Map-Reduce Framework
        Map input records=3
        Map output records=0
        Input split bytes=132
        Spilled Records=0
        Failed Shuffles=0
        Merged Map outputs=0
        GC time elapsed (ms)=1594
        CPU time spent (ms)=0
        Physical memory (bytes) snapshot=0
        Virtual memory (bytes) snapshot=0
        Total committed heap usage (bytes)=527958016
    Giraph Stats
        Aggregate edges=0
        Aggregate finished vertices=0
        Aggregate sent message message bytes=13535
        Aggregate sent messages=186
        Aggregate vertices=6
        Current master task partition=0
        Current workers=2
        Last checkpointed superstep=0
        Sent message bytes=438
        Sent messages=6
        Superstep=31
    Giraph Timers
        Initialize (ms)=2996
        Input superstep (ms)=5209
        Setup (ms)=59
        Shutdown (ms)=9324
        Superstep 0 GiraphComputation (ms)=3861
        Superstep 1 GiraphComputation (ms)=4027
        Superstep 10 GiraphComputation (ms)=4000
        Superstep 11 GiraphComputation (ms)=4004
        Superstep 12 GiraphComputation (ms)=3999
        Superstep 13 GiraphComputation (ms)=4000
        Superstep 14 GiraphComputation (ms)=4005
        Superstep 15 GiraphComputation (ms)=4003
        Superstep 16 GiraphComputation (ms)=4001
        Superstep 17 GiraphComputation (ms)=4007
        Superstep 18 GiraphComputation (ms)=3998
        Superstep 19 GiraphComputation (ms)=4006
        Superstep 2 GiraphComputation (ms)=4007
        Superstep 20 GiraphComputation (ms)=3996
        Superstep 21 GiraphComputation (ms)=4006
        Superstep 22 GiraphComputation (ms)=4002
        Superstep 23 GiraphComputation (ms)=3998
        Superstep 24 GiraphComputation (ms)=4003
        Superstep 25 GiraphComputation (ms)=4001
        Superstep 26 GiraphComputation (ms)=4003
        Superstep 27 GiraphComputation (ms)=4005
        Superstep 28 GiraphComputation (ms)=4002
        Superstep 29 GiraphComputation (ms)=4001
        Superstep 3 GiraphComputation (ms)=3988
        Superstep 30 GiraphComputation (ms)=4248
        Superstep 4 GiraphComputation (ms)=4010
        Superstep 5 GiraphComputation (ms)=3998
        Superstep 6 GiraphComputation (ms)=3996
        Superstep 7 GiraphComputation (ms)=4005
        Superstep 8 GiraphComputation (ms)=4009
        Superstep 9 GiraphComputation (ms)=3994
        Total (ms)=138788
    File Input Format Counters
        Bytes Read=0
    File Output Format Counters
        Bytes Written=0
$ hdfs dfs -cat output/~g/*
{"id":1,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.15000000000000002}],"name":[{"id":0,"value":"marko"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":3.0}],"age":[{"id":1,"value":29}]}}
{"id":5,"label":"software","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":35,"value":0.23181250000000003}],"name":[{"id":8,"value":"ripple"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":6,"value":0.0}],"lang":[{"id":9,"value":"java"}]}}
{"id":3,"label":"software","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.4018125}],"name":[{"id":4,"value":"lop"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":0.0}],"lang":[{"id":5,"value":"java"}]}}
{"id":4,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.19250000000000003}],"name":[{"id":6,"value":"josh"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":2.0}],"age":[{"id":7,"value":32}]}}
{"id":2,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":35,"value":0.19250000000000003}],"name":[{"id":2,"value":"vadas"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":6,"value":0.0}],"age":[{"id":3,"value":27}]}}
{"id":6,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":35,"value":0.15000000000000002}],"name":[{"id":10,"value":"peter"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":6,"value":1.0}],"age":[{"id":11,"value":35}]}}

Vertex 4 ("josh") is isolated below:

{
  "id":4,
  "label":"person",
  "properties": {
    "gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.19250000000000003}],
    "name":[{"id":6,"value":"josh"}],
    "gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":2.0}],
    "age":[{"id":7,"value":32}]}
  }
}

Conclusion

tinkerpop-character The world that we know, you and me, is but a subset of the world that Gremlin has weaved within The TinkerPop. Gremlin has constructed a fully connected graph and only the subset that makes logical sense to our traversing thoughts is the fragment we have come to know and have come to see one another within. But there are many more out there, within other webs of logics unfathomed. From any thought, every other thought, we come to realize that which is — The TinkerPop.

Acknowledgements

YourKit supports the TinkerPop open source project with its full-featured Java Profiler. YourKit, LLC is the creator of innovative and intelligent tools for profiling Java and .NET applications. YourKit’s leading software products: YourKit Java Profiler and YourKit .NET Profiler

ketrina-tinkerpop3 Ketrina Yim — Designing Gremlin and his friends for TinkerPop was one of my first major projects as a freelancer, and it’s delightful to see them on the Web and all over the documentation! Drawing and tweaking the characters over time is like watching them grow up. They’ve gone from sketches on paper to full-color logos, and from logos to living characters that cheerfully greet visitors to the TinkerPop website. And it’s been a great time all throughout!

…in the beginning.