3.4.2

# TinkerPop Documentation

# Preface

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 | For more information about differences between TinkerPop 3.x and earlier versions, please see the link:http://tinkerpop.apache.org/docs/3.4.2/upgrade/#appendix

# Introduction

Welcome to the Reference Documentation for Apache TinkerPop™ - the backbone for all details on how to work with TinkerPop and the Gremlin graph traversal language. This documentation is not meant to be a "book", but a source from which to spawn more detailed accounts of specific topics and a target to which all other resources point. The Reference Documentation makes some general assumptions about the reader:

1. They have a sense of what a graph is - not sure? see Practical Gremlin - Why Graph?

2. They know what it means for a graph system to be TinkerPop-enabled - not sure? see TinkerPop-enabled Providers

3. They know what the role of Gremlin is - not sure? see link:Introduction to Gremlin

Given those assumptions, it’s possible to dive more quickly into the details without spending a lot of time repeating what is written elsewhere.

It is fairly certain that readers of the Reference Documentation are coming from the most diverse software development backgrounds that TinkerPop has ever engaged in over the decade or so of its existence. While TinkerPop holds some roots in Java, and thus, languages bound to the Java Virtual Machine (JVM), it long ago branched out into other languages such as Python, Javascript, .NET, and others. To compound upon that diversity, it is also seeing extensive support from different graph systems which have chosen TinkerPop as their standard method for allowing users to interface with their graph. Moreover, the graph systems themselves are not only separated by OLTP and OLAP style workloads, but also by their implementation patterns, which range everywhere from being an embedded graph system to a cloud-only graph. One might even find diversity parallel to Gremlin if considering other graph query languages.

Despite all this diversity and disparity, Gremlin remains the unifying interface for all these different elements of the graph community. As a user, choosing a TinkerPop-enabled graph and using Gremlin in the correct way when building applications shields them from change and disparity in the space. As a graph provider, choosing to become TinkerPop-enabled not only expands the reach their system can get into different development ecosystems, but also provides access to other query languages through bytecode compilation as seen in sparql-gremlin.

Irrespective of the programming language being used, graph system chosen or other development background that might be driving a user to this documentation, the critical point to remember is that "Gremlin is Gremlin is Gremlin". The same Gremlin that is written for an OLTP query over an in-memory TinkerGraph is the same Gremlin that is written to execute over a multi-billion edge graph using OLAP through Spark. That same Gremlin for either of those cases is written in the same way whether using Java or Python or Javascript. The Gremlin is always fundamentally the same aside from syntactical differences that might be language specific - e.g. the construction of a lambda in Groovy is different than the construction of a lambda in Python or a reserved word in Javascript forces a Gremlin step to have slightly different naming than Java.

While learning the Gremlin language and its patterns is largely agnostic to all the diversity in the space, it is not really possible to ignore the impact of the diversity from an application development perspective and the Reference Documentation makes an effort to try to point out where differences and inconsistencies might lie without diving too deeply into specific graph provider implementations. Users are strongly encouraged to consult the documentation of their chosen graph provider to understand all of the capabilities and limitations that may restrict or inhibit usage of certain aspects of TinkerPop APIs which are defined here in this Reference Documentation.

The following introductory sections and separately referenced content will be of varying interest to different readers. The summaries below will hopefully be helpful in directing individuals to the appropriate place to start their learning process.

- Graph Computing is an introduction to what "graph computing" means to TinkerPop and describes many of the provider and user-facing TinkerPop APIs and concepts that enable Gremlin.

- Connecting Gremlin provides descriptions for the different modes by which users will connect to graphs depending on their environment.

- Basic Gremlin describes how to use a connection to start writing Gremlin.

- Staying Agnostic provides tips on ways to keep Gremlin as portable as possible among different graph providers.

New users should not ignore TinkerPop’s Getting Started tutorial or The Gremlin Console tutorial. Both contain a large set of basic information and tips that can help readers avoid some general pitfalls early on. Both also focus on Gremlin usage in the Gremlin Console, which tends to be a critical tool for Gremlin developers of any development background.

More advanced and experience users will appreciate Gremlin Recipes which provide examples of common Gremlin traversal patterns.

Finally, all Gremlin developers should become familiar with "Practical Gremlin" by Kelvin Lawrence. This book is freely available and published online. It contains great examples and details that are applicable to anyone building applications with Gremlin.

## Graph Computing

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.

Figure 1. TinkerPop Modern

Tip | Get to know this graph structure as it is used extensively throughout the documentation and in wider circles as well. It is referred to as "TinkerPop Modern" as it is a modern variation of 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 | All of the toy graphs available in TinkerPop are described in The Gremlin Console tutorial.

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.

TinkerPop’s role in graph computing is to provide the appropriate interfaces for graph providers and users to interact with graphs over their structure and process. When a graph system implements the TinkerPop structure and process APIs, their technology is considered TinkerPop-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 TinkerPop for the sole purpose of graph system-agnostic graph computing.

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

Generally speaking, the structure or "graph" API is meant for graph providers who are implementing the TinkerPop interfaces and the process or "traversal" API (i.e. Gremlin) is meant for end-users who are utilizing a graph system from a graph provider. While the components of the process API are itemized below, they are described in greater detail in the Gremlin’s Anatomy tutorial.

Primary components of the TinkerPop 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 TinkerPop 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 computation that analyzes all vertices in the graph in parallel and yields a single reduced result.

Note | The TinkerPop API rides a fine line between providing concise "query language" method names and respecting Java method naming standards. The general convention used throughout TinkerPop 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`. Conceptual knowledge of how a graph is composed is essential to end-users working with graphs, however, as mentioned earlier, the structure API is not the appropriate way for users to think when building applications with TinkerPop. The structure API is reserved for usage by graph providers. Those interested in implementing the structure API to make their graph system TinkerPop enabled can learn more about it in the Graph Provider documentation.

### The Graph Process

The primary way in which graphs are processed are via graph traversals. The TinkerPop 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.

console (groovy) groovy

    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

    marko = g.V().has('name','marko').next() //1\
    g.V(marko).out('knows') //2\
    g.V(marko).out('knows').values('name') //3

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").

The objects propagating through the traversal are wrapped in a `Traverser<T>`. The traverser 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.

console (groovy) groovy

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

    g.V(marko).out('knows').values('name').path()

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).

console (groovy) groovy

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

    g.V(marko).repeat(out()).times(2).values('name')

Warning | TinkerPop does not guarantee the order of results returned from a traversal. It only guarantees not to modify the iteration order provided by the underlying graph. Therefore it is important to understand the order guarantees of the graph database being used. A traversal’s result is never ordered by TinkerPop unless performed explicitly by means of `order()`-step.

## Connecting Gremlin

It was established in the initial introductory section that Gremlin is Gremlin is Gremlin, meaning that irrespective of programming language, graph system, etc. the Gremlin written is always of the same general construct making it possible for users to move between development languages and TinkerPop-enabled graph technology easily. This quality of Gremlin generally applies to the traversal language itself. It applies less to the way in which the user connects to a graph to utilize Gremlin, which might differ considerably depending on the programming language or graph database chosen.

How one connects to a graph is a multi-faceted subject that essentially divides along a simple lines determined by the answer to this question: Where is the Gremlin Traversal Machine (GTM)? The reason that this question is so important is because the GTM is responsible for processing traversals. One can write Gremlin traversals in any language, but without a GTM there will be no way to execute that traversal against a TinkerPop-enabled graph. The GTM is typically in one of the following places:

- Embedded in a Java application (i.e. Java Virtual Machine)

- Hosted in Gremlin Server

- Hosted by a Remote Gremlin Provider (RGP)

The following sections outline each of these models and what impact they have to using Gremlin.

### Embedded

TinkerPop maintains the reference implementation for the GTM, which is written in Java and thus available for the Java Virtual Machine (JVM). This is the classic model that TinkerPop has long been based on and many examples, blog posts and other resources on the internet will be demonstrated in this style. It is worth noting that the embedded mode is not restricted to just Java as a programming language. Any JVM language can take this approach and in some cases there are language specific wrappers that can help make Gremlin more convenient to use in the style and capability of that language. Examples of these wrappers include gremlin-scala and Ogre (for Clojure).

In this mode, users will start by creating a `Graph` instance, followed by a `GraphTraversalSource` which is the class from which Gremlin traversals are spawned. Graphs that allow this sort of direct instantiation are obviously ones that are JVM-based (or have a JVM-based connector) and directly implement TinkerPop interfaces.

    Graph graph = TinkerGraph.open();

The "graph" then spawns a `GraphTraversalSource` as follows and typically, by convention, this variable is named "g":

    GraphTraversalSource g = graph.traversal();
    List<Vertex> vertices = g.V().toList()

Note | It may be helpful to read the Gremlin Anatomy tutorial, which describes the component parts of Gremlin to get a better understanding of the terminology before proceeding further.

While the TinkerPop Community strives to ensure consistent behavior among all modes of usage, the embedded mode does provide the greatest level of flexibility and control. There are a number of features that can only work if using a JVM language. The following list outlines a number of these available options:

- Lambdas can be written in the native language which is convenient, however, it will reduce the portability of Gremlin to do so should the need arise to switch away from the embedded mode. See more in the Note on Lambdas Section.

- Any features that involve extending TinkerPop Java interfaces - e.g. `VertexProgram`, `TraversalStrategy`, etc. are bound to the JVM. In some cases, these features can be made accessible to non-JVM languages, but they obviously must be initially developed for the JVM.

- Certain built-in `TraversalStrategy` implementations that rely on lambdas or other JVM-only configurations may not be available for use any other way.

- There are no boundaries put in place by serialization (e.g. GraphSON) as embedded graphs are only dealing with Java objects.

- Greater control of graph transactions.

- Direct access to lower-levels of the API - e.g. "structure" API methods like `Vertex` and `Edge` interface methods. As mentioned elsewhere in this documentation, TinkerPop does not recommend direct usage of these methods by end-users.

### Gremlin Server

A JVM-based graph maybe be hosted in TinkerPop’s Gremlin Server. Gremlin Server exposes the graph as an endpoint to which different clients can connect, essentially providing a remote GTM. Gremlin Server supports multiple methods for clients to interface with it:

- Websockets with a custom sub-protocol

  - String-based Gremlin scripts

  - Bytecode-based Gremlin traversals

- HTTP for string-based scripts

Users are encouraged to use the bytecode-based approach with websockets because it allows them to write Gremlin in the language of their choice. Connecting looks somewhat similar to the embedded approach in that there is a need to create a `GraphTraversalSource`. In the embedded approach, the means for that object’s creation is derived from a `Graph` object which spawns it. In this case, however, the `Graph` instance exists only on the server which means that there is no `Graph` instance to create locally. The approach is to instead create a `GraphTraversalSource` anonymously with `AnonymousTraversalSource` and then apply some "remote" options that describe the location of the Gremlin Server to connect to:

java groovy csharp javascript python

    import static org.apache.tinkerpop.gremlin.process.traversal.AnonymousTraversalSource.traversal;

    GraphTraversalSource g = traversal().withRemote('conf/remote-graph.properties');

    import static org.apache.tinkerpop.gremlin.process.traversal.AnonymousTraversalSource.traversal;

    def g = traversal().withRemote('conf/remote-graph.properties')

    using static Gremlin.Net.Process.Traversal.AnonymousTraversalSource;

    var g = Traversal().WithRemote(
        new DriverRemoteConnection(new GremlinClient(new GremlinServer("localhost", 8182))));

    const traversal = gremlin.process.AnonymousTraversalSource.traversal;

    const g = traversal().withRemote(
                    new DriverRemoteConnection('ws://localhost:8182/gremlin'));

    from gremlin_python.process.anonymous_traversal_source import traversal

    g = traversal().withRemote(
              DriverRemoteConnection('ws://localhost:8182/gremlin','g'))

As shown in the embedded approach in the previous section, once "g" is defined, writing Gremlin is structurally and conceptually the same irrespective of programming language.

#### Limitations

The previous section on the embedded model outlined a number of areas where it has some advantages that it gains due to the fact that the full GTM is available to the user in the language of it’s origin, i.e. Java. Some of those items touch upon important concepts to focus on here.

The first of these points is serialization. When Gremlin Server receives a request, the results must be serialized to the form requested by the client and then the client deserializes those into objects native to the language. TinkerPop has two such formats that it uses with Gryo and GraphSON. Gryo is a JVM-only format and thus carries the advantage that serializing and deserializing occurs on the classes native to the JVM on both the client and server side. As the client has full access to the same classes that the server does it basically has a full GTM on its own and therefore has the ability to do some slightly more advanced things.

A good example is the `subgraph()`-step which returns a `Graph` instance as its result. The subgraph returned from the server can be deserialized into an actual `Graph` instance on the client, which then means it is possible to spawn a `GraphTraversalSource` from that to do local Gremlin traversals on the client-side. For non-JVM Gremlin Language Variants there is no local graph to deserialize that result into and no GTM to process Gremlin so there isn’t much that can be done with such a result.

The second point is related to this issue. As there is no GTM, there is no "structure" API and thus graph elements like `Vertex` and `Edge` are "references" only. A "reference" means that they only contain the `id` and `label` of the element and not the properties. To be consistent, even JVM-based languages hold this limitation when talking to a remote Gremlin Server.

Important | Most SQL developers would not write a query as `SELECT * FROM table`. They would instead write the individual names of the fields they wanted in place of the wildcard. Writing "good" Gremlin is no different with this regard. Prefer explicit property key names in Gremlin unless it is completely impossible to do so.

The third and final point involves transactions. Under this model, one traversal is equivalent to a single transaction and there is no way in TinkerPop to string together multiple traversals into the same transaction.

### Remote Gremlin Provider

Remote Gremlin Providers (RGPs) are showing up more and more often in the graph database space. In TinkerPop terms, this category of graph providers is defined by those who simply support the Gremlin language. Typically, these are server-based graphs, often cloud-based, which accept Gremlin scripts or bytecode as a request and return results. They will often implement Gremlin Server protocols, which enables TinkerPop drivers to connect to them as they would with Gremlin Server. Therefore, the typical connection approach is identical to the method of connection presented in the previous section with the exact same caveats pointed out toward the end.

Despite leveraging TinkerPop protocols and drivers as being typical, RGPs are not required to do so to be considered TinkerPop-enabled. RGPs may well have their own drivers and protocols that may plug into <<gremlin-drivers-variants,Gremlin Language Variants> and may allow for more advanced options like better security, cluster awareness, batched requests or other features. The details of these different systems are outside the scope of this documentation, so be sure to consult their documentation for more information.

## Basic Gremlin

The `GraphTraversalSource` is basically the connection to a graph instance. That graph instance might be embedded, hosted in Gremlin Server or hosted in a RGP, but the `GraphTraversalSource` is agnostic to that. Assuming "g" is the `GraphTraversalSource`, getting data into the graph regardless of programming language or mode of operation is just some basic Gremlin:

console (groovy) groovy csharp java javascript python

    gremlin> v1 = g.addV('person').property('name','marko').next()
    ==>v[0]
    gremlin> v2 = g.addV('person').property('name','stephen').next()
    ==>v[2]
    gremlin> g.V(v1).addE('knows').to(v2).property('weight',0.75).iterate()

    v1 = g.addV('person').property('name','marko').next()
    v2 = g.addV('person').property('name','stephen').next()
    g.V(v1).addE('knows').to(v2).property('weight',0.75).iterate()

    var v1 = g.AddV("person").Property("name", "marko").Next();
    var v2 = g.AddV("person").Property("name", "stephen").Next();
    g.V(v1).AddE("knows").To(v2).Property("weight", 0.75).Iterate();

    Vertex v1 = g.addV("person").property("name","marko").next();
    Vertex v2 = g.addV("person").property("name","stephen").next();
    g.V(v1).addE("knows").to(v2).property("weight",0.75).iterate();

    const v1 = g.addV('person').property('name','marko').next();
    const v2 = g.addV('person').property('name','stephen').next();
    g.V(v1).addE('knows').to(v2).property('weight',0.75).iterate();

    v1 = g.addV('person').property('name','marko').next()
    v2 = g.addV('person').property('name','stephen').next()
    g.V(Bindings.of('id',v1)).addE('knows').to(v2).property('weight',0.75).iterate()

The first two lines add a vertex each with the vertex label of "person" and the associated "name" property. The third line adds an edge with the "knows" label between them and an associated "weight" property. Note the use of `next()` and `iterate()` at the end of the lines - their effect as terminal steps is described in The Gremlin Console Tutorial.

Important | Writing Gremlin is just one way to load data into the graph. Some graphs may have special data loaders which could be more efficient and make the task easier and faster. It is worth looking into those tools especially if there is a large one-time load to do.

Retrieving this data is also a just writing a Gremlin statement:

console (groovy) groovy csharp java javascript python

    gremlin> marko = g.V().has('person','name','marko').next()
    ==>v[0]
    gremlin> peopleMarkoKnows = g.V().has('person','name','marko').out('knows').toList()
    ==>v[2]

    marko = g.V().has('person','name','marko').next()
    peopleMarkoKnows = g.V().has('person','name','marko').out('knows').toList()

    var marko = g.V().Has("person", "name", "marko").Next();
    var peopleMarkoKnows = g.V().Has("person", "name", "marko").Out("knows").ToList();

    Vertex marko = g.V().has("person","name","marko").next()
    List<Vertex> peopleMarkoKnows = g.V().has("person","name","marko").out("knows").toList()

    const marko = g.V().has('person','name','marko').next()
    const peopleMarkoKnows = g.V().has('person','name','marko').out('knows').toList()

    marko = g.V().has('person','name','marko').next()
    peopleMarkoKnows = g.V().has('person','name','marko').out('knows').toList()

In all these examples presented so far there really isn’t a lot of difference in how the Gremlin itself looks. There are a few language syntax specific odds and ends, but for the most part Gremlin looks like Gremlin in all of the different languages.

The library of Gremlin steps with examples for each can be found in The Traversal Section. This section is meant as a reference guide and will not necessarily provide methods for applying Gremlin to solve particular problems. Please see the aforementioned Tutorials Recipes and the Practical Gremlin book for that sort of information.

Note | A full list of helpful Gremlin resources can be found on the TinkerPop Compendium page.

## Staying Agnostic

A good deal has been written in these introductory sections on how TinkerPop enables an agnostic approach to building graph application and that agnosticism is enabled through Gremlin. As good a job as Gremlin can do in this area, it’s evident from the Connecting Gremlin Section that TinkerPop is just an enabler. It does not prevent a developer from making design choices that can limit its protective power.

There are several places to be concerned when considering this issue:

- Data types - Different graphs will support different types of data. Something like TinkerGraph will accept any JVM object, but another graph like Neo4j has a small tight subset of possible types. Choosing a type that is exotic or perhaps is a custom type that only a specific graph supports might create migration friction should the need arise.

- Schemas/Indices - TinkerPop does not provide abstractions for schemas and/or index management. Users will work directly with the API of the graph provider. It is considered good practice to attempt to enclose such code in a graph provider specific class or set of classes to isolate or abstract it.

- Extensions - Graphs may provide extensions to the Gremlin language, which will not be designed to be compatible with other graph providers. There may be a special helper syntax or expressions which can make certain features of that specific graph shine in powerful ways. Using those options is probably recommended, but users should be aware that doing so ties them more tightly to that graph.

- Graph specific semantics - TinkerPop tries to enforce specific semantics through its test suite which is quite extensive, but some graph providers may not completely respect all the semantics of the Gremlin language or TinkerPop’s model for its APIs. For the most part, that doesn’t disqualify them from being any less TinkerPop-enabled than another provider that might meet the semantics perfectly. Take care when considering a new graph and pay attention to what it supports and does not support.

- Graph API - The Graph API (also referred to as the Structure API) is not always accessible to users. Its accessibility is dependent on the choice of graph system and programming language. It is therefore recommended that users avoid usage of methods like `Graph.addVertex()` or `Vertex.properties()` and instead prefer use of Gremlin with `g.addV()` or `g.V(1).properties()`.

Outside of considering these points, the best practice for ensuring the greatest level of compatibility across graphs is to avoid embedded mode and stick to the bytecode based approaches explained in the Gremlin Server and the RGP sections above. It creates the least opportunity to stray from the agnostic path as anything that can be done with those two modes also works in embedded mode. If using embedded mode, simply write code as though the `Graph` instance is "remote" and not local to the JVM. In other words, write code as though the GTM is not available locally. Taking that approach and isolating the points of concern above makes it so that swapping graph providers largely comes down to a configuration task (i.e. modifying configuration files to point at a different graph system).

# The Graph

The Introduction discussed the diversity of TinkerPop-enabled graphs, with special attention paid to the different connection models, and how TinkerPop makes it possible to bridge that diversity in an agnostic manner. This particular section deals with elements of the Graph API which was noted as an API to avoid when trying to build an agnostic system. The Graph API refers to the core elements of what composes the structure of a graph within the Gremlin Traversal Machine (GTM), such as the `Graph`, `Vertex` and `Edge` Java interfaces.

To maintain the most portable code, users should only reference these interfaces. To "reference", simply means to utilize it as a pointer. For `Graph`, that means holding a pointer to the location of graph data and then using it to spawn `GraphTraversalSource` instances so as to write Gremlin:

console (groovy) groovy

    gremlin> graph = TinkerGraph.open()
    ==>tinkergraph[vertices:0 edges:0]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.addV('person')
    ==>v[0]

    graph = TinkerGraph.open()
    g = graph.traversal()
    g.addV('person')

In the above example, "graph" is the `Graph` interface produced by calling `open()` on `TinkerGraph` which creates the instance. Note that while the end intent of the code is to create a "person" vertex, it does not use the APIs on `Graph` to do that - e.g. `graph.addVertex(T.label,'person')`.

Even if the developer desired to use the `graph.addVertex()` method there are only a handful of scenarios where it is possible:

- The application is being developed on the JVM and the developer is using embedded mode

- The architecture includes Gremlin Server and the user is sending Gremlin scripts to the server

- The graph system chosen is a Remote Gremlin Provider and they expose the Graph API via scripts

Note that Gremlin Language Variants force developers to use the Graph API by reference. There is no `addVertex()` method available to GLVs on their respective `Graph` instances, nor are their graph elements filled with data at the call of `properties()`. Developing applications to meet this lowest common denominator in API usage will go a long way to making that application portable across TinkerPop-enabled systems.

When considering the remaining sub-sections that follow, recall that they are all generally bound to the Graph API. They are described here for reference and in some sense backward compatibility with older recommended models of development. In the future, the contents of this section will become less and less relevant.

## 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`:

console (groovy) groovy

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

    graph = TinkerGraph.open()
    graph.features()

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

console (groovy) groovy

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

    graph.features().graph().supportsTransactions()
    graph.features().graph().supportsTransactions() ? g.tx().commit() : "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 | Features of reference graphs which are used to connect to remote graphs do not reflect the features of the graph to which it connects. It reflects the features of instantiated graph itself, which will likely be quite different considering that reference graphs will typically be immutable.

## Vertex Properties

TinkerPop 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.

console (groovy) groovy

    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

    graph = TinkerGraph.open()
    g = graph.traversal()
    v = g.addV().property('name','marko').property('name','marko a. rodriguez').next()
    g.V(v).properties('name').count() //1\
    v.property(list, 'name', 'm. a. rodriguez') //2\
    g.V(v).properties('name').count()
    g.V(v).properties()
    g.V(v).properties('name')
    g.V(v).properties('name').hasValue('marko')
    g.V(v).properties('name').hasValue('marko').property('acl','private') //3\
    g.V(v).properties('name').hasValue('marko a. rodriguez')
    g.V(v).properties('name').hasValue('marko a. rodriguez').property('acl','public')
    g.V(v).properties('name').has('acl','public').value()
    g.V(v).properties('name').has('acl','public').drop() //4\
    g.V(v).properties('name').has('acl','public').value()
    g.V(v).properties('name').has('acl','private').value()
    g.V(v).properties()
    g.V(v).properties().properties() //5\
    g.V(v).properties().property('date',2014) //6\
    g.V(v).properties().property('creator','stephen')
    g.V(v).properties().properties()
    g.V(v).properties('name').valueMap()
    g.V(v).property('name','okram') //7\
    g.V(v).properties('name')
    g.V(v).values('name') //8

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 TinkerPop graph structure features is available at `TinkerFactory.createTheCrew()` and `data/tinkerpop-crew*`. This graph demonstrates multi-properties and meta-properties.

Figure 3. TinkerPop Crew
console (groovy) groovy

    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',asc).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]

    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
    g.V().has('name','gremlin').inE('uses').
          order().by('skill',asc).as('a').
          outV().as('b').
          select('a','b').by('skill').by('name') // rank the users of gremlin by their skill level

## Graph Variables

`Graph.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:

console (groovy) groovy

    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

    graph = TinkerGraph.open()
    graph.variables()
    graph.variables().set('systemAdmins',['stephen','peter','pavel'])
    graph.variables().set('systemUsers',['matthias','marko','josh'])
    graph.variables().keys()
    graph.variables().get('systemUsers')
    graph.variables().get('systemUsers').get()
    graph.variables().remove('systemAdmins')
    graph.variables().keys()

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.

Warning | Attempting to set graph variables in a reference graph will not promote them to the remote graph. Typically, a reference graph has immutable features and will not support this features.

## Graph Transactions

A database transaction represents a unit of work to execute against the database. Transactions in TinkerPop can be considered in several contexts: transactions for embedded graphs via the Graph API, transactions for Gremlin Server and transactions within Remote Gremlin Providers. For those following recommended patterns, the concepts presented in the embedded section should generally be of little interest and are present mainly for reference. Utilizing those transactional features will greatly reduce the portability of an application’s Gremlin code.

### Embedded

When on the JVM using an embedded graph, there is considerable flexibility for working with transactions. With the Graph API, 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(Consumer<Transaction> consumer);

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

    GraphTraversalSource g = graph.traversal();
    g.addV("person").("name","stephen").iterate();

    Thread t1 = new Thread(() -> {
        g.addV("person").("name","josh").iterate();
    });

    Thread t2 = new Thread(() -> {
        g.addV("person").("name","marko").iterate();
    });

    t1.start()
    t2.start()

    t1.join()
    t2.join()

    g.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 `addV()` 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();
    GraphTraversalSource g = graph.traversal();
    g.addV("person").("name","stephen").iterate();

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

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

    t1.start()
    t2.start()

    t1.join()
    t2.join()

    g.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 Server

The available capability for transactions with Gremlin Server is dependent upon the method of interaction that is used. The preferred method for interacting with Gremlin Server is via websockets and bytecode based requests. In this mode of operations each Gremlin traversal that is executed will be treated as a single transaction. Traversals that fail will have their transaction rolled back and successful iteration of a traversal will conclude with a transactional commit. How the graph hosted in Gremlin Server reacts to those commands is dependent on the graph chosen and it is therefore important to understand the transactional semantics of that graph when developing an application.

Gremlin Server also has the option to accept Gremlin-based scripts. The scripting approach provides access to the Graph API and thus also the transactional model described in the embedded section. Therefore a single script can have the ability to execute multiple transactions per request with complete control provided to the developer to commit or rollback transactions as needed.

There are two methods for sending scripts to Gremlin Server: sessionless and session-based. With sessionless requests there will always be an attempt to close the transaction at the end of the request with a commit if there are no errors or a rollback if there is a failure. It is therefore unnecessary to close transactions manually within scripts themselves. By default, session-based requests do not have this quality. The transaction will be held open on the server until the user closes it manually. There is an option to have automatic transaction management for sessions. More information on this topic can be found in the Considering Transactions Section and the Considering Sessions Section.

While those sections provide some additional details, the short advice is to avoid scripts when possible and prefer bytecode based requests.

### Remote Gremlin Providers

At this time, transactional patterns for Remote Gremlin Providers are largely in line with Gremlin Server. Most offer bytecode or script based sessionless requests, which have automatic transaction management, such that a successful traversal will commit on success and a failing traversal will rollback. As most of these RGPs do not expose a `Graph` instances, access to lower level transactional functions even in a sessionless fashion are not typically allowed. The nature of what a "transaction" means will be dependent on the RGP as is the case with any TinkerPop-enabled graph system, so it is important to consult that systems documentation for more details.

## 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 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 TinkerPop 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)`.

console (groovy) groovy

    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

    g.V(1).out().values('name') //1\
    g.V(1).out().map {it.get().value('name')} //2\
    g.V(1).out().map(values('name')) //3

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()`.

console (groovy) groovy

    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]

    g.V().filter {it.get().label() == 'person'} //1\
    g.V().filter(label().is('person')) //2\
    g.V().hasLabel('person') //3

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.

console (groovy) groovy

    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]]

    g.V().hasLabel('person').sideEffect(System.out.&println) //1\
    g.V().sideEffect(outE().count().store("o")).
          sideEffect(inE().count().store("i")).cap("o","i") //2

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.

console (groovy) groovy

    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

    g.V().branch {it.get().value('name')}.
          option('marko', values('age')).
          option(none, values('name')) //1\
    g.V().branch(values('name')).
          option('marko', values('age')).
          option(none, values('name')) //2\
    g.V().choose(has('name','marko'),
                 values('age'),
                 values('name')) //3

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.

console (groovy) groovy

    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\

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

1. `hasNext()` determines whether there are available results (not supported in `gremlin-javascript`).

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

3. `next(n)` will return the next `n` results in a list (not supported in `gremlin-javascript` or Gremlin.NET).

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

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

6. `toSet()` will return all results in a set and thus, duplicates removed (not supported in `gremlin-javascript`).

7. `toBulkSet()` will return all results in a weighted set and thus, duplicates preserved via weighting (only supported for JVM languages).

8. `fill(collection)` will put all results in the provided collection and return the collection when complete (only supported for JVM languages).

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.

There is also the `promise()` terminator step, which can only be used with remote traversals to Gremlin Server or RGPs. It starts a promise to execute a function on the current `Traversal` that will be completed in the future.

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).

console (groovy) groovy

    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]

    g.V(1).as('a').out('created').in('created').where(neq('a')).
      addE('co-developer').from('a').property('year',2009) //1\
    g.V(3,4,5).aggregate('x').has('name','josh').as('a').
      select('x').unfold().hasLabel('software').addE('createdBy').to('a') //2\
    g.V().as('a').out('created').addE('createdBy').to('a').property('acl','public') //3\
    g.V(1).as('a').out('knows').
      addE('livesNear').from('a').property('year',2009).
      inV().inE('livesNear').values('year') //4\
    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\
    g.E(23).valueMap()
    marko = g.V().has('name','marko').next()
    peter = g.V().has('name','peter').next()
    g.V(marko).addE('knows').to(peter) //6\
    g.addE('knows').from(marko).to(peter) //7

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.

console (groovy) groovy

    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()

    g.addV('person').property('name','stephen')
    g.V().values('name')
    g.V().outE('knows').addV().property('name','nothing')
    g.V().has('name','nothing')
    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.

console (groovy) groovy

    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]

    g.V(1).property('country','usa')
    g.V(1).property('city','santa fe').property('state','new mexico').valueMap()
    g.V(1).property(list,'age',35) //1\
    g.V(1).valueMap()
    g.V(1).property('friendWeight',outE('knows').values('weight').sum(),'acl','private') //2\
    g.V(1).properties('friendWeight').valueMap() //3

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.

console (groovy) groovy

    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

    g.V(1).out('created') //1\
    g.V(1).out('created').aggregate('x') //2\
    g.V(1).out('created').aggregate('x').in('created') //3\
    g.V(1).out('created').aggregate('x').in('created').out('created') //4\
    g.V(1).out('created').aggregate('x').in('created').out('created').
           where(without('x')).values('name') //5

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.

console (groovy) groovy

    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]

    g.V().out('knows').aggregate('x').cap('x')
    g.V().out('knows').aggregate('x').by('name').cap('x')

Additional References

`aggregate(String)`

### And Step

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

Python | The term `and` is a reserved word in Python, and therefore must be referred to in Gremlin with `and_()`.

console (groovy) groovy

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

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

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.

console (groovy) groovy

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

    g.V().where(outE('created').and().outE('knows')).values('name')

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.

Groovy | The term `as` is a reserved word in Groovy, and when therefore used as part of an anonymous traversal must be referred to in Gremlin with the double underscore `__.as()`.

Python | The term `as` is a reserved word in Python, and therefore must be referred to in Gremlin with `as_()`.

console (groovy) groovy

    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]

    g.V().as('a').out('created').as('b').select('a','b') //1\
    g.V().as('a').out('created').as('b').select('a','b').by('name') //2

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.

console (groovy) groovy

    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]]

    g.V().hasLabel('software').as('a','b','c').
       select('a','b','c').
         by('name').
         by('lang').
         by(__.in('created').values('name').fold())

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).

console (groovy) groovy

    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]

    g.V().sideEffect{println "first: ${it}"}.sideEffect{println "second: ${it}"}.iterate()
    g.V().sideEffect{println "first: ${it}"}.barrier().sideEffect{println "second: ${it}"}.iterate()

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.

console (groovy) groovy

    gremlin> graph = TinkerGraph.open()
    ==>tinkergraph[vertices:0 edges:0]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io('data/grateful-dead.xml').read().iterate()
    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\
    ==>10419.126311
    ==>126653966
    gremlin> clockWithResult(1){g.V().repeat(both()).times(3).count().next()} //3\
    ==>32.934236
    ==>126653966
    gremlin> clockWithResult(1){g.V().both().barrier().both().barrier().both().barrier().count().next()} //4\
    ==>14.656827999999999
    ==>126653966

    graph = TinkerGraph.open()
    g = graph.traversal()
    g.io('data/grateful-dead.xml').read().iterate()
    g = graph.traversal().withoutStrategies(LazyBarrierStrategy) //1\
    clockWithResult(1){g.V().both().both().both().count().next()} //2\
    clockWithResult(1){g.V().repeat(both()).times(3).count().next()} //3\
    clockWithResult(1){g.V().both().barrier().both().barrier().both().barrier().count().next()} //4

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."

console (groovy) groovy

    gremlin> graph = TinkerGraph.open()
    ==>tinkergraph[vertices:0 edges:0]
    gremlin> g = graph.traversal() //1\
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io('data/grateful-dead.xml').read().iterate()
    gremlin> clockWithResult(1){g.V().both().both().both().count().next()}
    ==>9.826167
    ==>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]

    graph = TinkerGraph.open()
    g = graph.traversal() //1\
    g.io('data/grateful-dead.xml').read().iterate()
    clockWithResult(1){g.V().both().both().both().count().next()}
    g.V().both().both().both().count().iterate().toString()  //2

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.

console (groovy) groovy

    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]

    g.V().group().by(bothE().count()) //1\
    g.V().group().by(bothE().count()).by('name') //2\
    g.V().group().by(bothE().count()).by(count())  //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.

console (groovy) groovy

    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]]

    g.V().groupCount('a').by(label).cap('a') //1\
    g.V().groupCount('a').by(label).groupCount('b').by(outE().count()).cap('a','b')   //2

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.

console (groovy) groovy

    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

    g.V().hasLabel('person').
          choose(values('age').is(lte(30)),
            __.in(),
            __.out()).values('name') //1\
    g.V().hasLabel('person').
          choose(values('age')).
            option(27, __.in()).
            option(32, __.out()).values('name') //2

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.

console (groovy) groovy

    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

    g.V().choose(hasLabel('person'), out('created')).values('name') //1\
    g.V().choose(hasLabel('person'), out('created'), identity()).values('name') //2

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.

console (groovy) groovy

    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

    g.V().hasLabel('person').
          choose(values('name')).
            option('marko', values('age')).
            option('josh', values('name')).
            option('vadas', valueMap()).
            option('peter', label())

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

console (groovy) groovy

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

    g.V().hasLabel('person').
          choose(values('name')).
            option('marko', values('age')).
            option(none, values('name'))

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.

console (groovy) groovy

    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

    g.V(1).coalesce(outE('knows'), outE('created')).inV().path().by('name').by(label)
    g.V(1).coalesce(outE('created'), outE('knows')).inV().path().by('name').by(label)
    g.V(1).property('nickname', 'okram')
    g.V().hasLabel('person').coalesce(values('nickname'), values('name'))

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."

console (groovy) groovy

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

    g.V().coin(0.5)
    g.V().coin(0.0)
    g.V().coin(1.0)

Additional References

`coin(double)`

### ConnectedComponent Step

The `connectedComponent()` step performs a computation to identify Connected Component instances in a graph. When this step completes, the vertices will be labelled with a component identifier to denote the component to which they are associated.

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

console (groovy) groovy

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().
               connectedComponent().
                 with(ConnectedComponent.propertyName, 'component').
               project('name','component').
                 by('name').
                 by('component')
    ==>[name:lop,component:1]
    ==>[name:vadas,component:1]
    ==>[name:marko,component:1]
    ==>[name:josh,component:1]
    ==>[name:ripple,component:1]
    ==>[name:peter,component:1]
    gremlin> g.V().hasLabel('person').
               connectedComponent().
                 with(ConnectedComponent.propertyName, 'component').
                 with(ConnectedComponent.edges, outE('knows')).
               project('name','component').
                 by('name').
                 by('component')
    ==>[name:marko,component:1]
    ==>[name:vadas,component:1]
    ==>[name:josh,component:1]
    ==>[name:peter,component:6]

    g = graph.traversal().withComputer()
    g.V().
      connectedComponent().
        with(ConnectedComponent.propertyName, 'component').
      project('name','component').
        by('name').
        by('component')
    g.V().hasLabel('person').
      connectedComponent().
        with(ConnectedComponent.propertyName, 'component').
        with(ConnectedComponent.edges, outE('knows')).
      project('name','component').
        by('name').
        by('component')

Note the use of the `with()` modulating step which provides configuration options to the algorithm. It takes configuration keys from the `ConnectedComponent` class and is automatically imported to the Gremlin Console.

Additional References

`connectedComponent()`

### 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.

console (groovy) groovy

    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

    g.V().choose(hasLabel('person'),
        values('name'),
        constant('inhuman')) //1\
    g.V().coalesce(
        hasLabel('person').values('name'),
        constant('inhuman')) //2

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).

console (groovy) groovy

    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]

    g.V().count()
    g.V().hasLabel('person').count()
    g.V().hasLabel('person').outE('created').count().path() //1\
    g.V().hasLabel('person').outE('created').count().map {it.get() * 10}.path() //2

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()`.

console (groovy) groovy

    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()

    g.V(1).both().both()
    g.V(1).both().both().cyclicPath()
    g.V(1).both().both().cyclicPath().path()
    g.V(1).as('a').out('created').as('b').
      in('created').as('c').
      cyclicPath().
      path()
    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.

console (groovy) groovy

    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]]

    g.V().values('lang')
    g.V().values('lang').dedup()
    g.V(1).repeat(bothE('created').dedup().otherV()).emit().path() //1

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.

console (groovy) groovy

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

    g.V().valueMap('name').with(WithOptions.tokens)
    g.V().dedup().by(label).values('name')

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.

console (groovy) groovy

    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]]

    g.V().as('a').out('created').as('b').in('created').as('c').select('a','b','c')
    g.V().as('a').out('created').as('b').in('created').as('c').dedup('a','b').select('a','b','c') //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.

console (groovy) groovy

    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()

    g.V().outE().drop()
    g.E()
    g.V().properties('name').drop()
    g.V().valueMap()
    g.V().drop()
    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.

console (groovy) groovy

    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))]
    EarlyLimitStrategy           [O]   [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))]
    AdjacentToIncidentStrategy   [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))]
    CountStrategy                [O]   [GraphStep(vertex,[]), HasStep([~label.eq(person)]), VertexStep(OUT,edge), IdentityStep, EdgeVertexStep(IN), RangeGlobalStep(0,6), CountGlobalStep, IsStep(gt(5))]
    PathRetractionStrategy       [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))]

    g.V().hasLabel('person').outE().identity().inV().count().is(gt(5)).explain()

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.

console (groovy) groovy

    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

    g.V(1).out('knows').values('name')
    g.V(1).out('knows').values('name').fold() //1\
    g.V(1).out('knows').values('name').fold().next().getClass() //2\
    g.V(1).out('knows').values('name').fold(0) {a,b -> a + b.length()} //3\
    g.V().values('age').fold(0) {a,b -> a + b} //4\
    g.V().values('age').fold(0, sum) //5\
    g.V().values('age').sum() //6

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()`.

Javascript | The term `from` is a reserved word in Javascript, and therefore must be referred to in Gremlin with `from_()`.

Python | The term `from` is a reserved word in Python, and therefore must be referred to in Gremlin with `from_()`.

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.

console (groovy) groovy

    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]

    g.V().has('name', within('marko', 'vadas', 'josh')).as('person').
      V().has('name', within('lop', 'ripple')).addE('uses').from('person')

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.

console (groovy) groovy

    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]

    g.V().has('name', within('marko', 'vadas', 'josh')).as('person').
      V().has('name', within('lop', 'ripple')).addE('uses').from('person').toString() //1\
    g.V().has('name', within('marko', 'vadas', 'josh')).as('person').
      V().has('name', within('lop', 'ripple')).addE('uses').from('person').iterate().toString() //2

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.

console (groovy) groovy

    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]

    g.V().group().by(label) //1\
    g.V().group().by(label).by('name') //2\
    g.V().group().by(label).by(count()) //3

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?"

console (groovy) groovy

    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]

    g.V().hasLabel('person').values('age').groupCount()
    g.V().hasLabel('person').groupCount().by('age') //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."

console (groovy) groovy

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

    g.V().repeat(both().groupCount('m').by(label)).times(10).cap('m')

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.

console (groovy) groovy

    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

    g.V().hasLabel('person')
    g.V().hasLabel('person').out().has('name',within('vadas','josh'))
    g.V().hasLabel('person').out().has('name',within('vadas','josh')).
          outE().hasLabel('created')
    g.V().has('age',inside(20,30)).values('age') //1\
    g.V().has('age',outside(20,30)).values('age') //2\
    g.V().has('name',within('josh','marko')).valueMap() //3\
    g.V().has('name',without('josh','marko')).valueMap() //4\
    g.V().has('name',not(within('josh','marko'))).valueMap() //5\
    g.V().properties().hasKey('age').value() //6\
    g.V().hasNot('age').values('name') //7

1. Find all vertices whose ages are between 20 (exclusive) and 30 (exclusive). In other words, the age must be greater than 20 and less than 30.

2. Find all vertices whose ages are not between 20 (inclusive) and 30 (inclusive). In other words, the age must be less than 20 or greater than 30.

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.

console (groovy) groovy

    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

    g.V().id()
    g.V(1).out().id().is(2)
    g.V(1).outE().id()
    g.V(1).properties().id()

Additional References

`id()`

### Identity Step

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

console (groovy) groovy

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

    g.V().identity()

Additional References

`identity()`

### Index Step

The `index()`-step (map) indexes each element in the current collection. If the current traverser’s value is not a collection, then it’s treated as a single-item collection. There are two indexers available, which can be chosen using the `with()` modulator. The list indexer (default) creates a list for each collection item, with the first item being the original element and the second element being the index. The map indexer created a linked hash map in which the index represents the key and the original item is used as the value.

console (groovy) groovy

    gremlin> g.V().hasLabel("software").index() //1\
    ==>[[v[3],0]]
    ==>[[v[5],0]]
    gremlin> g.V().hasLabel("software").values("name").fold().
               order(Scope.local).
               index().
               unfold().
               order().
                 by(__.tail(Scope.local, 1)) //2\
    ==>[lop,0]
    ==>[ripple,1]
    gremlin> g.V().hasLabel("software").values("name").fold().
               order(Scope.local).
               index().
                 with(WithOptions.indexer, WithOptions.list).
               unfold().
               order().
                 by(__.tail(Scope.local, 1)) //3\
    ==>[lop,0]
    ==>[ripple,1]
    gremlin> g.V().hasLabel("person").values("name").fold().
               order(Scope.local).
               index().
                 with(WithOptions.indexer, WithOptions.map) //4\
    ==>[0:josh,1:marko,2:peter,3:vadas]

    g.V().hasLabel("software").index() //1\
    g.V().hasLabel("software").values("name").fold().
      order(Scope.local).
      index().
      unfold().
      order().
        by(__.tail(Scope.local, 1)) //2\
    g.V().hasLabel("software").values("name").fold().
      order(Scope.local).
      index().
        with(WithOptions.indexer, WithOptions.list).
      unfold().
      order().
        by(__.tail(Scope.local, 1)) //3\
    g.V().hasLabel("person").values("name").fold().
      order(Scope.local).
      index().
        with(WithOptions.indexer, WithOptions.map)  //4

1. Indexing non-collection items results in multiple indexed single-item collections.

2. Index all software names in their alphabetical order.

3. Same as statement 1, but with an explicitely specified list indexer.

4. Index all person names in their alphabetical order and store the result in an ordered map.

Additional References

`index()`

### Inject Step

The concept of "injectable steps" makes it possible to insert objects arbitrarily into a traversal stream. In general, `inject()`-step (sideEffect) exists and a few examples are provided below.

console (groovy) groovy

    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]

    g.V(4).out().values('name').inject('daniel')
    g.V(4).out().values('name').inject('daniel').map {it.get().length()}
    g.V(4).out().values('name').inject('daniel').map {it.get().length()}.path()

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.

console (groovy) groovy

    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

    inject(1,2)
    inject(1,2).map {it.get() + 1}
    inject(1,2).map {it.get() + 1}.map {g.V(it.get()).next()}.values('name')

Additional References

`inject(Object)`

### IO Step

The task of importing and exporting the data of `Graph` instances is the job of the `io()`-step. By default, TinkerPop supports three formats for importing and exporting graph data in GraphML, GraphSON, and Gryo.

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

By itself the `io()`-step merely configures the kind of importing and exporting that is going to occur and it is the follow-on call to the `read()` or `write()` step that determines which of those actions will execute. Therefore, a typical usage of the `io()`-step would look like this:

    g.io(someInputFile).read().iterate()
    g.io(someOutputFile).write().iterate()

Important | The commands above are still traversals and therefore require iteration to be executed, hence the use of `iterate()` as a termination step.

By default, the `io()`-step will try to detect the right file format using the file name extension. To gain greater control of the format use the `with()` step modulator to provide further information to `io()`. For example:

    g.io(someInputFile).
        with(IO.reader, IO.graphson).
      read().iterate()
    g.io(someOutputFile).
        with(IO.writer,IO.graphml).
      write().iterate()

The `IO` class is a helper for the `io()`-step that provides expressions that can be used to help configure it and in this case it allows direct specification of the "reader" or "writer" to use. The "reader" actually refers to a `GraphReader` implementation and the "writer" refers to a `GraphWriter` implementation. The implementations of those interfaces provided by default are the standard TinkerPop implementations.

That default is an important point to consider for users. The default TinkerPop implementations are not designed with massive, complex, parallel bulk loading in mind. They are designed to do single-threaded, OLTP-style loading of data in the most generic way possible so as to accommodate the greatest number of graph databases out there. As such, from a reading perspective, they work best for small datasets (or perhaps medium datasets where memory is plentiful and time is not critical) that are loading to an empty graph - incremental loading is not supported. The story from the writing perspective is not that different in there are no parallel operations in play, however streaming the output to disk requires a single pass of the data without high memory requirements for larger datasets.

In general, TinkerPop recommends that users examine the native bulk import/export tools of the graph implementation that they choose. Those tools will often outperform the `io()`-step and perhaps be easier to use with a greater feature set. That said, graph providers do have the option to optimize `io()` to back it with their own import/export utilities and therefore the default behavior provided by TinkerPop described above might be overridden by the graph.

An excellent example of this lies in HadoopGraph with SparkGraphComputer which replaces the default single-threaded implementation with a more advanced OLAP style bulk import/export functionality internally using CloneVertexProgram. With this model, graphs of arbitrary size can be imported/exported assuming that there is a Hadoop `InputFormat` or `OutputFormat` to support it.

Important | Remote Gremlin Console users or Gremlin Language Variant (GLV) users (e.g. gremlin-python) who utilize the `io()`-step should recall that their `read()` or `write()` operation will occur on the server and not locally and therefore the file specified for import/export must be something accessible by the server.

GraphSON and Gryo formats are extensible allowing users and graph providers to extend supported serialization options. These extensions are exposed through `IoRegistry` implementations. To apply an `IoRegistry` use the `with()` option and the `IO.registry` key, where the value is either an actual `IoRegistry` instance or the fully qualified class name of one.

    g.io(someInputFile).
        with(IO.reader, IO.gryo).
        with(IO.registry, TinkerIoRegistryV3d0.instance())
      read().iterate()
    g.io(someOutputFile).
        with(IO.writer,IO.graphson).
        with(IO.registry, "org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerIoRegistryV3d0")
      write().iterate()

GLVs will obviously always be forced to use the latter form as they can’t explicitly create an instance of an `IoRegistry` to pass to the server (nor are `IoRegistry` instances necessarily serializable).

The version of the formats (e.g. GraphSON 2.0 or 3.0) utilized by `io()` is determined entirely by the `IO.reader` and `IO.writer` configurations or their defaults. The defaults will always be the latest version for the current release of TinkerPop. It is also possible for graph providers to override these defaults, so consult the documentation of the underlying graph database in use for any details on that.

For more advanced configuration of `GraphReader` and `GraphWriter` operations (e.g. normalized output for GraphSON, disabling class registrations for Gryo, etc.) then construct the appropriate `GraphReader` and `GraphWriter` using the `build()` method on their implementations and use it directly. It can be passed directly to the `IO.reader` or `IO.writer` options. Obviously, these are JVM based operations and thus not available to GLVs as portable features.

#### GraphML

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

- Generate a graph using NetworkX, export it with GraphML and import it to TinkerPop.

- Produce a subgraph and export it to GraphML to be consumed by and visualized in Gephi.

- Migrate the data of an entire graph to a different graph database not supported by TinkerPop.

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.

    g.io("graph.xml").read().iterate()
    g.io("graph.xml").write().iterate()

Note | If using GraphML generated from TinkerPop 2.x, read more about its incompatibilities in the Upgrade Documentation.

#### GraphSON

GraphSON is a JSON-based format extended from earlier versions of TinkerPop. It is important to note that TinkerPop’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.)

    g.io("graph.json").read().iterate()
    g.io("graph.json").write().iterate()

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

#### Gryo

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.

    g.io("graph.kryo").read().iterate()
    g.io("graph.kryo").write().iterate()

Additional References

`io(String)`

### Is Step

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

Python | The term `is` is a reserved word in Python, and therefore must be referred to in Gremlin with `is_()`.

console (groovy) groovy

    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

    g.V().values('age').is(32)
    g.V().values('age').is(lte(30))
    g.V().values('age').is(inside(30, 40))
    g.V().where(__.in('created').count().is(1)).values('name') //1\
    g.V().where(__.in('created').count().is(gte(2))).values('name') //2\
    g.V().where(__.in('created').values('age').
                               mean().is(inside(30d, 35d))).values('name') //3

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.

console (groovy) groovy

    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

    g.V(1).properties().key()
    g.V(1).properties().properties().key()

Additional References

`key()`

### Label Step

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

console (groovy) groovy

    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

    g.V().label()
    g.V(1).outE().label()
    g.V(1).properties().label()

Additional References

`label()`

### Limit Step

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

console (groovy) groovy

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

    g.V().limit(2)
    g.V().range(0, 2)

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

console (groovy) groovy

    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]]

    g.V().valueMap().select('location').limit(local,2) //1\
    g.V().valueMap().limit(local, 1) //2

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.

console (groovy) groovy

    gremlin> g.V().as('person').
                   properties('location').order().by('startTime',asc).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',asc).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]

    g.V().as('person').
          properties('location').order().by('startTime',asc).limit(2).value().as('location').
          select('person','location').by('name').by() //1\
    g.V().as('person').
          local(properties('location').order().by('startTime',asc).limit(2)).value().as('location').
          select('person','location').by('name').by() //2

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:

console (groovy) groovy

    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]

    g.V().both().barrier().flatMap(groupCount().by("name"))
    g.V().both().barrier().local(groupCount().by("name"))

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.

console (groovy) groovy

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

    g.V().emit(__.has("name", "marko").or().loops().is(2)).repeat(__.out()).values("name")

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."

console (groovy) groovy

    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]

    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')

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

console (groovy) groovy

    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]

    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')

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.

console (groovy) groovy

    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]

    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

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):

console (groovy) groovy

    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io('data/grateful-dead.xml').read().iterate()
    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

    g = graph.traversal()
    g.io('data/grateful-dead.xml').read().iterate()
    g.V().match(
            __.as('a').has('name', 'Garcia'),
            __.as('a').in('writtenBy').as('b'),
            __.as('a').in('sungBy').as('b')).
          select('b').values('name')

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

console (groovy) groovy

    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

    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

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.

console (groovy) groovy

    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

    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')

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).

console (groovy) groovy

    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]

    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')

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.

console (groovy) groovy

    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]

    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')

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.

console (groovy) groovy

    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]

    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

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()`.

console (groovy) groovy

    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]

    g.V().match(
            __.as('a').out('created').as('b'),
            __.as('b').in('created').as('c')).
            where('a', neq('c')).
          select('a','c').by('name')

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.

console (groovy) groovy

    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)])]

    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\
    traversal.toString() //4\
    traversal // (5) (6) (5)
    traversal.toString() //7

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.

console (groovy) groovy

    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

    g.V().as('a').out('knows').as('b').math('a + b').by('age')
    g.V().as('a').out('created').as('b').
      math('b + a').
        by(both().count().math('_ + 100')).
        by('age')
    g.withSideEffect('x',10).V().values('age').math('_ / x')
    g.withSack(1).V(1).repeat(sack(sum).by(constant(1))).times(10).emit().sack().math('sin _')

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 comparable objects and determines which is the last object according to its natural order in the stream.

console (groovy) groovy

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

    g.V().values('age').max()
    g.V().repeat(both()).times(3).values('age').max()
    g.V().values('name').max()

Important | `max(local)` determines the max of the current, local object (not the objects in the traversal stream). This works for `Collection` and `Comparable`-type objects.

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.

console (groovy) groovy

    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

    g.V().values('age').mean()
    g.V().repeat(both()).times(3).values('age').mean() //1\
    g.V().repeat(both()).times(3).values('age').dedup().mean()

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.

Additional References

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

### Min Step

The `min()`-step (map) operates on a stream of comparable objects and determines which is the first object according to its natural order in the stream.

console (groovy) groovy

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

    g.V().values('age').min()
    g.V().repeat(both()).times(3).values('age').min()
    g.V().values('name').min()

Important | `min(local)` determines the min of the current, local object (not the objects in the traversal stream). This works for `Collection` and `Comparable`-type objects.

Additional References

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

### None Step

The `none()`-step (filter) filters all objects from a traversal stream. It is especially useful for to traversals that are executed remotely where returning results is not useful and the traversal is only meant to generate side-effects. Choosing not to return results saves in serialization and network costs as the objects are filtered on the remote end and not returned to the client side. Typically, this step does not need to be used directly and is quietly used by the `iterate()` terminal step which appends `none()` to the traversal before actually cycling through results.

Additional References

'none()' 'iterate()'

### Not Step

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

Groovy | The term `not` is a reserved word in Groovy, and when therefore used as part of an anonymous traversal must be referred to in Gremlin with the double underscore `__.not()`.

Python | The term `not` is a reserved word in Python, and therefore must be referred to in Gremlin with `not_()`.

console (groovy) groovy

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

    g.V().not(hasLabel('person')).valueMap().with(WithOptions.tokens)
    g.V().hasLabel('person').
      not(out('created').count().is(gt(1))).values('name')   //1

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()`.

console (groovy) groovy

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

    g.V(2).optional(out('knows')) //1\
    g.V(2).optional(__.in('knows')) //2

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`.

console (groovy) groovy

    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]]

    g.V().hasLabel('person').optional(out('knows').optional(out('created'))).path() //1

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.

Python | The term `or` is a reserved word in Python, and therefore must be referred to in Gremlin with `or_()`.

console (groovy) groovy

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

    g.V().or(
       __.outE('created'),
       __.inE('created').count().is(gt(1))).
         values('name')

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.

console (groovy) groovy

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

    g.V().where(outE('created').or().outE('knows')).values('name')

Additional References

`or(Traversal…​)`

### Order Step

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

console (groovy) groovy

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

    g.V().values('name').order()
    g.V().values('name').order().by(desc)
    g.V().hasLabel('person').order().by('age', asc).values('name')

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.

console (groovy) groovy

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

    g.V().values('name')
    g.V().order().by('name',asc).values('name')
    g.V().order().by('name',desc).values('name')

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.

console (groovy) groovy

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

    g.V().hasLabel('person').order().by(outE('created').count(), asc).
                                     by('age', asc).values('name')
    g.V().hasLabel('person').order().by(outE('created').count(), asc).
                                     by('age', desc).values('name')

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

console (groovy) groovy

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

    g.V().hasLabel('person').order().by(shuffle)
    g.V().hasLabel('person').order().by(shuffle)

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.

console (groovy) groovy

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

    g.V().values('age').fold().order(local).by(desc) //1\
    g.V().values('age').order(local).by(desc) //2\
    g.V().groupCount().by(inE().count()).order(local).by(values, desc) //3\
    g.V().groupCount().by(inE().count()).order(local).by(keys, asc) //4

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`.

Note | Prior to version 3.3.4, ordering was defined by `Order.incr` for ascending order and `Order.decr` for descending order. That approach is now deprecated with the preferred method shown in the examples which uses the more common forms for query languages in `Order.asc` and Order.desc.

Additional References

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

### 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).

console (groovy) groovy

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().pageRank().by('pageRank').values('pageRank')
    ==>0.11375510357865536
    ==>0.11375510357865536
    ==>0.145985401527191
    ==>0.145985401527191
    ==>0.3047200907912248
    ==>0.17579889899708226
    gremlin> g.V().hasLabel('person').
               pageRank().
                 with(PageRank.edges, __.outE('knows')).
                 with(PageRank.propertyName, 'friendRank').
               order().by('friendRank',desc).valueMap('name','friendRank')
    ==>[friendRank:[0.8321166533236799],name:[vadas]]
    ==>[friendRank:[0.8321166533236799],name:[josh]]
    ==>[friendRank:[0.5839416733381598],name:[marko]]
    ==>[friendRank:[0.5839416733381598],name:[peter]]

    g = graph.traversal().withComputer()
    g.V().pageRank().by('pageRank').values('pageRank')
    g.V().hasLabel('person').
      pageRank().
        with(PageRank.edges, __.outE('knows')).
        with(PageRank.propertyName, 'friendRank').
      order().by('friendRank',desc).valueMap('name','friendRank')

Note the use of the `with()` modulating step which provides configuration options to the algorithm. It takes configuration keys from the `PageRank` and is automatically imported to the Gremlin Console.

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

console (groovy) groovy

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().hasLabel('person').
               pageRank().
                 with(PageRank.edges, __.outE('knows')).
                 with(PageRank.propertyName, 'friendRank').
               order().by('friendRank',desc).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), desc]]), 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), desc]]), 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), desc]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]
    EarlyLimitStrategy              [O]   [TraversalVertexProgramStep([GraphStep(vertex,[]), HasStep([~label.eq(person)])],graphfilter[none]), PageRankVertexProgramStep([VertexStep(OUT,[knows],edge)],friendRank,20,graphfilter[none]), Travers
                                             alVertexProgramStep([OrderGlobalStep([[value(friendRank), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), 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), desc]]), PropertyMapStep([name, friendRank],value)],graphfilter[none]), ComputerResultStep]

    g = graph.traversal().withComputer()
    g.V().hasLabel('person').
      pageRank().
        with(PageRank.edges, __.outE('knows')).
        with(PageRank.propertyName, 'friendRank').
      order().by('friendRank',desc).valueMap('name','friendRank').explain()

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).

console (groovy) groovy

    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]

    g.V().out().out().values('name')
    g.V().out().out().values('name').path()

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

console (groovy) groovy

    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]]

    g.V().outE().inV().outE().inV().path()

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

console (groovy) groovy

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

    g.V().out().out().path().by('name').by('age')

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.

console (groovy) groovy

    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]]

    g.V().out().out().path().by(
                       choose(hasLabel('person'),
                                     out('created').values('name'),
                                     __.in('created').values('name')).fold())

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.

console (groovy) groovy

    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

    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()
    path.size()
    path.objects()
    path.labels()
    path.a
    path.b
    path.c
    path.d == path.e

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).

console (groovy) groovy

    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().
                 with(PeerPressure.propertyName, 'cluster').
               group().
                 by('cluster').
                 by('name')
    ==>[1:[vadas,marko,josh],6:[peter]]

    g = graph.traversal().withComputer()
    g.V().peerPressure().by('cluster').values('cluster')
    g.V().hasLabel('person').
      peerPressure().
        with(PeerPressure.propertyName, 'cluster').
      group().
        by('cluster').
        by('name')

Note the use of the `with()` modulating step which provides configuration options to the algorithm. It takes configuration keys from the `PeerPressure` class and is automatically imported to the Gremlin Console.

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.

console (groovy) groovy

    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           0.169    16.20
    VertexStep(OUT,[created],vertex)                                       4           4           0.251    23.96
    NoOpBarrierStep(2500)                                                  4           2           0.071     6.87
    VertexStep(BOTH,vertex)                                               10           4           0.030     2.93
    NoOpBarrierStep(2500)                                                 10           3           0.028     2.70
    VertexStep(BOTH,vertex)                                               24           7           0.034     3.31
    NoOpBarrierStep(2500)                                                 24           5           0.034     3.33
    VertexStep(BOTH,vertex)                                               58          11           0.052     5.02
    NoOpBarrierStep(2500)                                                 58           6           0.066     6.39
    HasStep([~label.eq(person)])                                          48           4           0.037     3.59
    PropertiesStep([age],value)                                           48           4           0.046     4.44
    SumGlobalStep                                                          1           1           0.222    21.25
                                                >TOTAL                     -           -           1.047        -

    g.V().out('created').repeat(both()).times(3).hasLabel('person').values('age').sum().profile()

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:

console (groovy) groovy

    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           0.055    33.53
    VertexStep(OUT,[created],vertex)                                       4           4           0.056    34.33
    NoOpBarrierStep(2500)                                                  4           2           0.053    32.14
                                                >TOTAL                     -           -           0.166        -

    t = g.V().out('created').profile('metrics')
    t.iterate()
    metrics = t.getSideEffects().get('metrics')

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.

console (groovy) groovy

    gremlin> g.V().out('created').
               project('a','b').
                 by('name').
                 by(__.in('created').count()).
               order().by(select('b'),desc).
               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]

    g.V().out('created').
      project('a','b').
        by('name').
        by(__.in('created').count()).
      order().by(select('b'),desc).
      select('a')
    g.V().has('name','marko').
                   project('out','in').
                     by(outE().count()).
                     by(inE().count())

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

    public void setup(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(Vertex vertex, Messenger messenger, 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(Memory memory) {
      // the master-traversal will have halted traversers
      assert memory.exists(TraversalVertexProgram.HALTED_TRAVERSERS);
      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.

console (groovy) groovy

    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', asc).
               valueMap('name', 'rank')
    ==>[name:[marko],rank:[0.11375510357865537]]
    ==>[name:[peter],rank:[0.11375510357865537]]
    ==>[name:[vadas],rank:[0.14598540152719103]]
    ==>[name:[josh],rank:[0.14598540152719103]]

    g = graph.traversal().withComputer()
    g.V().hasLabel('person').
      program(PageRankVertexProgram.build().property('rank').create(graph)).
        order().by('rank', asc).
      valueMap('name', 'rank')

### Properties Step

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

console (groovy) groovy

    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]

    g.V(1).properties()
    g.V(1).properties('location').valueMap()
    g.V(1).properties('location').has('endTime').valueMap()

Additional References

`properties(String…​)`

### PropertyMap Step

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

console (groovy) groovy

    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]]

    g.V().propertyMap()
    g.V().propertyMap('age')
    g.V().propertyMap('age','blah')
    g.E().propertyMap()

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.

console (groovy) groovy

    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]

    g.V().range(0,3)
    g.V().range(1,3)
    g.V().range(1, -1)
    g.V().repeat(both()).times(1000000).emit().range(6,10)

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).

console (groovy) groovy

    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]

    g.V().as('a').out().as('b').in().as('c').select('a','b','c').by('name').range(local,1,2)

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

console (groovy) groovy

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

    g.V().valueMap().select('location').range(local, 1, 3)

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.

console (groovy) groovy

    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]

    g.V(1).repeat(out()).times(2).path().by('name') //1\
    g.V().until(has('name','ripple')).
          repeat(out()).path().by('name') //2

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).

console (groovy) groovy

    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]

    g.V(1).repeat(out()).times(2).emit().path().by('name') //1\
    g.V(1).emit().repeat(out()).times(2).path().by('name') //2

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.

console (groovy) groovy

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

    g.V(1).repeat(out()).times(2).emit(has('lang')).path().by('name')

console (groovy) groovy

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

    g.V(1).repeat(out()).times(2).emit().path().by('name')

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.

`repeat()`-steps may be nested inside each other or inside the `emit()` or `until()` predicates and they can also be 'named' by passing a string as the first parameter to `repeat()`. The loop counter of a named repeat step can be accessed within the looped context with `loops(loopName)` where `loopName` is the name set whe creating the `repeat()`-step.

console (groovy) groovy

    gremlin> g.V(1).
               repeat(out("knows")).
                 until(repeat(out("created")).emit(has("name", "lop"))) //1\
    ==>v[4]
    gremlin> g.V(6).
               repeat('a', both('created').simplePath()).
                 emit(repeat('b', both('knows')).
                        until(loops('b').as('b').where(loops('a').as('b'))).
               hasId(2)).dedup() //2\
    ==>v[4]

    g.V(1).
      repeat(out("knows")).
        until(repeat(out("created")).emit(has("name", "lop"))) //1\
    g.V(6).
      repeat('a', both('created').simplePath()).
        emit(repeat('b', both('knows')).
               until(loops('b').as('b').where(loops('a').as('b'))).
      hasId(2)).dedup() //2

1. Starting from vertex 1, keep going taking outgoing 'knows' edges until the vertex was created by 'lop'.

2. Starting from vertex 6, keep taking created edges in either direction until the vertex is same distance from vertex 2 over knows edges as it is from vertex 6 over created edges.

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.

console (groovy) groovy

    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]

    g.V(1).repeat(out()).until(hasLabel('software')).path().by('name') //1\
    g.V(1).emit(hasLabel('person')).repeat(out()).path().by('name') //2\
    g.V(1).repeat(out()).until(outE().count().is(0)).path().by('name') //3

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.

console (groovy) groovy

    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@751831f4
    gremlin> g.withSack {rand.nextFloat()}.V().sack()
    ==>0.7704409
    ==>0.04574257
    ==>0.60025656
    ==>0.14623755
    ==>0.9399091
    ==>0.157359

    g.withSack(1.0f).V().sack()
    rand = new Random()
    g.withSack {rand.nextFloat()}.V().sack()

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.

console (groovy) groovy

    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]]

    g.withSack(1.0f).V().repeat(outE().sack(mult).by('weight').inV()).times(2)
    g.withSack(1.0f).V().repeat(outE().sack(mult).by('weight').inV()).times(2).sack()
    g.withSack(1.0f).V().repeat(outE().sack(mult).by('weight').inV()).times(2).path().
          by().by('weight')

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.

console (groovy) groovy

    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]

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

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.

console (groovy) groovy

    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>

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

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.

console (groovy) groovy

    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')
    ==>1.0
    ==>1.0

    g.V().outE().sample(1).values('weight')
    g.V().outE().sample(1).by('weight').values('weight')
    g.V().outE().sample(2).by('weight').values('weight')

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.

console (groovy) groovy

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

    g.V(1).repeat(local(
             bothE().sample(1).by('weight').otherV()
           )).times(5)
    g.V(1).repeat(local(
             bothE().sample(1).by('weight').otherV()
           )).times(5).path()
    g.V(1).repeat(local(
             bothE().sample(1).by('weight').otherV()
           )).times(10).path()

As a clarification, note that in the above example `local()` is not strictly required as it only does the random walk over a single vertex, but note what happens without it if multiple vertices are traversed:

console (groovy) groovy

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

    g.V().repeat(bothE().sample(1).by('weight').otherV()).times(5).path()

The use of `local()` ensures that the traversal over `bothE()` occurs once per vertex traverser that passes through, thus allowing one random walk per vertex.

console (groovy) groovy

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

    g.V().repeat(local(bothE().sample(1).by('weight').otherV())).times(5).path()

So, while not strictly required, it is likely better to be explicit with the use of `local()` so that the proper intent of the traversal is expressed.

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.

console (groovy) groovy

    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]

    g.V().as('a').out().as('b').out().as('c') // no select
    g.V().as('a').out().as('b').out().as('c').select('a','b','c')
    g.V().as('a').out().as('b').out().as('c').select('a','b')
    g.V().as('a').out().as('b').out().as('c').select('a','b').by('name')
    g.V().as('a').out().as('b').out().as('c').select('a') //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.

console (groovy) groovy

    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]

    g.V().out().out()
    g.V().out().out().path()
    g.V().as('x').out().out().select('x')
    g.V().out().as('x').out().select('x')
    g.V().out().out().as('x').select('x') // pointless

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.

console (groovy) groovy

    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io('data/grateful-dead.xml').read().iterate()
    gremlin> g.V().hasLabel('song').out('followedBy').groupCount().by('name').
                   order(local).by(values,desc).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,desc).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,desc).limit(local, 5).select(keys).unfold()
    ==>PLAYING IN THE BAND
    ==>JACK STRAW
    ==>TRUCKING
    ==>DRUMS
    ==>ME AND MY UNCLE

    g = graph.traversal()
    g.io('data/grateful-dead.xml').read().iterate()
    g.V().hasLabel('song').out('followedBy').groupCount().by('name').
          order(local).by(values,desc).limit(local, 5)
    g.V().hasLabel('song').out('followedBy').groupCount().by('name').
          order(local).by(values,desc).limit(local, 5).select(keys)
    g.V().hasLabel('song').out('followedBy').groupCount().by('name').
          order(local).by(values,desc).limit(local, 5).select(keys).unfold()

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

console (groovy) groovy

    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io('data/grateful-dead.xml').read().iterate()
    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,desc).limit(local, 5) //3\
    ==>[1:22,2:12,3:7,4:4,6:2]

    g = graph.traversal()
    g.io('data/grateful-dead.xml').read().iterate()
    g.V().hasLabel('song').out('sungBy').groupCount().by('name') //1\
    g.V().hasLabel('song').out('sungBy').groupCount().by('name').select(values) //2\
    g.V().hasLabel('song').out('sungBy').groupCount().by('name').select(values).unfold().
          groupCount().order(local).by(values,desc).limit(local, 5) //3

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`:

console (groovy) groovy

    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]]

    g.V(1).as("a").repeat(out().as("a")).times(2).select(first, "a")
    g.V(1).as("a").repeat(out().as("a")).times(2).select(last, "a")
    g.V(1).as("a").repeat(out().as("a")).times(2).select(all, "a")

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.

console (groovy) groovy

    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

    g.withSideEffect("alias", ["marko":"okram"]).V(). //1\
      values("name").sack(assign). //2\
      optional(select("alias").select(sack()))         //3

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.

console (groovy) groovy

    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]

    g.V().as('a').out('created').in('created').as('b').select('a','b').by('name') //1\
    g.V().as('a').out('created').in('created').as('b').
          select('a','b').by('name').where('a',neq('b')) //2\
    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')

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`

### ShortestPath step

The `shortestPath()`-step provides an easy way to find shortest non-cyclic paths in a graph. It is configurable using the `with()`-modulator with the options given below.

Key | Type | Description | Default
--- | --- | --- | ---
`target` | `Traversal` | Sets a filter traversal for the end vertices (e.g. `__.has('name','marko')`). | all vertices (`__.identity()`)
`edges` | `Traversal` or `Direction` | Sets a `Traversal` that emits the edges to traverse from the current vertex or the `Direction` to traverse during the shortest path discovery. | `Direction.BOTH`
`distance` | `Traversal` or `String` | Sets the `Traversal` that calculates the distance for the current edge or the name of an edge property to use for the distance calculations. | `__.constant(1)`
`maxDistance` | `Number` | Sets the distance limit for all shortest paths. | none
`includeEdges` | `Boolean` | Whether to include edges in the result or not. | `false`

console (groovy) groovy

    gremlin> g = g.withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().shortestPath() //1\
    ==>[v[1],v[4]]
    ==>[v[1],v[4],v[5]]
    ==>[v[1],v[3],v[6]]
    ==>[v[1],v[2]]
    ==>[v[1],v[3]]
    ==>[v[1]]
    ==>[v[2],v[1],v[4]]
    ==>[v[2],v[1],v[4],v[5]]
    ==>[v[2],v[1],v[3],v[6]]
    ==>[v[2]]
    ==>[v[2],v[1],v[3]]
    ==>[v[2],v[1]]
    ==>[v[3],v[4]]
    ==>[v[3],v[4],v[5]]
    ==>[v[3],v[6]]
    ==>[v[3],v[1],v[2]]
    ==>[v[3]]
    ==>[v[3],v[1]]
    ==>[v[4]]
    ==>[v[4],v[5]]
    ==>[v[4],v[3],v[6]]
    ==>[v[4],v[1],v[2]]
    ==>[v[4],v[3]]
    ==>[v[4],v[1]]
    ==>[v[5],v[4]]
    ==>[v[5]]
    ==>[v[5],v[4],v[3],v[6]]
    ==>[v[5],v[4],v[1],v[2]]
    ==>[v[5],v[4],v[3]]
    ==>[v[5],v[4],v[1]]
    ==>[v[6],v[3],v[4]]
    ==>[v[6],v[3],v[4],v[5]]
    ==>[v[6]]
    ==>[v[6],v[3],v[1],v[2]]
    ==>[v[6],v[3]]
    ==>[v[6],v[3],v[1]]
    gremlin> g.V().has('person','name','marko').shortestPath() //2\
    ==>[v[1],v[2]]
    ==>[v[1]]
    ==>[v[1],v[3]]
    ==>[v[1],v[4]]
    ==>[v[1],v[4],v[5]]
    ==>[v[1],v[3],v[6]]
    gremlin> g.V().shortestPath().with(ShortestPath.target, __.has('name','peter')) //3\
    ==>[v[1],v[3],v[6]]
    ==>[v[2],v[1],v[3],v[6]]
    ==>[v[3],v[6]]
    ==>[v[4],v[3],v[6]]
    ==>[v[5],v[4],v[3],v[6]]
    ==>[v[6]]
    gremlin> g.V().shortestPath().
                     with(ShortestPath.edges, Direction.IN).
                     with(ShortestPath.target, __.has('name','josh')) //4\
    ==>[v[3],v[4]]
    ==>[v[4]]
    ==>[v[5],v[4]]
    gremlin> g.V().has('person','name','marko').
                   shortestPath().
                     with(ShortestPath.target, __.has('name','josh')) //5\
    ==>[v[1],v[4]]
    gremlin> g.V().has('person','name','marko').
                   shortestPath().
                     with(ShortestPath.target, __.has('name','josh')).
                     with(ShortestPath.distance, 'weight') //6\
    ==>[v[1],v[3],v[4]]
    gremlin> g.V().has('person','name','marko').
                   shortestPath().
                     with(ShortestPath.target, __.has('name','josh')).
                     with(ShortestPath.includeEdges, true) //7\
    ==>[v[1],e[8][1-knows->4],v[4]]

    g = g.withComputer()
    g.V().shortestPath() //1\
    g.V().has('person','name','marko').shortestPath() //2\
    g.V().shortestPath().with(ShortestPath.target, __.has('name','peter')) //3\
    g.V().shortestPath().
            with(ShortestPath.edges, Direction.IN).
            with(ShortestPath.target, __.has('name','josh')) //4\
    g.V().has('person','name','marko').
          shortestPath().
            with(ShortestPath.target, __.has('name','josh')) //5\
    g.V().has('person','name','marko').
          shortestPath().
            with(ShortestPath.target, __.has('name','josh')).
            with(ShortestPath.distance, 'weight') //6\
    g.V().has('person','name','marko').
          shortestPath().
            with(ShortestPath.target, __.has('name','josh')).
            with(ShortestPath.includeEdges, true) //7

1. Find all shortest paths.

2. Find all shortest paths from `marko`.

3. Find all shortest paths to `peter`.

4. Find all in-directed paths to `josh`.

5. Find all shortest paths from `marko` to `josh`.

6. Find all shortest paths from `marko` to `josh` using a custom distance property.

7. Find all shortest paths from `marko` to `josh` and include edges in the result.

console (groovy) groovy

    gremlin> g.inject(g.withComputer().V().shortestPath().
                        with(ShortestPath.distance, 'weight').
                        with(ShortestPath.includeEdges, true).
                        with(ShortestPath.maxDistance, 1).toList().toArray()).
               map(unfold().values('name','weight').fold()) //1\
    ==>[marko]
    ==>[marko,0.5,vadas]
    ==>[marko,0.4,lop]
    ==>[marko,0.4,lop,0.4,josh]
    ==>[marko,0.4,lop,0.2,peter]
    ==>[vadas,0.5,marko]
    ==>[vadas]
    ==>[vadas,0.5,marko,0.4,lop]
    ==>[josh,0.4,lop,0.4,marko]
    ==>[josh,0.4,lop]
    ==>[josh]
    ==>[josh,1.0,ripple]
    ==>[josh,0.4,lop,0.2,peter]
    ==>[ripple,1.0,josh]
    ==>[ripple]
    ==>[peter,0.2,lop,0.4,marko]
    ==>[peter,0.2,lop]
    ==>[peter,0.2,lop,0.4,josh]
    ==>[peter]
    ==>[lop,0.4,marko]
    ==>[lop,0.4,marko,0.5,vadas]
    ==>[lop]
    ==>[lop,0.4,josh]
    ==>[lop,0.2,peter]

    g.inject(g.withComputer().V().shortestPath().
               with(ShortestPath.distance, 'weight').
               with(ShortestPath.includeEdges, true).
               with(ShortestPath.maxDistance, 1).toList().toArray()).
      map(unfold().values('name','weight').fold()) //1

1. Find all shortest paths using a custom distance property and limit the distance to 1. Inject the result into a OLTP `GraphTraversal` in order to be able to select properties from all elements in all paths.

Additional References

`shortestPath()`

### 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()`.

console (groovy) groovy

    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')

    g.V(1).both().both()
    g.V(1).both().both().simplePath()
    g.V(1).both().both().simplePath().path()
    g.V().out().as('a').out().as('b').out().as('c').
      simplePath().by(label).
      path()
    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.

console (groovy) groovy

    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]]

    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()
    g.V('A').repeat(both().simplePath()).times(3).path() //1\
    g.V('D').repeat(both().simplePath()).times(3).path() //2\
    g.V('A').as('a').
      repeat(both().simplePath().from('a')).times(3).as('b').
      repeat(both().simplePath().from('b')).times(3).path()  //3

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.

console (groovy) groovy

    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

    g.V().values('age').order()
    g.V().values('age').order().skip(2)
    g.V().values('age').order().range(2, -1)

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

console (groovy) groovy

    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:[]]

    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

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.

console (groovy) groovy

    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]]
    gremlin> g.withoutStrategies(EarlyLimitStrategy).V().store('x').limit(1).cap('x')
    ==>[v[1],v[2]]

    g.V().aggregate('x').limit(1).cap('x')
    g.V().store('x').limit(1).cap('x')
    g.withoutStrategies(EarlyLimitStrategy).V().store('x').limit(1).cap('x')

It is important to note that `EarlyLimitStrategy` introduced in 3.3.5 alters the behavior of `store()`. Without that strategy (which is installed by default), 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.

console (groovy) groovy

    gremlin> g.E().store('x').by('weight').cap('x')
    ==>[0.5,1.0,1.0,0.4,0.4,0.2]

    g.E().store('x').by('weight').cap('x')

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:

console (groovy) groovy

    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]

    subGraph = g.E().hasLabel('knows').subgraph('subGraph').cap('subGraph').next() //1\
    sg = subGraph.traversal()
    sg.E() //2

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:

console (groovy) groovy

    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]

    subGraph = g.V(3).repeat(__.inE().subgraph('subGraph').outV()).times(3).cap('subGraph').next() //1\
    sg = subGraph.traversal()
    sg.E()

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).

console (groovy) groovy

    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]

    t = g.V().outE('knows').subgraph('knowsG').inV().outE('created').subgraph('createdG').
              inV().inE('created').subgraph('createdG').iterate()
    t.sideEffects.get('knowsG').traversal().E()
    t.sideEffects.get('createdG').traversal().E()

TinkerGraph is the ideal (and default) `Graph` into which a subgraph is extracted as it’s fast, in-memory, and supports user-supplied identifiers which can be any Java object. It is this last feature that needs some focus as many TinkerPop-enabled graphs have complex identifier types and TinkerGraph’s ability to consume those makes it a perfect host for an incoming subgraph. However care needs to be taken when using the elements of the TinkerGraph subgraph. The original graph’s identifiers may be preserved, but the elements of the graph are now TinkerGraph objects like, `TinkerVertex` and `TinkerEdge`. As a result, they can not be used directly in Gremlin running against the original graph. For example, the following traversal would likely return an error:

    Vertex v = sg.V().has('name','marko').next();  //1
    List<Vertex> vertices = g.V(v).out().toList(); //2

1. Here "sg" is a reference to a TinkerGraph subgraph and "v" is a `TinkerVertex`.

2. The `g.V(v)` has the potential to fail as "g" is the original `Graph` instance and not a TinkerGraph - it could reject the `TinkerVertex` instance as it will not recognize it.

It is safer to wrap the `TinkerVertex` in a `ReferenceVertex` or simply reference the `id()` as follows:

    Vertex v = sg.V().has('name','marko').next();
    List<Vertex> vertices = g.V(v.id()).out().toList();

    // OR

    Vertex v = new ReferenceVertex(sg.V().has('name','marko').next());
    List<Vertex> vertices = g.V(v).out().toList();

Additional References

`subgraph(String)`

### Sum Step

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

console (groovy) groovy

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

    g.V().values('age').sum()
    g.V().repeat(both()).times(3).values('age').sum()

Important | `sum(local)` determines the sum of the current, local object (not the objects in the traversal stream). This works for `Collection`-type objects.

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.

console (groovy) groovy

    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

    g.V().values('name').order()
    g.V().values('name').order().tail() //1\
    g.V().values('name').order().tail(1) //2\
    g.V().values('name').order().tail(3) //3

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.

console (groovy) groovy

    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]]

    g.V().as('a').out().as('a').out().as('a').select('a').by(tail(local)).values('name') //1\
    g.V().as('a').out().as('a').out().as('a').select('a').by(unfold().values('name').fold()).tail(local) //2\
    g.V().as('a').out().as('a').out().as('a').select('a').by(unfold().values('name').fold()).tail(local, 2) //3\
    g.V().valueMap().tail(local) //4

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.

console (groovy) groovy

    gremlin> g.V().repeat(both().groupCount('m')).times(16).cap('m').order(local).by(values,desc).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,desc).next()}
    ==>5.133465
    gremlin> g.V().repeat(timeLimit(2).both().groupCount('m')).times(16).cap('m').order(local).by(values,desc).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,desc).next()}
    ==>2.3989659999999997

    g.V().repeat(both().groupCount('m')).times(16).cap('m').order(local).by(values,desc).next()
    clock(1) {g.V().repeat(both().groupCount('m')).times(16).cap('m').order(local).by(values,desc).next()}
    g.V().repeat(timeLimit(2).both().groupCount('m')).times(16).cap('m').order(local).by(values,desc).next()
    clock(1) {g.V().repeat(timeLimit(2).both().groupCount('m')).times(16).cap('m').order(local).by(values,desc).next()}

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.

console (groovy) groovy

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

    tree = g.V().out().out().tree().next()

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).

console (groovy) groovy

    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

    tree = g.V().out().out().tree().by('name').next()
    tree['marko']
    tree['marko']['josh']
    tree.getObjectsAtDepth(3)

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:

console (groovy) groovy

    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:[]]]]

    g.V().has('name','josh').out('created').values('name').tree() //1\
    g.V().has('name','josh').out('created').values('name').
      tree().by('name').by(label).by() //2

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.

console (groovy) groovy

    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]

    g.V(1).out().fold().inject('gremlin',[1.23,2.34])
    g.V(1).out().fold().inject('gremlin',[1.23,2.34]).unfold()

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

console (groovy) groovy

    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

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

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.

console (groovy) groovy

    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]

    g.V(4).union(
             __.in().values('age'),
             out().values('lang'))
    g.V(4).union(
             __.in().values('age'),
             out().values('lang')).path()

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.

console (groovy) groovy

    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

    g.V(1).properties().value()
    g.V(1).properties().properties().value()

Additional References

`value()`

### ValueMap Step

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

console (groovy) groovy

    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]

    g.V().valueMap()
    g.V().valueMap('age')
    g.V().valueMap('age','blah')
    g.E().valueMap()

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 TinkerPop leverage vertex properties which support multiple values per key. Using the The Crew toy graph, the point is made explicit.

console (groovy) groovy

    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]

    g.V().valueMap()
    g.V().has('name','marko').properties('location')
    g.V().has('name','marko').properties('location').valueMap()

To turn list of values into single items, the `by()` modulator can be used as shown below.

console (groovy) groovy

    gremlin> g.V().valueMap().by(unfold())
    ==>[name:marko,location:san diego]
    ==>[name:stephen,location:centreville]
    ==>[name:matthias,location:bremen]
    ==>[name:daniel,location:spremberg]
    ==>[name:gremlin]
    ==>[name:tinkergraph]
    gremlin> g.V().valueMap('name','location').by().by(unfold())
    ==>[name:[marko],location:san diego]
    ==>[name:[stephen],location:centreville]
    ==>[name:[matthias],location:bremen]
    ==>[name:[daniel],location:spremberg]
    ==>[name:[gremlin]]
    ==>[name:[tinkergraph]]

    g.V().valueMap().by(unfold())
    g.V().valueMap('name','location').by().by(unfold())

If the `id`, `label`, `key`, and `value` of the `Element` is desired, then the `with()` modulator can be used to trigger its insertion into the returned map.

console (groovy) groovy

    gremlin> g.V().hasLabel('person').valueMap().with(WithOptions.tokens)
    ==>[id:1,label:person,name:[marko],location:[san diego,santa cruz,brussels,santa fe]]
    ==>[id:7,label:person,name:[stephen],location:[centreville,dulles,purcellville]]
    ==>[id:8,label:person,name:[matthias],location:[bremen,baltimore,oakland,seattle]]
    ==>[id:9,label:person,name:[daniel],location:[spremberg,kaiserslautern,aachen]]
    gremlin> g.V().hasLabel('person').valueMap('name').with(WithOptions.tokens, WithOptions.labels)
    ==>[label:person,name:[marko]]
    ==>[label:person,name:[stephen]]
    ==>[label:person,name:[matthias]]
    ==>[label:person,name:[daniel]]
    gremlin> g.V().hasLabel('person').properties('location').valueMap().with(WithOptions.tokens, WithOptions.values)
    ==>[value:san diego,startTime:1997,endTime:2001]
    ==>[value:santa cruz,startTime:2001,endTime:2004]
    ==>[value:brussels,startTime:2004,endTime:2005]
    ==>[value:santa fe,startTime:2005]
    ==>[value:centreville,startTime:1990,endTime:2000]
    ==>[value:dulles,startTime:2000,endTime:2006]
    ==>[value:purcellville,startTime:2006]
    ==>[value:bremen,startTime:2004,endTime:2007]
    ==>[value:baltimore,startTime:2007,endTime:2011]
    ==>[value:oakland,startTime:2011,endTime:2014]
    ==>[value:seattle,startTime:2014]
    ==>[value:spremberg,startTime:1982,endTime:2005]
    ==>[value:kaiserslautern,startTime:2005,endTime:2009]
    ==>[value:aachen,startTime:2009]

    g.V().hasLabel('person').valueMap().with(WithOptions.tokens)
    g.V().hasLabel('person').valueMap('name').with(WithOptions.tokens, WithOptions.labels)
    g.V().hasLabel('person').properties('location').valueMap().with(WithOptions.tokens, WithOptions.values)

Additional References

`valueMap(String…​)`

### Values Step

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

console (groovy) groovy

    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

    g.V(1).values()
    g.V(1).values('location')
    g.V(1).properties('location').values()

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.

Groovy | The term `in` is a reserved word in Groovy, and when therefore used as part of an anonymous traversal must be referred to in Gremlin with the double underscore `__.in()`.

Javascript | The term `in` is a reserved word in Python, and therefore must be referred to in Gremlin with `in_()`.

Python | The term `in` is a reserved word in Python, and therefore must be referred to in Gremlin with `in_()`.

console (groovy) groovy

    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]

    g.V(4)
    g.V(4).outE() //1\
    g.V(4).inE('knows') //2\
    g.V(4).inE('created') //3\
    g.V(4).bothE('knows','created','blah')
    g.V(4).bothE('knows','created','blah').otherV()
    g.V(4).both('knows','created','blah')
    g.V(4).outE().inV() //4\
    g.V(4).out() //5\
    g.V(4).inE().outV()
    g.V(4).inE().bothV()

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.

console (groovy) groovy

    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

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

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.

console (groovy) groovy

    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]

    g.V().where(out('created')).values('name') //1\
    g.V().out('knows').where(out('created')).values('name') //2\
    g.V().where(out('created').count().is(gte(2))).values('name') //3\
    g.V().where(out('knows').where(out('created'))).values('name') //4\
    g.V().where(__.not(out('created'))).where(__.in('knows')).values('name') //5\
    g.V().where(__.not(out('created')).and().in('knows')).values('name') //6\
    g.V().as('a').out('knows').as('b').
      where('a',gt('b')).
        by('age').
      select('a','b').
        by('name') //7\
    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

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.

Additional References

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

### With Step

The `with()`-step is not an actual step, but is instead a "step modulator" which modifies the behavior of the step prior to it. The `with()`-step provides additional "configuration" information to steps that implement the `Configuring` interface. Steps that allow for this type of modulation will explicitly state so in their documentation.

## A Note on Predicates

A `P` is a predicate of the form `Function<Object,Boolean>`. That is, given some object, return true or false. As of the relase of TinkerPop 3.4.0, Gremlin also supports simple text predicates, which only work on `String` values. The `TextP` text predicates extend the `P` predicates, but are specialized in that they are of the form `Function<String,Boolean>`. 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
--- | ---
`P.eq(object)` | Is the incoming object equal to the provided object?
`P.neq(object)` | Is the incoming object not equal to the provided object?
`P.lt(number)` | Is the incoming number less than the provided number?
`P.lte(number)` | Is the incoming number less than or equal to the provided number?
`P.gt(number)` | Is the incoming number greater than the provided number?
`P.gte(number)` | Is the incoming number greater than or equal to the provided number?
`P.inside(number,number)` | Is the incoming number greater than the first provided number and less than the second?
`P.outside(number,number)` | Is the incoming number less than the first provided number or greater than the second?
`P.between(number,number)` | Is the incoming number greater than or equal to the first provided number and less than the second?
`P.within(objects…​)` | Is the incoming object in the array of provided objects?
`P.without(objects…​)` | Is the incoming object not in the array of the provided objects?
`TextP.startingWith(string)` | Does the incoming `String` start with the provided `String`?
`TextP.endingWith(string)` | Does the incoming `String` end with the provided `String`?
`TextP.containing(string)` | Does the incoming `String` contain the provided `String`?
`TextP.notStartingWith(string)` | Does the incoming `String` not start with the provided `String`?
`TextP.notEndingWith(string)` | Does the incoming `String` not end with the provided `String`?
`TextP.notContaining(string)` | Does the incoming `String` not contain the provided `String`?

console (groovy) groovy

    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))

    eq(2)
    not(neq(2)) //1\
    not(within('a','b','c'))
    not(within('a','b','c')).test('d') //2\
    not(within('a','b','c')).test('a')
    within(1,2,3).and(not(eq(2))).test(3) //3\
    inside(1,4).or(eq(5)).test(3) //4\
    inside(1,4).or(eq(5)).test(5)
    between(1,2) //5\
    not(between(1,2))

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.

console (groovy) groovy

    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

    g.V().as('a').both().both().as('b').count()
    g.V().as('a').both().both().as('b').where('a',neq('b')).count()

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`).

Python | The term `global` is a reserved word in Python, and therefore a `Scope` using that term must be referred as `global_`.

console (groovy) groovy

    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

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

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:

console (groovy) groovy

    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,desc)
    ==>[person:4,software:2]
    gremlin> g.V().fold().sample(local,2)
    ==>[v[1],v[5]]

    g.V().both().group().by(label).select('software').dedup(local)
    g.V().groupCount().by(label).select(values).min(local)
    g.V().groupCount().by(label).order(local).by(values,desc)
    g.V().fold().sample(local,2)

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:

console (groovy) groovy

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

    g.V().fold().local(unfold().count())
    g.V().fold().count(local)

## 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.

console (groovy) groovy

    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

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

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 natively written 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.

console (groovy) groovy

    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]]

    g.V().out().out().path().by {it.value('name')}.
                             by {it.value('name')}.
                             by {g.V(it).in('created').values('name').fold().next()} //1\
    g.V().out().out().path().by('name').
                             by('name').
                             by(__.in('created').values('name').fold()) //2

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 TinkerPop 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(Traversal.Admin<?, ?> traversal) {
            if (traversal.getSteps().size() <= 1)
                return;

            for (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(Traversal.Admin<?, ?> traversal) {
            if (TraversalHelper.onGraphComputer(traversal))
                return;

            for (GraphStep originalGraphStep : TraversalHelper.getStepsOfClass(GraphStep.class, traversal)) {
                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 (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-TinkerPop provided steps, it should go into the `ProviderOptimizationStrategy` category to ensure the added step does not interfere with the assumptions of the `OptimizationStrategy` strategies.

console (groovy) groovy

    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]

    t = g.V().has('name','marko'); null
    t.toString()
    t.iterate(); null
    t.toString()

Warning | The reason that `OptimizationStrategy` and `ProviderOptimizationStrategy` are two different categories is that optimization strategies should only rewrite the traversal using TinkerPop steps. This ensures that the optimizations executed at the end of the optimization strategy round are TinkerPop 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 TinkerPop 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.

console (groovy) groovy

    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])]
    EarlyLimitStrategy           [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)]
                                          ]), 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])]
    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), Rep
                                          eatStep([VertexStep(OUT,vertex), RepeatEndStep],until(loops(2)),emit(false)), MatchEndStep(b)], [MatchStartStep(a), WherePredicateStep(neq(b)), MatchEndStep], [MatchStartStep(b), WhereTraversalStep([Whe
                                          reStartStep, VertexStep(BOTH,edge), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]
    RepeatUnrollStrategy         [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), CountGlobalStep, IsStep(gt(1))]), MatchEndStep]]), SelectOneStep(last,b), GroupCountStep([VertexStep(OUT,edge), CountGlobalStep])]
    CountStrategy                [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])]
    PathRetractionStrategy       [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])]

    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()

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 TinkerPop 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:

console (groovy) groovy

    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]

    g = TinkerGraph.open().traversal()
    v = g.addV().property(id,'42a').next()
    g.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.

console (groovy) groovy

    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

    graph = Neo4jGraph.open('/tmp/neo4j')
    strategy = ElementIdStrategy.build().create()
    g = graph.traversal().withStrategies(strategy)
    g.addV().property(id, '42a').id()

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:

console (groovy) groovy

    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.traversal.step.util.*, 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.traversal.step.map.*, org.apache.tinkerpop.gremlin.process.computer.clustering.connected.*, org.apache.tinkerpop.gremlin.process.computer.clone.*, 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.search.path.*, 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.AnonymousTraversalSource.*, static org.apache.tinkerpop.gremlin.process.traversal.TextP.*, 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.*, org.apache.tinkerpop.gremlin.groovy.jsr223.dsl.credential.*, org.apache.tinkerpop.gremlin.tinkergraph.structure.*, org.apache.tinkerpop.gremlin.tinkergraph.process.computer.*, 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.spark.process.computer.*, org.apache.tinkerpop.gremlin.spark.structure.*, org.apache.tinkerpop.gremlin.spark.structure.io.*, 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.sparql.process.traversal.dsl.sparql.*, org.apache.tinkerpop.gremlin.sparql.process.traversal.strategy.*, 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]]

    import org.apache.tinkerpop.gremlin.process.traversal.step.util.event.*
    graph = TinkerFactory.createModern()
    l = new ConsoleMutationListener(graph)
    strategy = EventStrategy.build().addListener(l).create()
    g = graph.traversal().withStrategies(strategy)
    g.addV().property('name','stephen')
    g.E().drop()

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.

console (groovy) groovy

    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]

    graph = TinkerFactory.createModern()
    strategyA = PartitionStrategy.build().partitionKey("_partition").writePartition("a").readPartitions("a").create()
    strategyB = PartitionStrategy.build().partitionKey("_partition").writePartition("b").readPartitions("b").create()
    gA = graph.traversal().withStrategies(strategyA)
    gA.addV() // this vertex has a property of {_partition:"a"}
    gB = graph.traversal().withStrategies(strategyB)
    gB.addV() // this vertex has a property of {_partition:"b"}
    gA.V()
    gB.V()

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.

console (groovy) groovy

    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])]
    EarlyLimitStrategy           [O]   [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])]
    AdjacentToIncidentStrategy   [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    RepeatUnrollStrategy         [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    CountStrategy                [O]   [GraphStep(vertex,[])@[a], PropertiesStep([location],property), NotStep([PropertiesStep([endTime],property)]), PropertyValueStep@[b], SelectStep(last,[a, b],[value(name), identity])]
    PathRetractionStrategy       [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])]

    graph = TinkerFactory.createTheCrew()
    g = graph.traversal()
    g.V().as('a').values('location').as('b'). //1\
      select('a','b').by('name').by()
    g = g.withStrategies(SubgraphStrategy.build().vertexProperties(hasNot('endTime')).create()) //2\
    g.V().as('a').values('location').as('b'). //3\
      select('a','b').by('name').by()
    g.V().as('a').values('location').as('b').
      select('a','b').by('name').by().explain()

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.

console (groovy) groovy

    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().with(WithOptions.tokens)
    ==>[id:1,label:person,name:[marko],location:[santa fe]]
    ==>[id:8,label:person,name:[matthias],location:[seattle]]
    ==>[id:10,label:software,name:[gremlin]]
    ==>[id:11,label:software,name:[tinkergraph]]
    gremlin> g.E().valueMap().with(WithOptions.tokens)
    ==>[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]

    graph = TinkerFactory.createTheCrew()
    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())
    g.V().valueMap().with(WithOptions.tokens)
    g.E().valueMap().with(WithOptions.tokens)
    g.V().outE().inV().
      path().
        by('name').
        by().
        by('name')

## 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()

Learn more about how to implement these DSLs in the Gremlin Language Variants section specific to the programming language of interest.

# The GraphComputer

TinkerPop 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, TinkerPop’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).

console (groovy) groovy

    gremlin> result = graph.compute().program(PageRankVertexProgram.build().create()).submit().get()
    ==>result[tinkergraph[vertices:6 edges:0],memory[size:0]]
    gremlin> result.memory().runtime
    ==>82
    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]]

    result = graph.compute().program(PageRankVertexProgram.build().create()).submit().get()
    result.memory().runtime
    g = result.graph().traversal()
    g.V().valueMap()

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. TinkerPop 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. TinkerPop 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 TinkerPop 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.

console (groovy) groovy

    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]]

    graph = TinkerFactory.createModern()
    result = graph.compute().program(PeerPressureVertexProgram.build().create()).mapReduce(ClusterPopulationMapReduce.build().create()).submit().get()
    result.memory().get('clusterPopulation')
    g = result.graph().traversal()
    g.V().values(PeerPressureVertexProgram.CLUSTER).groupCount().next()
    g.V().valueMap()

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.

console (groovy) groovy

    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

    result = graph.compute().program(PeerPressureVertexProgram.build().create()).
               mapReduce(ClusterPopulationMapReduce.build().create()).
               mapReduce(ClusterCountMapReduce.build().create()).submit().get()
    result.memory().clusterPopulation
    result.memory().clusterCount

Important | The MapReduce model of TinkerPop 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

TinkerPop 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 3.4.2. 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.

console (groovy) groovy

    gremlin> result = graph.compute().program(PageRankVertexProgram.build().create()).submit().get()
    ==>result[tinkergraph[vertices:6 edges:0],memory[size:0]]
    gremlin> result.memory().runtime
    ==>20
    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]]

    result = graph.compute().program(PageRankVertexProgram.build().create()).submit().get()
    result.memory().runtime
    g = result.graph().traversal()
    g.V().valueMap()

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

console (groovy) groovy

    gremlin> g = graph.traversal().withComputer()
    ==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
    gremlin> g.V().pageRank().valueMap()
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.11375510357865541],name:[marko],age:[29]]
    ==>[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]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.3047200907912249],name:[lop],lang:[java]]
    ==>[gremlin.pageRankVertexProgram.pageRank:[0.14598540152719106],name:[vadas],age:[27]]
    gremlin> g.V().pageRank().by('pageRank').times(5).order().by('pageRank').valueMap()
    ==>[pageRank:[0.11362166126141333],name:[marko],age:[29]]
    ==>[pageRank:[0.11362166126141333],name:[peter],age:[35]]
    ==>[pageRank:[0.14598422136890218],name:[vadas],age:[27]]
    ==>[pageRank:[0.14598422136890218],name:[josh],age:[32]]
    ==>[pageRank:[0.1756689971547068],name:[ripple],lang:[java]]
    ==>[pageRank:[0.30511923758466225],name:[lop],lang:[java]]

    g = graph.traversal().withComputer()
    g.V().pageRank().valueMap()
    g.V().pageRank().by('pageRank').times(5).order().by('pageRank').valueMap()

### 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.

console (groovy) groovy

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

    g = graph.traversal().withComputer()
    g.V().peerPressure().by('cluster').valueMap()
    g.V().peerPressure().by(outE('knows')).by('cluster').valueMap()

### ConnectedComponentVertexProgram

The `ConnectedComponentVertexProgram` identifies Connected Component instances in a graph. See `connectedComponent()`-step for more information.

### ShortestPathVertexProgram

The `ShortestPathVertexProram` provides an easy way to find shortest non-cyclic paths in the graph. It provides several options to configure the output format, the start- and end-vertices, the direction, a custom distance function, as well as a distance limitation. By default it just finds all undirected, shortest paths in the graph.

console (groovy) groovy

    gremlin> spvp = ShortestPathVertexProgram.build().create() //1\
    ==>ShortestPathVertexProgram[includeEdges=false]
    gremlin> result = graph.compute().program(spvp).submit().get() //2\
    ==>result[tinkergraph[vertices:6 edges:6],memory[size:1]]
    gremlin> result.memory().get(ShortestPathVertexProgram.SHORTEST_PATHS) //3\
    ==>[v[1],v[2]]
    ==>[v[2]]
    ==>[v[3],v[1],v[2]]
    ==>[v[4],v[1],v[2]]
    ==>[v[5],v[4],v[1],v[2]]
    ==>[v[6],v[3],v[1],v[2]]
    ==>[v[1]]
    ==>[v[2],v[1]]
    ==>[v[3],v[1]]
    ==>[v[4],v[1]]
    ==>[v[5],v[4],v[1]]
    ==>[v[6],v[3],v[1]]
    ==>[v[1],v[3]]
    ==>[v[2],v[1],v[3]]
    ==>[v[3]]
    ==>[v[4],v[3]]
    ==>[v[5],v[4],v[3]]
    ==>[v[6],v[3]]
    ==>[v[1],v[4]]
    ==>[v[2],v[1],v[4]]
    ==>[v[3],v[4]]
    ==>[v[4]]
    ==>[v[5],v[4]]
    ==>[v[6],v[3],v[4]]
    ==>[v[1],v[4],v[5]]
    ==>[v[2],v[1],v[4],v[5]]
    ==>[v[3],v[4],v[5]]
    ==>[v[4],v[5]]
    ==>[v[5]]
    ==>[v[6],v[3],v[4],v[5]]
    ==>[v[1],v[3],v[6]]
    ==>[v[2],v[1],v[3],v[6]]
    ==>[v[3],v[6]]
    ==>[v[4],v[3],v[6]]
    ==>[v[5],v[4],v[3],v[6]]
    ==>[v[6]]

    spvp = ShortestPathVertexProgram.build().create() //1\
    result = graph.compute().program(spvp).submit().get() //2\
    result.memory().get(ShortestPathVertexProgram.SHORTEST_PATHS) //3

1. Create a `ShortestPathVertexProgram` with its default configuration.

2. Execute the `ShortestPathVertexProgram`.

3. Get all shortest paths from the results memory.

console (groovy) groovy

    gremlin> spvp = ShortestPathVertexProgram.build().includeEdges(true).create() //1\
    ==>ShortestPathVertexProgram[includeEdges=true]
    gremlin> result = graph.compute().program(spvp).submit().get() //2\
    ==>result[tinkergraph[vertices:6 edges:6],memory[size:1]]
    gremlin> result.memory().get(ShortestPathVertexProgram.SHORTEST_PATHS) //3\
    ==>[v[1]]
    ==>[v[2],e[7][1-knows->2],v[1]]
    ==>[v[3],e[9][1-created->3],v[1]]
    ==>[v[4],e[8][1-knows->4],v[1]]
    ==>[v[5],e[10][4-created->5],v[4],e[8][1-knows->4],v[1]]
    ==>[v[6],e[12][6-created->3],v[3],e[9][1-created->3],v[1]]
    ==>[v[1],e[7][1-knows->2],v[2]]
    ==>[v[2]]
    ==>[v[3],e[9][1-created->3],v[1],e[7][1-knows->2],v[2]]
    ==>[v[4],e[8][1-knows->4],v[1],e[7][1-knows->2],v[2]]
    ==>[v[5],e[10][4-created->5],v[4],e[8][1-knows->4],v[1],e[7][1-knows->2],v[2]]
    ==>[v[6],e[12][6-created->3],v[3],e[9][1-created->3],v[1],e[7][1-knows->2],v[2]]
    ==>[v[1],e[9][1-created->3],v[3]]
    ==>[v[2],e[7][1-knows->2],v[1],e[9][1-created->3],v[3]]
    ==>[v[3]]
    ==>[v[4],e[11][4-created->3],v[3]]
    ==>[v[5],e[10][4-created->5],v[4],e[11][4-created->3],v[3]]
    ==>[v[6],e[12][6-created->3],v[3]]
    ==>[v[1],e[8][1-knows->4],v[4]]
    ==>[v[2],e[7][1-knows->2],v[1],e[8][1-knows->4],v[4]]
    ==>[v[3],e[11][4-created->3],v[4]]
    ==>[v[4]]
    ==>[v[5],e[10][4-created->5],v[4]]
    ==>[v[6],e[12][6-created->3],v[3],e[11][4-created->3],v[4]]
    ==>[v[1],e[8][1-knows->4],v[4],e[10][4-created->5],v[5]]
    ==>[v[2],e[7][1-knows->2],v[1],e[8][1-knows->4],v[4],e[10][4-created->5],v[5]]
    ==>[v[3],e[11][4-created->3],v[4],e[10][4-created->5],v[5]]
    ==>[v[4],e[10][4-created->5],v[5]]
    ==>[v[5]]
    ==>[v[6],e[12][6-created->3],v[3],e[11][4-created->3],v[4],e[10][4-created->5],v[5]]
    ==>[v[1],e[9][1-created->3],v[3],e[12][6-created->3],v[6]]
    ==>[v[2],e[7][1-knows->2],v[1],e[9][1-created->3],v[3],e[12][6-created->3],v[6]]
    ==>[v[3],e[12][6-created->3],v[6]]
    ==>[v[4],e[11][4-created->3],v[3],e[12][6-created->3],v[6]]
    ==>[v[5],e[10][4-created->5],v[4],e[11][4-created->3],v[3],e[12][6-created->3],v[6]]
    ==>[v[6]]

    spvp = ShortestPathVertexProgram.build().includeEdges(true).create() //1\
    result = graph.compute().program(spvp).submit().get() //2\
    result.memory().get(ShortestPathVertexProgram.SHORTEST_PATHS) //3

1. Create a `ShortestPathVertexProgram` as before, but configure it to include edges in the result.

2. Execute the `ShortestPathVertexProgram`.

3. Get all shortest paths from the results memory.

The `ShortestPathVertexProgram.Builder` provides the following configuration methods:

Method | Description | Default
--- | --- | ---
`source(Traversal)` | Sets a filter traversal for the start vertices (e.g. `__.has('name','marko')`). | all vertices (`__.identity()`)
`target(Traversal)` | Sets a filter traversal for the end vertices. | all vertices
`edgeDirection(Direction)` | Sets the direction to traverse during the shortest path discovery. | `Direction.BOTH`
`edgeTraversal(Traversal)` | Sets a traversal that emits the edges to traverse from the current vertex. | `__.bothE()`
`distanceProperty(String)` | Sets the edge property to use for the distance calculations. | none
`distanceTraversal(Traversal)` | Sets the traversal that calculates the distance for the current edge. | `__.constant(1)`
`maxDistance(Traversal)` | Limits the shortest path distance. | none
`includeEdges(Boolean)` | Whether to include edges in shortest paths or not. | `false`

Important | If a maximum distance is provided, the discovery process will only stop to follow a path at this distance if there was no custom distance property or traversal provided. Custom distances can be negative, hence exceeding the maximum distance doesn’t mean that there can’t be any more valid paths. However, paths will be filtered at the end, when no more non-cyclic paths can be found. The bottom line is that custom distance properties or traversals can lead to much longer runtimes and a much higher memory consumption.

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

### CloneVertexProgram

The `CloneVertexProgram` (known in versions prior to 3.2.10 as `BulkDumperVertexProgram`) copies a whole graph from any graph `InputFormat` to any graph `OutputFormat`. TinkerPop provides the following:

- `OutputFormat`

  - `GraphSONOutputFormat`

  - `GryoOutputFormat`

  - `ScriptOutputFormat`

- `InputFormat`

  - `GraphSONInputFormat`

  - `GryoInputFormat`

  - `ScriptInputFormat`).

An example is provided in the SparkGraphComputer section.

Graph Providers should consider writing their own `OutputFormat` and `InputFormat` which would allow bulk loading and export capabilities through this `VertexProgram`. This topic is discussed further in the Provider Documentation.

### 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.

console (groovy) groovy

    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

    g = graph.traversal()
    g.V().both().hasLabel('person').values('age').groupCount().next() // OLTP
    g = graph.traversal().withComputer()
    g.V().both().hasLabel('person').values('age').groupCount().next() // OLAP

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`.

console (groovy) groovy

    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
    ==>11

    result = graph.compute().program(TraversalVertexProgram.build().traversal(g.V().both().hasLabel('person').values('age').groupCount('a')).create()).submit().get()
    result.memory().a
    result.memory().iteration
    result.memory().runtime

#### 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 a Gremlin Traversal Machine 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.

console (groovy) groovy

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

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

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

console (groovy) groovy

    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

    g = TinkerFactory.createModern().traversal()
    g.V()
    g.V().values('name')
    g.V().has('name','marko').out('knows').values('name')

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`.
:bytecode | :bc | Provides options for translating and evaluating `Bytecode` for debugging purposes.

Many of the above commands are described elsewhere or are generally self explanatory, but the `:bytecode` command could use some additional explanation. The following code shows example usage:

    gremlin> :bytecode from g.V().out('knows')  //1
    ==>{"@type":"g:Bytecode","@value":{"step":[["V"],["out","knows"]]}}
    gremlin> :bytecode translate g {"@type":"g:Bytecode","@value":{"step":[["V"],["out","knows"]]}} //2
    ==>g.V().out("knows")

1. Generates a GraphSON 3.0 representation of the traversal as bytecode.

2. Converts bytecode in GraphSON 3.0 format to a traversal string.

Note | The Console does expose the `:record` command which is inherited from the Groovy Shell. This command works well with local commands, but may record session outputs differently for `:remote` commands. If there is a need to use `:record` it may be best to manually create a `Cluster` object and issue commands that way so that they evaluate locally in the shell.

### Interrupting Evaluations

If there is some input that is taking too long to evaluate or to iterate through, use `ctrl+c` to attempt to interrupt that process. It is an "attempt" in the sense that the long running process is only informed of the interruption by the user and must respond to it (as with any call to `interrupt()` on a `Thread`). A `Traversal` will typically respond to such requests as do most commands, including `:remote` operations.

    gremlin> java.util.stream.IntStream.range(0, 1000).iterator()
    ==>0
    ==>1
    ==>2
    ==>3
    ==>4
    ...
    ==>348
    ==>349
    ==>350
    ==>351
    ==>352
    Execution interrupted by ctrl+c
    gremlin>

### 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 3.4.2  //3
    ==>loaded: [org.apache.tinkerpop, neo4j-gremlin, 3.4.2]
    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.

### Docker Image

The Gremlin Console can also be started as a Docker image:

    $ docker run -it tinkerpop/gremlin-console:3.4.2
    Feb 25, 2018 3:47:24 PM java.util.prefs.FileSystemPreferences$1 run
    INFO: Created user preferences directory.

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

The Docker image offers the same options as the standalone Console. It can be used for example to execute scripts:

    $ docker run -it tinkerpop/gremlin-console:3.4.2 -e gremlin.groovy
    v[1]
    v[2]
    v[3]
    v[4]
    v[5]
    v[6]

## Gremlin Server

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

- Allows any Gremlin Structure-enabled graph (i.e. implements the `Graph` API on the JVM) 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.

- Provides a method for non-JVM languages which may not have a Gremlin Traversal Machine (e.g. Python, Javascript, etc.) to communicate with the TinkerPop stack on the JVM.

- 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 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".
    // ReferenceElementStrategy converts all graph elements (vertices/edges/vertex properties)
    // to "references" (i.e. just id and label without properties). this strategy was added
    // in 3.4.0 to make all Gremlin Server results consistent across all protocols and
    // serialization formats aligning it with TinkerPop recommended practices for writing
    // Gremlin.
    globals << [g : graph.traversal().withStrategies(ReferenceElementStrategy.instance())]

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 Drivers

TinkerPop offers client-side drivers for the Gremlin Server websocket sub-protocol in a variety of languages:

- C#

- Java

- Javascript

- Python

These drivers provide methods to send Gremlin based requests and get back traversal results as a response. The requests may be script-based or bytecode-based. As discussed earlier in the introduction the recommendation is to use bytecode-based requests. The difference between sending scripts and sending bytecode are demonstrated below in some basic examples:

java groovy csharp javascript python

    // script
    Cluster cluster = Cluster.open();
    Client client = cluster.connect();
    Map<String,Object> params = new HashMap<>();
    params.put("name","marko");
    List<Result> list = client.submit("g.V().has('person','name',name).out('knows')", params).all().get();

    // bytecode
    GraphTraversalSource g = traversal().withRemote(DriverRemoteConnection.using("localhost",8182,"g"));
    List<Vertex> list = g.V().has("person","name","marko").out("knows").toList();

    // script
    def cluster = Cluster.open()
    def client = cluster.connect()
    def list = client.submit("g.V().has('person','name',name).out('knows')", [name: "marko"]).all().get();

    // bytecode
    def g = traversal().withRemote(DriverRemoteConnection.using("localhost",8182,"g"))
    def list = g.V().has('person','name','marko').out('knows').toList()

    // script
    var gremlinServer = new GremlinServer("localhost", 8182);
    using (var gremlinClient = new GremlinClient(gremlinServer))
    {
        var bindings = new Dictionary<string, object>
        {
            {"name", "marko"}
        };

        var response =
            await gremlinClient.SubmitWithSingleResultAsync<object>("g.V().has('person','name',name).out('knows')",
                bindings);
    }

    // bytecode
    using (var gremlinClient = new GremlinClient(new GremlinServer("localhost", 8182)))
    {
        var g = Traversal().WithRemote(new DriverRemoteConnection(gremlinClient));
        var list = g.V().Has("person", "name", "marko").Out("knows").ToList();
    }

    // script
    const client = new Client('ws://localhost:45940/gremlin', { traversalSource: "g" });
    const conn = client.open();
    const list = conn.submit("g.V().has('person','name',name).out('knows')",{name: 'marko'}).then(function (response) { ... });

    // bytecode
    const g = gtraversal().withRemote(new DriverRemoteConnection('ws://localhost:8182/gremlin'));
    const list = g.V().has("person","name","marko").out("knows").toList();

    # script
    client = Client('ws://localhost:8182/gremlin', 'g')
    list = client.submit("g.V().has('person','name',name).out('knows')",{'name': 'marko'}).all()

    # bytecode
    g = traversal().withRemote(DriverRemoteConnection('ws://localhost:8182/gremlin','g'))
    list = g.V().has("person","name","marko").out("knows").toList()

The advantage of bytecode over scripts should be apparent from the above examples. Scripts are just strings that are embedded in code (in the above examples, the strings are Groovy-based) whereas bytecode based requests are themselves code written in the native language of use. Obviously, the advantage of the Gremlin being actual code is that there are checks (e.g. compile-time, auto-complete and other IDE support, language level checks, etc.) that help validate the Gremlin during the development process.

TinkerPop makes an effort to ensure a high-level of consistency among the drivers and their features, but there are differences in capabilities and features as they are each developed independently. The Java driver was the first and is therefore the most advanced. Please see the related documentation for the driver of interest for more information and details in the Gremlin Drivers and Variants Section of this documentation.

### 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:

console (groovy) groovy

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

    :remote connect tinkerpop.server conf/remote.yaml

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:

console (groovy) groovy

    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]

    :> g.V().values('name')
    :> g.V().has('name','marko').out('created').values('name')
    :> g.E().label().groupCount()
    result
    :remote close

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":

console (groovy) groovy

    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 = """
                      g.V().hasLabel('person').
                        out('knows').
                        out('created').
                        group().
                          by('name')
                      """
    ==>
             g.V().hasLabel('person').
               out('knows').
               out('created').
               group().
                 by('name')

    gremlin> :> @script //5\
    ==>[ripple:[v[5]],lop:[v[3]]]
    gremlin> :remote close
    ==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]

    :remote connect tinkerpop.server conf/remote-objects.yaml //1\
    :remote list //2\
    :> g.E().label().groupCount() //3\
    m = result[0].object //4\
    m.sort {it.value}
    script = """
             g.V().hasLabel('person').
               out('knows').
               out('created').
               group().
                 by('name')
             """
    :> @script //5\
    :remote close

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.

console (groovy) groovy

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

    cluster = Cluster.open()
    :remote connect tinkerpop.server cluster

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 the Console 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:

console (groovy) groovy

    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,knows:2]
    gremlin> :remote close
    ==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]

    :remote connect tinkerpop.server conf/remote-objects.yaml
    :remote config alias x g
    :> x.E().label().groupCount()
    :remote close

#### 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:

console (groovy) groovy

    gremlin> :remote connect tinkerpop.server conf/remote.yaml session
    ==>Configured localhost/127.0.0.1:8182-[02d9d5dc-90cf-4ebe-ac08-15b58929970a]
    gremlin> :> x = 1
    ==>1
    gremlin> :> y = 2
    ==>2
    gremlin> :> x + y
    ==>3
    gremlin> :remote close
    ==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]-[02d9d5dc-90cf-4ebe-ac08-15b58929970a]

    :remote connect tinkerpop.server conf/remote.yaml session
    :> x = 1
    :> y = 2
    :> x + y
    :remote close

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.

console (groovy) groovy

    gremlin> :remote connect tinkerpop.server conf/remote.yaml session
    ==>Configured localhost/127.0.0.1:8182-[01ebed12-04d5-4cc9-a00c-132385abfcbc]
    gremlin> :remote console
    ==>All scripts will now be sent to Gremlin Server - [localhost/127.0.0.1:8182]-[01ebed12-04d5-4cc9-a00c-132385abfcbc] - 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]-[01ebed12-04d5-4cc9-a00c-132385abfcbc]
    gremlin> :remote close
    ==>Removed - Gremlin Server - [localhost/127.0.0.1:8182]-[01ebed12-04d5-4cc9-a00c-132385abfcbc]

    :remote connect tinkerpop.server conf/remote.yaml session
    :remote console
    x = 1
    y = 2
    x + y
    :remote console
    :remote close

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 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.

Important | TinkerPop provides and supports this HTTP endpoint as a convenience and for legacy reasons, but users should prefer the recommended approach of bytcode based requests as described in Connecting Gremlin section.

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 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.

### 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 3.4.2

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 and iteration of result times out. 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.keyStore | The private key in JKS or PKCS#12 format. | none
ssl.keyStorePassword | The password of the `keyStore` if it is password-protected. | none
ssl.keyStoreType | `JKS` (Java 8 default) or `PKCS12` (Java 9+ default) | none
ssl.needClientAuth | Optional. One of NONE, REQUIRE. Enables client certificate authentication at the enforcement level specified. Can be used in combination with Authenticator. | none
ssl.sslCipherSuites | The list of JSSE ciphers to support for SSL connections. If specified, only the ciphers that are listed and supported will be enabled. If not specified, the JVM default is used. | none
ssl.sslEnabledProtocols | The list of SSL protocols to support for SSL connections. If specified, only the protocols that are listed and supported will be enabled. If not specified, the JVM default is used. | none
ssl.trustStore | Required when needClientAuth is REQUIRE. Trusted certificates for verifying the remote endpoint’s certificate. If this value is not provided and SSL is enabled, the default `TrustManager` will be used. | none
ssl.trustStorePassword | The password of the `trustStore` if it is password-protected | 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
--- | --- | ---
globalFunctionCacheEnabled | Determines if the script engine cache for global functions is enabled and behaves as an override to the plugin specific setting of the same name. | true
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. Client SSL options are described in the Gremlin Drivers and Variants" sections with varying capability depending on the driver chosen. 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.NET | PLAIN SASL | 3.3.0
Gremlin-Python | PLAIN SASL | 3.2.2
Gremlin.Net | PLAIN SASL | 3.2.7
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. To secure the transport for the credentials, SSL should be enabled. For this Quick Start, a self-signed certificate will be created but this should not be used in a production environment.

Generate the self-signed SSL certificate:

    $ keytool -genkey -alias localhost -keyalg RSA -keystore server.jks
    Enter keystore password:
    Re-enter new password:
    What is your first and last name?
      [Unknown]:  localhost
    What is the name of your organizational unit?
      [Unknown]:
    What is the name of your organization?
      [Unknown]:
    What is the name of your City or Locality?
      [Unknown]:
    What is the name of your State or Province?
      [Unknown]:
    What is the two-letter country code for this unit?
      [Unknown]:
    Is CN=localhost, OU=Unknown, O=Unknown, L=Unknown, ST=Unknown, C=Unknown correct?
      [no]:  yes

    Enter key password for <localhost>
        (RETURN if same as keystore password):

Next, uncomment the `keyStore` and `keyStorePassword` lines in `conf/gremlin-server-secure.yaml`.

    ssl: {
      enabled: true,
      sslEnabledProtocols: [TLSv1.2],
      keyStore: server.jks,
      keyStorePassword: changeit
    }

    $ 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
    ...
    [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.

When SSL is enabled on the server, it must also be enabled on the client when connecting. To connect to Gremlin Server with the `gremlin-driver`, set the `credentials`, `enableSsl`, and `trustStore` when constructing the `Cluster`.

    Cluster cluster = Cluster.build().credentials("stephen", "password")
                                     .enableSsl(true).trustStore("server.jks").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. Be sure to configure the trustStore if using self-signed certificates.

Similarly, Gremlin Server can be configured for REST and security. Follow the steps above for configuring the SSL certificate.

    $ 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
    ...
    [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:

console (groovy) groovy

    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$04$rTl63Kn8OEmV36idOfEe6OY0QpGI7SbbYF9vs6DSDaiZVwZQO0b6u],username:[marko]]
    gremlin> credentials.users().count()
    ==>3
    gremlin> credentials.users("daniel").drop()
    gremlin> credentials.users().count()
    ==>2

    graph = TinkerGraph.open()
    graph.createIndex("username",Vertex.class)
    credentials = graph.traversal(CredentialTraversalSource.class)
    credentials.user("stephen","password")
    credentials.user("daniel","better-password")
    credentials.user("marko","rainbow-dash")
    credentials.users("marko").valueMap()
    credentials.users().count()
    credentials.users("daniel").drop()
    credentials.users().count()

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 expanded 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: {enableThreadInterrupt: true}}}}

This configuration sets up the script engine with to ensure that loops (like `while`) will respect interrupt requests. 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) { }
    ==>Script evaluation exceeded the configured 'scriptEvaluationTimeout' threshold of 30000 ms or evaluation was otherwise cancelled directly for request [while(true) {}]

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 script 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.
`globalFunctionCacheEnabled` | Determines if the global function cache is enabled. By default, this value is `true` - described in more detail in the Cache Management Section.
`classMapCacheSpecification` | The cache specification for the `GremlinGroovyScriptEngine` class map cache - described in more detail in the Cache Management Section.
`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.

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, 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 three different serializers: GraphSON, Gryo, and GraphBinary.

##### 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. The format itself is described in the IO Documentation.

      - { 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. The format itself is described in the IO Documentation.

      - { 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. Note that the `ioRegistries` setting is applied first, followed 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.

##### GraphBinary

GraphBinary is a binary serialization format suitable for object trees, designed to reduce serialization overhead on both the client and the server, as well as limiting the size of the payload that is transmitted over the wire. The format itself is described in the IO Documentation.

Important | GraphBinary is currently only supported on the JVM.

      - { className: org.apache.tinkerpop.gremlin.driver.ser.GraphBinaryMessageSerializerV1 }

It has the MIME type of `application/vnd.graphbinary-v1.0` and the following configuration options:

Key | Description | Default
--- | --- | ---
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
builder | Name of the `TypeSerializerRegistry.Builder` instance to be used to construct the `TypeSerializerRegistry`. | none

As described above, there are multiple ways in which to register serializers for GraphBinary-based serialization. Note that the `ioRegistries` setting is applied first, followed by the `custom` setting.

#### 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-3.4.2/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().with(WithOptions.tokens)` 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. There are at least two good reasons for doing so: script caching and protection from "Gremlin injection" (conceptually the same as the notion of SQL injection).

With respect to caching, 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.

On the topic of Gremlin injection, note that it is possible to take advantage of Gremlin scripts in the same fashion as SQL scripts that are submitted as strings. When using string building patterns for queries without proper input scrubbing, it would be quite simple to do:

    String lbl = "person"
    String nodeId = "mary').next();g.V().drop().iterate();g.V().has('id', 'thomas";
    String query = "g.addV('" + lbl + "').property('identifier','" + nodeId + "')";
    client.submit(query);

The above case would `drop()` all vertices in the graph. By using script parameterization, there is a different outcome in that the `nodeId` string is not treated as something executable, but rather as a literal string that just becomes part of the "identifier" for the vertex on insertion:

    String lbl = "person"
    String nodeId = "mary').next();g.V().drop().iterate();g.V().has('id', 'thomas";
    String query = "g.addV(lbl).property('identifier',nodeId)";

    Map<String,Object> params = new HashMap<>();
    params.put("lbl",lbl);
    params.put("nodeId",nodeId);
    client.submit(query, params);

Gremlin injection should not be possible with `Bytecode` based traversals - only scripts - because `Bytecode` traversals will treat all arguments as literal values. There is potential for concern if lambda based steps are utilized as they execute arbitrary code, which is string based, but configuring `TraversalSource` instances with `LambdaRestrictionStrategy`, which prevents lambdas all together, using a graph that does not allow lambdas at all, or configuring appropriate sandbox options in Gremlin Server (or such options available to the graph database in use) should each help mitigate problems related to this issue.

#### 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 depending on the serializer being used those detached elements may or may not have their properties carried with them. Gryo and GraphSON serializers will write all properties in the return payload in this case but GraphBinary will not. Therefore, script-based requests that use Gryo or GraphSON 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
    GraphTraversalSource g = 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('name').with(WithOptions.tokens)");

    GraphTraversalSource g = traversal().withRemote('conf/remote-graph.properties');
    List<Vertex> results = g.V().hasLabel("person").valueMap('name').with(WithOptions.tokens).toList();

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

Tip | The example graph configurations pre-packaged with Gremlin Server utilize `ReferenceElementStrategy` which convert all graph elements to references by initializing "g" using `withStrategies(ReferenceElementStrategy.instance()`. Consider utilizing `ReferenceElementStrategy` whenever creating a `GraphTraversalSource` in Java to ensure the most portable Gremlin.

#### 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("#jsr223.groovy.engine.keep.globals", "soft");
    client.submit("def addItUp(x,y){x+y}", params);

In cases where maintaining the expense of the global function cache is unecessary this cache can be disabled with the `globalFunctionCacheEnabled` configuration on the `GroovyCompilerGremlinPlugin`.

Gremlin Server also has a "class map" cache which holds compiled scripts which helps avoid recompilation costs on future requests. This cache can be tuned in the Gremlin Server configuration with the `GroovyCompilerGremlinPlugin` in the following fashion:

    scriptEngines: {
      gremlin-groovy: {
        plugins: { ...
                   org.apache.tinkerpop.gremlin.groovy.jsr223.GroovyCompilerGremlinPlugin: {classMapCacheSpecification: "initialCapacity=1000,maximumSize=10000"},
                   ...}

The specifics for this comma delimited format can be found here. By default, the cache is set to `softValues` which means they are garbage collected in a globally least-recently-used manner as memory gets low. For production systems, it is likely that a more predictable strategy be taken as shown above with the use of the `maximumSize`.

#### Considering Sessions

The preferred approach for issuing script-based 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.

### Docker Image

The Gremlin Server can also be started as a Docker image:

    $ docker run tinkerpop/gremlin-server:3.4.2
    [INFO] GremlinServer -
             \,,,/
             (o o)
    -----oOOo-(3)-oOOo-----

    [INFO] GremlinServer - Configuring Gremlin Server from conf/gremlin-server.yaml
    ...
    [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.

By default, Gremlin Server listens on port 8182. So that port needs to be exposed if it should be reachable on the host:

    $ docker run -p 8182:8182 tinkerpop/gremlin-server:3.4.2

Arguments provided with `docker run` are forwarded to the script that starts Gremlin Server. This allows for example to use an alternative config file:

    $ docker run tinkerpop/gremlin-server:3.4.2 conf/gremlin-server-secure.yaml

## 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:

console (groovy) groovy

    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

    :plugin use tinkerpop.gephi
    graph = TinkerFactory.createModern()
    :remote connect tinkerpop.gephi
    :> graph

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:

console (groovy) groovy

    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

    :remote config visualTraversal graph //1\
    traversal = vg.V(2).in().out('knows').
                        has('age',gt(30)).outE('created').
                        has('weight',gt(0.5d)).inV();[] //2\
    :> traversal                                           //3

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:

console (groovy) groovy

    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

    :remote config startRGBColor [0.0,0.3,1.0]
    :remote config colorToFade b
    :remote config colorFadeRate 0.5
    :> traversal

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.

The instructions above assume that the `Graph` instance being visualized is local to the Gremlin Console. It makes that assumption because the Gephi plugin requires a locally held `Graph`. If the intent is to visualize a `Graph` instance hosted in Gremlin Server or a TinkerPop-enabled graph that can only be connected to in a "remote" fashion, then it is still possible to use the Gephi plugin, but the requirement for a locally held `Graph` remains the same. To use the Gephi plugin in these situations simply use subgraph()-step to extract the portion of the remote graph that will be visualized. Use of that step will return a `TinkerGraph` instance to the Gremlin Console at which point it can be used locally with the Gephi plugin. The following example demonstrates the general steps:

    gremlin> :remote connect tinkerpop.server conf/remote-objects.yaml //1
    ...
    gremlin> :> g.E().hasLabel('knows').subgraph('subGraph').cap('subGraph') //2
    ...
    gremlin> graph = result[0].object //3
    ...

1. Be sure to connect with a serializer configured to return objects and not their `toString()` representation which is discussed in more detail in the Connecting Via Console Section.

2. Use the `:>` command to subgraph the remote graph as needed.

3. The `TinkerGraph` of that previous traversal can be found in the `result` object and now that the `Graph` is local to Gremlin Console it can be used with Gephi as shown in the prior instruction set.

### 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 TinkerPop, 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 TinkerPop 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)`.

console (groovy) groovy

    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

    g.V //1\
    g.V.name //2\
    g.V.outE.weight //3

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.

console (groovy) groovy

    gremlin> g.V[0..2]
    ==>v[1]
    ==>v[2]
    gremlin> g.V[0..<2]
    ==>v[1]
    gremlin> g.V[2]
    ==>v[3]

    g.V[0..2]
    g.V[0..<2]
    g.V[2]

#### 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.

console (groovy) groovy

    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)]

    g.V.where(outE('knows') & outE('created')).name //1\
    t = g.V.where(outE('knows') | inE('created')).name; null //2\
    t.toString()
    t
    t.toString()

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.

console (groovy) groovy

    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

    g.V().map{it.get().value('name')}  // conventional
    g.V.map{it.name}  // sugar

### 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.

#### 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`:

console (groovy) groovy

    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 23 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.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."
    > org.apache.tinkerpop.gremlin.process.traversal.step.map.ReadTest$Traversals#g_io_readXxmlX
            "Hadoop-Gremlin does not support reads/writes with GraphML."
    > org.apache.tinkerpop.gremlin.process.traversal.step.map.ReadTest$Traversals#g_io_read_withXreader_graphmlX
            "Hadoop-Gremlin does not support reads/writes with GraphML."
    > org.apache.tinkerpop.gremlin.process.traversal.step.map.WriteTest$Traversals#g_io_writeXxmlX
            "Hadoop-Gremlin does not support reads/writes with GraphML."
    > org.apache.tinkerpop.gremlin.process.traversal.step.map.WriteTest$Traversals#g_io_write_withXwriter_graphmlX
            "Hadoop-Gremlin does not support reads/writes with GraphML."
    - 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.

    describeGraph(HadoopGraph)

# Gremlin Drivers and Variants

At this point, readers should be well familiar with the Introduction to this Reference Documentation and will likely be thinking about implementation details specific to the graph provider they have selected as well as the programming language they intend to use. The choice of programming language could have implications to the architecture and design of the application and the choice itself may have limits imposed upon it by the chosen graph provider. For example, a Remote Gremlin Provider will require the selection of a driver to interact with it. On the other hand, a graph system that is designed for embedded use, like TinkerGraph, needs the Java Virtual Machine (JVM) environment so will require Gremlin Server, if using a programming language that is not on the JVM and will further require driver selection.

TinkerPop provides an array of drivers in different programming languages as a way to connect to a remote Gremlin Server or Remote Gremlin Provider. Drivers allow the developer to make requests to that remote system and get back results from the TinkerPop-enabled graphs hosted within. A driver can submit Gremlin strings and Gremlin bytecode over this sub-protocol. Gremlin strings are written in the scripting language made available by the remote system that the driver is connecting to (typically, Groovy-based). This connection approach is quite similar to what developers are likely familiar with when using JDBC and SQL. While it is familiar, it is not recommended and for TinkerPop it is considered an out-dated concept and is largely still present for the purpose of supporting applications that might still be using that method of interaction.

The preferred approach is to use bytecode-based requests, which essentially allows the ability to craft Gremlin directly in the programming language of choice. As Gremlin makes use of two fundamental programming constructs: function composition and function nesting. it is possible to embed the Gremlin language in any modern programming language. It is a far more natural way to program, because it enables IDE interaction, compile time checks, and language level checks that can help prevent errors prior to execution. The differences between these two approaches were outlined in the Connecting Via Drivers Section, which applies to Gremlin Server, but also to Remote Gremlin Providers.

In addition to the languages and drivers that TinkerPop supports, there are also third-party implementations, as well as extensions to the Gremlin language that might be specific to a particular graph provider. That listing can be found on the TinkerPop home page. Their description is beyond the scope of this documentation.

Tip | When possible, it is typically best to align the version of TinkerPop used on the client with the version supported on the server. While it is not impossible to have a different version between client and server, it may require additional configuration and/or a deeper knowledge of that changes introduced between versions. It’s simply safer to avoid the conflict, when allowed to do so.

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.

The following sections describe each language variant and driver that is officially TinkerPop a part of the project, provided more detailed information about usage, configuration and known limitations.

## Gremlin-Java

Apache TinkerPop’s Gremlin-Java implements Gremlin within the Java language and can be used by any Java 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. As the Gremlin Traversal Machine that processes Gremlin queries is also written in Java, it can be used in all three connection methods described in the Connecting Gremlin Section.

    <dependency>
       <groupId>org.apache.tinkerpop</groupId>
       <artifactId>gremlin-core</artifactId>
       <version>3.4.2</version>
    </dependency>

    <!-- when using Gremlin Server or Remote Gremlin Provider a driver is required -->
    <dependency>
       <groupId>org.apache.tinkerpop</groupId>
       <artifactId>gremlin-driver</artifactId>
       <version>3.4.2</version>
    </dependency>

### Connecting

The pattern for connecting is described in Connecting Gremlin and it basically distills down to creating a `GraphTraversalSource`. For embedded mode, this involves first creating a `Graph` and then spawning the `GraphTraversalSource`:

    Graph graph = ...;
    GraphTraversalSource g = graph.traversal();

Using "g" it is then possible to start writing Gremlin. The "g" allows for the setting of many configuration options which affect traversal execution. The Traversal Section describes some of these options and some are only suitable with embedded style usage. For remote options however there are some added configurations to consider and this section looks to address those.

When connecting to Gremlin Server or Remote Gremlin Providers it is possible to configure the `DriverRemoteConnection` manually as shown in earlier examples where the host and port are provided as follows:

    GraphTraversalSource g = traversal().withRemote(DriverRemoteConnection.using("localhost",8182,"g"));

It is also possible to create it from a configuration. The most basic way to do so involves the following line of code:

    GraphTraversalSource g = traversal().withRemote('conf/remote-graph.properties');

The `remote-graph.properties` file simply provides connection information to the `GraphTraversalSource` which is used to configure a `RemoteConnection`. That file looks like this:

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

The `RemoteConnection` is an interface that provides the transport mechanism for "g" and makes it possible to for that mechanism to be altered (typically by graph providers who have their own protocols). TinkerPop provides one such implementation called the `DriverRemoteConnection` which enables transport over Gremlin Server protocols using the TinkerPop driver. The driver is configured by the specified `gremlin.remote.driver.clusterFile` and the local "g" is bound to the `GraphTraversalSource` on the remote end with `gremlin.remote.driver.sourceName` which in this case is also "g".

There are other ways to configure the traversal using `withRemote()` as it has other overloads. It can take an Apache Commons `Configuration` object which would have keys similar to those shown in the properties file and it can also take a `RemoteConnection` instance directly. The latter is interesting in that it means it is possible to programmatically construct all aspects of the `RemoteConnection`. For TinkerPop usage, that might mean directly constructing the `DriverRemoteConnection` and the driver instance that supplies the transport mechanism. For example, the command shown above could be re-written using programmatic construction as follows:

    Cluster cluster = Cluster.open();
    GraphTraversalSource g = traversal().withRemote(DriverRemoteConnection.using(cluster, "g"));

Please consider the following example:

console (groovy) groovy java

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

    g = traversal().withRemote('conf/remote-graph.properties')
    g.V().valueMap(true)
    g.close()

    GraphTraversalSource g = traversal().withRemote("conf/remote-graph.properties");
    List<Map> list = g.V().valueMap(true);
    g.close();

Note the call to `close()` above. The call to `withRemote()` internally instantiates a connection via the driver 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 `Cluster` and `Client objects and then re-use them.

console (groovy) groovy

    gremlin> cluster = Cluster.open('conf/remote-objects.yaml')
    ==>localhost/127.0.0.1:8182
    gremlin> client = cluster.connect()
    ==>org.apache.tinkerpop.gremlin.driver.Client$ClusteredClient@d919544
    gremlin> g = traversal().withRemote(DriverRemoteConnection.using(client, "g"))
    ==>graphtraversalsource[emptygraph[empty], standard]
    gremlin> g.V().valueMap(true)
    ==>[id:1,label:person,name:[marko],age:[29]]
    ==>[id:2,label:person,name:[vadas],age:[27]]
    ==>[id:3,label:software,name:[lop],lang:[java]]
    ==>[id:4,label:person,name:[josh],age:[32]]
    ==>[id:5,label:software,name:[ripple],lang:[java]]
    ==>[id:6,label:person,name:[peter],age:[35]]
    gremlin> g.close()
    gremlin> client.close()
    gremlin> cluster.close()

    cluster = Cluster.open('conf/remote-objects.yaml')
    client = cluster.connect()
    g = traversal().withRemote(DriverRemoteConnection.using(client, "g"))
    g.V().valueMap(true)
    g.close()
    client.close()
    cluster.close()

If the `Client` instance is supplied externally, as is shown above, then it is not closed implicitly by the close of "g". Closing "g" will have no effect on "client" or "cluster". When supplying them externally, the `Client` and `Cluster` objects must also be closed explicitly. It’s worth noting that the close of a `Cluster` will close all `Client` instances spawned by the `Cluster`.

Important | Bytecode-based traversals use 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) in the Gremlin Server configuration file as described here.

### Common Imports

There are a number of classes, functions and tokens that are typically used with Gremlin. The following imports provide most of the common functionality required to use Gremlin:

    import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource;
    import org.apache.tinkerpop.gremlin.process.traversal.IO;
    import static org.apache.tinkerpop.gremlin.process.traversal.AnonymousTraversalSource.traversal;
    import static org.apache.tinkerpop.gremlin.process.traversal.Operator.*;
    import static org.apache.tinkerpop.gremlin.process.traversal.Order.*;
    import static org.apache.tinkerpop.gremlin.process.traversal.P.*;
    import static org.apache.tinkerpop.gremlin.process.traversal.Pop.*;
    import static org.apache.tinkerpop.gremlin.process.traversal.SackFunctions.*;
    import static org.apache.tinkerpop.gremlin.process.traversal.Scope.*;
    import static org.apache.tinkerpop.gremlin.process.traversal.TextP.*;
    import static org.apache.tinkerpop.gremlin.structure.Column.*;
    import static org.apache.tinkerpop.gremlin.structure.Direction.*;
    import static org.apache.tinkerpop.gremlin.structure.T.*;
    import static org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.__.*;

### 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.keyStore | The private key in JKS or PKCS#12 format. | none
connectionPool.keyStorePassword | The password of the `keyStore` if it is password-protected. | none
connectionPool.keyStoreType | `JKS` (Java 8 default) or `PKCS12` (Java 9+ default) | 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.sslCipherSuites | The list of JSSE ciphers to support for SSL connections. If specified, only the ciphers that are listed and supported will be enabled. If not specified, the JVM default is used. | none
connectionPool.sslEnabledProtocols | The list of SSL protocols to support for SSL connections. If specified, only the protocols that are listed and supported will be enabled. If not specified, the JVM default is used. | none
connectionPool.sslSkipCertValidation | Configures the `TrustManager` to trust all certs without any validation. Should not be used in production. | false
connectionPool.trustStore | File location for a SSL Certificate Chain to use when SSL is enabled. If this value is not provided and SSL is enabled, the default `TrustManager` will be used. | none
connectionPool.trustStorePassword | The password of the `trustStore` if it is password-protected | none
connectionPool.validationRequest | A script that is used to test server connectivity. A good script to use is one that evaluates quickly and returns no data. The default simply returns an empty string, but if a graph is required by a particular provider, a good traversal might be `g.inject()`. | ''
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
path | The URL path to the Gremlin Server. | /gremlin
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.

### Serialization

Remote systems like Gremlin Server and Remote Gremlin Providers respond to requests made in a particular serialization format and respond by serializing results to some format to be interpreted by the client. For JVM-based languages, there are three options for serialization: Gryo, GraphSON and GraphBinary. When using Gryo serialization (the default serializer for the Java 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:

    IoRegistry registry = ...; // an IoRegistry instance exposed by a specific graph provider
    GryoMapper kryo = GryoMapper.build().addRegistry(registry).create();
    MessageSerializer serializer = new GryoMessageSerializerV3d0(kryo);
    Cluster cluster = Cluster.build().
                              serializer(serializer).
                              create();
    Client client = cluster.connect();
    GraphTraversalSource g = traversal().withRemote(DriverRemoteConnection.using(client, "g"));

The `IoRegistry` tells the serializer what classes from the graph provider 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. Obviously, it is possible to switch to GraphSON or GraphBinary by building the appropriate `MessageSerializer` (`GraphSONMessageSerializerV3d0` or `GraphBinaryMessageSerializerV1` respectively) in the same way and building that into the `Cluster` object.

### 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 and with embedded usage, lambdas can be leveraged directly:

    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 Java `Function` is used to map a `Traverser<S>` to an object `E`.

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

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

When sending traversals remotely to Gremlin Server or Remote Gremlin Providers, the static methods of `Lambda` should be used and should denote a particular JSR-223 `ScriptEngine` that is available on the remote end (typically, this is Groovy). `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()"))

Finally, Gremlin `Bytecode` that includes lambdas requires that the traversal be processed by the `ScriptEngine`. To avoid continued recompilation costs, it supports the encoding of bindings, which allow Gremlin Server 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 request, it is possible to simply execute. To express bindings in Java, use `Bindings`.

    b = Bindings.instance()
    g.V(b.of('id',1)).out('created').values('name').map{t -> "name: " + t.get() }
    g.V(b.of('id',4)).out('created').values('name').map{t -> "name: " + t.get() }
    g.V(b.of('id',4)).out('created').values('name').getBytecode()
    g.V(b.of('id',4)).out('created').values('name').getBytecode().getBindings()
    cluster.close()

Both traversals are abstractly defined as `g.V(id).out('created').values('name').map{t → "name: " + t.get() }` and thus, the first submission can be cached for faster evaluation on the next submission.

Warning | It is generally advised to avoid lambda usage. Please consider A Note On Lambdas for more information.

### Submitting Scripts

Warning | TinkerPop does not recommend submitting script-based requests and generally continues to support this feature for legacy reasons and corner use cases which are still not completely addressed by the Gremlin language. Please consider using bytecode-based requests instead when possible.

TinkerPop 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 scripts are 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 Groovy scripts:

    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.

#### Per Request Settings

There are a number of overloads to `Client.submit()` that accept a `RequestOptions` object. The `RequestOptions` provide a way to include options that are specific to the request made with the call to `submit()`. A good use-case for this feature is to set a per-request override to the `scriptEvaluationTimeout` so that it only applies to the current request.

    Cluster cluster = Cluster.open();
    Client client = cluster.connect();
    RequestOptions options = RequestOptions.build().timeout(500).create();
    List<Result> result = client.submit("g.V()", options).all().get();

#### 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.

### Domain Specific Languages

Creating a Domain Specific Language (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(Graph graph, TraversalStrategies traversalStrategies) {
            super(graph, traversalStrategies);
        }

        public SocialTraversalSourceDsl(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.

### Application Examples

The available Maven 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.

Use Maven to generate these example projects with a command like:

    $ mvn archetype:generate -DarchetypeGroupId=org.apache.tinkerpop -DarchetypeArtifactId=gremlin-archetype-server \
          -DarchetypeVersion=3.4.2 -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.

## 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 and Gremlin Server.

    compile group: 'org.apache.tinkerpop', name: 'gremlin-core', version: '3.3.4'
    compile group: 'org.apache.tinkerpop', name: 'gremlin-driver', version: '3.3.4'

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.

To install Gremlin-Python, use Python’s pip package manager.

    pip install gremlinpython

### Connecting

The pattern for connecting is described in Connecting Gremlin and it basically distills down to creating a `GraphTraversalSource`. A `GraphTraversalSource` is created from the anonymous `traversal()` method where the "g" provided to the `DriverRemoteConnection` corresponds to the name of a `GraphTraversalSource` on the remote end.

    g = traversal().withRemote(DriverRemoteConnection('ws://localhost:8182/gremlin','g'))

### Common Imports

There are a number of classes, functions and tokens that are typically used with Gremlin. The following imports provide most of the typical functionality required to use Gremlin:

    from gremlin_python import statics
    from gremlin_python.process.anonymous_traversal import traversal
    from gremlin_python.process.graph_traversal import __
    from gremlin_python.process.strategies import *
    from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
    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
    from gremlin_python.process.traversal import Bindings
    from gremlin_python.process.traversal import WithOptions

These can be used analogously to how they are used in Gremlin-Java.

console (python) python

    >>> g.V().hasLabel('person').has('age',P.gt(30)).order().by('age',Order.desc).toList()
    [v[6], v[4]]

    g.V().hasLabel('person').has('age',P.gt(30)).order().by('age',Order.desc).toList()

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.

console (python) python

    >>> g.V().hasLabel('person').has('age',gt(30)).order().by('age',desc).toList()
    [v[6], v[4]]

    g.V().hasLabel('person').has('age',gt(30)).order().by('age',desc).toList()

Finally, statics includes all the `__`-methods and thus, anonymous traversals like `__.out()` can be expressed as below. That is, without the `__.`-prefix.

console (python) python

    >>> g.V().repeat(out()).times(2).name.fold().toList()
    [[ripple, lop]]

    g.V().repeat(out()).times(2).name.fold().toList()

### Configuration

The following table describes the various configuration options for the Gremlin-Python Driver. They can be passed to the `Client` or `DriverRemoteConnection` 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. | ""

### 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.

console (python) python

    >>> 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().with_(WithOptions.tokens).toList()
    [[id:8, label:knows, weight:1.0]]
    >>> g = g.withoutStrategies(SubgraphStrategy)
    null
    >>> g.V().name.toList()
    [marko, vadas, lop, josh, ripple, peter]
    >>> g.V().outE().valueMap().with_(WithOptions.tokens).toList()
    [[id:9, label:created, weight:0.4], [id:7, label:knows, weight:0.5], [id:8, label:knows, weight:1.0], [id:10, label:created, weight:1.0], [id:11, label:created, weight:0.4], [id:12, label:created, weight:0.2]]
    >>> g = g.withComputer(workers=2,vertices=has('name','marko'))
    null
    >>> g.V().name.toList()
    [marko]
    >>> g.V().outE().valueMap().with_(WithOptions.tokens).toList()
    [[id:9, label:created, weight:0.4], [id:7, label:knows, weight:0.5], [id:8, label:knows, weight:1.0]]

    g = g.withStrategies(SubgraphStrategy(vertices=hasLabel('person'),edges=has('weight',gt(0.5))))
    g.V().name.toList()
    g.V().outE().valueMap().with_(WithOptions.tokens).toList()
    g = g.withoutStrategies(SubgraphStrategy)
    g.V().name.toList()
    g.V().outE().valueMap().with_(WithOptions.tokens).toList()
    g = g.withComputer(workers=2,vertices=has('name','marko'))
    g.V().name.toList()
    g.V().outE().valueMap().with_(WithOptions.tokens).toList()

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.

console (python) python

    >>> 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]

    g.V().out().map(lambda: "lambda x: len(x.get().value('name'))").sum().toList() #(1)
    statics.default_lambda_language #(2)
    g.V().out().map(lambda: ("it.get().value('name').length()", "gremlin-groovy")).sum().toList() #(3)
    statics.default_lambda_language = 'gremlin-groovy' #(4)
    g.V().out().map(lambda: "it.get().value('name').length()").sum().toList() #(5)
    g.V().out().map(lambda: ("lambda x: len(x.get().value('name'))", "gremlin-python")).sum().toList() #(6)
    statics.default_lambda_language = 'gremlin-python' #(7)
    g.V().out().map(lambda: "x: len(x.get().value('name'))").sum().toList()                            //8

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.

Finally, Gremlin `Bytecode` that includes lambdas requires that the traversal be processed by the `ScriptEngine`. To avoid continued recompilation costs, it supports the encoding of bindings, which allow a remote engine to 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.

console (python) python

    >>> g.V(Bindings.of('id',1)).out('created').map(lambda: ("it.get().value('name').length()", "gremlin-groovy")).sum().toList()
    [3]
    >>> g.V(Bindings.of('id',4)).out('created').map(lambda: ("it.get().value('name').length()", "gremlin-groovy")).sum().toList()
    [9]

    g.V(Bindings.of('id',1)).out('created').map(lambda: ("it.get().value('name').length()", "gremlin-groovy")).sum().toList()
    g.V(Bindings.of('id',4)).out('created').map(lambda: ("it.get().value('name').length()", "gremlin-groovy")).sum().toList()

#### Native Python Lambdas

To process lambdas in Python, the `GremlinJythonScriptEngine` must be enabled on the remote end. If that remote is Gremlin Server, then these instructions can help configuration it. As an example, the `conf/gremlin-server-modern-py.yaml` configuration maintains a `GremlinJythonScriptEngine`.

    $ bin/gremlin-server.sh install org.apache.tinkerpop gremlin-python 3.4.2
    $ 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$Slf4jReporterMetrics
    [INFO] GraphManager - Graph [graph] was successfully configured via [conf/tinkergraph-empty.properties].
    [INFO] ServerGremlinExecutor - Initialized Gremlin thread pool.  Threads in pool named with pattern gremlin-*
    [INFO] ScriptEngines - Loaded gremlin-jython ScriptEngine
    [INFO] ScriptEngines - Loaded gremlin-python ScriptEngine
    [INFO] ScriptEngines - Loaded gremlin-groovy ScriptEngine
    [INFO] GremlinExecutor - Initialized gremlin-groovy ScriptEngine with scripts/generate-modern.groovy
    [INFO] ServerGremlinExecutor - Initialized GremlinExecutor and configured ScriptEngines.
    [INFO] ServerGremlinExecutor - A GraphTraversalSource is now bound to [g] with graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    [INFO] OpLoader - Adding the standard OpProcessor.
    [INFO] OpLoader - Adding the session OpProcessor.
    [INFO] OpLoader - Adding the traversal OpProcessor.
    [INFO] TraversalOpProcessor - Initialized cache for TraversalOpProcessor with size 1000 and expiration time of 600000 ms
    [INFO] GremlinServer - Executing start up LifeCycleHook
    [INFO] Logger$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.

Warning | As explained throughout the documentation, when possible avoid lambdas. If lambdas must be used, then consider submitting Groovy lambdas as opposed to Python-based ones. The `GremlinGroovyScriptEngine` is far more featured and performant than its Jython sibling and will likely yield better results.

### Submitting Scripts

Warning | TinkerPop does not recommend submitting script-based requests and generally continues to support this feature for legacy reasons and corner use cases which are still not completely addressed by the Gremlin language. Please consider using bytecode-based requests instead when possible.

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.

### Domain Specific Languages

Writing a Gremlin Domain Specific Language (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()

### Syntactic 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.

console (python) python

    >>> 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]

    g.V().both()[1:3].toList()
    g.V().both()[1].toList()
    g.V().both().name.toList()

### Limitations

- Traversals that return a `Set` might be coerced to a `List` in Python. In the case of Python, number equality is different from JVM languages which produces different `Set` results when those types are in use. When this case is detected during deserialization, the `Set` is coerced to a `List` so that traversals return consistent results within a collection across different languages. If a `Set` is needed then convert `List` results to `Set` manually.

- Gremlin is capable of returning `Dictionary` results that use non-hashable keys (e.g. Dictionary as a key) and Python does not support that at a language level. Gremlin that returns such results will need to be re-written to avoid that.

## 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

### Connecting

The pattern for connecting is described in Connecting Gremlin and it basically distills down to creating a `GraphTraversalSource`. A `GraphTraversalSource` is created from the `AnonymousTraversalSource.traversal()` method where the "g" provided to the `DriverRemoteConnection` corresponds to the name of a `GraphTraversalSource` on the remote end.

    var remoteConnection = new DriverRemoteConnection(new GremlinClient(new GremlinServer("localhost", 8182)));
    var g = Traversal().WithRemote(remoteConnection);

### Common Imports

There are a number of classes, functions and tokens that are typically used with Gremlin. The following imports provide most of the typical functionality required to use Gremlin:

    using static Gremlin.Net.Process.Traversal.AnonymousTraversalSource;
    using static Gremlin.Net.Process.Traversal.__;
    using static Gremlin.Net.Process.Traversal.P;
    using static Gremlin.Net.Process.Traversal.Order;
    using static Gremlin.Net.Process.Traversal.Operator;
    using static Gremlin.Net.Process.Traversal.Pop;
    using static Gremlin.Net.Process.Traversal.Scope;
    using static Gremlin.Net.Process.Traversal.TextP;
    using static Gremlin.Net.Process.Traversal.Column;
    using static Gremlin.Net.Process.Traversal.Direction;
    using static Gremlin.Net.Process.Traversal.T;

### Configuration

The connection properties for the Gremlin.Net driver can be passed to the `GremlinServer` instance as keyword arguments:

Key | Description | Default
--- | --- | ---
hostname | The hostname that the driver will connect to. | localhost
port | The port on which Gremlin Server can be reached. | 8182
enableSsl | Determines if SSL should be enabled or not. If enabled on the server then it must be enabled on the client. | false
username | The username to submit on requests that require authentication. | none
password | The password to submit on requests that require authentication. | none

#### Connection Pool

It is also possible to configure the `ConnectionPool` of the Gremlin.Net driver. These configuration options can be set as properties on the `ConnectionPoolSettings` instance that can be passed to the `GremlinClient`:

Key | Description | Default
--- | --- | ---
PoolSize | The size of the connection pool. | 4
MaxInProcessPerConnection | The maximum number of in-flight requests that can occur on a connection. | 32

A `NoConnectionAvailableException` is thrown if all connections have reached the `MaxInProcessPerConnection` limit when a new request comes in.

#### Serialization

The Gremlin.Net driver uses by default GraphSON 3.0 but it is also possible to use GraphSON 2.0 which can be necessary when the server does not support GraphSON 3.0 yet:

    var client = new GremlinClient(new GremlinServer("localhost", 8182), new GraphSON2Reader(),
        new GraphSON2Writer(), GremlinClient.GraphSON2MimeType);

### 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(vertices: HasLabel("person"),
        edges: Has("weight", Gt(0.5))));
    var names = g.V().Values<string>("name").ToList();  // names: [marko, vadas, josh, peter]

    g = g.WithoutStrategies(typeof(SubgraphStrategy));
    names = g.V().Values<string>("name").ToList(); // names: [marko, vadas, lop, josh, ripple, peter]

    var edgeValueMaps = g.V().OutE().ValueMap<object, object>().With(WithOptions.Tokens).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<string>("name").ToList();  // names: [marko]

    edgeValueMaps = g.V().OutE().ValueMap<object, object>().With(WithOptions.Tokens).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.

### Submitting Scripts

Warning | TinkerPop does not recommend submitting script-based requests and generally continues to support this feature for legacy reasons and corner use cases which are still not completely addressed by the Gremlin language. Please consider using bytecode-based requests instead when possible.

Gremlin scripts are sent to the server from a `IGremlinClient` instance. A `IGremlinClient` is created as follows:

    var gremlinServer = new GremlinServer("localhost", 8182);
    using (var gremlinClient = new GremlinClient(gremlinServer))
    {
        var response =
            await gremlinClient.SubmitWithSingleResultAsync<string>("g.V().has('person','name','marko')");
    }

If the remote system has authentication and SSL enabled, then the `GremlinServer` object can be configured as follows:

    var username = "username";
    var password = "password";
    var gremlinServer = new GremlinServer("localhost", 8182, true, username, password);

### Domain Specific Languages

Developing a Domain Specific Language (DSL) 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 connection = new DriverRemoteConnection(new GremlinClient(new GremlinServer("localhost", 8182)));
    var social = Traversal().WithRemote(connection);

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

### Application Examples

This dotnet template helps getting started with Gremlin.Net. It creates a new C# console project that shows how to connect to a Gremlin Server with Gremlin.Net.

You can install the template with the dotnet CLI tool:

    dotnet new -i Gremlin.Net.Template

After the template is installed, a new project based on this template can be installed:

    dotnet new gremlin

Specify the output directory for the new project which will then also be used as the name of the created project:

    dotnet new gremlin -o MyFirstGremlinProject

## 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

### Connecting

The pattern for connecting is described in Connecting Gremlin and it basically distills down to creating a `GraphTraversalSource`. A `GraphTraversalSource` is created from the `AnonymousTraversalSource.traversal()` method where the "g" provided to the `DriverRemoteConnection` corresponds to the name of a `GraphTraversalSource` on the remote end.

    const g = traversal().withRemote(new DriverRemoteConnection('ws://localhost:8182/gremlin'));

Gremlin-JavaScript supports plain text SASL authentication, you can set it on the connection options.

    const authenticator = new gremlin.driver.auth.PlainTextSaslAuthenticator('myuser', 'mypassword');
    const g = traversal().withRemote(new DriverRemoteConnection('ws://localhost:8182/gremlin', { authenticator });

Given that I/O operations in Node.js are asynchronous by default, Terminal Steps 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));

When using `async` functions it is possible to `await` the promises:

    const names = await g.V().hasLabel('person').values('name').toList();
    console.log(names);

### Common Imports

There are a number of classes, functions and tokens that are typically used with Gremlin. The following imports provide most of the typical functionality required to use Gremlin:

    const gremlin = require('gremlin');
    const traversal = gremlin.process.AnonymousTraversalSource.traversal;
    const DriverRemoteConnection = gremlin.driver.DriverRemoteConnection;
    const column = gremlin.process.traversal.column
    const direction = gremlin.process.traversal.direction
    const p = gremlin.process.traversal.P
    const pick = gremlin.process.traversal.pick
    const pop = gremlin.process.traversal.pop
    const order = gremlin.process.traversal.order
    const scope = gremlin.process.traversal.scope
    const t = gremlin.process.traversal.t

### Submitting Scripts

Warning | TinkerPop does not recommend submitting script-based requests and generally continues to support this feature for legacy reasons and corner use cases which are still not completely addressed by the Gremlin language. Please consider using bytecode-based requests instead when possible.

It is possible to submit parametrized Gremlin scripts to the server as strings, using the `Client` class:

    const gremlin = require('gremlin');
    const client = new gremlin.driver.Client('ws://localhost:8182/gremlin', { traversalSource: 'g' });

    const result1 = await client.submit('g.V(vid)', { vid: 1 });
    const vertex = result1.first();

    const result2 = await client.submit('g.V().hasLabel(label).tail(n)', { label: 'person', n: 3 });

    // ResultSet is an iterable
    for (const vertex of result2) {
      console.log(vertex.id);
    }

### Domain Specific Languages

Developing Gremlin DSLs in JavaScript largely requires extension of existing core classes with use of standalone functions for anonymous traversal spawning. The pattern is demonstrated in the following example:

    class SocialTraversal extends GraphTraversal {
      constructor(graph, traversalStrategies, bytecode) {
        super(graph, traversalStrategies, bytecode);
      }

      aged(age) {
        return this.has('person', 'age', age);
      }
    }

    class SocialTraversalSource extends GraphTraversalSource {
      constructor(graph, traversalStrategies, bytecode) {
        super(graph, traversalStrategies, bytecode, SocialTraversalSource, SocialTraversal);
      }

      person(name) {
        return this.V().has('person', 'name', name);
      }
    }

    function anonymous() {
      return new SocialTraversal(null, null, new Bytecode());
    }

    function aged(age) {
      return anonymous().aged(age);
    }

`SocialTraversal` extends the core `GraphTraversal` class and has a three argument constructor which is immediately proxied to the `GraphTraversal` constructor. New DSL steps are then added to this class using available steps to construct the underlying traversal to execute as demonstrated in the `aged()` step.

The `SocialTraversal` is spawned from a `SocialTraversalSource` which is extended from `GraphTraversalSource`. Steps added here are meant to be start steps. In the above case, the `person()` start step find a "person" vertex to begin the traversal from.

Typically, steps that are made available on a `GraphTraversal` (i.e. SocialTraversal in this example) should also be made available as spawns for anonymous traversals. The recommendation is that these steps be exposed in the module as standalone functions. In the example above, the standalone `aged()` step creates an anonymous traversal through an `anonymous()` utility function. The method for creating these standalone functions can be handled in other ways if desired.

To use the DSL, simply initialize the `g` as follows:

    const g = traversal(SocialTraversalSource).withRemote(connection);
    g.person('marko').aged(29).values('name').toList().
      then(names => console.log(names));

# 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>3.4.2</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 TinkerPop and serves as the reference implementation for other providers to study in order to understand the semantics of the various methods of the TinkerPop 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.

console (groovy) groovy

    gremlin> graph = TinkerGraph.open()
    ==>tinkergraph[vertices:0 edges:0]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io('data/grateful-dead.xml').read().iterate()
    gremlin> clock(1000) {g.V().has('name','Garcia').iterate()} //1\
    ==>0.32740126499999994
    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> g.io('data/grateful-dead.xml').read().iterate()
    gremlin> clock(1000){g.V().has('name','Garcia').iterate()} //2\
    ==>0.07934517599999999

    graph = TinkerGraph.open()
    g = graph.traversal()
    g.io('data/grateful-dead.xml').read().iterate()
    clock(1000) {g.V().has('name','Garcia').iterate()} //1\
    graph = TinkerGraph.open()
    g = graph.traversal()
    graph.createIndex('name',Vertex.class)
    g.io('data/grateful-dead.xml').read().iterate()
    clock(1000){g.V().has('name','Garcia').iterate()} //2

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. TinkerPop 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:

console (groovy) groovy

    gremlin> graph = TinkerGraph.open()
    ==>tinkergraph[vertices:0 edges:0]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io("data/tinkerpop-crew.kryo").read().iterate()
    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@4aed311e
    gremlin> conf.setProperty("gremlin.tinkergraph.defaultVertexPropertyCardinality","list")
    gremlin> graph = TinkerGraph.open(conf)
    ==>tinkergraph[vertices:0 edges:0]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
    gremlin> g.io("data/tinkerpop-crew.kryo").read().iterate()
    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]

    graph = TinkerGraph.open()
    g = graph.traversal()
    g.io("data/tinkerpop-crew.kryo").read().iterate()
    g.V().properties()
    conf = new BaseConfiguration()
    conf.setProperty("gremlin.tinkergraph.defaultVertexPropertyCardinality","list")
    graph = TinkerGraph.open(conf)
    g = graph.traversal()
    g.io("data/tinkerpop-crew.kryo").read().iterate()
    g.V().properties()

## Neo4j-Gremlin

    <dependency>
       <groupId>org.apache.tinkerpop</groupId>
       <artifactId>neo4j-gremlin</artifactId>
       <version>3.4.2</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>

Neo4j, Inc. are the developers of the OLTP-based Neo4j graph database.

Warning | Unless under a commercial agreement with Neo4j, Inc., 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 3.4.2
    ==>Loaded: [org.apache.tinkerpop, neo4j-gremlin, 3.4.2] - 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 3.4.2`. 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. TinkerPop 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.

console (groovy) groovy

    gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
    ==>neo4jgraph[community single [/tmp/neo4j]]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/tmp/neo4j]], standard]
    gremlin> graph.cypher("CREATE INDEX ON :person(name)")
    gremlin> graph.tx().commit() //1\
    gremlin> g.addV('person').property('name','marko')
    ==>v[0]
    gremlin> g.addV('dog').property('name','puppy')
    ==>v[1]
    gremlin> g.V().hasLabel('person').has('name','marko').values('name')
    ==>marko
    gremlin> graph.close()

    graph = Neo4jGraph.open('/tmp/neo4j')
    g = graph.traversal()
    graph.cypher("CREATE INDEX ON :person(name)")
    graph.tx().commit() //1\
    g.addV('person').property('name','marko')
    g.addV('dog').property('name','puppy')
    g.V().hasLabel('person').has('name','marko').values('name')
    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.

console (groovy) groovy

    gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
    ==>neo4jgraph[community single [/tmp/neo4j]]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/tmp/neo4j]], standard]
    gremlin> g.io('data/grateful-dead.xml').read().iterate()
    gremlin> g.tx().commit()
    gremlin> clock(1000) {g.V().hasLabel('artist').has('name','Garcia').iterate()} //1\
    ==>2.215955697
    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\
    ==>0.322810508
    gremlin> clock(1000) {g.V().has('name','Garcia').iterate()} //5\
    ==>2.135554544
    gremlin> graph.cypher("DROP INDEX ON :artist(name)") //6\
    gremlin> g.tx().commit()
    gremlin> graph.close()

    graph = Neo4jGraph.open('/tmp/neo4j')
    g = graph.traversal()
    g.io('data/grateful-dead.xml').read().iterate()
    g.tx().commit()
    clock(1000) {g.V().hasLabel('artist').has('name','Garcia').iterate()} //1\
    graph.cypher("CREATE INDEX ON :artist(name)") //2\
    g.tx().commit()
    Thread.sleep(5000) //3\
    clock(1000) {g.V().hasLabel('artist').has('name','Garcia').iterate()} //4\
    clock(1000) {g.V().has('name','Garcia').iterate()} //5\
    graph.cypher("DROP INDEX ON :artist(name)") //6\
    g.tx().commit()
    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.

console (groovy) groovy

    gremlin> conf = new BaseConfiguration()
    ==>org.apache.commons.configuration.BaseConfiguration@4e8dd68d
    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()

    conf = new BaseConfiguration()
    conf.setProperty('gremlin.neo4j.directory','/tmp/neo4j')
    conf.setProperty('gremlin.neo4j.multiProperties',true)
    conf.setProperty('gremlin.neo4j.metaProperties',true)
    graph = Neo4jGraph.open(conf)
    g = graph.traversal()
    g.addV().property('name','michael').property('name','michael hunger').property('name','mhunger')
    g.V().properties('name').property('acl', 'public')
    g.V(0).valueMap()
    g.V(0).properties()
    g.V(0).properties().valueMap()
    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.

console (groovy) groovy

    gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
    ==>neo4jgraph[community single [/tmp/neo4j]]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/tmp/neo4j]], standard]
    gremlin> g.io('data/tinkerpop-modern.kryo').read().iterate()
    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()

    graph = Neo4jGraph.open('/tmp/neo4j')
    g = graph.traversal()
    g.io('data/tinkerpop-modern.kryo').read().iterate()
    graph.cypher('MATCH (a {name:"marko"}) RETURN a')
    graph.cypher('MATCH (a {name:"marko"}) RETURN a').select('a').out('knows').values('name')
    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

TinkerPop 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.

console (groovy) groovy

    gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
    ==>neo4jgraph[community single [/tmp/neo4j]]
    gremlin> g = graph.traversal()
    ==>graphtraversalsource[neo4jgraph[community single [/tmp/neo4j]], standard]
    gremlin> vertex = (Neo4jVertex) g.addV('human::animal').next() //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.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()

    graph = Neo4jGraph.open('/tmp/neo4j')
    g = graph.traversal()
    vertex = (Neo4jVertex) g.addV('human::animal').next() //1\
    vertex.label() //2\
    vertex.labels() //3\
    vertex.addLabel('organism') //4\
    vertex.label()
    vertex.removeLabel('human') //5\
    vertex.labels()
    vertex.addLabel('organism') //6\
    vertex.labels()
    vertex.removeLabel('human') //7\
    vertex.label()
    g.V().has(label,'organism') //8\
    g.V().has(label,of('organism')) //9\
    g.V().has(label,of('organism')).has(label,of('animal'))
    g.V().has(label,of('organism').and(of('animal')))
    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 CloneVertexProgram 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:

console (groovy) groovy

    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()
    ==>[age:[29],name:[marko]]
    ==>[age:[27],name:[vadas]]
    ==>[lang:[java],name:[lop]]
    ==>[age:[32],name:[josh]]
    ==>[lang:[java],name:[ripple]]
    ==>[age:[35],name:[peter]]
    gremlin> graph.close()

    wgConf = 'conf/neo4j-standalone.properties'
    modern = TinkerFactory.createModern()
    blvp = BulkLoaderVertexProgram.build().
               keepOriginalIds(false).
               writeGraph(wgConf).create(modern)
    modern.compute().workers(1).program(blvp).submit().get()
    graph = GraphFactory.open(wgConf)
    g = graph.traversal()
    g.V().valueMap()
    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>3.4.2</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 TinkerPop graph, then Hadoop-Gremlin can be used to process the graph using both TinkerPop’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 `SparkGraphComputer` it is advisable that the reader also familiarize their self with 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 3.4.2
    ==>loaded: [org.apache.tinkerpop, hadoop-gremlin, 3.4.2] - 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.

### 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

A review of the Hadoop-Gremlin specific properties are provided in the table below. For the respective OLAP engines (`SparkGraphComputer` 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, 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.

console (groovy) groovy

    gremlin> hdfs.copyFromLocal('data/tinkerpop-modern.kryo', 'tinkerpop-modern.kryo')
    gremlin> hdfs.ls()
    ==>rwxr-xr-x smallette supergroup 0 (D) .sparkStaging
    ==>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-3.3.7-libs
    ==>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]

    hdfs.copyFromLocal('data/tinkerpop-modern.kryo', 'tinkerpop-modern.kryo')
    hdfs.ls()
    graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    g = graph.traversal()
    g.V().count()
    g.V().out().out().values('name')
    g.V().group().by{it.value('name')[1]}.by('name').next()

### 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 TinkerPop can support any number of `GraphComputer` implementations. Out of the box, Hadoop-Gremlin supports the following two implementations.

- `SparkGraphComputer`: Leverages Apache Spark to execute TinkerPop 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).

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.

    $ 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 spark-gremlin 3.4.2
    ==>loaded: [org.apache.tinkerpop, spark-gremlin, 3.4.2] - 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.spark
    ==>tinkerpop.spark activated

Warning | Hadoop and Spark 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 may be necessary to manually cleanup dependency conflicts among different plugins.

#### SparkGraphComputer

    <dependency>
       <groupId>org.apache.tinkerpop</groupId>
       <artifactId>spark-gremlin</artifactId>
       <version>3.4.2</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.

console (groovy) groovy

    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

    graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    g = graph.traversal().withComputer(SparkGraphComputer)
    g.V().count()
    g.V().out().out().values('name')

For using lambdas in Gremlin-Groovy, simply provide `:remote connect` a `TraversalSource` which leverages SparkGraphComputer.

console (groovy) groovy

    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/tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.4.2-standalone/ext/tinkergraph-gremlin/lib
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.4.2-standalone/ext/tinkergraph-gremlin/lib
    INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/smallette/git/apache/tinkerpop/gremlin-console/target/apache-tinkerpop-gremlin-console-3.4.2-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]]

    graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    g = graph.traversal().withComputer(SparkGraphComputer)
    :remote connect tinkerpop.hadoop graph g
    :> g.V().group().by{it.value('name')[1]}.by('name')

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).

##### Using CloneVertexProgram

The CloneVertexProgram copies a whole graph from any graph `InputFormat` to any graph `OutputFormat`. TinkerPop provides formats such as `GraphSONOutputFormat`, `GryoOutputFormat` or `ScriptOutputFormat`. The example below takes a Hadoop graph as the input (in `GryoInputFormat`) and exports it as a GraphSON file (`GraphSONOutputFormat`).

console (groovy) groovy

    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}}]}}

    hdfs.copyFromLocal('data/tinkerpop-modern.kryo', 'tinkerpop-modern.kryo')
    graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    graph.configuration().setProperty('gremlin.hadoop.graphWriter', 'org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONOutputFormat')
    graph.compute(SparkGraphComputer).program(BulkDumperVertexProgram.build().create()).submit().get()
    hdfs.ls('output')
    hdfs.head('output/~g')

### 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`.

console (groovy) groovy

    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) .sparkStaging
    ==>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-3.3.7-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) .sparkStaging
    ==>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-3.3.7-libs
    ==>rw-r--r-- smallette supergroup 781 tinkerpop-modern.kryo

    graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    graph.compute(SparkGraphComputer).program(PeerPressureVertexProgram.build().create(graph)).mapReduce(ClusterCountMapReduce.build().memoryKey('clusterCount').create()).submit().get();
    hdfs.ls()
    hdfs.ls('output')
    hdfs.head('output', GryoInputFormat)
    hdfs.head('output', 'clusterCount', SequenceFileInputFormat)
    hdfs.rm('output')
    hdfs.ls()

#### 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` respectively. Persisted RDDs can be accessed using `spark`.

console (groovy) groovy

    gremlin> Spark.create('local[4]')
    ==>org.apache.spark.SparkContext@41aebbb4
    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()

    Spark.create('local[4]')
    graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
    graph.configuration().setProperty('gremlin.hadoop.graphWriter', PersistedOutputRDD.class.getCanonicalName())
    graph.configuration().setProperty('gremlin.spark.persistContext',true)
    graph.compute(SparkGraphComputer).program(PeerPressureVertexProgram.build().create(graph)).mapReduce(ClusterCountMapReduce.build().memoryKey('clusterCount').create()).submit().get();
    spark.ls()
    spark.ls('output')
    spark.head('output', PersistedInputRDD)
    spark.head('output', 'clusterCount', PersistedInputRDD)
    spark.rm('output')
    spark.ls()

# Gremlin Compilers

There are many languages built to query data. SQL is typically used to query relational data. There is SPARQL for RDF data. Cypher is used to do pattern matching in graph data. The list could go on. Compilers convert languages like these to Gremlin so that it becomes possible to use them in any context that Gremlin is used. In other words, a Gremlin Compiler enables a particular query language to work on any TinkerPop-enabled graph system.

## SPARQL-Gremlin

The SPARQL-Gremlin compiler, transforms SPARQL queries into Gremlin traversals. It uses the Apache Jena SPARQL processor ARQ, which provides access to a syntax tree of a SPARQL query.

The goal of this work is to bridge the query interoperability gap between the two famous, yet fairly disconnected, graph communities: Semantic Web (which relies on the RDF data model) and Graph database (which relies on property graph data model).

Note | The foundational research work on SPARQL-Gremlin compiler (aka Gremlinator) can be found in the Gremlinator paper. This paper presents the graph query language semantics of SPARQL and Gremlin, and a formal mapping between SPARQL pattern matching graph patterns and Gremlin traversals.

    <dependency>
       <groupId>org.apache.tinkerpop</groupId>
       <artifactId>sparql-gremlin</artifactId>
       <version>3.4.2</version>
    </dependency>

The SPARQL-Gremlin compiler converts SPARQL queries into Gremlin so that they can be executed across any TinkerPop-enabled graph system. To use this compiler in the Gremlin Console, first install and activate the "tinkerpop.sparql" plugin:

    gremlin> :install org.apache.tinkerpop sparql-gremlin 3.4.2
    ==>Loaded: [org.apache.tinkerpop, sparql-gremlin, 3.4.2]
    gremlin> :plugin use tinkerpop.sparql
    ==>tinkerpop.sparql activated

Installing this plugin will download appropriate dependencies and import certain classes to the console so that they may be used as follows:

console (groovy) groovy

    gremlin> graph = TinkerFactory.createModern()
    ==>tinkergraph[vertices:6 edges:6]
    gremlin> g = graph.traversal(SparqlTraversalSource) //1\
    ==>sparqltraversalsource[tinkergraph[vertices:6 edges:6], standard]
    gremlin> g.sparql("""SELECT ?name ?age
                         WHERE { ?person v:name ?name . ?person v:age ?age }
                         ORDER BY ASC(?age)""") //2\
    ==>[name:vadas,age:27]
    ==>[name:marko,age:29]
    ==>[name:josh,age:32]
    ==>[name:peter,age:35]

    graph = TinkerFactory.createModern()
    g = graph.traversal(SparqlTraversalSource) //1\
    g.sparql("""SELECT ?name ?age
                WHERE { ?person v:name ?name . ?person v:age ?age }
                ORDER BY ASC(?age)""")                                                                     //2

1. Define `g` as a `TraversalSource` that uses the `SparqlTraversalSource` - by default, the `traversal()` method usually returns a `GraphTraversalSource` which includes the standard Gremlin starts steps like `V()` or `E()`. In this case, the `SparqlTraversalSource` enables starts steps that are specific to SPARQL only - in this case the `sparql()` start step.

2. Execute a SPARQL query against the TinkerGraph instance. The `SparqlTraversalSource` uses a TraversalStrategy to transparently converts that SPARQL query into a standard Gremlin traversal and then when finally iterated, executes that against the TinkerGraph.

### Prefixes

The SPARQL-Gremlin compiler supports the following prefixes to traverse the graph:

Prefix | Purpose
--- | ---
`v:<id|label|<name>>` | access to vertex id, label or property value
`e:<label>` | out-edge traversal
`p:<name>` | property traversal

Note that element IDs and labels are treated like normal properties, hence they can be accessed using the same pattern:

console (groovy) groovy

    gremlin> g.sparql("""SELECT ?name ?id ?label
                 WHERE {
                 ?element v:name ?name .
                 ?element v:id ?id .
                 ?element v:label ?label .}""")
    ==>[name:marko,id:1,label:person]
    ==>[name:vadas,id:2,label:person]
    ==>[name:lop,id:3,label:software]
    ==>[name:josh,id:4,label:person]
    ==>[name:ripple,id:5,label:software]
    ==>[name:peter,id:6,label:person]

    g.sparql("""SELECT ?name ?id ?label
        WHERE {
        ?element v:name ?name .
        ?element v:id ?id .
        ?element v:label ?label .}""")

### Supported Queries

The SPARQL-Gremlin compiler currently supports translation of the SPARQL 1.0 specification, especially `SELECT` queries, though there is an on-going effort to cover the entire SPARQL 1.1 query feature spectrum. The supported SPARQL query types are:

- Union

- Optional

- Order-By

- Group-By

- STAR-shaped or neighbourhood queries

- Query modifiers, such as:

  - Filter with restrictions

  - Count

  - LIMIT

  - OFFSET

### Limitations

The current implementation of SPARQL-Gremlin compiler (i.e. SPARQL-Gremlin) does not support the following cases:

- SPARQL queries with variables in the predicate position are not currently covered, with an exception of the following case:

    g.sparql("""SELECT * WHERE { ?x ?y ?z . }""")

- A SPARQL Union query with un-balanced patterns, i.e. a gremlin union traversal can only be generated if the input SPARQL query has the same number of patterns on both the side of the union operator. For instance, the following SPARQL query cannot be mapped, since a union is executed between different number of graph patterns (two patterns `union` 1 pattern).

    g.sparql("""SELECT *
                WHERE {
                    {?person e:created ?software .
                    ?person v:name "josh" .}
                    UNION
                    {?software v:lang "java" .} }""")

- A non-Group key variable cannot be projected in a SPARQL query. This is a SPARQL language limitation rather than that of Gremlin/TinkerPop. Apache Jena throws the exception "Non-group key variable in SELECT" if this occurs. For instance, in a SPARQL query with GROUP-BY clause, only the variable on which the grouping is declared, can be projected. The following query is valid:

    g.sparql("""SELECT ?age
                WHERE {
                    ?person v:label "person" .
                    ?person v:age ?age .
                    ?person v:name ?name .} GROUP BY (?age)""")

Whereas, the following SPARQL query will be invalid:

    g.sparql("""SELECT ?person
                WHERE {
                  ?person v:label "person" .
                  ?person v:age ?age .
                  ?person v:name ?name .} GROUP BY (?age)""")

- In a SPARQL query with an ORDER-BY clause, the ordering occurs with respect to the first projected variable in the query. It is possible to choose any number of variable to be projected, however, the first variable in the selection will be the ordering decider. For instance, in the query:

    g.sparql("""SELECT ?name ?age
                WHERE {
                    ?person v:label "person" .
                    ?person v:age ?age .
                    ?person v:name ?name . } ORDER BY (?age)""")

the result set will be ordered according to the `?name` variable (in ascending order by default) despite having passed `?age` in the order by. Whereas, for the following query:

    g.sparql("""SELECT ?age ?name
                WHERE {
                    ?person v:label "person" .
                    ?person v:age ?age .
                    ?person v:name ?name . } ORDER BY (?age)""")

the result set will be ordered according to the `?age` (as it is the first projected variable). Finally, for the select all case (`SELECT *`):

    g.sparql("""SELECT *
                WHERE { ?person v:label "person" . ?person v:age ?age . ?person v:name ?name . } ORDER BY (?age)""")

the the variable encountered first will be the ordering decider, i.e. since we have `?person` encountered first, the result set will be ordered according to the `?person` variable (which are vertex id).

- In the current implementation, `OPTIONAL` clause doesn’t work under nesting with `UNION` clause (i.e. multiple optional clauses with in a union clause) and `ORDER-By` clause (i.e. declaring ordering over triple patterns within optional clauses). Everything else with SPARQL `OPTIONAL` works just fine.

### Examples

The following section presents examples of SPARQL queries that are currently covered by the SPARQL-Gremlin compiler.

#### Select All

Select all vertices in the graph.

console (groovy) groovy

    gremlin> g.sparql("""SELECT * WHERE { }""")
    ==>v[1]
    ==>v[2]
    ==>v[3]
    ==>v[4]
    ==>v[5]
    ==>v[6]

    g.sparql("""SELECT * WHERE { }""")

#### Match Constant Values

Select all vertices with the label `person`.

console (groovy) groovy

    gremlin> g.sparql("""SELECT * WHERE {  ?person v:label "person" .}""")
    ==>v[1]
    ==>v[2]
    ==>v[4]
    ==>v[6]

    g.sparql("""SELECT * WHERE {  ?person v:label "person" .}""")

#### Select Specific Elements

Select the values of the properties `name` and `age` for each `person` vertex.

console (groovy) groovy

    gremlin> g.sparql("""SELECT ?name ?age
             WHERE {
               ?person v:label "person" .
               ?person v:name ?name .
               ?person v:age ?age . }""")
    ==>[name:marko,age:29]
    ==>[name:vadas,age:27]
    ==>[name:josh,age:32]
    ==>[name:peter,age:35]

    g.sparql("""SELECT ?name ?age
    WHERE {
      ?person v:label "person" .
      ?person v:name ?name .
      ?person v:age ?age . }""")

#### Pattern Matching

Select only those persons who created a project.

console (groovy) groovy

    gremlin> g.sparql("""SELECT ?name ?age
             WHERE {
               ?person v:label "person" .
               ?person v:name ?name .
               ?person v:age ?age .
               ?person e:created ?project . }""")
    ==>[name:marko,age:29]
    ==>[name:josh,age:32]
    ==>[name:josh,age:32]
    ==>[name:peter,age:35]

    g.sparql("""SELECT ?name ?age
    WHERE {
      ?person v:label "person" .
      ?person v:name ?name .
      ?person v:age ?age .
      ?person e:created ?project . }""")

#### Filtering

Select only those persons who are older than 30.

console (groovy) groovy

    gremlin> g.sparql("""SELECT ?name ?age
             WHERE {
               ?person v:label "person" .
               ?person v:name ?name .
               ?person v:age ?age .
                 FILTER (?age > 30) }""")
    ==>[name:josh,age:32]
    ==>[name:peter,age:35]

    g.sparql("""SELECT ?name ?age
    WHERE {
      ?person v:label "person" .
      ?person v:name ?name .
      ?person v:age ?age .
        FILTER (?age > 30) }""")

#### Deduplication

Select the distinct names of the created projects.

console (groovy) groovy

    gremlin> g.sparql("""SELECT DISTINCT ?name
             WHERE {
               ?person v:label "person" .
               ?person v:age ?age .
               ?person e:created ?project .
               ?project v:name ?name .
                 FILTER (?age > 30)}""")
    ==>ripple
    ==>lop

    g.sparql("""SELECT DISTINCT ?name
    WHERE {
      ?person v:label "person" .
      ?person v:age ?age .
      ?person e:created ?project .
      ?project v:name ?name .
        FILTER (?age > 30)}""")

#### Multiple Filters

Select the distinct names of all Java projects.

console (groovy) groovy

    gremlin> g.sparql("""SELECT DISTINCT ?name
             WHERE {
               ?person v:label "person" .
               ?person v:age ?age .
               ?person e:created ?project .
               ?project v:name ?name .
               ?project v:lang ?lang .
                 FILTER (?age > 30 && ?lang = "java") }""")
    ==>ripple
    ==>lop

    g.sparql("""SELECT DISTINCT ?name
    WHERE {
      ?person v:label "person" .
      ?person v:age ?age .
      ?person e:created ?project .
      ?project v:name ?name .
      ?project v:lang ?lang .
        FILTER (?age > 30 && ?lang = "java") }""")

#### Union

Select all persons who have developed a software in java using union.

console (groovy) groovy

    gremlin> g.sparql("""SELECT *
             WHERE {
               {?person e:created ?software .}
               UNION
               {?software v:lang "java" .} }""")
    ==>[software:v[3],person:v[1]]
    ==>[software:v[3]]
    ==>[software:v[5],person:v[4]]
    ==>[software:v[3],person:v[4]]
    ==>[software:v[5]]
    ==>[software:v[3],person:v[6]]

    g.sparql("""SELECT *
    WHERE {
      {?person e:created ?software .}
      UNION
      {?software v:lang "java" .} }""")

#### Optional

Return the names of the persons who have created a software in java and optionally python.

    g.sparql("""SELECT ?person
    WHERE {
      ?person v:label "person" .
      ?person e:created ?software .
      ?software v:lang "java" .
      OPTIONAL {?software v:lang "python" . }}""")

#### Order By

Select all vertices with the label `person` and order them by their age.

console (groovy) groovy

    gremlin> g.sparql("""SELECT ?age ?name
             WHERE {
               ?person v:label "person" .
               ?person v:age ?age .
               ?person v:name ?name .
             } ORDER BY (?age)""")
    ==>[age:27,name:vadas]
    ==>[age:29,name:marko]
    ==>[age:32,name:josh]
    ==>[age:35,name:peter]

    g.sparql("""SELECT ?age ?name
    WHERE {
      ?person v:label "person" .
      ?person v:age ?age .
      ?person v:name ?name .
    } ORDER BY (?age)""")

#### Group By

Select all vertices with the label `person` and group them by their age.

console (groovy) groovy

    gremlin> g.sparql("""SELECT ?age
             WHERE {
               ?person v:label "person" .
               ?person v:age ?age .
             } GROUP BY (?age)""")
    ==>[32:[32],35:[35],27:[27],29:[29]]

    g.sparql("""SELECT ?age
    WHERE {
      ?person v:label "person" .
      ?person v:age ?age .
    } GROUP BY (?age)""")

#### Mixed/complex/aggregation-based queries

Count the number of projects which have been created by persons under the age of 30 and group them by age. Return only the top two.

    g.sparql("""SELECT (COUNT(?project) as ?p)
    WHERE {
      ?person v:label "person" .
      ?person v:age ?age . FILTER (?age < 30)
      ?person e:created ?project .
    } GROUP BY (?age) LIMIT 2""")

#### Meta-Property Access

Accessing the Meta-Property of a graph element. Meta-Property can be perceived as the reified statements in an RDF graph.

console (groovy) groovy

    gremlin> g = graph.traversal(SparqlTraversalSource)
    ==>sparqltraversalsource[tinkergraph[vertices:6 edges:14], standard]
    gremlin> g.sparql("""SELECT ?name ?startTime
             WHERE {
               ?person v:name "daniel" .
               ?person p:location ?location .
               ?location v:value ?name .
               ?location v:startTime ?startTime }""")
    ==>[name:spremberg,startTime:1982]
    ==>[name:kaiserslautern,startTime:2005]
    ==>[name:aachen,startTime:2009]

    g = graph.traversal(SparqlTraversalSource)
    g.sparql("""SELECT ?name ?startTime
    WHERE {
      ?person v:name "daniel" .
      ?person p:location ?location .
      ?location v:value ?name .
      ?location v:startTime ?startTime }""")

#### STAR-shaped queries

STAR-shaped queries are the queries that form/follow a star-shaped execution plan. These in terms of graph traversals can be perceived as path queries or neighborhood queries. For instance, getting all the information about a specific `person` or `software`.

console (groovy) groovy

    gremlin> g.sparql("""SELECT ?age ?software ?lang ?name
             WHERE {
               ?person v:name "josh" .
               ?person v:age ?age .
               ?person e:created ?software .
               ?software v:lang ?lang .
               ?software v:name ?name . }""")

    g.sparql("""SELECT ?age ?software ?lang ?name
    WHERE {
      ?person v:name "josh" .
      ?person v:age ?age .
      ?person e:created ?software .
      ?software v:lang ?lang .
      ?software v:name ?name . }""")

### With Gremlin

The `sparql()`-step takes a SPARQL query and returns a result. That result can be further processed by standard Gremlin steps as shown below:

console (groovy) groovy

    gremlin> g = graph.traversal(SparqlTraversalSource)
    ==>sparqltraversalsource[tinkergraph[vertices:6 edges:6], standard]
    gremlin> g.sparql("SELECT ?name ?age WHERE { ?person v:name ?name . ?person v:age ?age }")
    ==>[name:marko,age:29]
    ==>[name:vadas,age:27]
    ==>[name:josh,age:32]
    ==>[name:peter,age:35]
    gremlin> g.sparql("SELECT ?name ?age WHERE { ?person v:name ?name . ?person v:age ?age }").select("name")
    ==>marko
    ==>vadas
    ==>josh
    ==>peter
    gremlin> g.sparql("SELECT * WHERE { }").out("knows").values("name")
    ==>vadas
    ==>josh
    gremlin> g.withSack(1.0f).sparql("SELECT * WHERE { }").
               repeat(outE().sack(mult).by("weight").inV()).
                 times(2).
               sack()
    ==>1.0
    ==>0.4

    g = graph.traversal(SparqlTraversalSource)
    g.sparql("SELECT ?name ?age WHERE { ?person v:name ?name . ?person v:age ?age }")
    g.sparql("SELECT ?name ?age WHERE { ?person v:name ?name . ?person v:age ?age }").select("name")
    g.sparql("SELECT * WHERE { }").out("knows").values("name")
    g.withSack(1.0f).sparql("SELECT * WHERE { }").
      repeat(outE().sack(mult).by("weight").inV()).
        times(2).
      sack()

Mixing SPARQL with Gremlin steps introduces some interesting possibilities for complex traversals.

# 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.