3.3.3

# 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 wondered, 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 Gremlin 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 from nothing more than nothing. With each step towards The TinkerPop, more 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>3.3.3</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.

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

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);

1. Create a new in-memory `TinkerGraph` and assign it to the variable `graph`.

2. 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.

3. 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.

    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

1. 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

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:

1. Start at vertex 1.

2. Walk the incident knows-edges to the respective adjacent friend vertices of 1.

3. Move from those friend-vertices to software-vertices via created-edges.

4. 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.

1. `GraphTraversalSource.V(Object…​ ids)`: generates a traversal starting at vertices in the graph (if no ids are provided, all vertices).

2. `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.

1. `map`: transform the incoming traverser’s object to another object (S → E).

2. `flatMap`: transform the incoming traverser’s object to an iterator of other objects (S → E*).

3. `filter`: allow or disallow the traverser from proceeding to the next step (S → E ⊆ S).

4. `sideEffect`: allow the traverser to proceed unchanged, but yield some computational sideEffect in the process (S ↬ S).

5. `branch`: split the traverser and send each to an arbitrary location in the traversal (S → { S1 → E*, …​, Sn → 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()        //2
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
    gremlin> g.V().has('name','marko').out('knows').values('name') //3
    ==>vadas
    ==>josh

1. Open the toy graph and reference it by the variable `graph`.

2. Create a graph traversal source from the graph using the standard, OLTP traversal engine.

3. 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

1. Set the variable `marko` to the vertex in the graph `g` named "marko".

2. Get the vertices that are outgoing adjacent to the marko-vertex via knows-edges.

3. 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-Python (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 variants 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— 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('person','friend').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("person","friend").by((Function<Vertex, Integer>) v -> v.<String>value("name").length()) //4

1. All the non-lambda step chaining is identical in Gremlin-Groovy and Gremlin-Java. However, note that Groovy supports `'` strings as well as `"` strings.

2. 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.

3. The syntax for method references differs slightly between Java and Gremlin-Groovy.

4. Groovy is lenient on object typing and Java is not. When the parameter type of the lambda is not known, typecasting is required.

Please see the Gremlin Variants section for more information on this topic.

## Graph System 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:

1. It tells users the capabilities of their `Graph` instance.

2. 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
    >-- Transactions: false
    >-- Computer: true
    >-- Persistence: true
    >-- ConcurrentAccess: false
    >-- ThreadedTransactions: false
    > VariableFeatures
    >-- Variables: true
    >-- DoubleArrayValues: true
    >-- BooleanValues: true
    >-- ByteValues: true
    >-- DoubleValues: true
    >-- FloatValues: true
    >-- IntegerValues: true
    >-- LongValues: true
    >-- MapValues: true
    >-- MixedListValues: true
    >-- SerializableValues: true
    >-- StringValues: true
    >-- UniformListValues: true
    >-- BooleanArrayValues: true
    >-- FloatArrayValues: true
    >-- ByteArrayValues: true
    >-- IntegerArrayValues: true
    >-- LongArrayValues: true
    >-- StringArrayValues: true
    > VertexFeatures
    >-- MetaProperties: true
    >-- AddVertices: true
    >-- RemoveVertices: true
    >-- DuplicateMultiProperties: true
    >-- MultiProperties: true
    >-- AddProperty: true
    >-- RemoveProperty: true
    >-- NumericIds: true
    >-- StringIds: true
    >-- UuidIds: true
    >-- CustomIds: false
    >-- AnyIds: true
    >-- UserSuppliedIds: true
    > VertexPropertyFeatures
    >-- RemoveProperty: true
    >-- NumericIds: true
    >-- StringIds: true
    >-- UuidIds: true
    >-- CustomIds: false
    >-- AnyIds: true
    >-- UserSuppliedIds: true
    >-- Properties: true
    >-- DoubleArrayValues: true
    >-- BooleanValues: true
    >-- ByteValues: true
    >-- DoubleValues: true
    >-- FloatValues: true
    >-- IntegerValues: true
    >-- LongValues: true
    >-- MapValues: true
    >-- MixedListValues: true
    >-- SerializableValues: true
    >-- StringValues: true
    >-- UniformListValues: true
    >-- BooleanArrayValues: true
    >-- FloatArrayValues: true
    >-- ByteArrayValues: true
    >-- IntegerArrayValues: true
    >-- LongArrayValues: true
    >-- StringArrayValues: true
    > EdgeFeatures
    >-- AddEdges: true
    >-- RemoveEdges: true
    >-- AddProperty: true
    >-- RemoveProperty: true
    >-- NumericIds: true
    >-- StringIds: true
    >-- UuidIds: true
    >-- CustomIds: false
    >-- AnyIds: true
    >-- UserSuppliedIds: true
    > EdgePropertyFeatures
    >-- Properties: true
    >-- DoubleArrayValues: true
    >-- BooleanValues: true
    >-- ByteValues: true
    >-- DoubleValues: true
    >-- FloatValues: true
    >-- IntegerValues: true
    >-- LongValues: true
    >-- MapValues: true
    >-- MixedListValues: true
    >-- SerializableValues: true
    >-- StringValues: true
    >-- UniformListValues: true
    >-- BooleanArrayValues: true
    >-- FloatArrayValues: true
    >-- ByteArrayValues: true
    >-- IntegerArrayValues: true
    >-- LongArrayValues: true
    >-- StringArrayValues: 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

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:

1. Multiple properties (multi-properties): a vertex property key can have multiple values. For example, a vertex can have multiple "name" properties.

2. 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:

1. Permissions: Vertex properties can have key/value ACL-type permission information associated with them.

2. Auditing: When a vertex property is manipulated, it can have key/value information attached to it saying who the creator, deletor, etc. are.

3. 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()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> v = g.addV().property('name','marko').property('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

1. A vertex can have zero or more properties with the same key associated with it.

2. If a property is added with a cardinality of `Cardinality.list`, an additional property with the provided key will be added.

3. A vertex property can have standard key/value properties attached to it.

4. Vertex property removal is identical to property removal.

5. Gets the meta-properties of each vertex property.

6. A vertex property can have any number of key/value properties attached to it.

7. `property(…​)` will remove all existing key’d properties before adding the new single property (see `VertexProperty.Cardinality`).

8. 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'])
    gremlin> graph.variables().set('systemUsers',['matthias','marko','josh'])
    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')
    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

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. HTTP 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.

Warning | TinkerPop provides for basic transaction control, however, like many aspects of TinkerPop, it is up to the graph system provider to choose the specific aspects of how their implementation will work and how it fits into the TinkerPop stack. Be sure to understand the transaction semantics of the specific graph implementation that is being utilized as it may present differing functionality than described here.

### 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

1. Check `features` to ensure that the graph supports transactions.

2. By default, `Neo4jGraph` is configured with "automatic" transactions, so it is set here for demonstration purposes only.

3. 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.

4. Calling `commit` finalizes the transaction.

5. Change transaction behavior to require manual control.

6. Adding a vertex now results in failure because the transaction was not explicitly opened.

7. Explicitly open a transaction.

8. 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.

### 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

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`.

Note | Additional documentation for TinkerPop IO formats can be found in the IO Reference.

### GraphML Reader/Writer

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().create().readGraph(stream, newGraph);
    }

GraphML was a supported format in TinkerPop 2.x, but there were several issues that made it inconsistent with the specification that were corrected for 3.x. As a result, attempting to read a GraphML file generated by 2.x with the 3.x `GraphMLReader` will result in error. To help with this problem, an XSLT file is provided as a resource in `gremlin-core` which will transform 2.x GraphML to 3.x GraphML. It can be used as follows:

    import javax.xml.parsers.DocumentBuilderFactory;
    import javax.xml.transform.TransformerFactory;
    import javax.xml.transform.dom.DOMSource;
    import javax.xml.transform.stream.StreamSource;
    import javax.xml.transform.stream.StreamResult;

    InputStream stylesheet = Thread.currentThread().getContextClassLoader().getResourceAsStream("tp2-to-tp3-graphml.xslt");
    File datafile = new File('/tmp/tp2-graphml.xml');
    File outfile = new File('/tmp/tp3-graphml.xml');

    TransformerFactory tFactory = TransformerFactory.newInstance();
    StreamSource stylesource = new StreamSource(stylesheet);
    Transformer transformer = tFactory.newTransformer(stylesource);

    StreamSource source = new StreamSource(datafile);
    StreamResult result = new StreamResult(new FileWriter(outfile));
    transformer.transform(source, result);

### GraphSON Reader/Writer

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(graphson()).writeGraph("tinkerpop-modern.json");

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

Note | Using `graphson()`, which is a static helper method of `IoCore`, will default to the most current version of GraphSON which is 3.0.

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(graphson()).writer().mapper(mapper).create().writeGraph(os, graph)
    }

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

The following example shows how 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 ByteArrayOutputStream()
    ==>
    gremlin> graph.io(graphson()).writer().create().writeVertex(f, g.V(1).next(), BOTH)
    gremlin> f.close()

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

    {
        "id": {
            "@type": "g:Int32",
            "@value": 1
        },
        "label": "person",
        "outE": {
            "created": [{
                "id": {
                    "@type": "g:Int32",
                    "@value": 9
                },
                "inV": {
                    "@type": "g:Int32",
                    "@value": 3
                },
                "properties": {
                    "weight": {
                        "@type": "g:Double",
                        "@value": 0.4
                    }
                }
            }],
            "knows": [{
                "id": {
                    "@type": "g:Int32",
                    "@value": 7
                },
                "inV": {
                    "@type": "g:Int32",
                    "@value": 2
                },
                "properties": {
                    "weight": {
                        "@type": "g:Double",
                        "@value": 0.5
                    }
                }
            }, {
                "id": {
                    "@type": "g:Int32",
                    "@value": 8
                },
                "inV": {
                    "@type": "g:Int32",
                    "@value": 4
                },
                "properties": {
                    "weight": {
                        "@type": "g:Double",
                        "@value": 1.0
                    }
                }
            }]
        },
        "properties": {
            "name": [{
                "id": {
                    "@type": "g:Int64",
                    "@value": 0
                },
                "value": "marko"
            }],
            "age": [{
                "id": {
                    "@type": "g:Int64",
                    "@value": 1
                },
                "value": {
                    "@type": "g:Int32",
                    "@value": 29
                }
            }]
        }
    }

GraphSON has several versions and each has differences that prevent complete compatibility with one another. While the default version provided by `IoCore.graphson()` is recommended, it is possible to make changes to revert to an earlier version. The following shows an example of how to use 1.0 (with type embedding):

    gremlin> graph = TinkerFactory.createModern()
    ==>tinkergraph[vertices:6 edges:6]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
    gremlin> f = new ByteArrayOutputStream()
    ==>
    gremlin> mapper = graph.io(GraphSONIo.build(GraphSONVersion.V1_0)).mapper().typeInfo(TypeInfo.PARTIAL_TYPES).create()
    ==>org.apache.tinkerpop.gremlin.structure.io.graphson.GraphSONMapper@54bb1194
    gremlin> graph.io(GraphSONIo.build(GraphSONVersion.V1_0)).writer().mapper(mapper).create().writeVertex(f, g.V(1).next(), BOTH)
    gremlin> f.close()

Note | Additional documentation for GraphSON can be found in the IO Reference.

Important | When using the extended type system in Gremlin Server, support for these types when used in the context of Gremlin Language Variants is dependent on the programming language, the driver and its serializers. These implementations are only required to support the core types and not the extended ones.

### Gryo Reader/Writer

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(gryo()).writeGraph("tinkerpop-modern.kryo");

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

Note | Using `gryo()`, which is a static helper method of `IoCore`, will default to the most current version of Gryo which is 3.0.

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(GryoIo.build(GryoVersion.V1_0)).writer().create().writeGraph(os, graph);
    }

    final Graph newGraph = TinkerGraph.open();
    try (final InputStream stream = new FileInputStream("tinkerpop-modern.kryo")) {
        newGraph.io(GryoIo.build(GryoVersion.V1_0)).reader().create().readGraph(stream, newGraph);
    }

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

### TinkerPop2 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()
    ==>3.3.3
    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()
    ==>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:

1. `Step<S,E>`: an individual function applied to `S` to yield `E`. Steps are chained within a traversal.

2. `TraversalStrategy`: interceptor methods to alter the execution of the traversal (e.g. query re-writing).

3. `TraversalSideEffects`: key/value pairs that can be used to store global information about the traversal.

4. `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.

Important | The basics for starting a traversal are described in The Graph Process section as well as in the Getting Started tutorial.

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.

### General Steps

There are five general steps, each having a traversal and a lambda representation, by which all other specific steps described later extend.

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

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 in favor of their traversals representation and traversal verification strategies exist to disallow their use unless explicitly "turned off." For more information on the problems with lambdas, please read A Note on Lambdas.

The `Traverser<S>` object provides access to:

1. The current traversed `S` object —`Traverser.get()`.

2. 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)`.

3. The number of times the traverser has gone through the current loop —`Traverser.loops()`.

4. The number of objects represented by this traverser —`Traverser.bulk()`.

5. The local data structure associated with this traverser —`Traverser.sack()`.

6. 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)`.

    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
    gremlin> g.V(1).out().map(values('name')) //3\
    ==>lop
    ==>vadas
    ==>josh

1. An outgoing traversal from vertex 1 to the name values of the adjacent vertices.

2. The same operation, but using a lambda to access the name property values.

3. Again the same operation, but using the traversal representation of `map()`.

    gremlin> g.V().filter {it.get().label() == 'person'} //1\
    ==>v[1]
    ==>v[2]
    ==>v[4]
    ==>v[6]
    gremlin> g.V().filter(label().is('person')) //2\
    ==>v[1]
    ==>v[2]
    ==>v[4]
    ==>v[6]
    gremlin> g.V().hasLabel('person') //3\
    ==>v[1]
    ==>v[2]
    ==>v[4]
    ==>v[6]

1. A filter that only allows the vertex to pass if it has the "person" label

2. The same operation, but using the traversal representation of `filter()`.

3. The more specific `has()`-step is implemented as a `filter()` with respective predicate.

    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]
    gremlin> g.V().sideEffect(outE().count().store("o")).
                   sideEffect(inE().count().store("i")).cap("o","i") //2\
    ==>[i:[0,0,1,1,1,3],o:[3,0,0,0,2,1]]

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

2. Compute the out- and in-degree for each vertex. Both `sideEffect()` are fed with the same vertex.

    gremlin> g.V().branch {it.get().value('name')}.
                   option('marko', values('age')).
                   option(none, values('name')) //1\
    ==>29
    ==>vadas
    ==>lop
    ==>josh
    ==>ripple
    ==>peter
    gremlin> g.V().branch(values('name')).
                   option('marko', values('age')).
                   option(none, values('name')) //2\
    ==>29
    ==>vadas
    ==>lop
    ==>josh
    ==>ripple
    ==>peter
    gremlin> g.V().choose(has('name','marko'),
                          values('age'),
                          values('name')) //3\
    ==>29
    ==>vadas
    ==>lop
    ==>josh
    ==>ripple
    ==>peter

1. If the vertex is "marko", get his age, else get the name of the vertex.

2. The same operation, but using the traversal representing of `branch()`.

3. The more specific boolean-based `choose()`-step is implemented as a `branch()`.

### Terminal Steps

Typically, when a step is concatenated to a traversal a traversal is returned. In this way, a traversal is built up in a fluent, monadic fashion. However, some steps do not return a traversal, but instead, execute the traversal and return a result. These steps are known as terminal steps (terminal) and they are explained via the examples below.

    gremlin> g.V().out('created').hasNext() //1\
    ==>true
    gremlin> g.V().out('created').next() //2\
    ==>v[3]
    gremlin> g.V().out('created').next(2) //3\
    ==>v[3]
    ==>v[5]
    gremlin> g.V().out('nothing').tryNext() //4\
    ==>Optional.empty
    gremlin> g.V().out('created').toList() //5\
    ==>v[3]
    ==>v[5]
    ==>v[3]
    ==>v[3]
    gremlin> g.V().out('created').toSet() //6\
    ==>v[3]
    ==>v[5]
    gremlin> g.V().out('created').toBulkSet() //7\
    ==>v[3]
    ==>v[3]
    ==>v[3]
    ==>v[5]
    gremlin> results = ['blah',3]
    ==>blah
    ==>3
    gremlin> g.V().out('created').fill(results) //8\
    ==>blah
    ==>3
    ==>v[3]
    ==>v[5]
    ==>v[3]
    ==>v[3]
    gremlin> g.addV('person').iterate() // (9)

1. `hasNext()` determines whether there are available results.

2. `next()` will return the next result.

3. `next(n)` will return the next `n` results in a list.

4. `tryNext()` will return an `Optional` and thus, is a composite of `hasNext()`/`next()`.

5. `toList()` will return all results in a list.

6. `toSet()` will return all results in a set (thus, duplicates removed).

7. `toBulkSet()` will return all results in a weighted set (thus, duplicates preserved via weighting).

8. `fill(collection)` will put all results in the provided collection and return the collection when complete.

9. `iterate()` does not exactly fit the definition of a terminal step in that it doesn’t return a result, but still returns a traversal - it does however behave as a terminal step in that it iterates the traversal and generates side effects without returning the actual result.

Finally, `explain()`-step is also a terminal step and is described in its own section.

### 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[13][1-co-developer->4]
    ==>e[14][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[15][3-createdBy->4]
    ==>e[16][5-createdBy->4]
    gremlin> g.V().as('a').out('created').addE('createdBy').to('a').property('acl','public') //3\
    ==>e[17][3-createdBy->1]
    ==>e[18][5-createdBy->4]
    ==>e[19][3-createdBy->4]
    ==>e[20][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,23).property('project',select('c').values('name')) //5\
    ==>e[23][1-friendlyCollaborator->4]
    gremlin> g.E(23).valueMap()
    ==>[project:lop]
    gremlin> marko = g.V().has('name','marko').next()
    ==>v[1]
    gremlin> peter = g.V().has('name','peter').next()
    ==>v[6]
    gremlin> g.V(marko).addE('knows').to(peter) //6\
    ==>e[24][1-knows->6]
    gremlin> g.addE('knows').from(marko).to(peter) //7\
    ==>e[25][1-knows->6]

1. Add a co-developer edge with a year-property between marko and his collaborators.

2. Add incoming createdBy edges from the josh-vertex to the lop- and ripple-vertices.

3. Add an inverse createdBy edge for all created edges.

4. The newly created edge is a traversable object.

5. Two arbitrary bindings in a traversal can be joined `from()`→`to()`, where `id` can be provided for graphs that supports user provided ids.

6. Add an edge between marko and peter given the directed (detached) vertex references.

7. Add an edge between marko and peter given the directed (detached) vertex references.

Additional References

`addE(String)`, `addE(Traversal)`

### 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[13]
    gremlin> g.V().values('name')
    ==>marko
    ==>vadas
    ==>lop
    ==>josh
    ==>ripple
    ==>peter
    ==>stephen
    gremlin> g.V().outE('knows').addV().property('name','nothing')
    ==>v[15]
    ==>v[17]
    gremlin> g.V().has('name','nothing')
    ==>v[17]
    ==>v[15]
    gremlin> g.V().has('name','nothing').bothE()

Additional References

`addV()`, `addV(String)`), `addV(Traversal)`

### 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]

1. For vertices, a cardinality can be provided for vertex properties.

2. It is possible to select the property value (as well as key) via a traversal.

3. For vertices, the `property()`-step can add meta-properties.

Additional References

`property(Object, Object, Object…​)`, `property(Cardinality, Object, Object, Object…​)`, `Cardinality`

### 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

1. What has marko created?

2. Aggregate all his creations.

3. Who are marko’s collaborators?

4. What have marko’s collaborators created?

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

Additional References

`aggregate(String)`

### 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

Additional References

`and(Traversal…​)`

### 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]

1. Select the objects labeled "a" and "b" from the path.

2. 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]]

Additional References

`as(String,String…​)`

### Barrier Step

The `barrier()`-step (barrier) turns 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')
    gremlin> g = graph.traversal().withoutStrategies(LazyBarrierStrategy) //1\
    ==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
    gremlin> clockWithResult(1){g.V().both().both().both().count().next()} //2\
    ==>10339.5204
    ==>126653966
    gremlin> clockWithResult(1){g.V().repeat(both()).times(3).count().next()} //3\
    ==>29.239853
    ==>126653966
    gremlin> clockWithResult(1){g.V().both().barrier().both().barrier().both().barrier().count().next()} //4\
    ==>14.841833
    ==>126653966

1. Explicitly remove `LazyBarrierStrategy` which yields a bulking optimization.

2. A non-bulking traversal where each traverser is processed.

3. Each traverser entering `repeat()` has its recursion bulked.

4. 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 with the added benefit of reducing the risk of an out-of-memory exception.

`LazyBarrierStrategy` inserts `barrier()`-steps into 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')
    gremlin> g = graph.traversal() //1\
    ==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
    gremlin> clockWithResult(1){g.V().both().both().both().count().next()}
    ==>17.782709
    ==>126653966
    gremlin> g.V().both().both().both().count().iterate().toString() //2\
    ==>[TinkerGraphStep(vertex,[]), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), VertexStep(BOTH,vertex), NoOpBarrierStep(2500), VertexStep(BOTH,edge), CountGlobalStep, NoneStep]

1. `LazyBarrierStrategy` is a default strategy and thus, does not need to be explicitly activated.

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

Additional References

`barrier()`, `barrier(Consumer)`, `barrier(int)`

### 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]

1. `by(outE().count())` will group the elements by their edge count (traversal).

2. `by('name')` will process the grouped elements by their name (element property projection).

3. `by(count())` will count the number of elements in each group (traversal).

The following steps all support `by()`-modulation. Note that the semantics of such modulation should be understood on a step-by-step level and thus, as discussed in their respective section of the documentation.

- `dedup()`: dedup on the results of a `by()`-modulation.

- `cyclicPath()`: filter if the traverser’s path is cyclic given `by()`-modulation.

- `simplePath()`: filter if the traverser’s path is simple given `by()`-modulation.

- `sample()`: sample using the value returned by `by()`-modulation.

- `where()`: determine the predicate given the testing of the results of `by()`-modulation.

- `groupCount()`: count those groups where the group keys are the result of `by()`-modulation.

- `group()`: create group keys and values according to `by()`-modulation.

- `order()`: order the objects by the results of a `by()`-modulation.

- `path()`: get the path of the traverser where each path element is `by()`-modulated.

- `project()`: project a map of results given various `by()`-modulations off the current object.

- `select()`: select path elements and transform them via `by()`-modulation.

- `tree()`: get a tree of traversers objects where the objects have been `by()`-modulated.

- `aggregate()`: aggregate all objects into a set but only store their `by()`-modulated values.

- `store()`: store all objects into a set but only store their `by()`-modulated values.

Additional References

`by()`, `by(Comparator)`, `by(Function,Comparator)`, `by(Function)`, `by(Order)`, `by(String)`, `by(String,Comparator)`, `by(T)`, `by(Traversal)`, `by(Traversal,Comparator)`, `T`, `Order`

### 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]]

1. Group and count vertices by their label. Emit the side effect labeled 'a', which is the group count by label.

2. Same as statement 1, but also emit the side effect labeled 'b' which groups vertices by the number of out edges.

Additional References

`cap(String,String…​)`

### Choose Step

The `choose()`-step (branch) routes the current traverser to a particular traversal branch option. With `choose()`, it is possible to implement if/then/else-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

1. If the traversal yields an element, then do `in`, else do `out` (i.e. true/false-based option selection).

2. Use the result of the traversal as a key to the map of traversal options (i.e. value-based option selection).

If the "false"-branch is not provided, then if/then-semantics are implemented.

    gremlin> g.V().choose(hasLabel('person'), out('created')).values('name') //1\
    ==>lop
    ==>lop
    ==>ripple
    ==>lop
    ==>ripple
    ==>lop
    gremlin> g.V().choose(hasLabel('person'), out('created'), identity()).values('name') //2\
    ==>lop
    ==>lop
    ==>ripple
    ==>lop
    ==>ripple
    ==>lop

1. If the vertex is a person, emit the vertices they created, else emit the vertex.

2. If/then/else with an `identity()` on the false-branch is equivalent to if/then with no false-branch.

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

Additional References

`choose(Function)`, `choose(Predicate,Traversal)`, `choose(Predicate,Traversal,Traversal)`, `choose(Traversal,Traversal)`, `choose(Traversal,Traversal,Traversal)`, `choose(Traversal)`

### 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

Additional References

`coalesce(Traversal…​)`

### 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[5]
    ==>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]

Additional References

`coin(double)`

### 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

1. Show the names of people, but show "inhuman" for other vertices.

2. Same as statement 1 (unless there is a person vertex with no name).

Additional References

`constant(Object)`

### 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]

1. `count()`-step is a reducing barrier step meaning that all of the previous traversers are folded into a new traverser.

2. 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.

Additional References

`count()`, `count(Scope)`, `Scope`

### 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]]
    gremlin> g.V(1).as('a').out('created').as('b').
               in('created').as('c').
               cyclicPath().
               path()
    ==>[v[1],v[3],v[1]]
    gremlin> g.V(1).as('a').out('created').as('b').
               in('created').as('c').
               cyclicPath().from('a').to('b').
               path()

Additional References

`cyclicPath()`

### 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]]

1. 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,name:[marko],label:person]
    ==>[id:2,name:[vadas],label:person]
    ==>[id:3,name:[lop],label:software]
    ==>[id:4,name:[josh],label:person]
    ==>[id:5,name:[ripple],label:software]
    ==>[id:6,name:[peter],label:person]
    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]]

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

Additional References

`dedup(Scope,String…​)`, `dedup(String…​)`, `Scope`

### 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()

Additional References

- `drop()`

### Emit Step

The `emit`-step is not an actual step, but is instead a step modulator for `repeat()` (find more documentation on the `emit()` there).

Additional References

`emit()`, `emit(Predicate)`, `emit(Traversal)`

### Explain Step

The `explain()`-step (terminal) 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))]
    MatchPredicateStrategy       [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
    FilterRankingStrategy        [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
    InlineFilterStrategy         [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
    IncidentToAdjacentStrategy   [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
    RepeatUnrollStrategy         [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
    PathRetractionStrategy       [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), CountGlobalStep, IsStep(gt(5))]
    CountStrategy                [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
    AdjacentToIncidentStrategy   [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
    LazyBarrierStrategy          [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
    TinkerGraphCountStrategy     [P]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
    TinkerGraphStepStrategy      [P]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
    ProfileStrategy              [F]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
    StandardVerificationStrategy [V]   [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]

    Final Traversal                    [TinkerGraphStep(vertex,[~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), 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

1. A parameterless `fold()` will aggregate all the objects into a list and then emit the list.

2. A verification of the type of list returned.

3. `fold()` can be provided two arguments —  a seed value and a reduce bi-function ("vadas" is 5 characters + "josh" with 4 characters).

4. What is the total age of the people in the graph?

5. The same as before, but using a built-in bi-function.

6. The same as before, but using the `sum()`-step.

Additional References

`fold()`, `fold(Object,BiFunction)`

### From Step

The `from()`-step is not an actual step, but instead is a "step-modulator" similar to `as()` and `by()`. If a step is able to accept traversals or strings then `from()` is the means by which they are added. The general pattern is `step().from()`. See `to()`-step.

The list of steps that support `from()`-modulation are: `simplePath()`, `cyclicPath()`, `path()`, and `addE()`.

Additional References

`from(String)`, `from(Traversal)`, `from(Vertex)`

### 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[13][1-uses->3]
    ==>e[14][1-uses->5]
    ==>e[15][2-uses->3]
    ==>e[16][2-uses->5]
    ==>e[17][4-uses->3]
    ==>e[18][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({~from=[[SelectOneStep(last,person)]], label=[uses]})]
    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({~from=[[SelectOneStep(last,person)]], label=[uses]}), NoneStep]

1. Normally the `V()`-step will iterate over all vertices. However, graph strategies can fold `HasContainer’s into a `GraphStep` to allow index lookups.

2. 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.

Additional References

`V(Object…​)`

### 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]

1. Group the vertices by their label.

2. For each vertex in the group, get their name.

3. For each grouping, what is its size?

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

1. Key-projection: What feature of the object to group on (a function that yields the map key)?

2. Value-projection: What feature of the group to store in the key-list?

Additional References

`group()`, `group(String)`

### 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]

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.

Additional References

`groupCount()`, `groupCount(String)`

### 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 the property does not have all of the provided keys.

- `hasValue(values…​)`: Remove the traverser if its property does not have all of the provided 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]]
    gremlin> g.V().properties().hasKey('age').value() //6\
    ==>29
    ==>27
    ==>32
    ==>35
    gremlin> g.V().hasNot('age').values('name') //7\
    ==>lop
    ==>ripple

1. Find all vertices whose ages are between 20 (inclusive) and 30 (exclusive).

2. Find all vertices whose ages are not between 20 (inclusive) and 30 (exclusive).

3. Find all vertices whose names are exact matches to any names in the collection `[josh,marko]`, display all the key,value pairs for those vertices.

4. Find all vertices whose names are not in the collection `[josh,marko]`, display all the key,value pairs for those vertices.

5. Same as the prior example save using `not` on `within` to yield `without`.

6. Find all age-properties and emit their value.

7. Find all vertices that do not have an age-property and emit their name.

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

Additional References

`has(String)`, `has(String,Object)`, `has(String,P)`, `has(String,String,Object)`, `has(String,String,P)`, `has(String,Traversal)`, `has(T,Object)`, `has(T,P)`, `has(T,Traversal)`, `hasId(Object,Object…​)`, `hasId(P)`, `hasKey(P)`, `hasKey(String,String…​)`, `hasLabel(P)`, `hasLabel(String,String…​)`, `hasNot(String)`, `hasValue(Object,Object…​)`, `hasValue(P)`, `P`, `T`

### Id Step

The `id()`-step (map) takes an `Element` and extracts its identifier from it.

    gremlin> g.V().id()
    ==>1
    ==>2
    ==>3
    ==>4
    ==>5
    ==>6
    gremlin> g.V(1).out().id().is(2)
    ==>2
    gremlin> g.V(1).outE().id()
    ==>9
    ==>7
    ==>8
    gremlin> g.V(1).properties().id()
    ==>0
    ==>1

Additional References

`id()`

### Identity Step

The `identity()`-step (map) is an identity function which maps the current object to itself.

    gremlin> g.V().identity()
    ==>v[1]
    ==>v[2]
    ==>v[3]
    ==>v[4]
    ==>v[5]
    ==>v[6]

Additional References

`identity()`

### 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

Additional References

`inject(Object)`

### 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

1. Find projects having exactly one contributor.

2. Find projects having two or more contributors.

3. Find projects whose contributors average age is between 30 and 35.

Additional References

`is(Object)`, `is(P)`, `P`

### Key Step

The `key()`-step (map) takes a `Property` and extracts the key from it.

    gremlin> g.V(1).properties().key()
    ==>name
    ==>location
    ==>location
    ==>location
    ==>location
    gremlin> g.V(1).properties().properties().key()
    ==>startTime
    ==>endTime
    ==>startTime
    ==>endTime
    ==>startTime
    ==>endTime
    ==>startTime

Additional References

`key()`

### Label Step

The `label()`-step (map) takes an `Element` and extracts its label from it.

    gremlin> g.V().label()
    ==>person
    ==>person
    ==>software
    ==>person
    ==>software
    ==>person
    gremlin> g.V(1).outE().label()
    ==>created
    ==>knows
    ==>knows
    gremlin> g.V(1).properties().label()
    ==>name
    ==>age

Additional References

`label()`

### 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]

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]]

1. `List<String>` for each vertex containing the first two locations.

2. `Map<String, Object>` for each vertex, but containing only the first property value.

Additional References

`limit(long)`, `limit(Scope,long)` `Scope`

### 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]

1. Get the first two people and their respective location according to the most historic location start time.

2. 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.

Local Step is quite similar in functionality to Flat Map Step where it can often be confused. `local()` propagates the traverser through the internal traversal as is without splitting/cloning it. Thus, its a “global traversal” with local processing. Its use is subtle and primarily finds application in compilation optimizations (i.e. when writing `TraversalStrategy` implementations. As another example consider:

    gremlin> g.V().both().barrier().flatMap(groupCount().by("name"))
    ==>[lop:1]
    ==>[lop:1]
    ==>[lop:1]
    ==>[vadas:1]
    ==>[josh:1]
    ==>[josh:1]
    ==>[josh:1]
    ==>[marko:1]
    ==>[marko:1]
    ==>[marko:1]
    ==>[peter:1]
    ==>[ripple:1]
    gremlin> g.V().both().barrier().local(groupCount().by("name"))
    ==>[lop:3]
    ==>[vadas:1]
    ==>[josh:3]
    ==>[marko:3]
    ==>[peter:1]
    ==>[ripple:1]

Warning | The anonymous traversal of `local()` processes the current object "locally." In OLAP, where the atomic unit of computing is 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.

Additional References

`local(Traversal)`

### Loops Step

The `loops()`-step (map) extracts the number of times the `Traverser` has gone through the current loop.

    gremlin> g.V().emit(__.has("name", "marko").or().loops().is(2)).repeat(__.out()).values("name")
    ==>marko
    ==>ripple
    ==>lop

Additional References

`loops()`

### 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]

1. 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'.

2. Using these 'projects' vertices, find out their creators aged 29 and remember these as 'cocreators'.

3. 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')
    gremlin> g = graph.traversal()
    ==>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

1. Patterns of arbitrary complexity: `match()` is not restricted to triple patterns or property paths.

2. Recursion support: `match()` supports the branch-based steps within a pattern, including `repeat()`.

3. 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.

1. `as('a')…​as('b')`: both the start and end of the traversal have a declared variable.

2. `as('a')…​`: only the start of the traversal has a declared variable.

3. `…​`: 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]

1. A standard, step-labeled traversal can come prior to `match()`.

2. If the traverser’s path prior to entering `match()` has requisite label values, then those historic values are bound.

3. It is possible to use barrier steps though they are computed locally to the pattern (as one would expect).

4. It is possible to `not()` a pattern.

5. It is possible to nest `and()`- and `or()`-steps for conjunction matching.

6. Both infix and prefix conjunction notation is supported.

7. It is possible to "distinct" the specified label combination.

8. 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\
    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(last,[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(last,[a, c],[value(name)])]

1. 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.

2. A `where()`-step with a traversal containing variable bindings declared in `match()`.

3. A useful trick to ensure that the traversal is not iterated by Gremlin Console.

4. The string representation of the traversal prior to its strategies being applied.

5. The Gremlin Console will automatically iterate anything that is an iterator or is iterable.

6. Both marko and josh are co-developers and marko knows josh.

7. 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.

Additional References

`match(Traversal…​)`

### Math Step

The `math()`-step (math) enables scientific calculator functionality within Gremlin. This step deviates from the common function composition and nesting formalisms to provide an easy to read string-based math processor. Variables within the equation map to scopes in Gremlin — e.g. path labels, side-effects, or incoming map keys. This step supports `by()`-modulation where the `by()`-modulators are applied in the order in which the variables are first referenced within the equation. Note that the reserved variable `_` refers to the current numeric traverser object incoming to the `math()`-step.

    gremlin> g.V().as('a').out('knows').as('b').math('a + b').by('age')
    ==>56.0
    ==>61.0
    gremlin> g.V().as('a').out('created').as('b').
               math('b + a').
                 by(both().count().math('_ + 100')).
                 by('age')
    ==>132.0
    ==>133.0
    ==>135.0
    ==>138.0
    gremlin> g.withSideEffect('x',10).V().values('age').math('_ / x')
    ==>2.9
    ==>2.7
    ==>3.2
    ==>3.5
    gremlin> g.withSack(1).V(1).repeat(sack(sum).by(constant(1))).times(10).emit().sack().math('sin _')
    ==>0.9092974268256817
    ==>0.1411200080598672
    ==>-0.7568024953079282
    ==>-0.9589242746631385
    ==>-0.27941549819892586
    ==>0.6569865987187891
    ==>0.9893582466233818
    ==>0.4121184852417566
    ==>-0.5440211108893698
    ==>-0.9999902065507035

The operators supported by the calculator include: `*`, `+`, `\`, `^`, and `%`. Furthermore, the following built in functions are provided:

- `abs`: absolute value

- `acos`: arc cosine

- `asin`: arc sine

- `atan`: arc tangent

- `cbrt`: cubic root

- `ceil`: nearest upper integer

- `cos`: cosine

- `cosh`: hyperbolic cosine

- `exp`: euler’s number raised to the power (`e^x`)

- `floor`: nearest lower integer

- `log`: logarithmus naturalis (base e)

- `log10`: logarithm (base 10)

- `log2`: logarithm (base 2)

- `sin`: sine

- `sinh`: hyperbolic sine

- `sqrt`: square root

- `tan`: tangent

- `tanh`: hyperbolic tangent

- `signum`: signum function

Additional References

`math(String)`

### 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.

Additional References

`max()`, `max(Scope)`, `Scope`

### 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

1. 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.

Additional References

`mean()`, `mean(Scope)`, `Scope`

### 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.

Additional References

`min()`, `min(Scope)`, `Scope`

### Not Step

The `not()`-step (filter) removes objects from the traversal stream when the traversal provided as an argument does not return any objects.

    gremlin> g.V().not(hasLabel('person')).valueMap(true)
    ==>[id:3,name:[lop],label:software,lang:[java]]
    ==>[id:5,name:[ripple],label:software,lang:[java]]
    gremlin> g.V().hasLabel('person').
               not(out('created').count().is(gt(1))).values('name') //1\
    ==>marko
    ==>vadas
    ==>peter

1. josh created two projects and vadas none

Additional References

`not(Traversal)`

### Option Step

An option to a `branch()` or `choose()`.

Additional References

`option(Object,Traversal)`, `option(Traversal)`

### Optional Step

The `optional()`-step (branch/flatMap) returns the result of the specified traversal if it yields a result else it returns the calling element, i.e. the `identity()`.

    gremlin> g.V(2).optional(out('knows')) //1\
    ==>v[2]
    gremlin> g.V(2).optional(__.in('knows')) //2\
    ==>v[1]

1. vadas does not have an outgoing knows-edge so vadas is returned.

2. vadas does have an incoming knows-edge so marko is returned.

`optional` is particularly useful for lifting entire graphs when used in conjunction with `path` or `tree`.

    gremlin> g.V().hasLabel('person').optional(out('knows').optional(out('created'))).path() //1\
    ==>[v[1],v[2]]
    ==>[v[1],v[4],v[5]]
    ==>[v[1],v[4],v[3]]
    ==>[v[2]]
    ==>[v[4]]
    ==>[v[6]]

1. Returns the paths of everybody followed by who they know followed by what they created.

Additional References

`optional(Traversal)`

### 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

Additional References

`or(Traversal…​)`

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

1. The ages are gathered into a list and then that list is sorted in decreasing order.

2. The ages are not gathered and thus `order(local)` is "ordering" single integers and thus, does nothing.

3. The `groupCount()` map is ordered by its values in decreasing order.

4. 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`.

Additional References

`order()`, `order(Scope)`, `Scope`

### PageRank Step

The `pageRank()`-step (map/sideEffect) calculates PageRank using `PageRankVertexProgram`.

Important | The `pageRank()`-step is a `VertexComputing`-step and as such, can only be used against a graph that supports `GraphComputer` (OLAP).

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().pageRank().by('pageRank').values('pageRank')
    ==>0.11375510357865541
    ==>0.11375510357865541
    ==>0.14598540152719106
    ==>0.14598540152719106
    ==>0.17579889899708231
    ==>0.3047200907912249
    gremlin> g.V().hasLabel('person').
               pageRank().
                 by(outE('knows')).
                 by('friendRank').
               order().by('friendRank',decr).valueMap('name','friendRank')
    ==>[friendRank:[0.8321166533236799],name:[vadas]]
    ==>[friendRank:[0.8321166533236799],name:[josh]]
    ==>[friendRank:[0.5839416733381598],name:[marko]]
    ==>[friendRank:[0.5839416733381598],name:[peter]]

The `explain()`-step can be used to understand how the traversal is compiled into multiple `GraphComputer` jobs.

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().hasLabel('person').
               pageRank().
                 by(outE('knows')).
                 by('friendRank').
               order().by('friendRank',decr).valueMap('name','friendRank').explain()
    ==>Traversal Explanation
    =============================================================================================================================================================================================================================================
    Original Traversal                    [GraphStep(vertex,[]), HasStep([~label.eq(person)]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), OrderGlobalStep([[value(friendRank), decr]]), PropertyM
                                             apStep([name, friendRank],value)]

    ConnectiveStrategy              [D]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), OrderGlobalStep([[value(friendRank), decr]]), PropertyM
                                             apStep([name, friendRank],value)]
    VertexProgramStrategy           [D]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    MatchPredicateStrategy          [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    FilterRankingStrategy           [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    PathProcessorStrategy           [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    InlineFilterStrategy            [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    IncidentToAdjacentStrategy      [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    RepeatUnrollStrategy            [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    PathRetractionStrategy          [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    CountStrategy                   [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    AdjacentToIncidentStrategy      [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    LazyBarrierStrategy             [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    OrderLimitStrategy              [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    MessagePassingReductionStrategy [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    TinkerGraphCountStrategy        [P]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    TinkerGraphStepStrategy         [P]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    ProfileStrategy                 [F]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    ComputerVerificationStrategy    [V]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    StandardVerificationStrategy    [V]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    ComputerFinalizationStrategy    [T]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]

    Final Traversal                       [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), decr]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]

Additional References

`pageRank()`, `pageRank(double)`

### 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()
    ==>[b,a]
    ==>[]
    ==>[c]
    ==>[d,e]
    gremlin> path.a
    ==>v[1]
    gremlin> path.b
    ==>v[1]
    gremlin> path.c
    ==>v[5]
    gremlin> path.d == path.e
    ==>true

Additional References

`path()`

### PeerPressure Step

The `peerPressure()`-step (map/sideEffect) clusters vertices using `PeerPressureVertexProgram`.

Important | The `peerPressure()`-step is a `VertexComputing`-step and as such, can only be used against a graph that supports `GraphComputer` (OLAP).

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().peerPressure().by('cluster').values('cluster')
    ==>1
    ==>1
    ==>1
    ==>1
    ==>1
    ==>6
    gremlin> g.V().hasLabel('person').
               peerPressure().by('cluster').
               group().by('cluster').by('name')
    ==>[1:[marko,vadas,josh],6:[peter]]

Additional References

`peerPressure()`

### 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()
    ==>Traversal Metrics
    Step                                                               Count  Traversers       Time (ms)    % Dur
    =============================================================================================================
    TinkerGraphStep(vertex,[])                                             6           6           0.076    11.61
    VertexStep(OUT,[created],vertex)                                       4           4           0.108    16.39
    NoOpBarrierStep(2500)                                                  4           2           0.050     7.63
    VertexStep(BOTH,vertex)                                               10           4           0.045     6.86
    NoOpBarrierStep(2500)                                                 10           3           0.025     3.81
    VertexStep(BOTH,vertex)                                               24           7           0.028     4.34
    NoOpBarrierStep(2500)                                                 24           5           0.030     4.62
    VertexStep(BOTH,vertex)                                               58          11           0.040     6.15
    NoOpBarrierStep(2500)                                                 58           6           0.046     7.03
    HasStep([~label.eq(person)])                                          48           4           0.033     5.08
    PropertiesStep([age],value)                                           48           4           0.042     6.37
    SumGlobalStep                                                          1           1           0.133    20.11
                                                >TOTAL                     -           -           0.662        -

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.

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`.

A side effect key can also be passed to the `profile()`-step for situations when it is important to iterate the normal results of the Traversal and retrieve the `TraversalMetrics` afterwards, as shown here:

    gremlin> t = g.V().out('created').profile('metrics')
    ==>v[3]
    ==>v[3]
    ==>v[3]
    ==>v[5]
    gremlin> t.iterate()
    gremlin> metrics = t.getSideEffects().get('metrics')
    ==>Traversal Metrics
    Step                                                               Count  Traversers       Time (ms)    % Dur
    =============================================================================================================
    TinkerGraphStep(vertex,[])                                             6           6           0.051    35.58
    VertexStep(OUT,[created],vertex)                                       4           4           0.047    32.56
    NoOpBarrierStep(2500)                                                  4           2           0.046    31.86
                                                >TOTAL                     -           -           0.145        -

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

Additional References

`profile()`, `profile(String)`

### Project Step

The `project()`-step (map) projects the current object into a `Map<String,Object>` keyed by provided labels. It is similar to `select()`-step, save that instead of retrieving and modulating historic traverser state, it modulates the current state of the traverser.

    gremlin> g.V().out('created').
               project('a','b').
                 by('name').
                 by(__.in('created').count()).
               order().by(select('b'),decr).
               select('a')
    ==>lop
    ==>lop
    ==>lop
    ==>ripple
    gremlin> g.V().has('name','marko').
                            project('out','in').
                              by(outE().count()).
                              by(inE().count())
    ==>[out:3,in:0]

Additional References

`project(String,String…​)`

### Program Step

The `program()`-step (map/sideEffect) is the "lambda" step for `GraphComputer` jobs. The step takes a `VertexProgram` as an argument and will process the incoming graph accordingly. Thus, the user can create their own `VertexProgram` and have it execute within a traversal. The configuration provided to the vertex program includes:

- `gremlin.vertexProgramStep.rootTraversal` is a serialization of a `PureTraversal` form of the root traversal.

- `gremlin.vertexProgramStep.stepId` is the step string id of the `program()`-step being executed.

The user supplied `VertexProgram` can leverage that information accordingly within their vertex program. Example uses are provided below.

Warning | Developing a `VertexProgram` is for expert users. Moreover, developing one that can be used effectively within a traversal requires yet more expertise. This information is recommended to advanced users with a deep understanding of the mechanics of Gremlin OLAP (`GraphComputer`).

    private TraverserSet<Object> haltedTraversers;

    public void loadState(final Graph graph, final Configuration configuration) {
      VertexProgram.super.loadState(graph, configuration);
      this.traversal = PureTraversal.loadState(configuration, VertexProgramStep.ROOT_TRAVERSAL, graph);
      this.programStep = new TraversalMatrix<>(this.traversal.get()).getStepById(configuration.getString(ProgramVertexProgramStep.STEP_ID));
      // if the traversal sideEffects will be used in the computation, add them as memory compute keys
      this.memoryComputeKeys.addAll(MemoryTraversalSideEffects.getMemoryComputeKeys(this.traversal.get()));
      // if master-traversal traversers may be propagated, create a memory compute key
      this.memoryComputeKeys.add(MemoryComputeKey.of(TraversalVertexProgram.HALTED_TRAVERSERS, Operator.addAll, false, false));
      // returns an empty traverser set if there are no halted traversers
      this.haltedTraversers = TraversalVertexProgram.loadHaltedTraversers(configuration);
    }

    public void storeState(final Configuration configuration) {
      VertexProgram.super.storeState(configuration);
      // if halted traversers is null or empty, it does nothing
      TraversalVertexProgram.storeHaltedTraversers(configuration, this.haltedTraversers);
    }

    public void setup(final Memory memory) {
      if(!this.haltedTraversers.isEmpty()) {
        // do what you like with the halted master traversal traversers
      }
      // once used, no need to keep that information around (master)
      this.haltedTraversers = null;
    }

    public void execute(final Vertex vertex, final Messenger messenger, final Memory memory) {
      // once used, no need to keep that information around (workers)
      if(null != this.haltedTraversers)
        this.haltedTraversers = null;
      if(vertex.property(TraversalVertexProgram.HALTED_TRAVERSERS).isPresent()) {
        // haltedTraversers in execute() represent worker-traversal traversers
        // for example, from a traversal of the form g.V().out().program(...)
        TraverserSet<Object> haltedTraversers = vertex.value(TraversalVertexProgram.HALTED_TRAVERSERS);
        // create a new halted traverser set that can be used by the next OLAP job in the chain
        // these are worker-traversers that are distributed throughout the graph
        TraverserSet<Object> newHaltedTraversers = new TraverserSet<>();
        haltedTraversers.forEach(traverser -> {
           newHaltedTraversers.add(traverser.split(traverser.get().toString(), this.programStep));
        });
        vertex.property(VertexProperty.Cardinality.single, TraversalVertexProgram.HALTED_TRAVERSERS, newHaltedTraversers);
        // it is possible to create master-traversers that are localized to the master traversal (this is how results are ultimately delivered back to the user)
        memory.add(TraversalVertexProgram.HALTED_TRAVERSERS,
                   new TraverserSet<>(this.traversal().get().getTraverserGenerator().generate("an example", this.programStep, 1l)));
      }

    public boolean terminate(final Memory memory) {
      // the master-traversal will have halted traversers
      assert memory.exists(TraversalVertexProgram.HALTED_TRAVERSERS);
      final TraverserSet<String> haltedTraversers = memory.get(TraversalVertexProgram.HALTED_TRAVERSERS);
      // it will only have the traversers sent to the master traversal via memory
      assert haltedTraversers.stream().map(Traverser::get).filter(s -> s.equals("an example")).findAny().isPresent();
      // it will not contain the worker traversers distributed throughout the vertices
      assert !haltedTraversers.stream().map(Traverser::get).filter(s -> !s.equals("an example")).findAny().isPresent();
      return true;
    }

Note | The test case `ProgramTest` in `gremlin-test` has an example vertex program called `TestProgram` that demonstrates all the various ways in which traversal and traverser information is propagated within a vertex program and ultimately usable by other vertex programs (including `TraversalVertexProgram`) down the line in an OLAP compute chain.

Finally, an example is provided using `PageRankVertexProgram` which doesn’t use `pageRank()`-step.

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().hasLabel('person').
               program(PageRankVertexProgram.build().property('rank').create(graph)).
                 order().by('rank', incr).
               valueMap('name', 'rank')
    ==>[name:[marko],rank:[0.11375510357865541]]
    ==>[name:[peter],rank:[0.11375510357865541]]
    ==>[name:[vadas],rank:[0.14598540152719106]]
    ==>[name:[josh],rank:[0.14598540152719106]]

### Properties Step

The `properties()`-step (map) extracts properties from an `Element` in the traversal stream.

    gremlin> g.V(1).properties()
    ==>vp[name->marko]
    ==>vp[location->san diego]
    ==>vp[location->santa cruz]
    ==>vp[location->brussels]
    ==>vp[location->santa fe]
    gremlin> g.V(1).properties('location').valueMap()
    ==>[startTime:1997,endTime:2001]
    ==>[startTime:2001,endTime:2004]
    ==>[startTime:2004,endTime:2005]
    ==>[startTime:2005]
    gremlin> g.V(1).properties('location').has('endTime').valueMap()
    ==>[startTime:1997,endTime:2001]
    ==>[startTime:2001,endTime:2004]
    ==>[startTime:2004,endTime:2005]

Additional References

`properties(String…​)`

### PropertyMap Step

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

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

Additional References

`propertyMap(String…​)`

### 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 (inclusive) and high (exclusive) range, traversers are emitted. When above the high range, the traversal breaks out of iteration. Finally, the use of `-1` on the high range will emit remaining traversers after the low range begins.

    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().range(1, -1)
    ==>v[2]
    ==>v[3]
    ==>v[4]
    ==>v[5]
    ==>v[6]
    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]

Additional References

`range(long,long)`, `range(Scope,long,long)`, `Scope`

### 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]

1. do-while semantics stating to do `out()` 2 times.

2. 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]

1. The `emit()` comes after `repeat()` and thus, emission happens after the `repeat()` traversal is executed. Thus, no one vertex paths exist.

2. 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]

1. Starting from vertex 1, keep taking outgoing edges until a software vertex is reached.

2. Starting from vertex 1, and in an infinite loop, emit the vertex if it is a person and then traverser the outgoing edges.

3. 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 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.

Additional References

`repeat(Traversal)`

### Sack Step

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@5ddd3fbd
    gremlin> g.withSack {rand.nextFloat()}.V().sack()
    ==>0.7074782
    ==>0.3848706
    ==>0.9114991
    ==>0.55407107
    ==>0.8077923
    ==>0.64674765

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]]

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(1.0d).V(1).out('knows').in('knows') //1\
    ==>v[1]
    ==>v[1]
    gremlin> g.withSack(1.0d).V(1).out('knows').in('knows').sack() //2\
    ==>1.0
    ==>1.0
    gremlin> g.withSack(1.0d, sum).V(1).out('knows').in('knows').sack() //3\
    ==>2.0
    ==>2.0
    gremlin> g.withSack(1.0d).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').barrier() //4\
    ==>v[1]
    ==>v[1]
    gremlin> g.withSack(1.0d).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').barrier().sack() //5\
    ==>0.5
    ==>0.5
    gremlin> g.withSack(1.0d,sum).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').barrier().sack() //6\
    ==>1.0
    ==>1.0
    gremlin> g.withBulk(false).withSack(1.0f,sum).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').barrier().sack() //7\
    ==>1.0
    gremlin> g.withBulk(false).withSack(1.0f).V(1).local(outE('knows').barrier(normSack).inV()).in('knows').barrier().sack() //8\
    ==>0.5
    ==>0.5
    gremlin>

1. We find vertex 1 twice because he knows two other people

2. Without a merge operation the sack values are 1.0.

3. When specifying `sum` as the merge operation, the sack values are 2.0 because of bulking

4. Like 1, but using barrier internally

5. The `local(…​barrier(normSack)…​)` ensures that all traversers leaving vertex 1 have an evenly distributed amount of the initial 1.0 "energy" (50-50), i.e. the sack is 0.5 on each result

6. Like 3, but using `sum` as merge operator leads to the expected 1.0

7. 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

8. Like 7, but without the sum operator

Additional References

`sack()`, `sack(BiFunction)`

### 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')
    ==>0.5
    gremlin> g.V().outE().sample(1).by('weight').values('weight')
    ==>1.0
    gremlin> g.V().outE().sample(2).by('weight').values('weight')
    ==>0.4
    ==>1.0

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

Additional References

`sample(int)`, `sample(Scope,int)`, `Scope`

### 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.

1. Select labeled steps within a path (as defined by `as()` in a traversal).

2. 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]

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')
    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')
    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]

1. Which artist sung how many songs?

2. Get an anonymized set of song repertoire sizes.

3. What are the 5 most common song repertoire sizes?

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

There is also an option to supply a `Pop` operation to `select()` to manipulate `List` objects in the `Traverser`:

    gremlin> g.V(1).as("a").repeat(out().as("a")).times(2).select(first, "a")
    ==>v[1]
    ==>v[1]
    gremlin> g.V(1).as("a").repeat(out().as("a")).times(2).select(last, "a")
    ==>v[5]
    ==>v[3]
    gremlin> g.V(1).as("a").repeat(out().as("a")).times(2).select(all, "a")
    ==>[v[1],v[4],v[5]]
    ==>[v[1],v[4],v[3]]

In addition to the previously shown examples, where `select()` was used to select an element based on a static key, `select()` can also accept a traversal that emits a key.

Warning | Since the key used by `select(<traversal>)` cannot be determined at compile time, the `TraversalSelectStep` enables full path tracking.

    gremlin> g.withSideEffect("alias", ["marko":"okram"]).V(). //1\
               values("name").sack(assign). //2\
               optional(select("alias").select(sack())) //3\
    ==>okram
    ==>vadas
    ==>lop
    ==>josh
    ==>ripple
    ==>peter

1. Inject a name alias map and start the traversal from all vertices.

2. Select all `name` values and store them as the current traverser’s sack value.

3. Optionally select the alias for the current name from the injected map.

#### 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]

1. 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.

2. 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.

3. 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.

Additional References

`select(Column)`, `select(Pop,String)`, `select(String)`, `select(String,String,String…​)`, `select(Traversal)`, `select(Pop,Traversal)`, `Column`, `Pop`

### 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]]
    gremlin> g.V().out().as('a').out().as('b').out().as('c').
               simplePath().by(label).
               path()
    gremlin> g.V().out().as('a').out().as('b').out().as('c').
               simplePath().
                 by(label).
                 from('b').
                 to('c').
               path().
                 by('name')

By using the `from()` and `to()` modulators traversers can ensure that only certain sections of the path are acyclic.

    gremlin> g.addV().property(id, 'A').as('a').
               addV().property(id, 'B').as('b').
               addV().property(id, 'C').as('c').
               addV().property(id, 'D').as('d').
               addE('link').from('a').to('b').
               addE('link').from('b').to('c').
               addE('link').from('c').to('d').iterate()
    gremlin> g.V('A').repeat(both().simplePath()).times(3).path() //1\
    ==>[v[A],v[B],v[C],v[D]]
    gremlin> g.V('D').repeat(both().simplePath()).times(3).path() //2\
    ==>[v[D],v[C],v[B],v[A]]
    gremlin> g.V('A').as('a').
               repeat(both().simplePath().from('a')).times(3).as('b').
               repeat(both().simplePath().from('b')).times(3).path() //3\
    ==>[v[A],v[B],v[C],v[D],v[C],v[B],v[A]]

1. Traverse all acyclic 3-hop paths starting from vertex `A`

2. Traverse all acyclic 3-hop paths starting from vertex `D`

3. Traverse all acyclic 3-hop paths starting from vertex `A` and from there again all 3-hop paths. The second path may cross the vertices from the first path.

Additional References

`simplePath()`

### Skip Step

The `skip()`-step is analogous to `range()`-step save that the higher end range is set to -1.

    gremlin> g.V().values('age').order()
    ==>27
    ==>29
    ==>32
    ==>35
    gremlin> g.V().values('age').order().skip(2)
    ==>32
    ==>35
    gremlin> g.V().values('age').order().range(2, -1)
    ==>32
    ==>35

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

    gremlin> g.V().hasLabel('person').filter(outE('created')).as('p'). //1\
               map(out('created').values('name').fold()).
               project('person','primary','other').
                 by(select('p').by('name')).
                 by(limit(local, 1)). //2\
                 by(skip(local, 1)) //3\
    ==>[person:marko,primary:lop,other:[]]
    ==>[person:josh,primary:ripple,other:[lop]]
    ==>[person:peter,primary:lop,other:[]]

1. For each person who created something…​

2. …​select the first project (random order) as `primary` and…​

3. …​select all other projects as `other`.

Additional References

`skip(long)`, `skip(Scope,long)`, `Scope`

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

It is interesting to note that there are two results in the `store()` side-effect even though the interval selection is for 1 object. Realize that when the second 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')
    ==>[0.5,1.0,1.0,0.4,0.4,0.2]

Additional References

`store(String)`

### 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()
    ==>graphtraversalsource[tinkergraph[vertices:3 edges:2], standard]
    gremlin> sg.E() //2\
    ==>e[7][1-knows->2]
    ==>e[8][1-knows->4]

1. As this function produces "edge-induced" subgraphs, `subgraph()` must be called at edge steps.

2. 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()
    ==>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]

1. 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').traversal().E()
    ==>e[7][1-knows->2]
    ==>e[8][1-knows->4]
    gremlin> t.sideEffects.get('createdG').traversal().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.

Additional References

`subgraph(String)`

### 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.

Additional References

`sum()`, `sum(Scope)`, `Scope`

### 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

1. Last name (alphabetically).

2. Same as statement 1.

3. 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\
    ==>[ripple]
    ==>[lop]
    gremlin> g.V().valueMap().tail(local) //4\
    ==>[age:[29]]
    ==>[age:[27]]
    ==>[lang:[java]]
    ==>[age:[32]]
    ==>[lang:[java]]
    ==>[age:[35]]

1. Only the most recent name from the "a" step (`List<Vertex>` becomes `Vertex`).

2. Same result as statement 1 (`List<String>` becomes `String`).

3. `List<String>` for each path containing the last two names from the 'a' step.

4. `Map<String, Object>` for each vertex, but containing only the last property value.

Additional References

`tail()`, `tail(long)`, `tail(Scope)` tail(Scope,long)`` `Scope

### 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()}
    ==>2.907799
    gremlin> g.V().repeat(timeLimit(2).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(timeLimit(2).both().groupCount('m')).times(16).cap('m').order(local).by(values,decr).next()}
    ==>1.676544

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`.

Additional References

`timeLimit(long)`

### To Step

The `to()`-step is not an actual step, but instead is a "step-modulator" similar to `as()` and `by()`. If a step is able to accept traversals or strings then `to()` is the means by which they are added. The general pattern is `step().to()`. See `from()`-step.

The list of steps that support `to()`-modulation are: `simplePath()`, `cyclicPath()`, `path()`, and `addE()`.

Additional References

`to(Direction,String…​)`, `to(String)`, `to(Traversal)`, `to(Vertex)`, `toE(Direction,String)`, `toV(Direction)`, `Direction`

### 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

Note that when using `by()`-modulation, tree nodes are combined based on projection uniqueness, not on the uniqueness of the original objects being projected. For instance:

    gremlin> g.V().has('name','josh').out('created').values('name').tree() //1\
    ==>[v[4]:[v[3]:[lop:[]],v[5]:[ripple:[]]]]
    gremlin> g.V().has('name','josh').out('created').values('name').
               tree().by('name').by(label).by() //2\
    ==>[josh:[software:[ripple:[],lop:[]]]]

1. When the `tree()` is created, vertex 3 and 5 are unique and thus, form unique branches in the tree structure.

2. When the `tree()` is `by()`-modulated by `label`, then vertex 3 and 5 are both "software" and thus are merged to a single node in the tree.

Additional References

`tree()`, `tree(String)`

### 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

Additional References

`unfold()`

### 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]

Additional References

`union(Traversal…​)`

### Until Step

The `until`-step is not an actual step, but is instead a step modulator for `repeat()` (find more documentation on the `until()` there).

Additional References

`until(Predicate)`, `until(Traversal)`

### Value Step

The `value()`-step (map) takes a `Property` and extracts the value from it.

    gremlin> g.V(1).properties().value()
    ==>marko
    ==>san diego
    ==>santa cruz
    ==>brussels
    ==>santa fe
    gremlin> g.V(1).properties().properties().value()
    ==>1997
    ==>2001
    ==>2001
    ==>2004
    ==>2004
    ==>2005
    ==>2005

Additional References

`value()`

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

Additional References

`valueMap(boolean,String…​)`, `valueMap(String…​)`

### Values Step

The `values()`-step (map) extracts the values of properties from an `Element` in the traversal stream.

    gremlin> g.V(1).values()
    ==>marko
    ==>san diego
    ==>santa cruz
    ==>brussels
    ==>santa fe
    gremlin> g.V(1).values('location')
    ==>san diego
    ==>santa cruz
    ==>brussels
    ==>santa fe
    gremlin> g.V(1).properties('location').values()
    ==>1997
    ==>2001
    ==>2001
    ==>2004
    ==>2004
    ==>2005
    ==>2005

Additional References

`values(String…​)`

### 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]

1. All outgoing edges.

2. All incoming knows-edges.

3. All incoming created-edges.

4. Moving forward touching edges and vertices.

5. Moving forward only touching vertices.

Additional References

`both(String…​)`, `bothE(String…​)`, `bothV()`, `in(String…​)`, `inE(String…​)`, `inV()`, `otherV()`, `out(String…​)`, `outE(String…​)`, `outV()`

### 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 conjunction 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

1. Who are marko’s collaborators, where marko can not be his own collaborator? (predicate)

2. Of the co-creators of marko, only keep those whose name is josh or peter. (using a sideEffect)

3. 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
    gremlin> g.V().as('a').out('knows').as('b').
               where('a',gt('b')).
                 by('age').
               select('a','b').
                 by('name') //7\
    ==>[a:marko,b:vadas]
    gremlin> g.V().as('a').out('knows').as('b').
               where('a',gt('b').or(eq('b'))).
                 by('age').
                 by('age').
                 by(__.in('knows').values('age')).
               select('a','b').
                 by('name') //8\
    ==>[a:marko,b:vadas]
    ==>[a:marko,b:josh]

1. What are the names of the people who have created a project?

2. What are the names of the people that are known by someone one and have created a project?

3. What are the names of the people how have created two or more projects?

4. What are the names of the people who know someone that has created a project? (This only works in OLTP — see the `WARNING` below)

5. What are the names of the people who have not created anything, but are known by someone?

6. The concatenation of `where()`-steps is the same as a single `where()`-step with an and’d clause.

7. Marko knows josh and vadas but is only older than vadas.

8. Marko is younger than josh, but josh knows someone equal in age to marko (which is marko).

Warning | The anonymous traversal of `where()` processes the current object "locally". In OLAP, where the atomic unit of computing is 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).

Additional References

`where(P)`, `where(String,P)`, `where(Traversal)`, `P`

## 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))

1. The `not()` of a `P`-predicate is another `P`-predicate.

2. `P`-predicates are arguments to various steps which internally `test()` the incoming value.

3. `P`-predicates can be and’d together.

4. `P`-predicates can be or' together.

5. `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

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:

1. `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()`.

2. `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()`.

3. `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 it 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 Scopes

The `Scope` enum has two constants: `Scope.local` and `Scope.global`. Scope determines whether the particular step being scoped is with respects to the current object (`local`) at that step or to the entire stream of objects up to that step (`global`).

    gremlin> g.V().has('name','marko').out('knows').count() //1\
    ==>2
    gremlin> g.V().has('name','marko').out('knows').fold().count() //2\
    ==>1
    gremlin> g.V().has('name','marko').out('knows').fold().count(local) //3\
    ==>2
    gremlin> g.V().has('name','marko').out('knows').fold().count(global) //4\
    ==>1

1. Marko knows 2 people.

2. A list of Marko’s friends is created and thus, one object is counted (the single list).

3. A list of Marko’s friends is created and a `local`-count yields the number of objects in that list.

4. `count(global)` is the same as `count()` as the default behavior for most scoped steps is `global`.

The steps that support scoping are:

- `count()`: count the local collection or global stream.

- `dedup()`: dedup the local collection of global stream.

- `max()`: get the max value in the local collection or global stream.

- `mean()`: get the mean value in the local collection or global stream.

- `min()`: get the min value in the local collection or global stream.

- `order()`: order the objects in the local collection or global stream.

- `range()`: clip the local collection or global stream.

- `limit()`: clip the local collection or global stream.

- `sample()`: sample objects from the local collection or global stream.

- `tail()`: get the tail of the objects in the local collection or global stream.

A few more examples of the use of `Scope` are provided below:

    gremlin> g.V().both().group().by(label).select('software').dedup(local)
    ==>[v[3],v[5]]
    gremlin> g.V().groupCount().by(label).select(values).min(local)
    ==>2
    gremlin> g.V().groupCount().by(label).order(local).by(values,decr)
    ==>[person:4,software:2]
    gremlin> g.V().fold().sample(local,2)
    ==>[v[2],v[1]]

Finally, note that `local()`-step is a "hard-scoped step" that transforms any internal traversal into a locally-scoped operation. A contrived example is provided below:

    gremlin> g.V().fold().local(unfold().count())
    ==>6
    gremlin> g.V().fold().count(local)
    ==>6

## A Note On Lambdas

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

1. A lambda-rich Gremlin traversal which should and can be avoided. (bad)

2. 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). It is also not currently possible to send a lambda for remote execution to Gremlin-Server or a driver that supports remote execution.

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]]

1. The length-3 paths have each of their objects transformed by a lambda. (bad)

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

## TraversalStrategy

A `TraversalStrategy` analyzes a `Traversal` and, if the traversal meets its criteria, can mutate it accordingly. Traversal strategies are executed at compile-time and form the foundation of the Gremlin traversal machine’s compiler. There are 5 categories of strategies which are itemized below:

- 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 some final adjustments/cleanups/analyses 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 (traversal.getSteps().size() <= 1)
                return;

            for (final IdentityStep<?> identityStep : TraversalHelper.getStepsOfClass(IdentityStep.class, traversal)) {
                if (identityStep.getLabels().isEmpty() || !(identityStep.getPreviousStep() instanceof EmptyStep)) {
                    TraversalHelper.copyLabels(identityStep, identityStep.getPreviousStep(), false);
                    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 respective 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, provider 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. However, such generalization are strongly discouraged.

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 (TraversalHelper.onGraphComputer(traversal))
                return;

            for (final GraphStep originalGraphStep : TraversalHelper.getStepsOfClass(GraphStep.class, traversal)) {
                final TinkerGraphStep<?, ?> tinkerGraphStep = new TinkerGraphStep<>(originalGraphStep);
                TraversalHelper.replaceStep(originalGraphStep, tinkerGraphStep, traversal);
                Step<?, ?> currentStep = tinkerGraphStep.getNextStep();
                while (currentStep instanceof HasStep || currentStep instanceof NoOpBarrierStep) {
                    if (currentStep instanceof HasStep) {
                        for (final HasContainer hasContainer : ((HasContainerHolder) currentStep).getHasContainers()) {
                            if (!GraphStep.processHasContainerIds(tinkerGraphStep, hasContainer))
                                tinkerGraphStep.addHasContainer(hasContainer);
                        }
                        TraversalHelper.copyLabels(currentStep, currentStep.getPreviousStep(), false);
                        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 their `HasContainers` to `TinkerGraphStep`. Then its up to `TinkerGraphStep` to determine if an appropriate index exists. Given that the strategy uses non-TinkerPop3 provided steps, it should go into the `ProviderOptimizationStrategy` category to ensure the added step does not interfere with the assumptions of the `OptimizationStrategy` strategies.

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

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.

Finally, here is a complicated traversal that has various components that are optimized by the default TinkerPop strategies.

    gremlin> g.V().hasLabel('person'). //1\
                     and(has('name'), //2\
                         has('name','marko'),
                         filter(has('age',gt(20)))). //3\
               match(__.as('a').has('age',lt(32)), //4\
                     __.as('a').repeat(outE().inV()).times(2).as('b')). //5\
                 where('a',neq('b')). //6\
                 where(__.as('b').both().count().is(gt(1))). //7\
               select('b'). //8\
               groupCount().
                 by(out().count()). //9\
               explain()
    ==>Traversal Explanation
    ================================================================================================================================================================================================================================================
    Original Traversal                 [GraphStep(vertex,[]), HasStep([~label.eq(person)]), AndStep([[TraversalFilterStep([PropertiesStep([name],value)])], [HasStep([name.eq(marko)])], [TraversalFilterStep([HasStep([age.gt(20)])])]]), MatchS
                                          tep(AND,[[MatchStartStep(a), HasStep([age.lt(32)]), MatchEndStep], [MatchStartStep(a), RepeatStep([VertexStep(OUT,edge), EdgeVertexStep(IN), RepeatEndStep],until(loops(2)),emit(false)), MatchEndStep(b)]
                                          ]), WherePredicateStep(a,neq(b)), WhereTraversalStep([WhereStartStep(b), VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGl
                                          obalStep])]

    ConnectiveStrategy           [D]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), AndStep([[TraversalFilterStep([PropertiesStep([name],value)])], [HasStep([name.eq(marko)])], [TraversalFilterStep([HasStep([age.gt(20)])])]]), MatchS
                                          tep(AND,[[MatchStartStep(a), HasStep([age.lt(32)]), MatchEndStep], [MatchStartStep(a), RepeatStep([VertexStep(OUT,edge), EdgeVertexStep(IN), RepeatEndStep],until(loops(2)),emit(false)), MatchEndStep(b)]
                                          ]), WherePredicateStep(a,neq(b)), WhereTraversalStep([WhereStartStep(b), VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGl
                                          obalStep])]
    MatchPredicateStrategy       [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), AndStep([[TraversalFilterStep([PropertiesStep([name],value)])], [HasStep([name.eq(marko)])], [TraversalFilterStep([HasStep([age.gt(20)])])]]), MatchS
                                          tep(AND,[[MatchStartStep(a), HasStep([age.lt(32)]), MatchEndStep], [MatchStartStep(a), RepeatStep([VertexStep(OUT,edge), EdgeVertexStep(IN), RepeatEndStep],until(loops(2)),emit(false)), MatchEndStep(b)]
                                          , [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalStep([WhereStartStep, VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), Selec
                                          tOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGlobalStep])]
    FilterRankingStrategy        [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), AndStep([[TraversalFilterStep([PropertiesStep([name],value)])], [HasStep([name.eq(marko)])], [TraversalFilterStep([HasStep([age.gt(20)])])]]), MatchS
                                          tep(AND,[[MatchStartStep(a), HasStep([age.lt(32)]), MatchEndStep], [MatchStartStep(a), RepeatStep([VertexStep(OUT,edge), EdgeVertexStep(IN), RepeatEndStep],until(loops(2)),emit(false)), MatchEndStep(b)]
                                          , [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalStep([WhereStartStep, VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), Selec
                                          tOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGlobalStep])]
    InlineFilterStrategy         [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],value)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Repeat
                                          Step([VertexStep(OUT,edge), EdgeVertexStep(IN), RepeatEndStep],until(loops(2)),emit(false)), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTra
                                          versalStep([WhereStartStep, VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGlobalStep])]
    IncidentToAdjacentStrategy   [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],value)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Repeat
                                          Step([VertexStep(OUT,vertex), RepeatEndStep],until(loops(2)),emit(false)), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalStep([WhereS
                                          tartStep, VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGlobalStep])]
    RepeatUnrollStrategy         [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],value)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Vertex
                                          Step(OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversa
                                          lStep([WhereStartStep, VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGlobalStep])]
    PathRetractionStrategy       [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],value)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Vertex
                                          Step(OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversa
                                          lStep([WhereStartStep, VertexStep(BOTH,vertex), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGlobalStep])]
    CountStrategy                [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],value)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Vertex
                                          Step(OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversa
                                          lStep([WhereStartStep, VertexStep(BOTH,vertex), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,vertex), CountGlobalStep])]
    AdjacentToIncidentStrategy   [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],property)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Ver
                                          texStep(OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTrave
                                          rsalStep([WhereStartStep, VertexStep(BOTH,edge), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]
    LazyBarrierStrategy          [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],property)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Ver
                                          texStep(OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTrave
                                          rsalStep([WhereStartStep, VertexStep(BOTH,edge), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]
    TinkerGraphCountStrategy     [P]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],property)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), Ver
                                          texStep(OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTrave
                                          rsalStep([WhereStartStep, VertexStep(BOTH,edge), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]
    TinkerGraphStepStrategy      [P]   [TinkerGraphStep(vertex,[~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],property)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), VertexStep
                                          (OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalSte
                                          p([WhereStartStep, VertexStep(BOTH,edge), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]
    ProfileStrategy              [F]   [TinkerGraphStep(vertex,[~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],property)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), VertexStep
                                          (OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalSte
                                          p([WhereStartStep, VertexStep(BOTH,edge), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]
    StandardVerificationStrategy [V]   [TinkerGraphStep(vertex,[~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],property)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), VertexStep
                                          (OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalSte
                                          p([WhereStartStep, VertexStep(BOTH,edge), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]

    Final Traversal                    [TinkerGraphStep(vertex,[~label.eq(person)]), TraversalFilterStep([PropertiesStep([name],property)]), HasStep([name.eq(marko), age.gt(20), age.lt(32)])@[a], MatchStep(AND,[[MatchStartStep(a), VertexStep
                                          (OUT,vertex), NoOpBarrierStep(2500), VertexStep(OUT,vertex), NoOpBarrierStep(2500), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalSte
                                          p([WhereStartStep, VertexStep(BOTH,edge), RangeGlobalStep(0,2), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]

1. `TinkerGraphStepStrategy` pulls in `has()`-step predicates for global, graph-centric index lookups.

2. `FilterRankStrategy` sorts filter steps by their time/space execution costs.

3. `InlineFilterStrategy` de-nests filters to increase the likelihood of filter concatenation and aggregation.

4. `InlineFilterStrategy` pulls out named predicates from `match()`-step to more easily allow provider strategies to use indices.

5. `RepeatUnrollStrategy` will unroll loops and `IncidentToAdjacentStrategy` will turn `outE().inV()`-patterns into `out()`.

6. `MatchPredicateStrategy` will pull in `where()`-steps so that they can be subjected to `match()`-steps runtime query optimizer.

7. `CountStrategy` will limit the traversal to only the number of traversers required for the `count().is(x)`-check.

8. `PathRetractionStrategy` will remove paths from the traversers and increase the likelihood of bulking as path data is not required after `select('b')`.

9. `AdjacentToIncidentStrategy` will turn `out()` into `outE()` to increase data access locality.

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 indices under the hood.

    gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
    ==>neo4jgraph[community single [/tmp/neo4j]]
    gremlin> strategy = ElementIdStrategy.build().create()
    ==>ElementIdStrategy
    gremlin> g = graph.traversal().withStrategies(strategy)
    ==>graphtraversalsource[neo4jgraph[community single [/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> import org.apache.tinkerpop.gremlin.process.traversal.step.util.event.*
    ==>org.apache.tinkerpop.gremlin.structure.*, org.apache.tinkerpop.gremlin.structure.util.*, org.apache.tinkerpop.gremlin.process.traversal.*, org.apache.tinkerpop.gremlin.process.traversal.step.*, org.apache.tinkerpop.gremlin.process.remote.*, org.apache.tinkerpop.gremlin.structure.util.empty.*, org.apache.tinkerpop.gremlin.structure.io.*, org.apache.tinkerpop.gremlin.structure.io.graphml.*, org.apache.tinkerpop.gremlin.structure.io.graphson.*, org.apache.tinkerpop.gremlin.structure.io.gryo.*, org.apache.commons.configuration.*, org.apache.tinkerpop.gremlin.process.traversal.strategy.decoration.*, org.apache.tinkerpop.gremlin.process.traversal.strategy.optimization.*, org.apache.tinkerpop.gremlin.process.traversal.strategy.finalization.*, org.apache.tinkerpop.gremlin.process.traversal.strategy.verification.*, org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.*, org.apache.tinkerpop.gremlin.process.traversal.util.*, org.apache.tinkerpop.gremlin.process.computer.*, org.apache.tinkerpop.gremlin.process.computer.bulkdumping.*, org.apache.tinkerpop.gremlin.process.computer.bulkloading.*, org.apache.tinkerpop.gremlin.process.computer.clustering.peerpressure.*, org.apache.tinkerpop.gremlin.process.computer.traversal.*, org.apache.tinkerpop.gremlin.process.computer.ranking.pagerank.*, org.apache.tinkerpop.gremlin.process.computer.traversal.strategy.optimization.*, org.apache.tinkerpop.gremlin.process.computer.traversal.strategy.decoration.*, org.apache.tinkerpop.gremlin.util.*, org.apache.tinkerpop.gremlin.util.iterator.*, org.apache.tinkerpop.gremlin.util.function.*, static org.apache.tinkerpop.gremlin.structure.io.IoCore.*, static org.apache.tinkerpop.gremlin.process.traversal.P.*, static org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.__.*, static org.apache.tinkerpop.gremlin.process.computer.Computer.*, static org.apache.tinkerpop.gremlin.util.TimeUtil.*, static org.apache.tinkerpop.gremlin.util.function.Lambda.*, static org.apache.tinkerpop.gremlin.process.traversal.SackFunctions.Barrier.*, static org.apache.tinkerpop.gremlin.structure.VertexProperty.Cardinality.*, static org.apache.tinkerpop.gremlin.structure.Column.*, static org.apache.tinkerpop.gremlin.structure.Direction.*, static org.apache.tinkerpop.gremlin.process.traversal.Operator.*, static org.apache.tinkerpop.gremlin.process.traversal.Order.*, static org.apache.tinkerpop.gremlin.process.traversal.Pop.*, static org.apache.tinkerpop.gremlin.process.traversal.Scope.*, static org.apache.tinkerpop.gremlin.structure.T.*, static org.apache.tinkerpop.gremlin.process.traversal.step.TraversalOptionParent.Pick.*, org.apache.tinkerpop.gremlin.driver.*, org.apache.tinkerpop.gremlin.driver.exception.*, org.apache.tinkerpop.gremlin.driver.message.*, org.apache.tinkerpop.gremlin.driver.ser.*, org.apache.tinkerpop.gremlin.driver.remote.*, groovyx.gbench.*, groovyx.gprof.*, static groovyx.gprof.ProfileStaticExtension.*, org.apache.hadoop.conf.*, org.apache.hadoop.hdfs.*, org.apache.hadoop.fs.*, org.apache.hadoop.util.*, org.apache.hadoop.io.*, org.apache.hadoop.io.compress.*, org.apache.hadoop.mapreduce.lib.input.*, org.apache.hadoop.mapreduce.lib.output.*, org.apache.tinkerpop.gremlin.hadoop.*, org.apache.tinkerpop.gremlin.hadoop.structure.*, org.apache.tinkerpop.gremlin.hadoop.structure.util.*, org.apache.tinkerpop.gremlin.hadoop.structure.io.*, org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.*, org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.*, org.apache.tinkerpop.gremlin.hadoop.structure.io.script.*, org.apache.tinkerpop.gremlin.hadoop.process.computer.mapreduce.*, org.apache.tinkerpop.gremlin.giraph.process.computer.*, org.apache.tinkerpop.gremlin.groovy.jsr223.dsl.credential.*, static org.apache.tinkerpop.gremlin.groovy.jsr223.dsl.credential.CredentialGraph.*, org.apache.tinkerpop.gremlin.neo4j.structure.*, org.apache.tinkerpop.gremlin.neo4j.process.traversal.*, static org.apache.tinkerpop.gremlin.neo4j.process.traversal.LabelP.*, org.apache.tinkerpop.gremlin.spark.process.computer.*, org.apache.tinkerpop.gremlin.spark.structure.*, org.apache.tinkerpop.gremlin.spark.structure.io.*, org.apache.tinkerpop.gremlin.tinkergraph.structure.*, org.apache.tinkerpop.gremlin.tinkergraph.process.computer.*, org.apache.tinkerpop.gremlin.process.traversal.step.util.event.*
    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 = graph.traversal().withStrategies(strategy)
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
    gremlin> g.addV().property('name','stephen')
    Vertex [v[13]] added to graph [tinkergraph[vertices:7 edges:6]]
    ==>v[13]
    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.

Another default configuration for `EventStrategy` revolves around the concept of "detachment". Graph elements are detached from the graph as copies when passed to referring mutation events. Therefore, when adding a new `Vertex` in TinkerGraph, the event will not contain a `TinkerVertex` but will instead include a `DetachedVertex`. This behavior can be modified with the `detach()` method on the `EventStrategy.Builder` which accepts the following inputs: `null` meaning no detachment and the return of the original element, `DetachedFactory` which is the same as the default behavior, and `ReferenceFactory` which will return "reference" elements only with no properties.

Important | If setting the `detach()` configuration to `null`, be aware that transactional graphs will likely create a new transaction immediately following the `commit()` that raises the events. The graph elements raised in the events may also not behave as "snapshots" at the time of their creation as they are "live" references to actual database elements.

### 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`:

1. Partition Key - The property key that denotes a String value representing a partition.

2. Write Partition - A `String` denoting what partition all future written elements will be in.

3. 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").readPartitions("a").create()
    ==>PartitionStrategy
    gremlin> strategyB = PartitionStrategy.build().partitionKey("_partition").writePartition("b").readPartitions("b").create()
    ==>PartitionStrategy
    gremlin> gA = graph.traversal().withStrategies(strategyA)
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], standard]
    gremlin> gA.addV() // this vertex has a property of {_partition:"a"}
    ==>v[13]
    gremlin> gB = graph.traversal().withStrategies(strategyB)
    ==>graphtraversalsource[tinkergraph[vertices:7 edges:6], standard]
    gremlin> gB.addV() // this vertex has a property of {_partition:"b"}
    ==>v[15]
    gremlin> gA.V()
    ==>v[13]
    gremlin> gB.V()
    ==>v[15]

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 similar to `PartitionStrategy` in that it constrains a `Traversal` to certain vertices, edges, and vertex properties as determined by a `Traversal`-based criterion defined individually for each.

    gremlin> graph = TinkerFactory.createTheCrew()
    ==>tinkergraph[vertices:6 edges:14]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:14], standard]
    gremlin> g.V().as('a').values('location').as('b'). //1\
               select('a','b').by('name').by()
    ==>[a:marko,b:san diego]
    ==>[a:marko,b:santa cruz]
    ==>[a:marko,b:brussels]
    ==>[a:marko,b:santa fe]
    ==>[a:stephen,b:centreville]
    ==>[a:stephen,b:dulles]
    ==>[a:stephen,b:purcellville]
    ==>[a:matthias,b:bremen]
    ==>[a:matthias,b:baltimore]
    ==>[a:matthias,b:oakland]
    ==>[a:matthias,b:seattle]
    ==>[a:daniel,b:spremberg]
    ==>[a:daniel,b:kaiserslautern]
    ==>[a:daniel,b:aachen]
    gremlin> g = g.withStrategies(SubgraphStrategy.build().vertexProperties(hasNot('endTime')).create()) //2\
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:14], standard]
    gremlin> g.V().as('a').values('location').as('b'). //3\
               select('a','b').by('name').by()
    ==>[a:marko,b:santa fe]
    ==>[a:stephen,b:purcellville]
    ==>[a:matthias,b:seattle]
    ==>[a:daniel,b:aachen]
    gremlin> g.V().as('a').values('location').as('b').
               select('a','b').by('name').by().explain()
    ==>Traversal Explanation
    =============================================================================================================================================================================================================================================
    Original Traversal                 [GraphStep(vertex,[])@[a], PropertiesStep([location],value)@[b], SelectStep(last,[a, b],[value(name), identity])]

    SubgraphStrategy             [D]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), TraversalFilterStep([NotStep([PropertiesStep([endTime],value)])]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    ConnectiveStrategy           [D]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), TraversalFilterStep([NotStep([PropertiesStep([endTime],value)])]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    MatchPredicateStrategy       [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), TraversalFilterStep([NotStep([PropertiesStep([endTime],value)])]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    FilterRankingStrategy        [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), TraversalFilterStep([NotStep([PropertiesStep([endTime],value)])]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    InlineFilterStrategy         [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],value)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    IncidentToAdjacentStrategy   [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],value)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    RepeatUnrollStrategy         [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],value)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    PathRetractionStrategy       [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],value)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    CountStrategy                [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],value)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    AdjacentToIncidentStrategy   [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    LazyBarrierStrategy          [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    TinkerGraphCountStrategy     [P]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    TinkerGraphStepStrategy      [P]   [TinkerGraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    ProfileStrategy              [F]   [TinkerGraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    StandardVerificationStrategy [V]   [TinkerGraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]

    Final Traversal                    [TinkerGraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]

1. Get all vertices and their vertex property locations.

2. Create a `SubgraphStrategy` where vertex properties must not have an `endTime`-property (thus, the current location).

3. Get all vertices and their current vertex property locations.

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

The example below uses all three filters: vertex, edge, and vertex property. People vertices must have lived in more than three places, edges must be labeled "develops," and vertex properties must be the persons current location or a non-location property.

    gremlin> graph = TinkerFactory.createTheCrew()
    ==>tinkergraph[vertices:6 edges:14]
    gremlin> g = graph.traversal().withStrategies(SubgraphStrategy.build().
               vertices(or(hasNot('location'),properties('location').count().is(gt(3)))).
               edges(hasLabel('develops')).
               vertexProperties(or(hasLabel(neq('location')),hasNot('endTime'))).create())
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:14], standard]
    gremlin> g.V().valueMap(true)
    ==>[id:1,name:[marko],location:[santa fe],label:person]
    ==>[id:8,name:[matthias],location:[seattle],label:person]
    ==>[id:10,name:[gremlin],label:software]
    ==>[id:11,name:[tinkergraph],label:software]
    gremlin> g.E().valueMap(true)
    ==>[id:13,label:develops,since:2009]
    ==>[id:14,label:develops,since:2010]
    ==>[id:21,label:develops,since:2012]
    gremlin> g.V().outE().inV().
               path().
                 by('name').
                 by().
                 by('name')
    ==>[marko,e[13][1-develops->10],gremlin]
    ==>[marko,e[14][1-develops->11],tinkergraph]
    ==>[matthias,e[21][8-develops->10],gremlin]

## Domain Specific Languages

Gremlin is a domain specific language (DSL) for traversing graphs. It operates in the language of vertices, edges and properties. Typically, applications built with Gremlin are not of the graph domain, but instead model their domain within a graph. For example, the "modern" toy graph models software and person domain objects with the relationships between them (i.e. a person "knows" another person and a person "created" software).

An analyst who wanted to find out if "marko" knows "josh" could write the following Gremlin:

    g.V().hasLabel('person').has('name','marko').
      out('knows').hasLabel('person').has('name','josh').hasNext()

While this method achieves the desired answer, it requires the analyst to traverse the graph in the domain language of the graph rather than the domain language of the social network. A more natural way for the analyst to write this traversal might be:

    g.persons('marko').knows('josh').hasNext()

In the statement above, the traversal is written in the language of the domain, abstracting away the underlying graph structure from the query. The two traversal results are equivalent and, indeed, the "Social DSL" produces the same set of traversal steps as the "Graph DSL" thus producing equivalent strategy application and performance runtimes.

To further the example of the Social DSL consider the following:

    // Graph DSL - find the number of persons who created at least 2 projects
    g.V().hasLabel('person').
      where(outE("created").count().is(P.gte(2))).count()

    // Social DSL - find the number of persons who created at least 2 projects
    social.persons().where(createdAtLeast(2)).count()

    // Graph DSL - determine the age of the youngest friend "marko" has
    g.V().hasLabel('person').has('name','marko').
      out("knows").hasLabel("person").values("age").min()

    // Social DSL - determine the age of the youngest friend "marko" has
    social.persons("marko").youngestFriendsAge()

The following sections explain how to develop application specific DSLs for different Gremlin Language Variants using the examples above of the Social DSL as the API for the implementation.

### Gremlin-Java

Creating a DSL in Java requires the `@GremlinDsl` Java annotation in `gremlin-core`. This annotation should be applied to a "DSL interface" that extends `GraphTraversal.Admin`.

    @GremlinDsl
    public interface SocialTraversalDsl<S, E> extends GraphTraversal.Admin<S, E> {
    }

Important | The name of the DSL interface should be suffixed with "TraversalDSL". All characters in the interface name before that become the "name" of the DSL.

In this interface, define the methods that the DSL will be composed of:

    @GremlinDsl
    public interface SocialTraversalDsl<S, E> extends GraphTraversal.Admin<S, E> {
        public default GraphTraversal<S, Vertex> knows(String personName) {
            return out("knows").hasLabel("person").has("name", personName);
        }

        public default <E2 extends Number> GraphTraversal<S, E2> youngestFriendsAge() {
            return out("knows").hasLabel("person").values("age").min();
        }

        public default GraphTraversal<S, Long> createdAtLeast(int number) {
            return outE("created").count().is(P.gte(number));
        }
    }

Important | Follow the TinkerPop convention of using `<S,E>` in naming generics as those conventions are taken into account when generating the anonymous traversal class. The processor attempts to infer the appropriate type parameters when generating the anonymous traversal class. If it cannot do it correctly, it is possible to avoid the inference by using the `GremlinDsl.AnonymousMethod` annotation on the DSL method. It allows explicit specification of the types to use.

The `@GremlinDsl` annotation is used by the Java Annotation Processor to generate the boilerplate class structure required to properly use the DSL within the TinkerPop framework. These classes can be generated and maintained by hand, but it would be time consuming, monotonous and error-prone to do so. Typically, the Java compilation process is automatically configured to detect annotation processors on the classpath and will automatically use them when found. If that does not happen, it may be necessary to make configuration changes to the build to allow for the compilation process to be aware of the following `javax.annotation.processing.Processor` implementation:

    org.apache.tinkerpop.gremlin.process.traversal.dsl.GremlinDslProcessor

The annotation processor will generate several classes for the DSL:

- `SocialTraversal` - A `Traversal` interface that extends the `SocialTraversalDsl` proxying methods to its underlying interfaces (such as `GraphTraversal`) to instead return a `SocialTraversal`

- `DefaultSocialTraversal` - A default implementation of `SocialTraversal` (typically not used directly by the user)

- `SocialTraversalSource` - Spawns `DefaultSocialTraversal` instances.

- `__` - Spawns anonymous `DefaultSocialTraversal` instances.

Using the DSL then just involves telling the `Graph` to use it:

    SocialTraversalSource social = graph.traversal(SocialTraversalSource.class);
    social.V().has("name","marko").knows("josh");

The `SocialTraversalSource` can also be customized with DSL functions. As an additional step, include a class that extends from `GraphTraversalSource` and with a name that is suffixed with "TraversalSourceDsl". Include in this class, any custom methods required by the DSL:

    public class SocialTraversalSourceDsl extends GraphTraversalSource {

        public SocialTraversalSourceDsl(final Graph graph, final TraversalStrategies traversalStrategies) {
            super(graph, traversalStrategies);
        }

        public SocialTraversalSourceDsl(final Graph graph) {
            super(graph);
        }

        public GraphTraversal<Vertex, Vertex> persons(String... names) {
            GraphTraversalSource clone = this.clone();

            // Manually add a "start" step for the traversal in this case the equivalent of V(). GraphStep is marked
            // as a "start" step by passing "true" in the constructor.
            clone.getBytecode().addStep(GraphTraversal.Symbols.V);
            GraphTraversal<Vertex, Vertex> traversal = new DefaultGraphTraversal<>(clone);
            traversal.asAdmin().addStep(new GraphStep<>(traversal.asAdmin(), Vertex.class, true));

            traversal = traversal.hasLabel("person");
            if (names.length > 0) traversal = traversal.has("name", P.within(names));

            return traversal;
        }
    }

Then, back in the `SocialTraversal` interface, update the `GremlinDsl` annotation with the `traversalSource` argument to point to the fully qualified class name of the `SocialTraversalSourceDsl`:

    @GremlinDsl(traversalSource = "com.company.SocialTraversalSourceDsl")
    public interface SocialTraversalDsl<S, E> extends GraphTraversal.Admin<S, E> {
        ...
    }

It is then possible to use the `persons()` method to start traversals:

    SocialTraversalSource social = graph.traversal(SocialTraversalSource.class);
    social.persons("marko").knows("josh");

Note | Using Maven, as shown in the `gremlin-archetype-dsl` module, makes developing DSLs with the annotation processor straightforward in that it sets up appropriate paths to the generated code automatically.

### Gremlin-Python

Writing a Gremlin DSL in Python simply requires direct extension of several classes:

- `GraphTraversal` - which exposes the various steps used in traversal writing

- `__` - which spawns anonymous traversals from steps

- `GraphTraversalSource` - which spawns `GraphTraversal` instances

The Social DSL based on the "modern" toy graph might look like this:

    class SocialTraversal(GraphTraversal):

        def knows(self, person_name):
            return self.out("knows").hasLabel("person").has("name", person_name)

        def youngestFriendsAge(self):
            return self.out("knows").hasLabel("person").values("age").min()

        def createdAtLeast(self, number):
            return self.outE("created").count().is_(P.gte(number))

    class __(AnonymousTraversal):

        graph_traversal = SocialTraversal

        @classmethod
        def knows(cls, *args):
            return cls.graph_traversal(None, None, Bytecode()).knows(*args)

        @classmethod
        def youngestFriendsAge(cls, *args):
            return cls.graph_traversal(None, None, Bytecode()).youngestFriendsAge(*args)

        @classmethod
        def createdAtLeast(cls, *args):
            return cls.graph_traversal(None, None, Bytecode()).createdAtLeast(*args)

    class SocialTraversalSource(GraphTraversalSource):

        def __init__(self, *args, **kwargs):
            super(SocialTraversalSource, self).__init__(*args, **kwargs)
            self.graph_traversal = SocialTraversal

        def persons(self, *args):
            traversal = self.get_graph_traversal()
            traversal.bytecode.add_step("V")
            traversal.bytecode.add_step("hasLabel", "person")

            if len(args) > 0:
                traversal.bytecode.add_step("has", "name", P.within(args))

            return traversal

Note | The `AnonymousTraversal` class above is just an alias for `` as in `from gremlin_python.process.graph_traversal import as AnonymousTraversal`

Using the DSL is straightforward and just requires that the graph instance know the `SocialTraversalSource` should be used:

    social = Graph().traversal(SocialTraversalSource).withRemote(DriverRemoteConnection('ws://localhost:8182/gremlin','g'))
    social.persons("marko").knows("josh")
    social.persons("marko").youngestFriendsAge()
    social.persons().filter(__.createdAtLeast(2)).count()

### Gremlin.Net

Developing DSLs for .Net is most easily implemented using Extension Methods as they don’t require direct extension of classes in the TinkerPop hierarchy. Extension Method classes simply need to be constructed for the `GraphTraversal` and the `GraphTraversalSource`. Unfortunately, anonymous traversals (spawned from ``) can’t use the Extension Method approach as they do not work for static classes and static classes can’t be extended. The only option is to re-implement the methods of `` as a wrapper in the anonymous traversal for the DSL or to simply create a static class for the DSL and use the two anonymous traversals creators independently. The following example uses the latter approach as it saves a lot of boilerplate code with the minor annoyance of having a second static class to deal with when writing traversals rather than just calling `__` for everything.

    namespace Dsl
    {

        public static class SocialTraversalExtensions
        {
            public static GraphTraversal<Vertex,Vertex> Knows(this GraphTraversal<Vertex,Vertex> t, string personName)
            {
                return t.Out("knows").HasLabel("person").Has("name", personName);
            }

            public static GraphTraversal<Vertex, int> YoungestFriendsAge(this GraphTraversal<Vertex,Vertex> t)
            {
                return t.Out("knows").HasLabel("person").Values<int>("age").Min<int>();
            }

            public static GraphTraversal<Vertex,long> CreatedAtLeast(this GraphTraversal<Vertex,Vertex> t, long number)
            {
                return t.OutE("created").Count().Is(P.Gte(number));
            }
        }

        public static class __Social
        {
            public static GraphTraversal<object,Vertex> Knows(string personName)
            {
                return __.Out("knows").HasLabel("person").Has("name", personName);
            }

            public static GraphTraversal<object, int> YoungestFriendsAge()
            {
                return __.Out("knows").HasLabel("person").Values<int>("age").Min<int>();
            }

            public static GraphTraversal<object,long> CreatedAtLeast(long number)
            {
                return __.OutE("created").Count().Is(P.Gte(number));
            }
        }

        public static class SocialTraversalSourceExtensions
        {
            public static GraphTraversal<Vertex,Vertex> Persons(this GraphTraversalSource g, params string[] personNames)
            {
                GraphTraversal<Vertex,Vertex> t = g.V().HasLabel("person");

                if (personNames.Length > 0)
                {
                    t = t.Has("name", P.Within(personNames));
                }

                return t;
            }
        }
    }

Note the creation of `__Social` as the Social DSL’s "extension" to the available ways in which to spawn anonymous traversals. The use of the double underscore prefix in the name is just a convention to consider using and is not a requirement. To use the DSL, bring it into scope with the `using` directive:

    using Dsl;
    using static Dsl.__Social;

and then it can be called from the application as follows:

    var graph = new Graph();
    var connection = new DriverRemoteConnection(new GremlinClient(new GremlinServer("localhost", 8182)));
    var social = graph.Traversal().WithRemote(connection);

    social.Persons("marko").Knows("josh");
    social.Persons("marko").YoungestFriendsAge();
    social.Persons().Filter(CreatedAtLeast(2)).Count();

# The GraphComputer

TinkerPop3 provides two primary means of interacting with a graph: online transaction processing (OLTP) and online analytical processing (OLAP). OLTP-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

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
    ==>75
    gremlin> g = result.graph().traversal()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:0], standard]
    gremlin> g.V().valueMap()
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.11375510357865538],name:[marko],age:[29]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.14598540152719103],name:[vadas],age:[27]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.3047200907912249],name:[lop],lang:[java]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.14598540152719103],name:[josh],age:[32]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.17579889899708231],name:[ripple],lang:[java]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.11375510357865538],name:[peter],age:[35]]

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. 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`.

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:1]]
    gremlin> result.memory().get('clusterPopulation')
    ==>1=5
    ==>6=1
    gremlin> g = result.graph().traversal()
    ==>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.cluster:[1],name:[marko],age:[29]]
    ==>[gremlin.peerPressureVertexProgram.cluster:[1],name:[vadas],age:[27]]
    ==>[gremlin.peerPressureVertexProgram.cluster:[1],name:[lop],lang:[java]]
    ==>[gremlin.peerPressureVertexProgram.cluster:[1],name:[josh],age:[32]]
    ==>[gremlin.peerPressureVertexProgram.cluster:[1],name:[ripple],lang:[java]]
    ==>[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:2]]
    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 fed back into a `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 3.3.3. Over time, with future releases, more algorithms will be added.

### PageRankVertexProgram

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

        public static final String PAGE_RANK = "gremlin.pageRankVertexProgram.pageRank";
        private static final String EDGE_COUNT = "gremlin.pageRankVertexProgram.edgeCount";
        private static final String PROPERTY = "gremlin.pageRankVertexProgram.property";
        private static final String VERTEX_COUNT = "gremlin.pageRankVertexProgram.vertexCount";
        private static final String ALPHA = "gremlin.pageRankVertexProgram.alpha";
        private static final String EPSILON = "gremlin.pageRankVertexProgram.epsilon";
        private static final String MAX_ITERATIONS = "gremlin.pageRankVertexProgram.maxIterations";
        private static final String EDGE_TRAVERSAL = "gremlin.pageRankVertexProgram.edgeTraversal";
        private static final String INITIAL_RANK_TRAVERSAL = "gremlin.pageRankVertexProgram.initialRankTraversal";
        private static final String TELEPORTATION_ENERGY = "gremlin.pageRankVertexProgram.teleportationEnergy";
        private static final String CONVERGENCE_ERROR = "gremlin.pageRankVertexProgram.convergenceError";

        private MessageScope.Local<Double> incidentMessageScope = MessageScope.Local.of(__::outE); //2
        private MessageScope.Local<Double> countMessageScope = MessageScope.Local.of(new MessageScope.Local.ReverseTraversalSupplier(this.incidentMessageScope));
        private PureTraversal<Vertex, Edge> edgeTraversal = null;
        private PureTraversal<Vertex, ? extends Number> initialRankTraversal = null;
        private double alpha = 0.85d;
        private double epsilon = 0.00001d;
        private int maxIterations = 20;
        private String property = PAGE_RANK; //3
        private Set<VertexComputeKey> vertexComputeKeys;
        private Set<MemoryComputeKey> memoryComputeKeys;

        private PageRankVertexProgram() {

        }

        @Override
        public void loadState(final Graph graph, final Configuration configuration) { //4
            if (configuration.containsKey(INITIAL_RANK_TRAVERSAL))
                this.initialRankTraversal = PureTraversal.loadState(configuration, INITIAL_RANK_TRAVERSAL, graph);
            if (configuration.containsKey(EDGE_TRAVERSAL)) {
                this.edgeTraversal = PureTraversal.loadState(configuration, EDGE_TRAVERSAL, graph);
                this.incidentMessageScope = MessageScope.Local.of(() -> this.edgeTraversal.get().clone());
                this.countMessageScope = MessageScope.Local.of(new MessageScope.Local.ReverseTraversalSupplier(this.incidentMessageScope));
            }
            this.alpha = configuration.getDouble(ALPHA, this.alpha);
            this.epsilon = configuration.getDouble(EPSILON, this.epsilon);
            this.maxIterations = configuration.getInt(MAX_ITERATIONS, 20);
            this.property = configuration.getString(PROPERTY, PAGE_RANK);
            this.vertexComputeKeys = new HashSet<>(Arrays.asList(
                    VertexComputeKey.of(this.property, false),
                    VertexComputeKey.of(EDGE_COUNT, true))); //5
            this.memoryComputeKeys = new HashSet<>(Arrays.asList(
                    MemoryComputeKey.of(TELEPORTATION_ENERGY, Operator.sum, true, true),
                    MemoryComputeKey.of(VERTEX_COUNT, Operator.sum, true, true),
                    MemoryComputeKey.of(CONVERGENCE_ERROR, Operator.sum, false, true)));
        }

        @Override
        public void storeState(final Configuration configuration) {
            VertexProgram.super.storeState(configuration);
            configuration.setProperty(ALPHA, this.alpha);
            configuration.setProperty(EPSILON, this.epsilon);
            configuration.setProperty(PROPERTY, this.property);
            configuration.setProperty(MAX_ITERATIONS, this.maxIterations);
            if (null != this.edgeTraversal)
                this.edgeTraversal.storeState(configuration, EDGE_TRAVERSAL);
            if (null != this.initialRankTraversal)
                this.initialRankTraversal.storeState(configuration, INITIAL_RANK_TRAVERSAL);
        }

        @Override
        public GraphComputer.ResultGraph getPreferredResultGraph() {
            return GraphComputer.ResultGraph.NEW;
        }

        @Override
        public GraphComputer.Persist getPreferredPersist() {
            return GraphComputer.Persist.VERTEX_PROPERTIES;
        }

        @Override
        public Set<VertexComputeKey> getVertexComputeKeys() { //6
            return this.vertexComputeKeys;
        }

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

        @Override
        public Set<MemoryComputeKey> getMemoryComputeKeys() {
            return this.memoryComputeKeys;
        }

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

        @Override
        public PageRankVertexProgram clone() {
            try {
                final PageRankVertexProgram clone = (PageRankVertexProgram) super.clone();
                if (null != this.initialRankTraversal)
                    clone.initialRankTraversal = this.initialRankTraversal.clone();
                return clone;
            } catch (final CloneNotSupportedException e) {
                throw new IllegalStateException(e.getMessage(), e);
            }
        }

        @Override
        public void setup(final Memory memory) {
            memory.set(TELEPORTATION_ENERGY, null == this.initialRankTraversal ? 1.0d : 0.0d);
            memory.set(VERTEX_COUNT, 0.0d);
            memory.set(CONVERGENCE_ERROR, 1.0d);
        }

        @Override
        public void execute(final Vertex vertex, Messenger<Double> messenger, final Memory memory) { //7
            if (memory.isInitialIteration()) {
                messenger.sendMessage(this.countMessageScope, 1.0d);  //8
                memory.add(VERTEX_COUNT, 1.0d);
            } else {
                final double vertexCount = memory.<Double>get(VERTEX_COUNT);
                final double edgeCount;
                double pageRank;
                if (1 == memory.getIteration()) {
                    edgeCount = IteratorUtils.reduce(messenger.receiveMessages(), 0.0d, (a, b) -> a + b);
                    vertex.property(VertexProperty.Cardinality.single, EDGE_COUNT, edgeCount);
                    pageRank = null == this.initialRankTraversal ?
                            0.0d :
                            TraversalUtil.apply(vertex, this.initialRankTraversal.get()).doubleValue(); //9
                } else {
                    edgeCount = vertex.value(EDGE_COUNT);
                    pageRank = IteratorUtils.reduce(messenger.receiveMessages(), 0.0d, (a, b) -> a + b); //10
                }
                //////////////////////////
                final double teleporationEnergy = memory.get(TELEPORTATION_ENERGY);
                if (teleporationEnergy > 0.0d) {
                    final double localTerminalEnergy = teleporationEnergy / vertexCount;
                    pageRank = pageRank + localTerminalEnergy;
                    memory.add(TELEPORTATION_ENERGY, -localTerminalEnergy);
                }
                final double previousPageRank = vertex.<Double>property(this.property).orElse(0.0d);
                memory.add(CONVERGENCE_ERROR, Math.abs(pageRank - previousPageRank));
                vertex.property(VertexProperty.Cardinality.single, this.property, pageRank);
                memory.add(TELEPORTATION_ENERGY, (1.0d - this.alpha) * pageRank);
                pageRank = this.alpha * pageRank;
                if (edgeCount > 0.0d)
                    messenger.sendMessage(this.incidentMessageScope, pageRank / edgeCount);
                else
                    memory.add(TELEPORTATION_ENERGY, pageRank);
            }
        }

        @Override
        public boolean terminate(final Memory memory) { //11
            boolean terminate = memory.<Double>get(CONVERGENCE_ERROR) < this.epsilon || memory.getIteration() >= this.maxIterations;
            memory.set(CONVERGENCE_ERROR, 0.0d);
            return terminate;
        }

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

1. `PageRankVertexProgram` implements `VertexProgram<Double>` because the messages it sends are Java doubles.

2. The default path of energy propagation is via outgoing edges from the current vertex.

3. The resulting PageRank values for the vertices are stored as a vertex property.

4. A vertex program is constructed using an Apache `Configuration` to ensure easy dissemination across a cluster of JVMs.

5. `EDGE_COUNT` is a transient "scratch data" compute key while `PAGE_RANK` is not.

6. A vertex program must define the "compute keys" that are the properties being operated on during the computation.

7. The "while"-loop of the vertex program.

8. 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`.

9. Initially, each vertex is provided an equal amount of energy represented as a double.

10. Energy is aggregated, computed on according to the PageRank algorithm, and then disseminated according to the defined `MessageScope.Local`.

11. The computation is terminated after epsilon-convergence is met or a pre-defined number of iterations have taken place.

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
    ==>15
    gremlin> g = result.graph().traversal()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:0], standard]
    gremlin> g.V().valueMap()
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.11375510357865541],name:[marko],age:[29]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.14598540152719106],name:[vadas],age:[27]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.3047200907912249],name:[lop],lang:[java]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.14598540152719106],name:[josh],age:[32]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.17579889899708231],name:[ripple],lang:[java]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.11375510357865541],name:[peter],age:[35]]

Note that `GraphTraversal` provides a `pageRank()`-step.

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().pageRank().valueMap()
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.11375510357865537],name:[marko],age:[29]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.30472009079122486],name:[lop],lang:[java]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.14598540152719103],name:[josh],age:[32]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.1757988989970823],name:[ripple],lang:[java]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.11375510357865537],name:[peter],age:[35]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.14598540152719103],name:[vadas],age:[27]]
    gremlin> g.V().pageRank().by('pageRank').times(5).order().by('pageRank').valueMap()
    ==>[name:[marko],pageRank:[0.11362166126141332],age:[29]]
    ==>[name:[peter],pageRank:[0.11362166126141332],age:[35]]
    ==>[name:[vadas],pageRank:[0.14598422136890218],age:[27]]
    ==>[name:[josh],pageRank:[0.14598422136890218],age:[32]]
    ==>[name:[ripple],lang:[java],pageRank:[0.1756689971547068]]
    ==>[name:[lop],lang:[java],pageRank:[0.3051192375846622]]

### 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.

1. Every vertex assigns itself to a unique cluster ID (initially, its vertex ID).

2. Every vertex determines its per neighbor vote strength as 1.0d / incident edges count.

3. Every vertex sends its cluster ID and vote strength to its adjacent vertices as a `Pair<Serializable,Double>`

4. 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.

5. Steps 3 and 4 repeat until either a max number of iterations has occurred or no vertex has adjusted its cluster anymore.

Note that `GraphTraversal` provides a `peerPressure()`-step.

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().peerPressure().by('cluster').valueMap()
    ==>[name:[marko],cluster:[1],age:[29]]
    ==>[name:[vadas],cluster:[1],age:[27]]
    ==>[name:[lop],lang:[java],cluster:[1]]
    ==>[name:[josh],cluster:[1],age:[32]]
    ==>[name:[ripple],lang:[java],cluster:[1]]
    ==>[name:[peter],cluster:[6],age:[35]]
    gremlin> g.V().peerPressure().by(outE('knows')).by('cluster').valueMap()
    ==>[name:[marko],cluster:[1],age:[29]]
    ==>[name:[vadas],cluster:[1],age:[27]]
    ==>[name:[lop],lang:[java],cluster:[3]]
    ==>[name:[josh],cluster:[1],age:[32]]
    ==>[name:[ripple],lang:[java],cluster:[5]]
    ==>[name:[peter],cluster:[6],age:[35]]

### 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

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@537b3b2e
    gremlin> writeGraphConf.setProperty("gremlin.graph", "org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph")
    gremlin> writeGraphConf.setProperty("gremlin.tinkergraph.graphFormat", "gryo")
    gremlin> writeGraphConf.setProperty("gremlin.tinkergraph.graphLocation", "/tmp/tinkergraph.kryo")
    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()

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.

Warning | Edges in the input graph must be present in both directions, e.g. from the source vertex to the target vertex as an out-edge and from the target vertex to the source vertex as an in-edge. This is especially important if the input graph is a `HadoopGraph`. `BulkLoaderVertexProgram` will likely fail with a `FastNoSuchElementException` if one of the edges is missing.

### TraversalVertexProgram

The `TraversalVertexProgram` is a "special" VertexProgram in that it can be executed via a `Traversal` and a `GraphComputer`. In Gremlin, it is possible to have the same traversal executed using either the standard OLTP-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()
    ==>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().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    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:

1. `g.V()`: Put a traverser on each vertex in the graph.

2. `both()`: Propagate each traverser to the vertices `both`-adjacent to its current vertex.

3. `hasLabel('person')`: If the vertex is not a person, kill the traversers at that vertex.

4. `values('age')`: Have all the traversers reference the integer age of their current vertex.

5. `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 2 iterations. The reason being is that `g.V().both()` and `hasLabel('person').values('age').groupCount()` can 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.

The same OLAP traversal can be executed using the standard `graph.compute()` model, though at the expense of verbosity. `TraversalVertexProgram` provides a fluent `Builder` for constructing a `TraversalVertexProgram`. The specified `traversal()` can be either a direct `Traversal` object or a JSR-223 script that will generate a `Traversal`. There is no benefit to using the model below. It is demonstrated to help elucidate how Gremlin OLAP traversals are ultimately compiled for execution on a `GraphComputer`.

    gremlin> result = graph.compute().program(TraversalVertexProgram.build().traversal(g.V().both().hasLabel('person').values('age').groupCount('a')).create()).submit().get()
    ==>result[tinkergraph[vertices:6 edges:6],memory[size:2]]
    gremlin> result.memory().a
    ==>32=3
    ==>35=1
    ==>27=1
    ==>29=3
    gremlin> result.memory().iteration
    ==>1
    gremlin> result.memory().runtime
    ==>6

#### 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 "bulked." The second difference is that Gremlin OLTP is executed in a serial/streaming fashion, while Gremlin OLAP is executed in a parallel/step-wise fashion. These two fundamental differences lead to the behaviors enumerated below.

1. Traversal sideEffects are represented as a distributed data structure across `GraphComputer` workers. It is not possible to get a global view of a sideEffect until after an iteration has occurred and global sideEffects are re-broadcasted to the workers. In some situations, a "stale" local 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. To ensure a fresh global representation, use `barrier()` prior to accessing the global sideEffect. Note that this only comes into play with custom steps and lambda steps. The standard Gremlin step library is respective of OLAP semantics.

2. 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. In other words, bulking is very unlikely with traversers that maintain path information. For more information on this concept, please see Faunus Provides Big Graph Data.

3. 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), `TraversalVertexProgram` ensures a serial representation is ordered accordingly. Moreover, it is intelligent enough to maintain the ordering of `g.V().hasLabel("person").order().by("age").values("name")`. However, the OLAP traversal `g.V().hasLabel("person").order().by("age").out().values("name")` will lose the original ordering as the `out()`-step will rebroadcast traversers across the cluster.

## Graph Filter

Most OLAP jobs do not require the entire source graph to faithfully execute their `VertexProgram`. For instance, if `PageRankVertexProgram` is only going to compute the centrality of people in the friendship-graph, then the following `GraphFilter` can be applied.

    graph.computer().
      vertices(hasLabel("person")).
      edges(bothE("knows")).
      program(PageRankVertexProgram...)

There are two methods for constructing a `GraphFilter`.

- `vertices(Traversal<Vertex,Vertex>)`: A traversal that will be used that can only analyze a vertex and its properties. If the traversal `hasNext()`, the input `Vertex` is passed to the `GraphComputer`.

- `edges(Traversal<Vertex,Edge>)`: A traversal that will iterate all legal edges for the source vertex.

`GraphFilter` is a "push-down predicate" that providers can reason on to determine the most efficient way to provide graph data to the `GraphComputer`.

Important | Apache TinkerPop provides `GraphFilterStrategy` traversal strategy which analyzes a submitted OLAP traversal and, if possible, creates an appropriate `GraphFilter` automatically. For instance, `g.V().count()` would yield a `GraphFilter.edges(limit(0))`. Thus, for traversal submissions, users typically do not need to be aware of creating graph filters explicitly. Users can use the `explain()`-step to see the `GraphFilter` generated by `GraphFilterStrategy`.

# 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:

1. Gremlin Console - A REPL environment for interactive development and analysis

2. Gremlin Server - A server that hosts script engines thus enabling remote Gremlin execution

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 Customize 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="ibiblio" m2compatible="true"/>
        </chain>
      </resolvers>
    </ivysettings>

The Graph configuration can also be modified to include the local system’s Maven `.m2` directory:

    <ivysettings>
      <settings defaultResolver="downloadGrapes"/>
      <property name="m2-pattern" value="${user.home}/.m2/repository/org/apache/tinkerpop/[module]/[revision]/[module]-[revision](-[classifier]).[ext]" />
      <property name="m2-pattern-ivy" value="${user.home}/.m2/repository/org/apache/tinkerpop/[module]/[revision]/[module]-[revision](-[classifier]).pom" />
      <caches>
        <cache name="nocache" useOrigin="true" />
      </caches>
      <resolvers>
        <chain name="downloadGrapes">
          <!-- https://mvmn.wordpress.com/2016/02/02/grape-config-for-local-maven-repo/ -->
          <filesystem name="local-maven2" checkmodified="true" changingPattern=".*" changingMatcher="regexp" m2compatible="true" cache="nocache">
            <artifact pattern="${m2-pattern}"/>
            <ivy pattern="${m2-pattern-ivy}"/>
          </filesystem>
          <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="ibiblio" m2compatible="true"/>
          <ibiblio name="local" root="file:${user.home}/.m2/repository/" m2compatible="true"/>
        </chain>
      </resolvers>
    </ivysettings>

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.

## 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. Moreover, 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()
    ==>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 | :x | Ends the Console session.
import | :i | Import a class into the Console session.
:clear | :c | 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 | :l | 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`.

### Console Preferences

Preferences are set with `:set name value`. Values can contain spaces when quoted. All preferences are reset by `:purge preferences`

Preference | Type | Description
--- | --- | ---
max-iteration | int | Controls the maximum number of results that the Console will display. Default: 100 results.
colors | bool | Enable ANSI color rendering. Default: true
gremlin.color | colors | Color of the ASCII art gremlin on startup.
info.color | colors | Color of "info" type messages.
error.color | colors | Color of "error" type messages.
vertex.color | colors | Color of vertices results.
edge.color | colors | Color of edges in results.
string.color | colors | Colors of strings in results.
number.color | colors | Color of numbers in results.
T.color | colors | Color of Tokens in results.
input.prompt.color | colors | Color of the input prompt.
result.prompt.color | colors | Color of the result prompt.
input.prompt | string | Text of the input prompt.
result.prompt | string | Text of the result prompt.
result.indicator.null | string | Text of the void/no results indicator - setting to empty string (i.e. "" at the command line) will print no result line in these cases.

Colors can contain a comma-separated combination of 1 each of foreground, background, and attribute.

Foreground | Background | Attributes
--- | --- | ---
black | bg_black | bold
blue | bg_blue | faint
cyan | bg_cyan | underline
green | bg_green |
magenta | bg_magenta |
red | bg_red |
white | bg_white |
yellow | bg_yellow |

Example:

    :set gremlin.color bg_black,green,bold

### 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 3.3.3  //3
    ==>loaded: [org.apache.tinkerpop, neo4j-gremlin, 3.3.3]
    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]

1. 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.

2. To make a plugin "active" execute the `:plugin use` command and specify the name of the plugin to enable.

3. 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.

4. Note that there is a "tinkerpop.neo4j" plugin available, but it is not yet "active".

5. Again, to use the "tinkerpop.neo4j" plugin, it must be made "active" with `:plugin use`.

6. 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.

### Execution Mode

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

    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. 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:

    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]

It is also possible to pass multiple scripts by specifying multiple `-e` options. The scripts will execute in the order that they are specified. Note that only the arguments from the last script executed will be preserved in the console. Finally, if the arguments conflict with the reserved flags that `gremlin.sh` responds double quotes can be used to wrap all the arguments to the option:

    $ bin/gremlin.sh -e "gremlin.groovy -e -i --color"

### Interactive Mode

The Gremlin Console can be started in an "interactive" mode. Interactive mode is like execution mode but the console will not exit at the completion of the script, even if the script completes unsuccessfully. In such a case, it will simply stop processing on the line that of the script that failed. In this way the state of the console is such that a user could examine the state of things up to the point of failure which might make the script easier to debug.

In addition to debugging, interactive mode is a helpful way for users to initialize their console environment to avoid otherwise repetitive typing. For example, a user who spends a lot of time working with the TinkerPop "modern" graph might create a script called `init.groovy` like:

    graph = TinkerFactory.createModern()
    g = graph.traversal()

and then start Gremlin Console as follows:

    $ bin/gremlin.sh -i init.groovy

             \,,,/
             (o o)
    -----oOOo-(3)-oOOo-----
    plugin activated: tinkerpop.server
    plugin activated: tinkerpop.utilities
    plugin activated: tinkerpop.tinkergraph
    gremlin> g.V()
    ==>v[1]
    ==>v[2]
    ==>v[3]
    ==>v[4]
    ==>v[5]
    ==>v[6]

Note that the user can now reference `g` (and `graph` for that matter) at startup without having to directly type that variable initialization code into the console.

Like, execution mode, it is also possible to pass multiple scripts by specifying multiple `-i` options. See the Execution Mode Section for more information on the specifics of that capability.

## 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 WebSocket and exposes a custom sub-protocol for interacting with the server.

Warning | Gremlin Server allows for the execution of remotely submitted "scripts" (i.e. arbitrary code sent by a client to the server). Developers should consider the security implications involved in running Gremlin Server without the appropriate precautions. Please review the Security Section and more specifically, the Script Execution Section for more information.

### 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 /home/smallette/git/apache/incubator-tinkerpop/gremlin-server/target/apache-tinkerpop-gremlin-server-3.3.0-SNAPSHOT-standalone/conf/gremlin-server.yaml
    [INFO] MetricManager - Configured Metrics ConsoleReporter configured with report interval=180000ms
    [INFO] MetricManager - Configured Metrics CsvReporter configured with report interval=180000ms to fileName=/tmp/gremlin-server-metrics.csv
    [INFO] MetricManager - Configured Metrics JmxReporter configured with domain= and agentId=
    [INFO] MetricManager - Configured Metrics Slf4jReporter configured with interval=180000ms and loggerName=org.apache.tinkerpop.gremlin.server.Settings$Slf4jReporterMetrics
    [INFO] DefaultGraphManager - Graph [graph] was successfully configured via [conf/tinkergraph-empty.properties].
    [INFO] ServerGremlinExecutor - Initialized Gremlin thread pool.  Threads in pool named with pattern gremlin-*
    [INFO] ServerGremlinExecutor - Initialized GremlinExecutor and preparing GremlinScriptEngines instances.
    [INFO] ServerGremlinExecutor - Initialized gremlin-groovy GremlinScriptEngine and registered metrics
    [INFO] ServerGremlinExecutor - A GraphTraversalSource is now bound to [g] with graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    [INFO] OpLoader - Adding the standard OpProcessor.
    [INFO] OpLoader - Adding the session OpProcessor.
    [INFO] OpLoader - Adding the traversal OpProcessor.
    [INFO] TraversalOpProcessor - Initialized cache for TraversalOpProcessor with size 1000 and expiration time of 600000 ms
    [INFO] GremlinServer - Executing start up LifeCycleHook
    [INFO] Logger$info - Loading 'modern' graph data.
    [INFO] AbstractChannelizer - Configured application/vnd.gremlin-v3.0+gryo with org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV3d0
    [INFO] AbstractChannelizer - Configured application/vnd.gremlin-v3.0+gryo-stringd with org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV3d0
    [INFO] AbstractChannelizer - Configured application/vnd.gremlin-v3.0+json with org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerV3d0
    [INFO] AbstractChannelizer - Configured application/json with org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerV3d0
    [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.")
          org.apache.tinkerpop.gremlin.tinkergraph.structure.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]

1. 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.

2. 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 `:submit` will react to.

3. 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`.

4. 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.

5. 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 alternative syntax to connecting allows for the `Cluster` to be user constructed directly in the console as opposed to simply providing a static YAML file.

    gremlin> cluster = Cluster.open()
    ==>localhost/127.0.0.1:8182
    gremlin> :remote connect tinkerpop.server cluster
    ==>Configured localhost/127.0.0.1:8182

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]
    gremlin> :remote close
    ==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]

#### 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-[e453e97a-19a8-4533-b2fe-8e7d3ff72f57]
    gremlin> :> x = 1
    ==>1
    gremlin> :> y = 2
    ==>2
    gremlin> :> x + y
    ==>3
    gremlin> :remote close
    ==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]-[e453e97a-19a8-4533-b2fe-8e7d3ff72f57]

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-[95aef35a-3e7c-41d1-9c3a-61eb4ecafea2]
    gremlin> :remote console
    ==>All scripts will now be sent to Gremlin Server - [localhost/127.0.0.1:8182]-[95aef35a-3e7c-41d1-9c3a-61eb4ecafea2] - 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]-[95aef35a-3e7c-41d1-9c3a-61eb4ecafea2]
    gremlin> :remote close
    ==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]-[95aef35a-3e7c-41d1-9c3a-61eb4ecafea2]

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 evaluation. 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>3.3.3</version>
    </dependency>

                             gremlin-groovy
    image:gremlin-java.png[width=175,float=left] 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

1. Opens a reference to `localhost` - note that there are many configuration options available in defining a `Cluster` object.

2. 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

1. 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.

2. 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.

3. Submit a script, get a `ResultSet`, then return a `CompletableFuture` that will be called when all results have been returned.

4. Submit a script asynchronously without waiting for the request to be written to the server.

5. 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.keepAliveInterval | Length of time in milliseconds to wait on an idle connection before sending a keep-alive request. Set to zero to disable this feature. | 1800000
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 is 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. | 4
connectionPool.maxSimultaneousUsagePerConnection | The maximum number of times that a connection can be borrowed from the pool simultaneously. | 16
connectionPool.maxSize | The maximum size of a connection pool for a host. | 8
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. | 1
connectionPool.minSimultaneousUsagePerConnection | The maximum number of times that a connection can be borrowed from the pool simultaneously. | 8
connectionPool.minSize | The minimum size of a connection pool for a host. | 2
connectionPool.reconnectInterval | The amount of time in milliseconds to wait before trying to reconnect to a dead host. | 1000
connectionPool.resultIterationBatchSize | The override value for the size of the result batches to be returned from the server. | 64
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. | none
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 GryoMessageSerializerV3d0(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 Python

    pip install gremlinpython

TinkerPop3 also includes a client for Python-based applications. It is referred to as Gremlin-Python Driver. The `Client` class implementation/interface is based on the Java Driver, with some restrictions. Most notably, Gremlin-Python does not yet implement the `Cluster` class. Instead, `Client` is instantiated directly. Usage is as follows:

    from gremlin_python.driver import client //1
    client = client.Client('ws://localhost:8182/gremlin', 'g') //2

1. Import the Gremlin-Python `client` module.

2. Opens a reference to `localhost` - note that there are various configuration options that can be passed to the `Client` object upon instantiation as keyword arguments.

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

    result_set = client.submit("[1,2,3,4]")  //1
    future_results = result_set.all()  //2
    results = future_results.result() //3
    assert results == [1, 2, 3, 4] //4

    future_result_set = client.submitAsync("[1,2,3,4]") //5
    result_set = future_result_set.result() //6
    result = result_set.one() //7
    assert results == [1, 2, 3, 4] //8
    assert result_set.done.done() //9

    client.close() //10

1. Submit 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.

2. 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. The `all` method returns a `concurrent.futures.Future` that resolves to a list when it is complete.

3. Block until the the script is evaluated and results are sent back by the server.

4. Verify the result.

5. Submit the same script to the server but don’t block.

6. Wait until request is written to the server and `ResultSet` is constructed.

7. Read a single result off the result stream.

8. Again, verify the result.

9. Verify that the all results have been read and stream is closed.

10. Close client and underlying pool connections.

#### Configuration

The following table describes the various configuration options for the Gremlin-Python Driver. They can be passed to the `Client` instance as keyword arguments:

Key | Description | Default
--- | --- | ---
protocol_factory | A callable that returns an instance of `AbstractBaseProtocol`. | `gremlin_python.driver.protocol.GremlinServerWSProtocol`
transport_factory | A callable that returns an instance of `AbstractBaseTransport`. | `gremlin_python.driver.tornado.transport.TornadoTransport`
pool_size | The number of connections used by the pool. | 4
max_workers | Maximum number of worker threads. | Number of CPUs * 5
message_serializer | The message serializer implementation. | `gremlin_python.driver.serializer.GraphSONMessageSerializer`
password | The password to submit on requests that require authentication. | ""
username | The username to submit on requests that require authentication. | ""

### Connecting via HTTP

While the default behavior for Gremlin Server is to provide a WebSocket-based connection, it can also be configured to support plain HTTP web service. The HTTP 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, HTTP 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 HTTP 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. The `WsAndHttpChannelizer` may also be configured to enable both WebSockets and the REST endpoint. `WebSocketChannelizer`, in the configuration file:

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

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

The `HttpChannelizer` 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 HTTP 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 WebSocket configuration, which supports streaming, if that type of use case is required.

### Connecting via withRemote

    <dependency>
       <groupId>org.apache.tinkerpop</groupId>
       <artifactId>gremlin-driver</artifactId>
       <version>3.3.3</version>
    </dependency>

A `TraversalSource` has several `withRemote()` methods which provide an interesting alternative to the other methods for connecting to Gremlin Server. It is interesting in that all other methods involve construction of a `String` representation of the `Traversal` which is then submitted as a script to Gremlin Server (via driver or HTTP). This approach is quite akin to SQL, where query strings are embedded into code and submitted to a database server. While there are patterns for taking this approach that can lead to maintainable application code, using `withRemote()` could be a better method as it brings some good benefits with it:

- Get auto-complete when writing traversals in an IDE.

- Get compile-time errors in traversal writing.

- Get the feel of working with an embedded database.

One way to create a `Traversal` instance that is remote-enabled is by configuration file. Here is an example of what that file looks like:

    gremlin.remote.remoteConnectionClass=org.apache.tinkerpop.gremlin.driver.remote.DriverRemoteConnection
    gremlin.remote.driver.clusterFile=conf/remote-objects.yaml
    gremlin.remote.driver.sourceName=g

The `gremlin.remote.remoteConnectionClass` should be an implementation of the `RemoteConnection` interface in `gremlin-core`. In this case, it points at the `gremlin-driver` implementation, called `DriverRemoteConnection`. The other setting, `gremlin.remote.driver.clusterFile`, is a configuration to `DriverRemoteConnection`, and it provides a pointer to the config file to use to construct a `gremlin-driver` `Cluster` object to be utilized when connecting to Gremlin Server. Please see the Connecting Via Java section for more information on those classes and their usage. Finally, the `gremlin.remote.driver.sourceName` setting tells the `DriverRemoteConnection` the name of the `TraversalSource` in Gremlin Server to connect to.

Important | Gremlin Server supports configurable serialization options. The `withRemote()` feature works best with Gryo serialization. While it is compatible with GraphSON, unknown incompatibilities may arise

Gremlin Server needs to be running for this example to work. Use the following configuration:

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

To configure a "remote" traversal, there first needs to be a `TraversalSource`. A `TraversalSource` can be generated from any `Graph` instance with the `traversal()` method. Of course, any traversals generated from this source using the `withRemote()` configuration option will not execute against the local graph. That could be confusing and it may be easier to think of the local graph as being "empty". To that end, it is recommended that when using `withRemote()`, the `TraversalSource` be generated with `EmptyGraph` as follows:

    gremlin> graph = EmptyGraph.instance()
    ==>emptygraph[empty]
    gremlin> g = graph.traversal().withRemote('conf/remote-graph.properties')
    ==>graphtraversalsource[emptygraph[empty], standard]
    gremlin> g.V().valueMap(true)
    ==>[id:1,name:[marko],label:person,age:[29]]
    ==>[id:2,name:[vadas],label:person,age:[27]]
    ==>[id:3,name:[lop],label:software,lang:[java]]
    ==>[id:4,name:[josh],label:person,age:[32]]
    ==>[id:5,name:[ripple],label:software,lang:[java]]
    ==>[id:6,name:[peter],label:person,age:[35]]
    ==>[id:13,name:[matthias],label:vertex]
    gremlin> g.close()

Note the call to `close()` above. The call to `withRemote()` internally instantiates a `Client` instance that can only be released by "closing" the `GraphTraversalSource`. It is important to take that step to release resources created in that step.

If working with multiple remote `TraversalSource` instances it is more efficient to construct a `Cluster` object and then re-use it.

    gremlin> cluster = Cluster.open('conf/remote-objects.yaml')
    ==>localhost/127.0.0.1:8182
    gremlin> graph = EmptyGraph.instance()
    ==>emptygraph[empty]
    gremlin> g = graph.traversal().withRemote(DriverRemoteConnection.using(cluster, "g"))
    ==>graphtraversalsource[emptygraph[empty], standard]
    gremlin> g.V().valueMap(true)
    ==>[id:1,name:[marko],label:person,age:[29]]
    ==>[id:2,name:[vadas],label:person,age:[27]]
    ==>[id:3,name:[lop],label:software,lang:[java]]
    ==>[id:4,name:[josh],label:person,age:[32]]
    ==>[id:5,name:[ripple],label:software,lang:[java]]
    ==>[id:6,name:[peter],label:person,age:[35]]
    ==>[id:13,name:[matthias],label:vertex]
    gremlin> g.close()
    gremlin> cluster.close()

If the `Cluster` instance is supplied externally, as is shown above, then it is not closed implicitly by the close of "g". Closing "g" will only close the `Client` instance associated with that `TraversalSource`. In this case, the `Cluster` must also be closed explicitly. Closing "g" and the "cluster" aren’t actually both necessary - the close of a `Cluster` will close all `Client` instance spawned by the `Cluster`.

Important | `RemoteGraph` uses the `TraversalOpProcessor` in Gremlin Server which requires a cache to enable the retrieval of side-effects (if the `Traversal` produces any). That cache can be configured (e.g. controlling eviction times and sizing) can be done in the Gremlin Server configuration file as described here.

Finally, Gremlin `Bytecode` supports the encoding of bindings which allow GremlinServer to cache traversals that will be reused over and over again save that some parameterization may change. Thus, instead of translating, compiling, and then executing each submitted bytecode, it is possible to simply execute. To express bindings in Gremlin-Java and Gremlin-Groovy, use `Bindings`.

    gremlin> cluster = Cluster.open('conf/remote-objects.yaml')
    ==>localhost/127.0.0.1:8182
    gremlin> b = Bindings.instance()
    ==>bindings[main]
    gremlin> g = EmptyGraph.instance().traversal().withRemote(DriverRemoteConnection.using(cluster, "g"))
    ==>graphtraversalsource[emptygraph[empty], standard]
    gremlin> g.V(b.of('id',1)).out('created').values('name')
    ==>lop
    gremlin> g.V(b.of('id',4)).out('created').values('name')
    ==>ripple
    ==>lop
    gremlin> g.V(b.of('id',4)).out('created').values('name').getBytecode()
    ==>[[], [V(binding[id=4]), out(created), values(name)]]
    gremlin> g.V(b.of('id',4)).out('created').values('name').getBytecode().getBindings()
    ==>id=4
    gremlin> cluster.close()

Both traversals are abstractly defined as `g.V(id).out('created').values('name')` and thus, the first submission can be cached for faster evaluation on the next submission.

### Configuring

The `gremlin-server.sh` file serves multiple purposes. 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 `install` switch and supply the Maven coordinates for the dependency to "install". For example, to use Neo4j in Gremlin Server:

    bin/gremlin-server.sh install org.apache.tinkerpop neo4j-gremlin 3.3.3

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.

`bin/gremlin-server.sh` has several other options.

Parameter | Description
--- | ---
start | Start the server in the background.
stop | Shutdown the server.
restart | Shutdown a running server then start it again.
status | Check if the server is running.
console | Start the server in the foreground. Use ^C to kill it.
install <group> <artifact> <version> | Install dependencies into the server. "-i" exists for backwards compatibility but is deprecated.
<conf file> | Start the server in the foreground using the provided YAML config file.

The `bin/gremlin-server.sh` script can be customized with environment variables in `bin/gremlin-server.conf`.

Variable | Description
--- | ---
DEBUG | Enable debugging of the startup script
GREMLIN_HOME | The Gremlin Server install directory. Use this if the script has trouble finding itself.
GREMLIN_YAML | The default server YAML file (conf/gremlin-server.yaml)
LOG_DIR | Location of gremlin.log where stdout/stderr are captured (logs/)
PID_DIR | Location of gremlin.pid
RUNAS | User to run the server as
JAVA_HOME | Java install location. Will use $JAVA_HOME/bin/java
JAVA_OPTIONS | Options passed to the JVM

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

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

Warning | On Windows, gremlin-server.bat will always start in the foreground. When no parameter is provided, it will start with the default `conf/gremlin-server.yaml` file.

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

Key | Description | Default
--- | --- | ---
authentication.authenticator | The fully qualified classname of an `Authenticator` implementation to use. If this setting is not present, then authentication is effectively disabled. | `AllowAllAuthenticator`
authentication.authenticationHandler | The fully qualified classname of an `AbstractAuthenticationHandler` implementation to use. If this setting is not present, but the `authentication.authenticator` is, it will use that authenticator with the default `AbstractAuthenticationHandler` implementation for the specified `Channelizer` | none
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
authentication.enableAuditLog | The available authenticators can issue audit logging messages, binding the authenticated user to his remote socket address and binding requests with a gremlin query to the remote socket address. For privacy reasons, the default value of this setting is false. The audit logging messages are logged at the INFO level via the `audit.org.apache.tinkerpop.gremlin.server` logger, which can be configured using the log4j.properties file. | false
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. WebSocket, Java NIO, etc.). | `WebSocketChannelizer`
graphManager | The fully qualified classname of the `GraphManager` implementation to use. A `GraphManager` is a class that adheres to the TinkerPop `GraphManager` interface, allowing custom implementations for storing and managing graph references, as well as defining custom methods to open and close graphs instantiations. It is important to note that the TinkerPop HTTP and WebSocketChannelizers auto-commit and auto-rollback based on the graphs stored in the graphManager upon script execution completion. | `DefaultGraphManager`
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. When set to `0`, Gremlin Server will use the value provided by `Runtime.availableProcessors()`. | 0
host | The name of the host to bind the server to. | localhost
idleConnectionTimeout | Time in milliseconds that the server will allow a channel to not receive requests from a client before it automatically closes. If enabled, the value provided should typically exceed the amount of time given to `keepAliveInterval`. Note that while this value is to be provided as milliseconds it will resolve to second precision. Set this value to `0` to disable this feature. | 0
keepAliveInterval | Time in milliseconds that the server will allow a channel to not send responses to a client before it sends a "ping" to see if it is still present. If it is present, the client should respond with a "pong" which will thus reset the `idleConnectionTimeout` and keep the channel open. If enabled, this number should be smaller than the value provided to the `idleConnectionTimeout`. Note that while this value is to be provided as milliseconds it will resolve to second precision. Set this value to `0` to disable this feature. | 0
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. Additional setup is required. | 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. Additional setup is required. | 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
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. | 64
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`. If this value is not set, it will default to "gremlin-groovy". | gremlin-groovy
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. If this value is not set, then Gremlin Server will configure with GraphSON and Gryo but will not register any `ioRegistries` for configured graphs. | empty
serializers[X].className | The full class name of the `MessageSerializer` implementation. | none
serializers[X].config | A `Map` containing `MessageSerializer` specific configurations. | none
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 is password-protected | none
ssl.needClientAuth | Optional. One of NONE, OPTIONAL, REQUIRE. Enables client certificate authentication at the enforcement level specified. Can be used in combination with Authenticator. | none
ssl.trustCertChainFile | Required when needClientAuth is OPTIONAL or REQUIRE. Trusted certificates for verifying the remote endpoint’s certificate. The file should contain an X.509 certificate chain in PEM format. | 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`. | 1
threadPoolWorker | The number of threads available to Gremlin Server for processing non-blocking reads and writes. | 1
useEpollEventLoop | try to use epoll event loops (works only on Linux os) instead of netty NIO. | false
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

See the Metrics section for more information on how to configure Ganglia and Graphite.

#### OpProcessor Configurations

An `OpProcessor` provides a way to plug-in handlers to Gremlin Server’s processing flow. Gremlin Server uses this plug-in system itself to expose the packaged functionality that it exposes. Configurations can be supplied to an `OpProcessor` through the `processors` key in the Gremlin Server configuration file. Each `OpProcessor` can take a `Map` of arguments which are specific to a particular implementation:

    processors:
      - { className: org.apache.tinkerpop.gremlin.server.op.session.SessionOpProcessor, config: { sessionTimeout: 28800000 }}

The following sub-sections describe those configurations for each `OpProcessor` implementations supplied with Gremlin Server.

##### SessionOpProcessor

The `SessionOpProcessor` provides a way to interact with Gremlin Server over a session.

Name | Description | Default
--- | --- | ---
maxParameters | Maximum number of parameters that can be passed on the request. | 16
perGraphCloseTimeout | Time in milliseconds to wait for each configured graph to close any open transactions when the session is killed. | 10000
sessionTimeout | Time in milliseconds before a session will time out. | 28800000

##### StandardOpProcessor

The `StandardOpProcessor` provides a way to interact with Gremlin Server without use of sessions and is the default method for processing script evaluation requests.

Name | Description | Default
--- | --- | ---
maxParameters | Maximum number of parameters that can be passed on the request. | 16

##### TraversalOpProcessor

The `TraversalOpProcessor` provides a way to use RemoteGraph.

Name | Description | Default
--- | --- | ---
cacheExpirationTime | Time in milliseconds before side-effects from a `Traversal` will be evicted. | 60000
cacheMaxSize | The maximum number of entries in the side-effect cache. | 1000

#### Security

Gremlin Server provides for several features that aid in the security of the graphs that it exposes. In particular it supports SSL for transport layer security, protective measures against malicious script execution, and authentication. SSL options are described in the configuration settings table in the Connecting via Java section. Script execution options are covered at the end of this section. This section starts with authentication.

Gremlin Server supports a pluggable authentication framework using SASL (Simple Authentication and Security Layer). Depending on the client used to connect to Gremlin Server, different authentication mechanisms are accessible, see the table below.

Client | Authentication mechanism | Availability
--- | --- | ---
HTTP | BASIC | 3.0.0-incubating
Gremlin-Java/ Gremlin-Console | PLAIN SASL (username/password) | 3.0.0-incubating
Pluggable SASL | 3.0.0-incubating
GSSAPI SASL (Kerberos) | 3.3.0
Gremlin-Python | PLAIN SASL | 3.2.2
Gremlin-Javascript | PLAIN SASL | 3.3.0

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 two implementations called `SimpleAuthenticator` for plain text authentication using HTTP BASIC or PLAIN SASL and `Krb5Authenticator` for Kerberos authentication using GSSAPI SASL.

##### Plain text authentication

The `SimpleAuthenticator` implements the "PLAIN" SASL mechanism (i.e. plain text) to authenticate a request. It also supports handling basic authentication requests from http clients. It validates username/password pairs against a graph database, which must be provided to it as part of the configuration.

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

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 `conf/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 HTTP 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)
    gremlin> credentials = graph.traversal(CredentialTraversalSource.class)
    ==>credentialtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> credentials.user("stephen","password")
    ==>v[0]
    gremlin> credentials.user("daniel","better-password")
    ==>v[3]
    gremlin> credentials.user("marko","rainbow-dash")
    ==>v[6]
    gremlin> credentials.users("marko").valueMap()
    ==>[password:[$2a$04$kYNDmJPU4oIJOnEyd6C5VuvyknxSJbGpdyV25lNBuwoKkLHQY.wMm],username:[marko]]
    gremlin> credentials.users().count()
    ==>3
    gremlin> credentials.users("daniel").drop()
    gremlin> credentials.users().count()
    ==>2

Note | The Credentials DSL is built using TinkerPop’s DSL Annotation Processor described here.

Important | In the above example, an empty in-memory TinkerGraph was used for demonstrating the API of the DSL. Obviously, this data will not be retained and usable with Gremlin Server. It would be important to configure TinkerGraph to persist that data or to manually persist it (e.g. write the graph data to Gryo) once changes are complete. Alternatively, use a persistent graph to hold the credentials and configure Gremlin Server accordingly.

##### Kerberos Authentication

The `Krb5Authenticator` implements the "GSSAPI" SASL mechanism (i.e. Kerberos) to authenticate a request from a Gremlin client. It can be applied in an existing Kerberos environment and validates whether a valid authentication proof and service ticket are offered.

    authentication: {
      className: org.apache.tinkerpop.gremlin.server.auth.Krb5Authenticator,
      config: {
        principal: gremlinserver/hostname.your.org@YOUR.REALM,
        keytab: /etc/security/keytabs/gremlinserver.service.keytab}}

Krb5Authenticator needs a Kerberos service principal and a keytab that holds the secret key for that principal. The keytab location and service name, e.g. gremlinserver, are free to be chosen, but Gremlin clients have to specify this service name as the `protocol`. For Gremlin-Console the `protocol` is an entry in the remote.yaml file, for Gremlin-java the client builder has a `protocol()` method.

In addition to the `protocol`, the Gremlin client needs to specify a `jaasEntry`, an entry in the JAAS configuration file. Gremlin-Console comes with a sample gremlin-jaas.conf file with a `GremlinConsole` jaasEntry:

    GremlinConsole {
      com.sun.security.auth.module.Krb5LoginModule required
      doNotPrompt=true
      useTicketCache=true;
    };

This configuration tells Gremlin-Console to pass authentication requests from gremlin-server to the Krb5LoginModule, which is part of the java standard library. The Krb5LoginModule does not prompt the user for a username and password but uses the ticket cache that is normally refreshed when a user logs in to a host within the Kerberos realm.

Finally, the Gremlin client needs the location of the JAAS configuration file to be passed as a system property to the JVM. For Gremlin-Console the easiest way to do this is to pass it to the run script via the JAVA_OPTIONS environment property:

    export JAVA_OPTIONS="$JAVA_OPTIONS -Djava.security.auth.login.config=conf/gremlin-jaas.conf"

##### Protecting Script Execution

It is important to remember that Gremlin Server exposes `GremlinScriptEngine` instances 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.

Scripts have access to the full power of their language and the JVM on which they are running. This means that they can access certain APIs that have nothing to do with Gremlin itself, such as `java.lang.System` or the `java.io` and `java.net` packages. Scripts offer developers a lot of flexibility, but having that flexibility comes at the cost of safety. A Gremlin Server instance that is not secured appropriately provides for a big security risk.

The previous sections discussed methods for securing Gremlin Server through authentication and encryption, which is a good first step in protection. Another layer of protection comes in the form of specific configurations for the `GremlinGroovyScriptEngine`. A user can configure the script engine with a `GroovyCompilerGremlinPlugin` implementation. Consider the basic configuration from the Gremlin Server YAML file:

    scriptEngines: {
      gremlin-groovy: {
        plugins: { org.apache.tinkerpop.gremlin.server.jsr223.GremlinServerGremlinPlugin: {},
                   org.apache.tinkerpop.gremlin.tinkergraph.jsr223.TinkerGraphGremlinPlugin: {},
                   org.apache.tinkerpop.gremlin.jsr223.ImportGremlinPlugin: {classImports: [java.lang.Math], methodImports: [java.lang.Math#*]},
                   org.apache.tinkerpop.gremlin.jsr223.ScriptFileGremlinPlugin: {files: [scripts/empty-sample.groovy]}}}}

This configuration can be expaded to include a the `GroovyCompilerGremlinPlugin`:

    scriptEngines: {
      gremlin-groovy: {
        plugins: { org.apache.tinkerpop.gremlin.server.jsr223.GremlinServerGremlinPlugin: {},
                   org.apache.tinkerpop.gremlin.tinkergraph.jsr223.TinkerGraphGremlinPlugin: {}
                   org.apache.tinkerpop.gremlin.jsr223.ImportGremlinPlugin: {classImports: [java.lang.Math], methodImports: [java.lang.Math#*]},
                   org.apache.tinkerpop.gremlin.jsr223.ScriptFileGremlinPlugin: {files: [scripts/empty-sample-secure.groovy]},
                   org.apache.tinkerpop.gremlin.groovy.jsr223.GroovyCompilerGremlinPlugin: {timedInterrupt: 10000}}}}

This configuration sets up the script engine with to 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

The `GroovyCompilerGremlinPlugin` has a number of configuration options:

Customizer | Description
--- | ---
`compilation` | Allows for three configurations: `COMPILE_STATIC`, `TYPE_CHECKED` or `NONE` (default). When configured with `COMPILE_STATIC` or `TYPE_CHECKED` it applies `CompileStatic` or `TypeChecked` annotations (respectively) to incoming scripts thus removing dynamic dispatch. More information about static compilation can be found here and additional information on `TypeChecked` usage can be found here.
`compilerConfigurationOptions` | Allows configuration of the Groovy `CompilerConfiguration` object by taking a `Map` of key/value pairs where the "key" is a property to set on the `CompilerConfiguration`.
`enableThreadInterrupt` | Injects checks for thread interruption, thus allowing the thread to potentially respect calls to `Thread.interrupt()`
`expectedCompilationTime` | The amount of time in milliseconds a script is allowed to compile before a warning message is sent to the logs.
`extensions` | This setting is for use when `compilation` is configured with `COMPILE_STATIC` or `TYPE_CHECKED` and accepts a comma separated list of type checking extensions that can have the effect of securing calls to various methods.
`timedInterrupt` | Injects checks into loops to interrupt them if they exceed the configured timeout in milliseconds.

Note | 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.

To provide some basic out-of-the-box protections against troublesome scripts, the following configuration can be used:

    scriptEngines: {
      gremlin-groovy: {
        plugins: { org.apache.tinkerpop.gremlin.server.jsr223.GremlinServerGremlinPlugin: {},
                   org.apache.tinkerpop.gremlin.tinkergraph.jsr223.TinkerGraphGremlinPlugin: {}
                   org.apache.tinkerpop.gremlin.groovy.jsr223.GroovyCompilerGremlinPlugin: {enableThreadInterrupt: true, timedInterrupt: 10000, compilation: COMPILE_STATIC, extensions: org.apache.tinkerpop.gremlin.groovy.jsr223.customizer.SimpleSandboxExtension},
                   org.apache.tinkerpop.gremlin.jsr223.ImportGremlinPlugin: {classImports: [java.lang.Math], methodImports: [java.lang.Math#*]},
                   org.apache.tinkerpop.gremlin.jsr223.ScriptFileGremlinPlugin: {files: [scripts/empty-sample-secure.groovy]}}}}

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:

1. `autoTypeUnknown` - When set to `true`, unresolved variables are typed as `Object`.

2. `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)`.

3. `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 `GroovyCompilerGremlinPlugin` implementation is that it is not just for "security" (though is is demonstrated in that capacity here). It 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. Serializers implement the `MessageSerializer` interface. In doing so, they express the list of mime types they expect to support. When configuring multiple serializers it is possible for two or more serializers to support the same mime type. Such a situation may be common with a generic mime type such as `application/json`. Serializers are added in the order that they are encountered in the configuration file and the first one added for a specific mime type will not be overridden by other serializers that also support it.

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 }
      - { className: org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerV2d0 }

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
--- | --- | ---
ioRegistries | A list of `IoRegistry` implementations to be applied to the serializer. | 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
--- | --- | ---
ioRegistries | A list of `IoRegistry` implementations to be applied to the serializer. | 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

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. Note that Graphite and Ganglia are not included as part of the Gremlin Server distribution and must be installed to the server manually.

    bin/gremlin-server.sh install com.codahale.metrics metrics-ganglia 3.0.2
    bin/gremlin-server.sh install com.codahale.metrics metrics-graphite 3.0.2

Warning | Gremlin Server is built to work with Metrics 3.0.2. Usage of other versions may lead to unexpected problems.

Note | Installing Ganglia will include `org.acplt:oncrpc`, which is an LGPL licensed dependency.

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).

- `op.traversal` - the number of `Traversal` executions, 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.

- `engine-name.session.session-id.*` - metrics related to different `GremlinScriptEngine` instances configured for session-based requests where "engine-name" will be the actual name of the engine, such as "gremlin-groovy" and "session-id" will be the identifier for the session itself.

- `engine-name.sessionless.*` - metrics related to different `GremlinScriptEngine` instances configured for sessionless requests where "engine-name" will be the actual name of the engine, such as "gremlin-groovy".

#### As A Service

Gremlin server can be configured to run as a service.

##### Init.d (SysV)

Link `bin/gremlin-server.sh` to `init.d` Be sure to set RUNAS to the service user in `bin/gremlin-server.conf`

    # Install
    ln -s /path/to/apache-tinkerpop-gremlin-server-3.3.3/bin/gremlin-server.sh /etc/init.d/gremlin-server

    # Systems with chkconfig/service. E.g. Fedora, Red Hat
    chkconfig --add gremlin-server

    # Start
    service gremlin-server start

    # Or call directly
    /etc/init.d/gremlin-server restart

##### Systemd

To install, copy the service template below to /etc/systemd/system/gremlin.service and update the paths `/path/to/apache-tinkerpop-gremlin-server` with the actual install path of Gremlin Server.

    [Unit]
    Description=Apache TinkerPop Gremlin Server daemon
    Documentation=http://tinkerpop.apache.org/
    After=network.target

    [Service]
    Type=forking
    ExecStart=/path/to/apache-tinkerpop-gremlin-server/bin/gremlin-server.sh start
    ExecStop=/path/to/apache-tinkerpop-gremlin-server/bin/gremlin-server.sh stop
    PIDFile=/path/to/apache-tinkerpop-gremlin-server/run/gremlin.pid

    [Install]
    WantedBy=multi-user.target

Enable the service with `systemctl enable gremlin-server`

Start the service with `systemctl start gremlin-server`

### Best Practices

The following sections define best practices for working with Gremlin Server.

#### Tuning

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.conf`.

- 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.

- Scripts that are "slow" can really hurt Gremlin Server if they are not properly accounted for. `ScriptEngine` evaluations are blocking operations that aren’t always 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` to something "sane". In other words, test the traversals being sent to Gremlin Server and determine the maximum time they take to evaluate and iterate over results, then set the timeout value accordingly.

  - Note that `scriptEvaluationTimeout` can only attempt to interrupt the evaluation on timeout. It allows Gremlin Server to "ignore" the result of that evaluation, which means the thread in the `gremlinPool` that did the evaluation may still be consumed after the timeout if interruption does not succeed on the thread.

- Graph element serialization for `Vertex` and `Edge` can be expensive, as their data structures are complex given the possible existence of multi-properties and meta-properties. When returning data from Gremlin Server only return the data that is required. For example, if only two properties of a `Vertex` are needed then simply return the two rather than returning the entire `Vertex` object itself. Even with an entire `Vertex`, it is typically much faster to issue the query as `g.V(1).valueMap(true)` than `g.V(1)`, as the former returns a `Map` of the same data as a `Vertex`, but without all the associated structure which can slow the response.

#### Parameterized Scripts

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);

The more parameters that are used in a script the more expensive the compilation step becomes. Gremlin Server has a `OpProcessor` setting called `maxParameters`, which is mentioned in the OpProcessor Configuration section. It controls the maximum number of parameters that can be passed to the server for script evaluation purposes. Use of this setting can prevent accidental long run compilations, which individually are not terribly oppressive to the server, but taken as a group under high concurrency would be considered detrimental.

#### Properties of Elements

It was mentioned above at the start of this "Best Practices" section that serialization of graph elements (i.e. `Vertex`, `Edge`, and `VertexProperty`) can be expensive and that it is best to only return the data that is required by the requesting system. This point begs for further clarification as there are a number of ways to use and configure Gremlin Server which might influence its interpretation.

To begin to discuss these nuances, first consider the method of making requests to Gremlin Server: script or bytecode. For scripts, that will mean that users are sending string representation of Gremlin to the server directly through a driver over websockets or through the HTTP. For bytecode, users will be utilize a Gremlin GLV which will construct bytecode for them and submit the request to the server upon iteration of their traversal.

In either case, it is important to also consider the method of "detachment". Detachment refers to the manner in which a graph element is disconnected from the graph for purpose of serialization. Depending on the case and configuration, graph elements may be detached with or without properties. Cases where they include properties is generally referred to as "detached elements" and cases where properties are not included are "reference elements".

With the type of request and detachment model in mind, it is now possible to discuss how best to consider element properties in relation to them all in concert.

For script-based requests, users should take care when returning graph elements. By default, elements will be returned as detached elements and will thus serialize with all properties that are bound to them. As such, Gryo and GraphSON serializers will write all properties in the return payload. Script-based requests should definitely follow the best practice of only returning the data required by the application.

Note | Gryo does have the exception for the `GryoMessageSerializerGremlinV1d0` with the `serializeResultToString` option enabled, which will simply convert all results using the Java `toString()` method prior to serialization and is typically only use by the Gremlin Console for remote sessions where the actual object from the server is not of use.

For bytecode-based requests, graph elements have reference detachment and thus only return the `id` and `label` of the elements. While this approach alleviates a potential performance problem that the script approach exposes, it is still important to follow the practice of being specific about the data that is required by the requesting application as it won’t arrive on the client side without that declaration.

Ultimately, the detachment model should have little impact to Gremlin usage if the best practice of specifying only the data required by the application is adhered to. In other words, while there may be a difference in the contents of return values for these traversals:

    // properties returned from g.V().hasLabel('person') because this is using the
    // Script API with full detachment
    Cluster cluster = Cluster.open();
    Client client = cluster.connect();
    ResultSet results = client.submit("g.V().hasLabel('person')");

    // no properties returned from g.V().hasLabel("person") because this is using
    // Bytecode API with reference detachment
    Graph graph = EmptyGraph.instance();
    GraphTraversalSource g = graph.traversal().
                                   withRemote('conf/remote-graph.properties');
    List<Vertex> results = g.V().hasLabel("person").toList();

There is no difference if re-written using the best practice of requesting only the data the application needs:

    Cluster cluster = Cluster.open();
    Client client = cluster.connect();
    ResultSet results = client.submit("g.V().hasLabel('person').valueMap(true,'name')");

    Graph graph = EmptyGraph.instance();
    GraphTraversalSource g = graph.traversal().
                                   withRemote('conf/remote-graph.properties');
    List<Vertex> results = g.V().hasLabel("person").valueMap(true,'name').toList();

Both of the above requests return a list of `Map` instances that contain the `id`, `label` and the "name" property.

#### Cache Management

If Gremlin Server processes a large number of unique scripts, the global function 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 global function 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 functions could be garbage collected and thus removed from the cache, forcing Gremlin Server to recompile later if that script is later encountered.

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

    Map<String,Object> params = new HashMap<>();
    params.put("x",4);
    params.put("#jsr223.groovy.engine.keep.globals", "soft");
    client.submit("[1,2,3,x]", params);

#### 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

1. Opens a reference to `localhost` as previously shown.

2. 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 accessible 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 successfully 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 HTTP 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 HTTP 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 HTTP-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

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 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 graph data to a running Gephi application. The Gephi plugin for Gremlin Console utilizes this API to allow for graph and traversal visualization.

Important | These instructions have been tested with Gephi 0.9.1 and Graph Streaming plugin 1.0.3.

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/workspace1`:

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.

Warning | Do not skip the `File > New Project` step as it may prevent a newly started Gephi application from fully enabling the streaming tab.

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/workspace1 with stepDelay:1000, startRGBColor:[0.0, 1.0, 0.5], colorToFade:g, colorFadeRate:0.7, startSize:10.0,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/workspace1 with stepDelay:1000, startRGBColor:[0.0, 1.0, 0.5], colorToFade:g, colorFadeRate:0.7, startSize:10.0,sizeDecrementRate:0.33
    gremlin> traversal = vg.V(2).in().out('knows').
                                 has('age',gt(30)).outE('created').
                                 has('weight',gt(0.5d)).inV();[] //2\
    gremlin> :> traversal //3\
    ==>v[5]
    ==>false

1. Configure a "visual traversal" from your "graph" - this must be a `Graph` instance. This command will create a new `TraversalSource` called "vg" that must be used to visualize any spawned traversals in Gephi.

2. Define the traversal to be visualized. Note that ending the line with `;[]` simply prevents iteration of the traversal before it is submitted.

3. 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/workspace1 with stepDelay:1000, startRGBColor:[0.0, 0.3, 1.0], colorToFade:g, colorFadeRate:0.7, startSize:10.0,sizeDecrementRate:0.33
    gremlin> :remote config colorToFade b
    ==>Connection to Gephi - http://localhost:8080/workspace1 with stepDelay:1000, startRGBColor:[0.0, 0.3, 1.0], colorToFade:b, colorFadeRate:0.7, startSize:10.0,sizeDecrementRate:0.33
    gremlin> :remote config colorFadeRate 0.5
    ==>Connection to Gephi - http://localhost:8080/workspace1 with stepDelay:1000, startRGBColor:[0.0, 0.3, 1.0], colorToFade:b, colorFadeRate:0.5, startSize:10.0,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. | workspace1
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. | 20
colorToFade | A single char from the set `{r,g,b,R,G,B}` determining which color to fade for vertices visited in prior steps | g
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
visualTraversal | Creates a `TraversalSource` variable in the Console named `vg` which can be used for visualizing traversals. This configuration option takes two parameters. The first is required and is the name of the `Graph` instance variable that will generate the `TraversalSource`. The second parameter is the variable name that the `TraversalSource` should have when referenced in the Console. If left unspecified, this value defaults to `vg`. | vg

Note | This plugin is typically only useful to the Gremlin Console and is enabled in the there by default.

### Giraph Plugin

The Giraph Plugin installs as part of `giraph-gremlin` and provides a number of imports and utility functions to the environment within which it is used. Those classes and functions provide the basis for supporting OLAP based traversals using Giraph. This plugin is defined in greater detail in the GiraphGraphComputer section and is typically installed in conjuction with the Hadoop-Plugin.

### Graph Plugins

This section does not refer to a specific Gremlin Plugin, but a class of them. Graph Plugins are typically created by graph providers to make it easy to integrate their graph systems into Gremlin Console and Gremlin Server. As TinkerPop provides two reference `Graph` implementations in TinkerGraph and Neo4j, there is also one Gremlin Plugin for each of them.

The TinkerGraph plugin is installed and activated in the Gremlin Console by default and the sample configurations that are supplied with the Gremlin Server distribution include the `TinkerGraphGremlinPlugin` as part of the default setup. If using Neo4j, however, the plugin must be installed manually. Instructions for doing so can be found in the Neo4j section.

### Hadoop Plugin

The Hadoop Plugin installs as part of `hadoop-gremlin` and provides a number of imports and utility functions to the environment within which it is used. Those classes and functions provide the basis for supporting OLAP based traversals with Gremlin. This plugin is defined in greater detail in the Hadoop-Gremlin section.

### Server Plugin

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 | This plugin is typically only useful to the Gremlin Console and is enabled in the there by default.

The Server Plugin for remoting with the Gremlin Console should not be confused with a plugin of similar name that is used by the server. `GremlinServerGremlinPlugin` is typically only configured in Gremlin Server and provides a number of imports that are required for writing initialization scripts.

### Spark Plugin

The Spark Plugin installs as part of `spark-gremlin` and provides a number of imports and utility functions to the environment within which it is used. Those classes and functions provide the basis for supporting OLAP based traversals using Spark. This plugin is defined in greater detail in the SparkGraphComputer section and is typically installed in conjuction with the Hadoop-Plugin.

### Sugar Plugin

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

1. There is no need for the parentheses in `g.V()`.

2. The traversal is interpreted as `g.V().values('name')`.

3. 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\
    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)]

1. Introducing the `AndStep` with the `&` operator.

2. 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()}
             }.prettyPrint()
    Environment
    ===========
    * Groovy: 2.4.15
    * JVM: Java HotSpot(TM) 64-Bit Server VM (25.161-b12, Oracle Corporation)
        * JRE: 1.8.0_161
        * Total Memory: 1629 MB
        * Maximum Memory: 3566.5 MB
    * OS: Linux (3.13.0-141-generic, amd64)

    Options
    =======
    * Warm Up: Auto (- 60 sec)
    * CPU Time Measurement: On

              user  system    cpu   real

    sugar    15355       0  15355  15367
    nosugar   9475      39   9514   9520
    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
    44.5        0.00     0.00      1     0.67     0.67    0.67    0.67    0.67    0.67  org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.DefaultGraphTraversal.iterate
    43.9        0.00     0.00      1     0.66     1.52    0.66    1.52    0.66    1.52  groovysh_evaluate$_run_closure1.doCall
    11.4        0.00     0.00      1     0.17     0.17    0.17    0.17    0.17    0.17  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]      44.5  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]      11.4  0.00      0.00      1  org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource.V [3]
    ------------------------------------------------------------------------------------------------------------------------------------

#### 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 (3 of 4 suites)
    - Compliant with (3 of 11 suites)
    > org.apache.tinkerpop.gremlin.structure.StructureStandardSuite
    > org.apache.tinkerpop.gremlin.process.ProcessStandardSuite
    > org.apache.tinkerpop.gremlin.process.ProcessComputerSuite
    - Opts out of 22 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$Traversals#g_V_out_asXcX_matchXb_knows_a__c_created_eX_selectXcX
            "Giraph does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though."
    > org.apache.tinkerpop.gremlin.process.traversal.step.map.CountTest$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$Traversals#g_V_repeatXoutX_timesX5X_asXaX_outXwrittenByX_asXbX_selectXa_bX_count
            "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
    > org.apache.tinkerpop.gremlin.process.traversal.step.map.ProfileTest$Traversals#grateful_V_out_out_profile
            "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.ProfileTest$Traversals#grateful_V_out_out_profileXmetricsX
            "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_hasLabelXsongX_groupXaX_byXnameX_byXproperties_groupCount_byXlabelXX_out_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.GroupTest#g_V_outXfollowedByX_group_byXsongTypeX_byXbothE_group_byXlabelX_byXweight_sumXX
            "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.computer.GraphComputerTest#shouldStartAndEndWorkersForVertexProgramAndMapReduce
            "Spark executes map and combine in a lazy fashion and thus, fails the blocking aspect of this test"
    > org.apache.tinkerpop.gremlin.process.traversal.TraversalInterruptionTest#*
            "The interruption model in the test can't guarantee interruption at the right time with HadoopGraph."
    > org.apache.tinkerpop.gremlin.process.traversal.TraversalInterruptionComputerTest#*
            "This test makes use of a sideEffect to enforce when a thread interruption is triggered and thus isn't applicable to HadoopGraph"
    > org.apache.tinkerpop.gremlin.process.traversal.step.map.MatchTest$CountMatchTraversals#g_V_matchXa_followedBy_count_isXgtX10XX_b__a_0followedBy_count_isXgtX10XX_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.MatchTest$GreedyMatchTraversals#g_V_matchXa_followedBy_count_isXgtX10XX_b__a_0followedBy_count_isXgtX10XX_bX_count
            "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute."
    - 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-dsl` - An example project that demonstrates how to build Domain Specific Languages with Gremlin in Java.

- `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=3.3.3 -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>3.3.3</version>
    </dependency>

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. Its status as a reference implementation does not however imply that it is not suitable for production. TinkerGraph has many practical use cases in production applications and their development. Some examples of TinkerGraph use cases include:

- Ad-hoc analysis of large immutable graphs that fit in memory.

- Extract subgraphs, from larger graphs that don’t fit in memory, into TinkerGraph for further analysis or other purposes.

- Use TinkerGraph as a sandbox to develop and debug complex traversals by simulating data from a larger graph inside a TinkerGraph.

Constructing a simple graph using TinkerGraph in Java8 is presented below:

    Graph graph = TinkerGraph.open();
    GraphTraversalSource g = graph.traversal();
    Vertex marko = g.addV("person").property("name","marko").property("age",29).next();
    Vertex lop = g.addV("software").property("name","lop").property("lang","java").next();
    g.addE("created").from(marko).to(lop).property("weight",0.6d).iterate();

The above Gremlin creates two vertices named "marko" and "lop" and connects them via a created-edge with a weight=0.6 property. The addition of these two vertices and the edge between them could also be done in a single Gremlin statement as follows:

    g.addV("person").property("name","marko").property("age",29).as("m").
      addV("software").property("name","lop").property("lang","java").as("l").
      addE("created").from("m").to("l").property("weight",0.6d).iterate();

Important | Pay attention to the fact that traversals end with `next()` or `iterate()`. These methods advance the objects in the traversal stream and without those methods, the traversal does nothing. Review the Result Iteration Section of The Gremlin Console tutorial for more information.

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')
    gremlin> clock(1000) {g.V().has('name','Garcia').iterate()} //1\
    ==>0.21966102699999998
    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)
    gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
    gremlin> clock(1000){g.V().has('name','Garcia').iterate()} //2\
    ==>0.062264819

1. Determine the average runtime of 1000 vertex lookups when no `name`-index is defined.

2. 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 `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")
    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@68b11545
    gremlin> conf.setProperty("gremlin.tinkergraph.defaultVertexPropertyCardinality","list")
    gremlin> graph = TinkerGraph.open(conf)
    ==>tinkergraph[vertices:0 edges:0]
    gremlin> graph.io(gryo()).readGraph("data/tinkerpop-crew.kryo")
    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>3.3.3</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>0.7-3.2.3</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.

Tip | For configuring Grape, the dependency resolver of Groovy, please refer to the Gremlin Applications section.

    gremlin> :install org.apache.tinkerpop neo4j-gremlin 3.3.3
    ==>Loaded: [org.apache.tinkerpop, neo4j-gremlin, 3.3.3] - 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 install org.apache.tinkerpop neo4j-gremlin 3.3.3`. Once installed, the Gremlin Server configuration file must be edited to include the `Neo4jGremlinPlugin` as shown in `conf/gremlin-server.neo4j`.

### 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 single [/tmp/neo4j]]
    gremlin> graph.cypher("CREATE INDEX ON :person(name)")
    gremlin> graph.tx().commit() //1\
    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 single [/tmp/neo4j]], standard]
    gremlin> g.V().hasLabel('person').has('name','marko').values('name')
    ==>marko
    gremlin> graph.close()

1. 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 single [/tmp/neo4j]]
    gremlin> graph.io(graphml()).readGraph('data/grateful-dead.xml')
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/tmp/neo4j]], standard]
    gremlin> g.tx().commit()
    gremlin> clock(1000) {g.V().hasLabel('artist').has('name','Garcia').iterate()} //1\
    ==>1.6297199859999998
    gremlin> graph.cypher("CREATE INDEX ON :artist(name)") //2\
    gremlin> g.tx().commit()
    gremlin> Thread.sleep(5000) //3\
    gremlin> clock(1000) {g.V().hasLabel('artist').has('name','Garcia').iterate()} //4\
    ==>0.25174431399999997
    gremlin> clock(1000) {g.V().has('name','Garcia').iterate()} //5\
    ==>1.615392057
    gremlin> graph.cypher("DROP INDEX ON :artist(name)") //6\
    gremlin> g.tx().commit()
    gremlin> graph.close()

1. Find all artists whose name is Garcia which does a linear scan of the artist vertex-label partition.

2. Create an index for all artist vertices on their name property.

3. Neo4j indices are eventually consistent so this stalls to give the index time to populate itself.

4. Find all artists whose name is Garcia which uses the pre-defined schema index.

5. Find all vertices whose name is Garcia which requires a linear scan of all the data in the graph.

6. 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@5f59e8a7
    gremlin> conf.setProperty('gremlin.neo4j.directory','/tmp/neo4j')
    gremlin> conf.setProperty('gremlin.neo4j.multiProperties',true)
    gremlin> conf.setProperty('gremlin.neo4j.metaProperties',true)
    gremlin> graph = Neo4jGraph.open(conf)
    ==>neo4jgraph[community single [/tmp/neo4j]]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/tmp/neo4j]], standard]
    gremlin> g.addV().property('name','michael').property('name','michael hunger').property('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()

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 single [/tmp/neo4j]]
    gremlin> graph.io(gryo()).readGraph('data/tinkerpop-modern.kryo')
    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()

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 single [/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\
    gremlin> vertex.label()
    ==>animal::human::organism
    gremlin> vertex.removeLabel('human') //5\
    gremlin> vertex.labels()
    ==>animal
    ==>organism
    gremlin> vertex.addLabel('organism') //6\
    gremlin> vertex.labels()
    ==>animal
    ==>organism
    gremlin> vertex.removeLabel('human') //7\
    gremlin> vertex.label()
    ==>animal::organism
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/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()

1. Typecasting to a `Neo4jVertex` is only required in Java.

2. The standard `Vertex.label()` method returns all the labels in alphabetical order concatenated using `::`.

3. `Neo4jVertex.labels()` method returns the individual labels as a set.

4. `Neo4jVertex.addLabel()` method adds a single label.

5. `Neo4jVertex.removeLabel()` method removes a single label.

6. Labels are unique and thus duplicate labels don’t exist.

7. If a label that does not exist is removed, nothing happens.

8. `P.eq()` does a full string match and should only be used if multi-labels are not leveraged.

9. `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 single [/tmp/neo4j]]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/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()

    # neo4j-standalone.properties

    gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
    gremlin.neo4j.directory=/tmp/neo4j
    gremlin.neo4j.conf.dbms.auto_index.nodes.enabled=true
    gremlin.neo4j.conf.dbms.auto_index.relationships.enabled=true

### High Availability Configuration

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.host.coordination=localhost:5001
    gremlin.neo4j.conf.ha.host.data=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.host.coordination=localhost:5002
    gremlin.neo4j.conf.ha.host.data=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.host.coordination=localhost:5003
    gremlin.neo4j.conf.ha.host.data=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>3.3.3</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 3.3.3
    ==>loaded: [org.apache.tinkerpop, hadoop-gremlin, 3.3.3] - 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 contain jars that should be uploaded to a respective distributed cache (YARN or SparkServer). Note that the locations in `HADOOP_GREMLIN_LIBS` can be colon-separated (`:`) and all jars from all locations will be loaded into the cluster. Locations can be local paths (e.g. `/path/to/libs`), but may also be prefixed with a file scheme to reference files or directories in different file systems (e.g. `hdfs:///path/to/distributed/libs`). 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.

    gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
    gremlin.hadoop.inputLocation=tinkerpop-modern.kryo
    gremlin.hadoop.graphReader=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
    gremlin.hadoop.outputLocation=output
    gremlin.hadoop.graphWriter=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat
    gremlin.hadoop.jarsInDistributedCache=true
    gremlin.hadoop.defaultGraphComputer=org.apache.tinkerpop.gremlin.spark.process.computer.SparkGraphComputer
    ####################################
    # Spark Configuration              #
    ####################################
    spark.master=local[4]
    spark.executor.memory=1g
    spark.serializer=org.apache.tinkerpop.gremlin.spark.structure.io.gryo.GryoSerializer
    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.graphReader | The class that the graph input file(s) are read with (e.g. an `InputFormat`).
gremlin.hadoop.outputLocation | The location to write the computed HadoopGraph to.
gremlin.hadoop.graphWriter | The class that the graph output file(s) are written with (e.g. an `OutputFormat`).
gremlin.hadoop.jarsInDistributedCache | Whether to upload the Hadoop-Gremlin jars to a distributed cache (necessary if jars are not on the machines' classpaths).
gremlin.hadoop.defaultGraphComputer | The default `GraphComputer` to use when `graph.compute()` is called. This is optional.

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

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')
    gremlin> hdfs.ls()
    ==>rw-r--r-- smallette 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-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 two implementations.

- `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 3.3.3
    ==>loaded: [org.apache.tinkerpop, giraph-gremlin, 3.3.3] - restart the console to use [tinkerpop.giraph]
    gremlin> :install org.apache.tinkerpop spark-gremlin 3.3.3
    ==>loaded: [org.apache.tinkerpop, spark-gremlin, 3.3.3] - 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).

#### SparkGraphComputer

    <dependency>
       <groupId>org.apache.tinkerpop</groupId>
       <artifactId>spark-gremlin</artifactId>
       <version>3.3.3</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`.

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 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().withComputer(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().withComputer(SparkGraphComputer)
    ==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], sparkgraphcomputer]
    gremlin> :remote connect tinkerpop.hadoop graph g
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/giraph-gremlin/lib:/home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/tinkergraph-gremlin/lib
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/giraph-gremlin/lib:/home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/tinkergraph-gremlin/lib
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/giraph-gremlin/lib:/home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/tinkergraph-gremlin/lib
    ==>useTraversalSource=graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], sparkgraphcomputer]
    ==>useSugar=false
    gremlin> :> g.V().group().by{it.value('name')[1]}.by('name')
    ==>[a:[marko,vadas],i:[ripple],e:[peter],o:[lop,josh]]

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.hadoop.graphReader | A class for reading a graph-based RDD (e.g. an `InputRDD` or `InputFormat`).
gremlin.hadoop.graphWriter | A class for writing a graph-based RDD (e.g. an `OutputRDD` or `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. An `InputRDD` provides a read method that takes a `SparkContext` and returns a graphRDD. Likewise, and `OutputRDD` is used for writing a graphRDD.

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 (i.e. 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

1. spark.jobGroup.id

2. spark.job.description

3. spark.job.interruptOnCancel

4. 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')
    gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    ==>hadoopgraph[gryoinputformat->gryooutputformat]
    gremlin> graph.configuration().setProperty('gremlin.hadoop.graphWriter', 'org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONOutputFormat')
    gremlin> graph.compute(SparkGraphComputer).program(BulkDumperVertexProgram.build().create()).submit().get()
    ==>result[hadoopgraph[graphsoninputformat->graphsonoutputformat],memory[size:0]]
    gremlin> hdfs.ls('output')
    ==>rwxr-xr-x smallette supergroup 0 (D) ~g
    gremlin> hdfs.head('output/~g')
    ==>{"id":{"@type":"g:Int32","@value":4},"label":"person","inE":{"knows":[{"id":{"@type":"g:Int32","@value":8},"outV":{"@type":"g:Int32","@value":1},"properties":{"weight":{"@type":"g:Double","@value":1.0}}}]},"outE":{"created":[{"id":{"@type":"g:Int32","@value":10},"inV":{"@type":"g:Int32","@value":5},"properties":{"weight":{"@type":"g:Double","@value":1.0}}},{"id":{"@type":"g:Int32","@value":11},"inV":{"@type":"g:Int32","@value":3},"properties":{"weight":{"@type":"g:Double","@value":0.4}}}]},"properties":{"name":[{"id":{"@type":"g:Int64","@value":6},"value":"josh"}],"age":[{"id":{"@type":"g:Int64","@value":7},"value":{"@type":"g:Int32","@value":32}}]}}
    ==>{"id":{"@type":"g:Int32","@value":1},"label":"person","outE":{"created":[{"id":{"@type":"g:Int32","@value":9},"inV":{"@type":"g:Int32","@value":3},"properties":{"weight":{"@type":"g:Double","@value":0.4}}}],"knows":[{"id":{"@type":"g:Int32","@value":7},"inV":{"@type":"g:Int32","@value":2},"properties":{"weight":{"@type":"g:Double","@value":0.5}}},{"id":{"@type":"g:Int32","@value":8},"inV":{"@type":"g:Int32","@value":4},"properties":{"weight":{"@type":"g:Double","@value":1.0}}}]},"properties":{"name":[{"id":{"@type":"g:Int64","@value":0},"value":"marko"}],"age":[{"id":{"@type":"g:Int64","@value":1},"value":{"@type":"g:Int32","@value":29}}]}}
    ==>{"id":{"@type":"g:Int32","@value":6},"label":"person","outE":{"created":[{"id":{"@type":"g:Int32","@value":12},"inV":{"@type":"g:Int32","@value":3},"properties":{"weight":{"@type":"g:Double","@value":0.2}}}]},"properties":{"name":[{"id":{"@type":"g:Int64","@value":10},"value":"peter"}],"age":[{"id":{"@type":"g:Int64","@value":11},"value":{"@type":"g:Int32","@value":35}}]}}
    ==>{"id":{"@type":"g:Int32","@value":3},"label":"software","inE":{"created":[{"id":{"@type":"g:Int32","@value":9},"outV":{"@type":"g:Int32","@value":1},"properties":{"weight":{"@type":"g:Double","@value":0.4}}},{"id":{"@type":"g:Int32","@value":11},"outV":{"@type":"g:Int32","@value":4},"properties":{"weight":{"@type":"g:Double","@value":0.4}}},{"id":{"@type":"g:Int32","@value":12},"outV":{"@type":"g:Int32","@value":6},"properties":{"weight":{"@type":"g:Double","@value":0.2}}}]},"properties":{"name":[{"id":{"@type":"g:Int64","@value":4},"value":"lop"}],"lang":[{"id":{"@type":"g:Int64","@value":5},"value":"java"}]}}
    ==>{"id":{"@type":"g:Int32","@value":5},"label":"software","inE":{"created":[{"id":{"@type":"g:Int32","@value":10},"outV":{"@type":"g:Int32","@value":4},"properties":{"weight":{"@type":"g:Double","@value":1.0}}}]},"properties":{"name":[{"id":{"@type":"g:Int64","@value":8},"value":"ripple"}],"lang":[{"id":{"@type":"g:Int64","@value":9},"value":"java"}]}}
    ==>{"id":{"@type":"g:Int32","@value":2},"label":"person","inE":{"knows":[{"id":{"@type":"g:Int32","@value":7},"outV":{"@type":"g:Int32","@value":1},"properties":{"weight":{"@type":"g:Double","@value":0.5}}}]},"properties":{"name":[{"id":{"@type":"g:Int64","@value":2},"value":"vadas"}],"age":[{"id":{"@type":"g:Int64","@value":3},"value":{"@type":"g:Int32","@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')
    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()

    # hadoop-grateful-gryo.properties

    #
    # Hadoop Graph Configuration
    #
    gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
    gremlin.hadoop.graphReader=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>3.3.3</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().withComputer(GiraphGraphComputer)
    ==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], giraphgraphcomputer]
    gremlin> g.V().count()
    INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://localhost:8088/proxy/application_1525797629707_0001/
    INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1525797629707_0001
    INFO  org.apache.hadoop.mapreduce.Job  - Job job_1525797629707_0001 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_1525797629707_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=718002
                    FILE: Number of read operations=0
                    FILE: Number of large read operations=0
                    FILE: Number of write operations=0
                    HDFS: Number of bytes read=977
                    HDFS: Number of bytes written=1257
                    HDFS: Number of read operations=41
                    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)=214176
                    Total time spent by all reduces in occupied slots (ms)=0
                    Total time spent by all map tasks (ms)=107088
                    Total vcore-milliseconds taken by all map tasks=107088
                    Total megabyte-milliseconds taken by all map tasks=219316224
            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)=2540
                    CPU time spent (ms)=25800
                    Physical memory (bytes) snapshot=1658023936
                    Virtual memory (bytes) snapshot=8590405632
                    Total committed heap usage (bytes)=1329070080
            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)=2970
                    Input superstep (ms)=5356
                    Setup (ms)=65
                    Shutdown (ms)=9213
                    Superstep 0 GiraphComputation (ms)=4926
                    Total (ms)=19568
            Zookeeper base path
                    /_hadoopBsp/job_1525797629707_0001=0
            Zookeeper halt node
                    /_hadoopBsp/job_1525797629707_0001/_haltComputation=0
            Zookeeper server:port
                    localhost:22181=0
            File Input Format Counters
                    Bytes Read=0
            File Output Format Counters
                    Bytes Written=0
    ==>6
    gremlin> g.V().out().out().values('name')
    INFO  org.apache.hadoop.mapreduce.Job  - The url to track the job: http://localhost:8088/proxy/application_1525797629707_0002/
    INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1525797629707_0002
    INFO  org.apache.hadoop.mapreduce.Job  - Job job_1525797629707_0002 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_1525797629707_0002 completed successfully
    INFO  org.apache.hadoop.mapreduce.Job  - Counters: 52
            File System Counters
                    FILE: Number of bytes read=0
                    FILE: Number of bytes written=707760
                    FILE: Number of read operations=0
                    FILE: Number of large read operations=0
                    FILE: Number of write operations=0
                    HDFS: Number of bytes read=977
                    HDFS: Number of bytes written=1387
                    HDFS: Number of read operations=41
                    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)=256422
                    Total time spent by all reduces in occupied slots (ms)=0
                    Total time spent by all map tasks (ms)=128211
                    Total vcore-milliseconds taken by all map tasks=128211
                    Total megabyte-milliseconds taken by all map tasks=262576128
            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)=2603
                    CPU time spent (ms)=30590
                    Physical memory (bytes) snapshot=1642807296
                    Virtual memory (bytes) snapshot=8561393664
                    Total committed heap usage (bytes)=1388314624
            Giraph Stats
                    Aggregate edges=0
                    Aggregate finished vertices=0
                    Aggregate sent message message bytes=856
                    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)=2426
                    Input superstep (ms)=5298
                    Setup (ms)=67
                    Shutdown (ms)=9109
                    Superstep 0 GiraphComputation (ms)=5054
                    Superstep 1 GiraphComputation (ms)=3892
                    Superstep 2 GiraphComputation (ms)=3108
                    Total (ms)=26531
            Zookeeper base path
                    /_hadoopBsp/job_1525797629707_0002=0
            Zookeeper halt node
                    /_hadoopBsp/job_1525797629707_0002/_haltComputation=0
            Zookeeper server:port
                    localhost:22181=0
            File Input Format Counters
                    Bytes Read=0
            File Output Format Counters
                    Bytes Written=0
    ==>lop
    ==>ripple

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().withComputer(GiraphGraphComputer)
    ==>graphtraversalsource[hadoopgraph[gryoinputformat->gryooutputformat], giraphgraphcomputer]
    gremlin> :remote connect tinkerpop.hadoop graph g
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/giraph-gremlin/lib:/home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/tinkergraph-gremlin/lib
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/giraph-gremlin/lib:/home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/tinkergraph-gremlin/lib
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/giraph-gremlin/lib:/home/smallette/git/apache/incubator-tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.3.3-standalone/ext/tinkergraph-gremlin/lib
    ==>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://localhost:8088/proxy/application_1525797629707_0003/
    INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1525797629707_0003
    INFO  org.apache.hadoop.mapreduce.Job  - Job job_1525797629707_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_1525797629707_0003 completed successfully
    INFO  org.apache.hadoop.mapreduce.Job  - Counters: 50
            File System Counters
                    FILE: Number of bytes read=0
                    FILE: Number of bytes written=621357
                    FILE: Number of read operations=0
                    FILE: Number of large read operations=0
                    FILE: Number of write operations=0
                    HDFS: Number of bytes read=977
                    HDFS: Number of bytes written=1550
                    HDFS: Number of read operations=42
                    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)=299610
                    Total time spent by all reduces in occupied slots (ms)=0
                    Total time spent by all map tasks (ms)=149805
                    Total vcore-milliseconds taken by all map tasks=149805
                    Total megabyte-milliseconds taken by all map tasks=306800640
            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)=3601
                    CPU time spent (ms)=55840
                    Physical memory (bytes) snapshot=1782489088
                    Virtual memory (bytes) snapshot=8584286208
                    Total committed heap usage (bytes)=1507852288
            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)=2417
                    Input superstep (ms)=8634
                    Setup (ms)=84
                    Shutdown (ms)=9240
                    Superstep 0 GiraphComputation (ms)=10054
                    Total (ms)=28016
            Zookeeper base path
                    /_hadoopBsp/job_1525797629707_0003=0
            Zookeeper halt node
                    /_hadoopBsp/job_1525797629707_0003/_haltComputation=0
            Zookeeper server:port
                    localhost:22181=0
            File Input Format Counters
                    Bytes Read=0
            File Output Format Counters
                    Bytes Written=0
    ==>[a:[vadas,marko],e:[peter],i:[ripple],o:[josh,lop]]
    gremlin> result
    ==>result[hadoopgraph[gryoinputformat->gryooutputformat],memory[size:1]]
    gremlin> result.memory.runtime
    ==>68925

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

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 Giraph:

    gremlin> hdfs.copyFromLocal('data/grateful-dead.kryo', 'grateful-dead.kryo')
    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://localhost:8088/proxy/application_1525797629707_0004/
    INFO  org.apache.hadoop.mapreduce.Job  - Running job: job_1525797629707_0004
    INFO  org.apache.hadoop.mapreduce.Job  - Job job_1525797629707_0004 running in uber mode : false
    INFO  org.apache.hadoop.mapreduce.Job  -  map 100% reduce 0%
    INFO  org.apache.hadoop.mapreduce.Job  - Job job_1525797629707_0004 completed successfully
    INFO  org.apache.hadoop.mapreduce.Job  - Counters: 52
            File System Counters
                    FILE: Number of bytes read=0
                    FILE: Number of bytes written=344220
                    FILE: Number of read operations=0
                    FILE: Number of large read operations=0
                    FILE: Number of write operations=0
                    HDFS: Number of bytes read=332378
                    HDFS: Number of bytes written=223651
                    HDFS: Number of read operations=29
                    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)=127156
                    Total time spent by all reduces in occupied slots (ms)=0
                    Total time spent by all map tasks (ms)=63578
                    Total vcore-milliseconds taken by all map tasks=63578
                    Total megabyte-milliseconds taken by all map tasks=130207744
            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)=923
                    CPU time spent (ms)=31040
                    Physical memory (bytes) snapshot=1124392960
                    Virtual memory (bytes) snapshot=5705859072
                    Total committed heap usage (bytes)=907542528
            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)=1288
                    Input superstep (ms)=2182
                    Setup (ms)=54
                    Shutdown (ms)=9116
                    Superstep 0 GiraphComputation (ms)=3900
                    Superstep 1 GiraphComputation (ms)=4020
                    Superstep 2 GiraphComputation (ms)=2165
                    Total (ms)=21441
            Zookeeper base path
                    /_hadoopBsp/job_1525797629707_0004=0
            Zookeeper halt node
                    /_hadoopBsp/job_1525797629707_0004/_haltComputation=0
            Zookeeper server:port
                    localhost: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()

    # hadoop-grateful-gryo.properties

    #
    # Hadoop Graph Configuration
    #
    gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
    gremlin.hadoop.graphReader=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
    gremlin.hadoop.graphWriter=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

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) { ... }

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) {
        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 `fs`.

    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:1]]
    gremlin> hdfs.ls()
    ==>rwxr-xr-x smallette supergroup 0 (D) _bsp
    ==>rw-r--r-- smallette supergroup 332226 grateful-dead.kryo
    ==>rwxr-xr-x smallette supergroup 0 (D) hadoop-gremlin-3.3.3-libs
    ==>rwxr-xr-x smallette supergroup 0 (D) output
    ==>rw-r--r-- smallette supergroup 781 tinkerpop-modern.kryo
    gremlin> hdfs.ls('output')
    ==>rwxr-xr-x smallette supergroup 0 (D) clusterCount
    ==>rwxr-xr-x smallette 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 smallette supergroup 0 (D) _bsp
    ==>rw-r--r-- smallette supergroup 332226 grateful-dead.kryo
    ==>rwxr-xr-x smallette supergroup 0 (D) hadoop-gremlin-3.3.3-libs
    ==>rw-r--r-- smallette 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]')
    ==>org.apache.spark.SparkContext@4aeb0e2b
    gremlin> graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    ==>hadoopgraph[gryoinputformat->gryooutputformat]
    gremlin> graph.configuration().setProperty('gremlin.hadoop.graphWriter', PersistedOutputRDD.class.getCanonicalName())
    gremlin> graph.configuration().setProperty('gremlin.spark.persistContext',true)
    gremlin> graph.compute(SparkGraphComputer).program(PeerPressureVertexProgram.build().create(graph)).mapReduce(ClusterCountMapReduce.build().memoryKey('clusterCount').create()).submit().get();
    ==>result[hadoopgraph[persistedinputrdd->persistedoutputrdd],memory[size:1]]
    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-3.3.3-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}]}
      }
    }

# Gremlin Variants

Gremlin is a graph traversal language that makes use of two fundamental programming constructs: function composition and function nesting. Given this generality, it is possible to embed Gremlin in any modern programming language.

Important | Gremlin-Java is the canonical representation of Gremlin and any (proper) Gremlin language variant will emulate its structure as best as possible given the constructs of the host language. A strong correspondence between variants ensures that the general Gremlin reference documentation is applicable to all variants and that users moving between development languages can easily adopt the Gremlin variant for that language.

Note | The information herein describes how to use the Gremlin language variants distributed with Apache TinkerPop. For information on how to build a Gremlin language variant, please review the Gremlin Language Variants tutorial.

## Gremlin-Java

Apache TinkerPop’s Gremlin-Java implements Gremlin within the Java8 language and can be used by any Java8 compliant virtual machine. Gremlin-Java is considered the canonical, reference implementation of Gremlin and serves as the foundation by which all other Gremlin language variants should emulate.

### The Lambda Solution

Supporting anonymous functions across languages is difficult as most languages do not support lambda introspection and thus, code analysis. In Gremlin-Java, Java8 lambdas can be leveraged.

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

1. A Java8 function is used to map a `Traverser<S>` to an object `E`.

2. Gremlin steps that take consumer arguments can be passed Java8 method references.

3. Gremlin-Java may sometimes require explicit lambda typing when types can not be automatically inferred.

When sending traversals over the wire via a `RemoteConnection`, the static methods of `Lambda` should be used and should denote a particular JSR-223 `ScriptEngine`. `Lambda` creates a string-based lambda that is then converted into a lambda/closure/anonymous-function/etc. by the respective lambda language’s JSR-223 `ScriptEngine` implementation.

    g.V().out("knows").map(Lambda.function("it.get().value('name') + ' is the friend name'"))
    g.V().out("knows").sideEffect(Lambda.consumer("println it"))
    g.V().as("a").out("knows").as("b").select("b").by(Lambda.<Vertex,Integer>function("it.value('name').length()"))

## Gremlin-Groovy

Apache TinkerPop’s Gremlin-Groovy implements Gremlin within the Apache Groovy language. As a JVM-based language variant, Gremlin-Groovy is backed by Gremlin-Java constructs. Moreover, given its scripting nature, Gremlin-Groovy serves as the language of Gremlin Console.

Warning | In Groovy, `as`, `in`, and `not` are reserved words. Gremlin-Groovy does not allow these steps to be called statically from the anonymous traversal `` and therefore, must always be prefixed with `.` For instance: `g.V().as('a').in().as('b').where(.not(.as('a').out().as('b')))`

## Gremlin-Python

Apache TinkerPop’s Gremlin-Python implements Gremlin within the Python language and can be used on any Python virtual machine including the popular CPython machine. Python’s syntax has the same constructs as Java including "dot notation" for function chaining (`a.b.c`), round bracket function arguments (`a(b,c)`), and support for global namespaces (`a(b())` vs `a(__.b())`). As such, anyone familiar with Gremlin-Java will immediately be able to work with Gremlin-Python. Moreover, there are a few added constructs to Gremlin-Python that make traversals a bit more succinct.

Warning | In Python, `as`, `in`, `and`, `or`, `is`, `not`, `from`, and `global` are reserved words. Gremlin-Python simply postfixes `` to the end of these terms for their use with graph traversal. For instance: `g.V().as('a').in_().as_('b').select('a','b')`.

To install Gremlin-Python, use Python’s pip package manager.

    pip install gremlinpython

Gremlin-Python users will typically make use of the following classes.

    >>> from gremlin_python import statics
    >>> from gremlin_python.structure.graph import Graph
    >>> from gremlin_python.process.graph_traversal import __
    >>> from gremlin_python.process.strategies import *
    >>> from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection

In Gremlin-Python there exists `GraphTraversalSource`, `GraphTraversal`, and `__` which mirror the respective classes in Gremlin-Java. The `GraphTraversalSource` requires a driver in order to communicate with GremlinServer (or any `RemoteConnection`-enabled server). The `gremlin_python.driver.driver_remote_connection` is provided with Apache TinkerPop’s Gremlin-Python distribution.

Important | For developers wishing to provide another driver implementation, be sure to extend `RemoteConnection` in `gremlin_python.driver` so it can then be used by Gremlin-Python’s `GraphTraversal`.

When Gremlin Server is running, Gremlin-Python can communicate with Gremlin Server. The `conf/gremlin-server-modern-py.yaml` configuration maintains a `GremlinJythonScriptEngine` as well as the appropriate serializers for communicating `Bytecode`.

Important | Gremlin-Python is not compatible with GraphSON 1.0.

    $ bin/gremlin-server.sh install org.apache.tinkerpop gremlin-python 3.3.3
    $ bin/gremlin-server.sh conf/gremlin-server-modern-py.yaml
    [INFO] GremlinServer -
           \,,,/
           (o o)
    ---oOOo-(3)-oOOo---

    [INFO] GremlinServer - Configuring Gremlin Server from conf/gremlin-server-modern-py.yaml
    [INFO] MetricManager - Configured Metrics Slf4jReporter configured with interval=180000ms and loggerName=org.apache.tinkerpop.gremlin.server.Settings$Slf4jReporterMetrics
    [INFO] GraphManager - Graph [graph] was successfully configured via [conf/tinkergraph-empty.properties].
    [INFO] ServerGremlinExecutor - Initialized Gremlin thread pool.  Threads in pool named with pattern gremlin-*
    [INFO] Logger - 51 attributes loaded from 40 stream(s) in 16ms, 51 saved, 614 ignored: ["Ant-Version", "Archiver-Version", "Bnd-LastModified", "Boot-Class-Path", "Build-Jdk", "Build-Version", "Built-By", "Bundle-ClassPath", "Bundle-Description", "Bundle-DocURL", "Bundle-License", "Bundle-ManifestVersion", "Bundle-Name", "Bundle-RequiredExecutionEnvironment", "Bundle-SymbolicName", "Bundle-Vendor", "Bundle-Version", "Can-Redefine-Classes", "Created-By", "DynamicImport-Package", "Eclipse-BuddyPolicy", "Export-Package", "Extension-Name", "Extension-name", "Fragment-Host", "Ignore-Package", "Implementation-Build", "Implementation-Title", "Implementation-URL", "Implementation-Vendor", "Implementation-Vendor-Id", "Implementation-Version", "Import-Package", "Include-Resource", "JCabi-Build", "JCabi-Date", "JCabi-Version", "Main-Class", "Main-class", "Manifest-Version", "Originally-Created-By", "Private-Package", "Require-Capability", "Specification-Title", "Specification-Vendor", "Specification-Version", "Tool", "X-Compile-Source-JDK", "X-Compile-Target-JDK", "hash", "version"]
    [INFO] ScriptEngines - Loaded gremlin-jython ScriptEngine
    [INFO] ScriptEngines - Loaded gremlin-python ScriptEngine
    [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 session OpProcessor.
    [INFO] OpLoader - Adding the traversal OpProcessor.
    [INFO] TraversalOpProcessor - Initialized cache for TraversalOpProcessor with size 1000 and expiration time of 600000 ms
    [INFO] GremlinServer - Executing start up LifeCycleHook
    [INFO] Logger$info - Loading 'modern' graph data.
    [INFO] AbstractChannelizer - Configured application/vnd.gremlin-v3.0+gryo with org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV3d0
    [INFO] AbstractChannelizer - Configured application/vnd.gremlin-v3.0+gryo-stringd with org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV3d0
    [INFO] AbstractChannelizer - Configured application/vnd.gremlin-v3.0+json with org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerV3d0
    [INFO] AbstractChannelizer - Configured application/json with org.apache.tinkerpop.gremlin.driver.ser.GraphSONMessageSerializerV3d0
    [INFO] GremlinServer$1 - Gremlin Server configured with worker thread pool of 1, gremlin pool of 4 and boss thread pool of 1.
    [INFO] GremlinServer$1 - Channel started at port 8182.

Note | The command to use `install` need only be executed once to gather `gremlin-python` dependencies into Gremlin Servers' path. Future starts of Gremlin Server will not require that command.

Within the CPython console, an empty `Graph` is created and a traversal source is spawned with `RemoteStrategy`.

    >>> graph = Graph()
    >>> g = graph.traversal().withRemote(DriverRemoteConnection('ws://localhost:8182/gremlin','g'))

When a traversal from the `GraphTraversalSource` is iterated, the traversal’s `Bytecode` is sent over the wire via the registered `RemoteConnection`. The bytecode is used to construct the equivalent traversal at the remote traversal source. Moreover, typically the bytecode is analyzed to determine which language the bytecode should be translated to. If the traversal does not contain lambdas, the remote location (e.g. Gremlin Server) will typically use Gremlin-Java. If it has lambdas written in Groovy, it will use Gremlin-Groovy (e.g. `GremlinGroovyScriptEngine`). Likewise, if it has lambdas represented in Python, it will use Gremlin-Python (e.g. `GremlinJythonScriptEngine`).

Important | Gremlin-Python’s `Traversal` class supports the standard Gremlin methods such as `next()`, `nextTraverser()`, `toSet()`, `toList()`, etc. Such "terminal" methods trigger the evaluation of the traversal.

### RemoteConnection Submission

There are various ways to submit a traversal to a `RemoteConnection`. Just as in Gremlin-Java, there are various "terminal/action methods" off of `Traversal`.

- `Traversal.next()`

- `Traversal.nextTraverser()`

- `Traversal.toList()`

- `Traversal.toSet()`

- `Traversal.iterate()`

### Gremlin-Python Sugar

Python supports meta-programming and operator overloading. There are three uses of these techniques in Gremlin-Python that makes traversals a bit more concise.

    >>> g.V().both()[1:3].toList()
    [v[2], v[4]]
    >>> g.V().both()[1].toList()
    [v[2]]
    >>> g.V().both().name.toList()
    [lop, lop, lop, vadas, josh, josh, josh, marko, marko, marko, peter, ripple]

### Static Enums and Methods

Gremlin has various tokens (e.g. `T`, `P`, `Order`, `Operator`, etc.) that are represented in Gremlin-Python as Python `Enums`.

    >>> from gremlin_python.process.traversal import T
    >>> from gremlin_python.process.traversal import Order
    >>> from gremlin_python.process.traversal import Cardinality
    >>> from gremlin_python.process.traversal import Column
    >>> from gremlin_python.process.traversal import Direction
    >>> from gremlin_python.process.traversal import Operator
    >>> from gremlin_python.process.traversal import P
    >>> from gremlin_python.process.traversal import Pop
    >>> from gremlin_python.process.traversal import Scope
    >>> from gremlin_python.process.traversal import Barrier

These can be used analogously to how they are used in Gremlin-Java.

    >>> g.V().hasLabel('person').has('age',P.gt(30)).order().by('age',Order.decr).toList()
    [v[6], v[4]]

Moreover, by importing the `statics` of Gremlin-Python, the class prefixes can be omitted.

    >>> statics.load_statics(globals())

With statics loaded its possible to represent the above traversal as below.

    >>> g.V().hasLabel('person').has('age',gt(30)).order().by('age',decr).toList()
    [v[6], v[4]]

Finally, statics includes all the ``-methods and thus, anonymous traversals like `.out()` can be expressed as below. That is, without the `__.`-prefix.

    >>> g.V().repeat(out()).times(2).name.fold().toList()
    [[ripple, lop]]

### Bindings

When a traversal bytecode is sent over a `RemoteConnection` (e.g. Gremlin Server), it will be translated, compiled, and then executed. If the same traversal is sent again, translation and compilation can be skipped as the previously compiled version should be cached. Many traversals are unique up to some parameterization. For instance, `g.V(1).out('created').name` is considered different from `g.V(4).out('created').name'` as they have different script "string" representations. However, `g.V(x).out('created').name` with bindings of `{x : 1}` and `{x : 4}` are considered the same. If a traversal is going to be executed repeatedly, but with different parameters, then bindings should be used. In Gremlin-Python, bindings are 2-tuples and used as follows.

    >>> g.V(('id',1)).out('created').name.toList()
    [lop]
    >>> g.V(('id',4)).out('created').name.toList()
    [ripple, lop]

### Traversal Strategies

In order to add and remove traversal strategies from a traversal source, Gremlin-Python has a `TraversalStrategy` class along with a collection of subclasses that mirror the standard Gremlin-Java strategies.

    >>> g = g.withStrategies(SubgraphStrategy(vertices=hasLabel('person'),edges=has('weight',gt(0.5))))
    null
    >>> g.V().name.toList()
    [marko, vadas, josh, peter]
    >>> g.V().outE().valueMap(True).toList()
    [[weight:1.0, label:knows, id:8]]
    >>> g = g.withoutStrategies(SubgraphStrategy)
    null
    >>> g.V().name.toList()
    [marko, vadas, lop, josh, ripple, peter]
    >>> g.V().outE().valueMap(True).toList()
    [[weight:0.4, label:created, id:9], [weight:0.5, label:knows, id:7], [weight:1.0, label:knows, id:8], [weight:1.0, label:created, id:10], [weight:0.4, label:created, id:11], [weight:0.2, label:created, id:12]]
    >>> g = g.withComputer(workers=2,vertices=has('name','marko'))
    null
    >>> g.V().name.toList()
    [marko]
    >>> g.V().outE().valueMap(True).toList()
    [[weight:0.4, label:created, id:9], [weight:0.5, label:knows, id:7], [weight:1.0, label:knows, id:8]]

Note | Many of the `TraversalStrategy` classes in Gremlin-Python are proxies to the respective strategy on Apache TinkerPop’s JVM-based Gremlin traversal machine. As such, their `apply(Traversal)` method does nothing. However, the strategy is encoded in the Gremlin-Python bytecode and transmitted to the Gremlin traversal machine for re-construction machine-side.

### The Lambda Solution

Supporting anonymous functions across languages is difficult as most languages do not support lambda introspection and thus, code analysis. In Gremlin-Python, a Python lambda should be represented as a zero-arg callable that returns a string representation of a lambda. The default lambda language is `gremlin-python` and can be changed via `gremlin_python.statics.default_lambda_language`. When the lambda is represented in `Bytecode` its language is encoded such that the remote connection host can infer which translator and ultimate execution engine to use.

    >>> g.V().out().map(lambda: "lambda x: len(x.get().value('name'))").sum().toList() #(1)
    [24]
    >>> statics.default_lambda_language #(2)
    gremlin-python
    >>> g.V().out().map(lambda: ("it.get().value('name').length()", "gremlin-groovy")).sum().toList() #(3)
    [24]
    >>> statics.default_lambda_language = 'gremlin-groovy' #(4)
    null
    >>> g.V().out().map(lambda: "it.get().value('name').length()").sum().toList() #(5)
    [24]
    >>> g.V().out().map(lambda: ("lambda x: len(x.get().value('name'))", "gremlin-python")).sum().toList() #(6)
    [24]
    >>> statics.default_lambda_language = 'gremlin-python' #(7)
    null
    >>> g.V().out().map(lambda: "x: len(x.get().value('name'))").sum().toList() #(8)
    [24]

1. A zero-arg lambda yields a string representation of a lambda in Gremlin-Python.

2. The default lambda language is currently Gremlin-Python.

3. A zero-arg lambda yields a 2-tuple where the second element is the language of the lambda (Gremlin-Groovy).

4. The default lambda language can be statically changed.

5. A zero-arg lambda yields a string representation of a closure in Gremlin-Groovy.

6. A zero-arg lambda yields a 2-tuple where the second element is the language of the lambda (Gremlin-Python).

7. The default lambda language is changed back to Gremlin-Python.

8. If the `lambda`-prefix is not provided, then it is appended automatically in order to give a more natural look to the expression.

### Limitations

- Traversals that return a `Set` will be coerced to a `List` in Python so that traversals return consistent results within a collection across different languages. In the case of Python, number equality is different from JVM languages which produces different `Set` results when those types are in use. If a `Set` is needed then convert `List` results to `Set` manually.

## Gremlin.Net

Apache TinkerPop’s Gremlin.Net implements Gremlin within the C# language. It targets .NET Standard and can therefore be used on different operating systems and with different .NET frameworks, such as .NET Framework and .NET Core. Since the C# syntax is very similar to that of Java, it should be very easy to switch between Gremlin-Java and Gremlin.Net. The only major syntactical difference is that all method names in Gremlin.Net use PascalCase as opposed to camelCase in Gremlin-Java in order to comply with .NET conventions.

    nuget install Gremlin.Net

In Gremlin.Net there exists `GraphTraversalSource`, `GraphTraversal`, and `__` which mirror the respective classes in Gremlin-Java. The `GraphTraversalSource` requires a driver in order to communicate with GremlinServer (or any RemoteConnection-enabled server).

The `Gremlin.Net.Driver.Remote.DriverRemoteConnection` is provided as part of Apache TinkerPop’s Gremlin.Net.

Important | For developers wishing to provide another driver implementation, be sure to implement `IRemoteConnection` in `Gremlin.Net.Process.Remote` so it can then be used by Gremlin.Net’s `GraphTraversal`.

When Gremlin Server is running, Gremlin.Net can communicate with Gremlin Server by sending traversals serialized as `Bytecode`.

Important | Gremlin.Net is not compatible with GraphSON 1.0.

A traversal source can be spawned with `RemoteStrategy` from an empty `Graph`.

    var graph = new Graph();
    var g = graph.Traversal().WithRemote(new DriverRemoteConnection(new GremlinClient(new GremlinServer("localhost", 8182))));

When a traversal from the `GraphTraversalSource` is iterated, the traversal’s `Bytecode` is sent over the wire via the registered `IRemoteConnection`. The bytecode is used to construct the equivalent traversal at the remote traversal source. Moreover, typically the bytecode is analyzed to determine which language the bytecode should be translated to. If the traversal does not contain lambdas, the remote location (e.g. Gremlin Server) will typically use Gremlin-Java. If it has lambdas written in Groovy, it will use Gremlin-Groovy (e.g. `GremlinGroovyScriptEngine`). Likewise, if it has lambdas represented in Python, it will use Gremlin-Python (e.g. `GremlinJythonScriptEngine`).

Important | Gremlin.Net’s `ITraversal` interface supports the standard Gremlin methods such as `Next()`, `NextTraverser()`, `ToSet()`, `ToList()`, etc. Such "terminal" methods trigger the evaluation of the traversal.

### RemoteConnection Submission

Very similar to Gremlin-Python and Gremlin-Java, there are various ways to submit a traversal to a `IRemoteConnection` using terminal/action methods off of `ITraversal`.

- `ITraversal.Next()`

- `ITraversal.NextTraverser()`

- `ITraversal.ToList()`

- `ITraversal.ToSet()`

- `ITraversal.Iterate()`

### Static Enums and Methods

Gremlin has various tokens (e.g. `T`, `P`, `Order`, `Operator`, etc.) that are represented in Gremlin.Net as classes.

These can be used analogously to how they are used in Gremlin-Java.

    g.V().HasLabel("person").Has("age",P.Gt(30)).Order().By("age",Order.decr).ToList()

Moreover, the class prefixes can be omitted with a `using static`.

    using static Gremlin.Net.Process.Traversal.P;
    using static Gremlin.Net.Process.Traversal.Order;

Then it is possible to represent the above traversal as below.

    g.V().HasLabel("person").Has("age",Gt(30)).Order().By("age",decr).ToList()

Finally, with using static ``, anonymous traversals like `.Out()` can be expressed as below. That is, without the `__.`-prefix.

    g.V().Repeat(Out()).Times(2).Values("name").Fold().ToList()

### Bindings

When a traversal bytecode is sent over a `IRemoteConnection` (e.g. Gremlin Server), it will be translated, compiled, and then executed. If the same traversal is sent again, translation and compilation can be skipped as the previously compiled version should be cached. Many traversals are unique up to some parameterization. For instance, `g.V(1).Out("created").Values("name")` is considered different from `g.V(4).Out("created").Values("Name")` as they have different script "string" representations. However, `g.V(x).Out("created").Values("name")` with bindings of `{x : 1}` and `{x : 4}` are considered the same. If a traversal is going to be executed repeatedly, but with different parameters, then bindings should be used. In Gremlin.Net, bindings are objects that can be created as follows.

    var b = new Bindings();
    g.V(b.Of("id", 1)).Out("created").Values("name").toList()
    g.V(b.Of("id", 4)).Out("created").Values("name").toList()

### Traversal Strategies

In order to add and remove traversal strategies from a traversal source, Gremlin.Net has an `AbstractTraversalStrategy` class along with a collection of subclasses that mirror the standard Gremlin-Java strategies.

    g = g.WithStrategies(new SubgraphStrategy(vertexCriterion: HasLabel("person"),
        edgeCriterion: Has("weight", Gt(0.5))));
    var names = g.V().Values("name").ToList();  // names: [marko, vadas, josh, peter]

    g = g.WithoutStrategies(typeof(SubgraphStrategy));
    names = g.V().Values("name").ToList(); // names: [marko, vadas, lop, josh, ripple, peter]

    var edgeValueMaps = g.V().OutE().ValueMap(true).ToList();
    // edgeValueMaps: [[label:created, id:9, weight:0.4], [label:knows, id:7, weight:0.5], [label:knows, id:8, weight:1.0],
    //     [label:created, id:10, weight:1.0], [label:created, id:11, weight:0.4], [label:created, id:12, weight:0.2]]

    g = g.WithComputer(workers: 2, vertices: Has("name", "marko"));
    names = g.V().Values("name").ToList();  // names: [marko]

    edgeValueMaps = g.V().OutE().ValueMap(true).ToList();
    // edgeValueMaps: [[label:created, id:9, weight:0.4], [label:knows, id:7, weight:0.5], [label:knows, id:8, weight:1.0]]

Note | Many of the TraversalStrategy classes in Gremlin.Net are proxies to the respective strategy on Apache TinkerPop’s JVM-based Gremlin traversal machine. As such, their `Apply(ITraversal)` method does nothing. However, the strategy is encoded in the Gremlin.Net bytecode and transmitted to the Gremlin traversal machine for re-construction machine-side.

### The Lambda Solution

Supporting anonymous functions across languages is difficult as most languages do not support lambda introspection and thus, code analysis. While Gremlin.Net doesn’t support C# lambdas, it is still able to represent lambdas in other languages. When the lambda is represented in `Bytecode` its language is encoded such that the remote connection host can infer which translator and ultimate execution engine to use.

    g.V().Out().Map<int>(Lambda.Groovy("it.get().value('name').length()")).Sum<int>().ToList();      //1
    g.V().Out().Map<int>(Lambda.Python("lambda x: len(x.get().value('name'))")).Sum<int>().ToList(); //2

1. `Lambda.Groovy()` can be used to create a Groovy lambda.

2. `Lambda.Python()` can be used to create a Python lambda.

The `ILambda` interface returned by these two methods inherits interfaces like `IFunction` and `IPredicate` that mirror their Java counterparts which makes it possible to use lambdas with Gremlin.Net for the same steps as in Gremlin-Java.

## Gremlin-JavaScript

Apache TinkerPop’s Gremlin-JavaScript implements Gremlin within the JavaScript language. It targets Node.js runtime and can be used on different operating systems on any Node.js 6 or above. Since the JavaScript naming conventions are very similar to that of Java, it should be very easy to switch between Gremlin-Java and Gremlin-JavaScript.

    npm install gremlin

The Gremlin-JavaScript provides `GraphTraversalSource`, `GraphTraversal`, and `__` which mirror the respective classes in Gremlin-Java. The `GraphTraversalSource` requires a RemoteConnection implementation in order to communicate with GremlinServer.

    const gremlin = require('gremlin');
    const Graph = gremlin.structure.Graph;
    const DriverRemoteConnection = gremlin.driver.DriverRemoteConnection;

A traversal source can be spawned with `RemoteStrategy` from an empty `Graph`.

    const graph = new Graph();
    const g = graph.traversal().withRemote(new DriverRemoteConnection('ws://localhost:8182/gremlin'));

When a traversal from the `GraphTraversalSource` is iterated, the traversal’s `Bytecode` is sent over the wire via the registered `RemoteConnection`. The bytecode is used to construct the equivalent traversal at the remote traversal source.

Since Gremlin-JavaScript currently doesn’t support lambda expressions, all traversals can be translated to Gremlin-Java on the remote location (e.g. Gremlin Server).

Important | Gremlin-JavaScript’s `Traversal` base class supports the standard Gremlin methods such as `next()` and `toList()` Such "terminal" methods trigger the evaluation of the traversal.

### RemoteConnection Submission

In a similar way as in other GLVs, there are various ways to submit a traversal to a `RemoteConnection` using terminal/action methods off of `Traversal`. Given that I/O operations in Node.js are asynchronous by default, this terminal methods return a `Promise`.

- `Traversal.toList()`: Returns a `Promise` with an `Array` as result value.

- `Traversal.next()`: Returns a `Promise` with a `{ value, done }` tuple as result value, according to the async iterator proposal.

- `Traversal.iterate()`: Returns a `Promise` without a value.

For example:

    g.V().hasLabel('person').values('name').toList()
      .then(names => console.log(names));

You can `await` the promises if you are using `async` functions.

    const names = await g.V().hasLabel('person').values('name').toList();
    console.log(names);

### Static Enums and Methods

Gremlin has various tokens (e.g. `t`, `P`, `order`, `direction`, etc.) that are represented in Gremlin-JavaScript as objects.

    g.V().hasLabel('person').has('age', P.gt(30)).order().by('age', order.decr).toList()

These objects must be required manually from the `process` namespace:

    const gremlin = require('gremlin');
    const P = gremlin.process.P;

Finally, using static `` anonymous traversals like `.out()` can be expressed as below:

    const gremlin = require('gremlin');
    const __ = gremlin.process.statics;

    g.V().repeat(__.out()).times(2).values("name").fold().toList();

# Conclusion

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 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.