3.7.3

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 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:

They have a sense of what a graph is - not sure? see Practical Gremlin - Why Graph?
They know what it means for a graph system to be TinkerPop-enabled - not sure? see TinkerPop-enabled Providers
They know what the role of Gremlin is - not sure? see 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, GO, 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 enabled 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

gremlin standing A graph’s structure is the topology formed by the explicit references between its vertices, edges, and properties. A vertex has incident edges. A vertex is adjacent to another vertex if they share an incident edge. A property is attached to an element and an element has a set of properties. A property is a key/value pair, where the key is always a character String. 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

gremlin running 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:

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

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

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

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

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

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

Tip

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

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

$ bin/gremlin.sh

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

Open the toy graph and reference it by the variable graph.
Create a graph traversal source from the graph using the standard, OLTP traversal engine. This object should be created once and then re-used.
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

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

The Traverser

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

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

blueprints character 1 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" is then used to spawn a GraphTraversalSource as follows and typically, by convention, this variable is named "g":

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

rexster character 3 A JVM-based graph may 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 go

// gremlin-driver module
import org.apache.tinkerpop.gremlin.driver.remote.DriverRemoteConnection;

// gremlin-core module
import static org.apache.tinkerpop.gremlin.process.traversal.AnonymousTraversalSource.traversal;

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

// gremlin-driver module
import org.apache.tinkerpop.gremlin.driver.remote.DriverRemoteConnection;

// gremlin-core module
import static org.apache.tinkerpop.gremlin.process.traversal.AnonymousTraversalSource.traversal;

def g = traversal().withRemote(
                DriverRemoteConnection.using('localhost', 8182))

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

var g = Traversal().WithRemote(new DriverRemoteConnection("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'))

import (
    gremlingo "github.com/apache/tinkerpop/gremlin-go/v3/driver"
)

remote, err := gremlingo.NewDriverRemoteConnection("ws://localhost:8182/gremlin")
g := gremlingo.Traversal_().WithRemote(remote)

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.

Tip	The variable `g`, the `TraversalSource`, only needs to be instantiated once and should then be re-used.

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 its 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 GraphBinary and GraphSON. Users should prefer GraphBinary when available in the programming language being used.

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 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

language variants 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 go

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(v1).addE('knows').to(v2).property('weight',0.75).iterate()

v1, err := g.AddV("person").Property("name", "marko").Next()
v2, err := g.AddV("person").Property("name", "stephen").Next()
g.V(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 go

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()

marko, err := g.V().Has("person", "name", "marko").Next()
peopleMarkoKnows, err := 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 = traversal().withEmbedded(graph)
==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
gremlin> g.addV('person')
==>v[0]

graph = TinkerGraph.open()
g = traversal().withEmbedded(graph)
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:

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

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

console (groovy) groovy

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> graph.features()
==>FEATURES
> GraphFeatures
>-- Transactions: false
>-- Computer: true
>-- Persistence: true
>-- ConcurrentAccess: false
>-- ThreadedTransactions: false
>-- IoRead: true
>-- IoWrite: true
>-- OrderabilitySemantics: true
>-- ServiceCall: 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
>-- StringArrayValues: true
>-- LongArrayValues: true
> VertexFeatures
>-- MetaProperties: true
>-- Upsert: false
>-- AddVertices: true
>-- RemoveVertices: true
>-- MultiProperties: true
>-- DuplicateMultiProperties: true
>-- NullPropertyValues: false
>-- UserSuppliedIds: true
>-- AddProperty: true
>-- RemoveProperty: true
>-- NumericIds: true
>-- StringIds: true
>-- UuidIds: true
>-- CustomIds: false
>-- AnyIds: true
> VertexPropertyFeatures
>-- NullPropertyValues: false
>-- UserSuppliedIds: true
>-- RemoveProperty: true
>-- NumericIds: true
>-- StringIds: true
>-- UuidIds: true
>-- CustomIds: false
>-- AnyIds: 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
>-- StringArrayValues: true
>-- LongArrayValues: true
> EdgeFeatures
>-- AddEdges: true
>-- RemoveEdges: true
>-- Upsert: false
>-- NullPropertyValues: false
>-- UserSuppliedIds: true
>-- AddProperty: true
>-- RemoveProperty: true
>-- NumericIds: true
>-- StringIds: true
>-- UuidIds: true
>-- CustomIds: false
>-- AnyIds: 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
>-- StringArrayValues: true
>-- LongArrayValues: 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

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:

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

Possible use cases for meta-properties:

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

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

console (groovy) groovy

gremlin> graph = TinkerGraph.open()
==>tinkergraph[vertices:0 edges:0]
gremlin> g = traversal().withEmbedded(graph)
==>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 = traversal().withEmbedded(graph)
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

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

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

Tip	A toy graph demonstrating all of the new 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'])
==>null
gremlin> graph.variables().set('systemUsers',['matthias','marko','josh'])
==>null
gremlin> graph.variables().keys()
==>systemAdmins
==>systemUsers
gremlin> graph.variables().get('systemUsers')
==>Optional[[matthias, marko, josh]]
gremlin> graph.variables().get('systemUsers').get()
==>matthias
==>marko
==>josh
gremlin> graph.variables().remove('systemAdmins')
==>null
gremlin> graph.variables().keys()
==>systemUsers

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.

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:

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

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

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.

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.

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.

Traversal Transactions

gremlin coins A database transaction represents a unit of work to execute against the database. A traversals unit of work is affected by usage convention (i.e. the method of connecting) and the graph provider’s transaction model. Without diving deeply into different conventions and models the most general and recommended approach to working with transactions is demonstrated as follows:

GraphTraversalSource g = traversal().withEmbedded(graph);
// or
GraphTraversalSource g = traversal().withRemote(conn);

Transaction tx = g.tx();

// spawn a GraphTraversalSource from the Transaction. Traversals spawned
// from gtx will be essentially be bound to tx
GraphTraversalSource gtx = tx.begin();
try {
    gtx.addV('person').iterate();
    gtx.addV('software').iterate();

    tx.commit();
} catch (Exception ex) {
    tx.rollback();
}

The above example is straightforward and represents a good starting point for discussing the nuances of transactions in relation to the usage convention and graph provider caveats alluded to earlier.

Focusing on remote contexts first, note that it is still possible to issue traversals from g, but those will have a transaction scope outside of gtx and will simply commit() on the server if successfully executed or rollback() on the server otherwise (i.e. one traversal is one transaction). Each isolated transaction will require its own Transaction object. Multiple begin() calls on the same Transaction object will produce GraphTraversalSource instances that are bound to the same transaction, therefore:

GraphTraversalSource g = traversal().withRemote(conn);
Transaction tx1 = g.tx();
Transaction tx2 = g.tx();

// both gtx1a and gtx1b will be bound to the same transaction
GraphTraversalSource gtx1a = tx1.begin();
GraphTraversalSource gtx1b = tx1.begin();

// g and gtx2 will not have knowledge of what happens in tx1
GraphTraversalSource gtx2 = tx2.begin();

In remote cases, GraphTraversalSource instances spawned from begin() are safe to use in multiple threads though on the server side they will be processed serially as they arrive. The default behavior of close() on a Transaction for remote cases is to commit(), so the following re-write of the earlier example is also valid:

// note here that we dispense with creating a Transaction object and
// simply spawn the gtx in a more inline fashion
GraphTraversalSource gtx = g.tx().begin();
try {
    gtx.addV('person').iterate();
    gtx.addV('software').iterate();
    gtx.close();
} catch (Exception ex) {
    tx.rollback();
}

Important

Transactions with non-JVM languages are always "remote". For specific transaction syntax in a particular language, please see the "Transactions" sub-section of your language of interest in the Gremlin Drivers and Variants section.

In embedded cases, that initial recommended model for defining transactions holds, but users have more options here on deeper inspection. For embedded use cases (and perhaps even in configuration of a graph instance in Gremlin Server), the type of Transaction object that is returned from g.tx() is an important indicator as to the features of that graph’s transaction model. In most cases, inspection of that object will indicate an instance that derives from the AbstractThreadLocalTransaction class, which means that the transaction is bound to the current thread and therefore all traversals that execute within that thread are tied to that transaction.

A ThreadLocal transaction differs then from the remote case described before because technically any traversal spawned from g or from a Transaction will fall under the same transaction scope. As a result, it is wise, when trying to write context agnostic Gremlin, to follow the more rigid conventions of the initial example.

The sub-sections that follow offer a bit more insight into each of the usage contexts.

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.

Tip	Some graphs may throw an exception that implements `TemporaryException`. In this case, this marker interface is designed to inform the client that it may choose to retry the operation at a later time for possible success.

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.

Important

The following example is meant to demonstrate specific use of ThreadLocal transactions and is at odds with the more generalized transaction convention that is recommended for both embedded and remote contexts. Please be sure to understand the preferred approach described at in the Traversal Transactions Section before using this method.

gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[EmbeddedGraphDatabase [/tmp/neo4j]]
gremlin> g = traversal().withEmbedded(graph)
==>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

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

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

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. The start of the Transactions Section describes this approach in detail with examples.

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.

Remote Gremlin Providers

At this time, transactional patterns for Remote Gremlin Providers are largely in line with Gremlin Server. As most of RGPs do not expose a Graph instance, access to lower level transactional functions available to embedded graphs even in a sessionless fashion are not typically permitted. For example, without a Graph instance it is not possible to configure transaction close or read-write behaviors. 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.

Configuration Steps

Many of the methods on the GraphTraversalSource are meant to configure the source for usage. These configuration affect the manner in which a traversals are spawned from it. Configuration methods can be identified by their names with make use of "with" as a prefix:

With Configuration

The with() configuration adds arbitrary data to a TraversalSource which can then be used by graph providers as configuration options for a traversal execution. This configuration is similar to with()-modulator which has similar functionality when applied to an individual step.

g.with('providerDefinedVariable', 0.33).V()

The 0.33 value for the "providerDefinedVariable" will be bound to each traversal spawned that way. Consult the graph system being used to determine if any such configuration options are available.

WithBulk Configuration

The withBulk() configuration allows for control of bulking operations. This value is true by default allowing for normal bulking operations, but when set to false, introduces a subtle change in that behavior as shown in examples in sack()-step.

WithComputer Configuration

The withComputer() configuration adds a Computer that will be used to process the traversal and is necessary for OLAP based processing and steps that require that processing. See examples related to SparkGraphComputer or see examples in the computer required steps, like pageRank() or [shortestpath-shortestPath()].

WithSack Configuration

The withSack() configuration adds a "sack" that can be accessed by traversals spawned from this source. This functionality is shown in more detail in the examples for (sack())-step.

WithSideEffect Configuration

The withSideEffect() configuration adds an arbitrary Object to traversals spawned from this source which can be accessed as a side-effect given the supplied key.

console (groovy) groovy

gremlin> g.withSideEffect('x',['dog','cat','fish']).
           V().has('person','name','marko').select('x').unfold()
==>dog
==>cat
==>fish

g.withSideEffect('x',['dog','cat','fish']).
  V().has('person','name','marko').select('x').unfold()

More practical examples can be found in other examples elsewhere in the documentation. The math()-step example and the where()-step example should both be helpful in examining this configuration step more closely.

WithStrategies Configuration

The withStrategies() configuration allows inclusion of additional TraversalStrategy instances to be applied to any traversals spawned from the configured source. Please see the Traversal Strategy Section for more details on how this configuration works.

WithoutStrategies Configuration

The withoutStrategies() configuration removes a particular TraversalStrategy from those to be applied to traversals spawned from the configured source. Please see the Traversal Strategy Section for more details on how this configuration works.

Start Steps

Not all steps are capable of starting a GraphTraversal. Only those steps on the GraphTraversalSource can do that. Many of the methods on GraphTraversalSource are actually for its configuration and start steps should not be confused with those.

Spawn steps, which actually yield a traversal, typically match the names of existing steps:

addE() - Adds an Edge to start the traversal (example).
addV() - Adds a Vertex to start the traversal (example).
call() - Makes a provider-specific service call to start the traversal (example).
E() - Reads edges from the graph to start the traversal (example).
inject() - Inserts arbitrary objects to start the traversal (example).
mergeE() - Adds an Edge in a "create if not exist" fashion to start the traversal (example)
mergeV() - Adds a Vertex in a "create if not exist" fashion to start the traversal (example)
union() - Merges the results of an arbitrary number of child traversals to start the traversal (example).
V() - Reads vertices from the graph to start the traversal (example).

Graph Traversal Steps

Gremlin steps are chained together to produce the actual traversal and are triggered by way of start steps on the GraphTraversalSource.

Important

More details about the Gremlin language can be found in the Provider Documentation within the Gremlin Semantics Section.

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

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.

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:

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

map lambda

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

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

filter lambda

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

A filter that only allows the vertex to pass if it has the "person" label
The same operation, but using the traversal representation of filter().
The more specific has()-step is implemented as a filter() with respective predicate.

side effect lambda

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().aggregate(local,"o")).
               sideEffect(inE().count().aggregate(local,"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().aggregate(local,"o")).
      sideEffect(inE().count().aggregate(local,"i")).cap("o","i") //2

Whatever enters sideEffect() is passed to the next step, but some intervening process can occur.
Compute the out- and in-degree for each vertex. Both sideEffect() are fed with the same vertex.

branch lambda

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

If the vertex is "marko", get his age, else get the name of the vertex.
The same operation, but using the traversal representing of branch().
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

hasNext() determines whether there are available results (not supported in gremlin-javascript).
next() will return the next result.
next(n) will return the next n results in a list (not supported in gremlin-javascript or Gremlin.NET).
tryNext() will return an Optional and thus, is a composite of hasNext()/next() (only supported for JVM languages).
toList() will return all results in a list.
toSet() will return all results in a set and thus, duplicates removed (not supported in gremlin-javascript).
toBulkSet() will return all results in a weighted set and thus, duplicates preserved via weighting (only supported for JVM languages).
fill(collection) will put all results in the provided collection and return the collection when complete (only supported for JVM languages).
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.

AddE 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[0][1-co-developer->4]
==>e[13][1-co-developer->6]
gremlin> g.V(3,4,5).aggregate('x').has('name','josh').as('a').
           select('x').unfold().hasLabel('software').addE('createdBy').to('a') //// (2)
==>e[14][3-createdBy->4]
==>e[15][5-createdBy->4]
gremlin> g.V().as('a').out('created').addE('createdBy').to('a').property('acl','public') //// (3)
==>e[16][3-createdBy->1]
==>e[17][5-createdBy->4]
==>e[18][3-createdBy->4]
==>e[19][3-createdBy->6]
gremlin> g.V(1).as('a').out('knows').
           addE('livesNear').from('a').property('year',2009).
           inV().inE('livesNear').values('year') //// (4)
==>2009
==>2009
gremlin> g.V().match(
                 __.as('a').out('knows').as('b'),
                 __.as('a').out('created').as('c'),
                 __.as('b').out('created').as('c')).
               addE('friendlyCollaborator').from('a').to('b').
                 property(id,23).property('project',select('c').values('name')) //// (5)
==>e[23][1-friendlyCollaborator->4]
gremlin> g.E(23).valueMap()
==>[project:lop]
gremlin> vMarko = g.V().has('name','marko').next()
==>v[1]
gremlin> vPeter = g.V().has('name','peter').next()
==>v[6]
gremlin> g.V(vMarko).addE('knows').to(vPeter) //// (6)
==>e[22][1-knows->6]
gremlin> g.addE('knows').from(vMarko).to(vPeter) //// (7)
==>e[24][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()
vMarko = g.V().has('name','marko').next()
vPeter = g.V().has('name','peter').next()
g.V(vMarko).addE('knows').to(vPeter) //// (6)
g.addE('knows').from(vMarko).to(vPeter) //7

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

Additional References

addE(String), addE(Traversal)

AddV 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[0]
gremlin> g.V().values('name')
==>stephen
==>marko
==>vadas
==>lop
==>josh
==>ripple
==>peter
gremlin> g.V().outE('knows').addV().property('name','nothing')
==>v[13]
==>v[15]
gremlin> g.V().has('name','nothing')
==>v[13]
==>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)

Aggregate Step

The aggregate()-step (sideEffect) is used to aggregate all the objects at a particular point of traversal into a Collection. The step is uses Scope to help determine the aggregating behavior. For global scope this means that the step will use eager evaluation in that no objects continue on until all previous objects have been fully aggregated. The eager evaluation model is crucial in situations where everything at a particular point is required for future computation. By default, when the overload of aggregate() is called without a Scope, the default is global. 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(global, 'x') //// (3)
==>v[3]
gremlin> g.V(1).out('created').aggregate('x').in('created') //// (4)
==>v[1]
==>v[4]
==>v[6]
gremlin> g.V(1).out('created').aggregate('x').in('created').out('created') //// (5)
==>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') //// (6)
==>ripple

g.V(1).out('created') //// (1)
g.V(1).out('created').aggregate('x') //// (2)
g.V(1).out('created').aggregate(global, 'x') //// (3)
g.V(1).out('created').aggregate('x').in('created') //// (4)
g.V(1).out('created').aggregate('x').in('created').out('created') //// (5)
g.V(1).out('created').aggregate('x').in('created').out('created').
       where(without('x')).values('name') //6

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

In recommendation systems, the above pattern is used:

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

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

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]
gremlin> g.V().out('knows').aggregate('x').by('age').cap('x') //// (1)
==>[27,32]

g.V().out('knows').aggregate('x').cap('x')
g.V().out('knows').aggregate('x').by('name').cap('x')
g.V().out('knows').aggregate('x').by('age').cap('x')  //1

The "age" property is not productive for all vertices and therefore those values are not included in the aggregation.

For local scope the aggregation will occur in a lazy fashion.

Note	Prior to 3.4.3, `local` aggregation (i.e. lazy) evaluation was handled by `store()`-step.

console (groovy) groovy

gremlin> g.V().aggregate(global, 'x').limit(1).cap('x')
==>[v[1],v[2],v[3],v[4],v[5],v[6]]
gremlin> g.V().aggregate(local, 'x').limit(1).cap('x')
==>[v[1]]
gremlin> g.withoutStrategies(EarlyLimitStrategy).V().aggregate(local,'x').limit(1).cap('x')
==>[v[1],v[2]]

g.V().aggregate(global, 'x').limit(1).cap('x')
g.V().aggregate(local, 'x').limit(1).cap('x')
g.withoutStrategies(EarlyLimitStrategy).V().aggregate(local,'x').limit(1).cap('x')

It is important to note that EarlyLimitStrategy introduced in 3.3.5 alters the behavior of aggregate(local). Without that strategy (which is installed by default), there are two results in the aggregate() 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 aggregate() and thus, stored before filtered.

console (groovy) groovy

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

g.E().aggregate(local,'x').by('weight').cap('x')

Additional References

aggregate(String), aggregate(Scope,String)

All Step

It is possible to filter list traversers using all()-step (filter). Every item in the list will be tested against the supplied predicate and if all of the items pass then the traverser is passed along the stream, otherwise it is filtered. Empty lists are passed along but null or non-iterable traversers are filtered out.

Python

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

console (groovy) groovy

gremlin> g.V().values('age').fold().all(gt(25)) //// (1)
==>[29,27,32,35]

g.V().values('age').fold().all(gt(25)) //1

Return the list of ages only if everyone’s age is greater than 25.

Additional References

all(P), P

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…)

Any Step

It is possible to filter list traversers using any()-step (filter). All items in the list will be tested against the supplied predicate and if any of the items pass then the traverser is passed along the stream, otherwise it is filtered. Empty lists, null traversers, and non-iterable traversers are filtered out as well.

Python

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

console (groovy) groovy

gremlin> g.V().values('age').fold().any(gt(25)) //// (1)
==>[29,27,32,35]

g.V().values('age').fold().any(gt(25)) //1

Return the list of ages if anyone’s age is greater than 25.

Additional References

any(P), P

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

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

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

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…)

AsString Step

The asString()-step (map) returns the value of incoming traverser as strings. Null values are returned unchanged.

console (groovy) groovy

gremlin> g.V().hasLabel('person').values('age').asString() //// (1)
==>29
==>27
==>32
==>35
gremlin> g.V().hasLabel('person').values('age').asString().concat(' years old') //// (2)
==>29 years old
==>27 years old
==>32 years old
==>35 years old
gremlin> g.V().hasLabel('person').values('age').fold().asString(local) //// (3)
==>[29,27,32,35]

g.V().hasLabel('person').values('age').asString() //// (1)
g.V().hasLabel('person').values('age').asString().concat(' years old') //// (2)
g.V().hasLabel('person').values('age').fold().asString(local) //3

Return ages as string.
Return ages as string and use concat to generate phrases.
Use Scope.local to operate on individual string elements inside incoming list, which will return a list.

Additional References

asString() asString(Scope)

AsDate Step

The asDate()-step (map) converts string or numeric input to Date.

For string input only ISO-8601 format is supported. For numbers, the value is considered as the number of the milliseconds since "the epoch" (January 1, 1970, 00:00:00 GMT). Date input is passed without changes.

If the incoming traverser is not a string, number or Date then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.inject(1690934400000).asDate() //// (1)
==>Wed Aug 02 00:00:00 UTC 2023
gremlin> g.inject("2023-08-02T00:00:00Z").asDate() //// (2)
==>Wed Aug 02 00:00:00 UTC 2023
gremlin> g.inject(datetime("2023-08-24T00:00:00Z")).asDate() //// (3)
==>Thu Aug 24 00:00:00 UTC 2023

g.inject(1690934400000).asDate() //// (1)
g.inject("2023-08-02T00:00:00Z").asDate() //// (2)
g.inject(datetime("2023-08-24T00:00:00Z")).asDate() //3

Convert number to Date
Convert ISO-8601 string to Date
Pass Date without modification

Additional References

asDate()

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 = traversal().withEmbedded(graph)
==>graphtraversalsource[tinkergraph[vertices:0 edges:0], standard]
gremlin> g.io('data/grateful-dead.xml').read().iterate()
gremlin> g = traversal().withEmbedded(graph).withoutStrategies(LazyBarrierStrategy) //// (1)
==>graphtraversalsource[tinkergraph[vertices:808 edges:8049], standard]
gremlin> clockWithResult(1){g.V().both().both().both().count().next()} //// (2)
==>6997.450284
==>126653966
gremlin> clockWithResult(1){g.V().repeat(both()).times(3).count().next()} //// (3)
==>7050.683031
==>126653966
gremlin> clockWithResult(1){g.V().both().barrier().both().barrier().both().barrier().count().next()} //// (4)
==>8.607391999999999
==>126653966

graph = TinkerGraph.open()
g = traversal().withEmbedded(graph)
g.io('data/grateful-dead.xml').read().iterate()
g = traversal().withEmbedded(graph).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

Explicitly remove LazyBarrierStrategy which yields a bulking optimization.
A non-bulking traversal where each traverser is processed.
Each traverser entering repeat() has its recursion bulked.
A bulking traversal where implicit traversers are not processed.

If barrier() is provided an integer argument, then the barrier will only hold n-number of unique traversers in its barrier before draining the aggregated traversers to the next step. This is useful in the aforementioned bulking optimization scenario 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 = traversal().withEmbedded(graph) //// (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()}
==>5.572671
==>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 = traversal().withEmbedded(graph) //// (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

LazyBarrierStrategy is a default strategy and thus, does not need to be explicitly activated.
With LazyBarrierStrategy activated, barrier()-steps are automatically inserted where appropriate.

Additional References

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

Branch Step

The branch() step splits the traverser to all the child traversals provided to it. Please see the General Steps section for more information, but also consider that branch() is the basis for more robust steps like choose() and union().

Additional References

map(Traversal)

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

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

When a by() modulator does not produce a result, it is deemed "unproductive". An "unproductive" modulator will lead to the filtering of the traverser it is currently working with. The filtering will manifest in various ways depending on the step.

console (groovy) groovy

gremlin> g.V().sample(1).by('age') //// (1)
==>v[4]

g.V().sample(1).by('age') //1

The "age" property key is not present for all vertices, therefore sample() will ignore (i.e. filter) such vertices for consideration in the sampling.

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.

aggregate(): aggregate all objects into a set but only store their by()-modulated values.
cyclicPath(): filter if the traverser’s path is cyclic given by()-modulation.
dedup(): dedup on the results of a by()-modulation.
group(): create group keys and values according to by()-modulation.
groupCount(): count those groups where the group keys are the result of by()-modulation.
math(): transform a traverser provided to the step by way of the by() modulator before it processed by it.
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.
propertyMap(): transform the result of the values in the resulting Map using the by() modulator.
sack(): provides the transformation for a traverser to a value to be stored in the sack.
sample(): sample using the value returned by by()-modulation.
select(): select path elements and transform them via by()-modulation.
simplePath(): filter if the traverser’s path is simple given by()-modulation.
tree(): get a tree of traversers objects where the objects have been by()-modulated.
valueMap(): transform the result of the values in the resulting Map using the by() modulator.
where(): determine the predicate given the testing of the results of by()-modulation.

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

Call Step

The call() step allows for custom, provider-specific service calls either at the start of a traversal or mid-traversal. This allows Graph providers to expose operations not natively built into the Gremlin language, such as full text search, custom analytics, notification triggers, etc.

When called with no arguments, call() will produce a list of callable services available for the graph in use. This no-argument version is equivalent to call('--list'). This "directory service" is also capable of producing more verbose output describing all the services or an individual service:

console (groovy) groovy

gremlin> g.call() //// (1)
gremlin> g.call('--list') //// (1)
gremlin> g.call().with('verbose') //// (2)
gremlin> g.call().with('verbose').with('service', 'xyz-service') //// (3)

g.call() //// (1)
g.call('--list') //// (1)
g.call().with('verbose') //// (2)
g.call().with('verbose').with('service', 'xyz-service') //3

List available services by name
Produce a Map of detailed service information by name
Produce the detailed service information for the 'xyz-service'

The first argument to call() is always the name of the service call. Additionally, service calls can accept both static and dynamically produced parameters. Static parameters can be passed as a Map to the call() as the second argument. Individual static parameters can also be added using the .with() modulator. Dynamic parameters can be passed as a Map-producing Traversal as the second argument (no static parameters) or third argument (static + dynamic parameters). Additional individual dynamic parameters can be added using the .with() modulator.

g.call('xyz-service') //1
g.call('xyz-service', ['a':'b']) //2
g.call('xyz-service').with('a', 'b') //2
g.call('xyz-service', __.inject(['a':'b'])) //3
g.call('xyz-service').with('a', __.inject('b')) //3
g.call('xyz-service', ['a':'b'], __.inject(['c':'d'])) //4

Call the 'xyz-service' with no parameters
Examples of static parameters (constants known before execution)
Examples of dynamic parameters (these will be computed at execution time)
Example of static + dynamic parameters (these will be computed and merged into one set of parameters at execution time)

Additional References

GraphTraversalSource:

call() call(String) call(String, Map) call(String, Traversal) call(String, Map, Traversal)

GraphTraversal:

call(String) call(String, Map) call(String, Traversal) call(String, Map, Traversal)

Cap Step

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

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

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

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

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

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

If the vertex is a person, emit the vertices they created, else emit the vertex.
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', elementMap()).
                 option('peter', label())
==>29
==>[id:2,label:person,name:vadas,age:27]
==>josh
==>person

g.V().hasLabel('person').
      choose(values('name')).
        option('marko', values('age')).
        option('josh', values('name')).
        option('vadas', elementMap()).
        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[2]
==>v[5]
==>v[6]
gremlin> g.V().coin(0.0)
gremlin> g.V().coin(1.0)
==>v[1]
==>v[2]
==>v[3]
==>v[4]
==>v[5]
==>v[6]

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

Additional References

coin(double)

Combine Step

The combine()-step (map) combines the elements of the incoming list traverser and the provided list argument into one list. This is also known as appending or concatenating. This step only expects list data (array or Iterable) and will throw an IllegalArgumentException if any other type is encountered (including null). This differs from the merge()-step in that it allows duplicates to exist.

console (groovy) groovy

gremlin> g.V().values("name").fold().combine(["james","jen","marko","vadas"])
==>[marko,vadas,lop,josh,ripple,peter,james,jen,marko,vadas]
gremlin> g.V().values("name").fold().combine(__.constant("stephen").fold())
==>[marko,vadas,lop,josh,ripple,peter,stephen]

g.V().values("name").fold().combine(["james","jen","marko","vadas"])
g.V().values("name").fold().combine(__.constant("stephen").fold())

Additional References

combine(Object) Semantics

Concat Step

The concat()-step (map) concatenates one or more String values together to the incoming String traverser. This step can take either String varargs or Traversal varargs. Any null String values will be skipped when concatenated with non-null String values. If two null value are concatenated, the null value will be propagated and returned. If the incoming traverser is a non-String value then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.addV(constant('prefix_').concat(__.V(1).label())).property(id, 10) //// (1)
==>v[10]
gremlin> g.V(10).label()
==>prefix_person
gremlin> g.V().hasLabel('person').values('name').as('a').
             constant('Mr.').concat(__.select('a')) //// (2)
==>Mr.marko
==>Mr.vadas
==>Mr.josh
==>Mr.peter
gremlin> g.V().hasLabel('software').as('a').values('name').
             concat(' uses ').
             concat(select('a').values('lang')) //// (3)
==>lop uses java
==>ripple uses java
gremlin> g.V(1).outE().as('a').V(1).values('name').
             concat(' ').
             concat(select('a').label()).
             concat(' ').
             concat(select("a").inV().values('name')) //// (4)
==>marko created lop
==>marko knows vadas
==>marko knows josh
gremlin> g.V(1).outE().as('a').V(1).values('name').
             concat(constant(' '),
                 select("a").label(),
                 constant(' '),
                 select('a').inV().values('name')) //// (5)
==>marko created lop
==>marko knows vadas
==>marko knows josh
gremlin> g.inject('hello', 'hi').concat(__.V().values('name')) //// (6)
==>hellomarko
==>himarko
gremlin> g.inject('This').concat(' ').concat('is a ', 'gremlin.') //// (7)
==>This is a gremlin.

g.addV(constant('prefix_').concat(__.V(1).label())).property(id, 10) //// (1)
g.V(10).label()
g.V().hasLabel('person').values('name').as('a').
    constant('Mr.').concat(__.select('a')) //// (2)
g.V().hasLabel('software').as('a').values('name').
    concat(' uses ').
    concat(select('a').values('lang')) //// (3)
g.V(1).outE().as('a').V(1).values('name').
    concat(' ').
    concat(select('a').label()).
    concat(' ').
    concat(select("a").inV().values('name')) //// (4)
g.V(1).outE().as('a').V(1).values('name').
    concat(constant(' '),
        select("a").label(),
        constant(' '),
        select('a').inV().values('name')) //// (5)
g.inject('hello', 'hi').concat(__.V().values('name')) //// (6)
g.inject('This').concat(' ').concat('is a ', 'gremlin.') //7

Add a new vertex with id 10 which should be labeled like an existing vertex but with some prefix attached
Attach the prefix "Mr." to all the names using the constant()-step
Generate a string of software names and the language they use
Generate a string description for each of marko’s outgoing edges
Alternative way to generate the string description by using traversal varargs. Use the constant() step to add desired strings between arguments.
The concat() step will append the first result from the child traversal to the incoming traverser
A generic use of concat() to join strings together

Additional References

concat(String…) concat(Taversal, Traversal…) Semantics

Conjoin Step

The conjoin()-step (map) joins together the elements in the incoming list traverser together with the provided argument as a delimiter. The resulting String is added to the Traversal Stream. This step only expects list data (array or Iterable) in the incoming traverser and will throw an IllegalArgumentException if any other type is encountered (including null). Null values are skipped and not included in the result.

console (groovy) groovy

gremlin> g.V().values("name").fold().conjoin("+")
==>marko+vadas+lop+josh+ripple+peter

g.V().values("name").fold().conjoin("+")

Additional References

conjoin(String) Semantics

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

g = traversal().withEmbedded(graph).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

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

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

Important

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

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).both().both().cyclicPath().by('age').path() //// (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).both().both().cyclicPath().by('age').path() //// (1)
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()

The "age" property is not productive for all vertices and therefore those traversers are filtered.

Additional References

cyclicPath()

DateAdd Step

The dateAdd()-step (map) returns the value with the addition of the value number of units as specified by the DateToken. If the incoming traverser is not a Date, then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.inject("2023-08-02T00:00:00Z").asDate().dateAdd(DT.day, 7) //// (1)
==>Wed Aug 09 00:00:00 UTC 2023
gremlin> g.inject(["2023-08-02T00:00:00Z", "2023-08-03T00:00:00Z"]).unfold().asDate().dateAdd(DT.minute, 1) //// (2)
==>Wed Aug 02 00:01:00 UTC 2023
==>Thu Aug 03 00:01:00 UTC 2023

g.inject("2023-08-02T00:00:00Z").asDate().dateAdd(DT.day, 7) //// (1)
g.inject(["2023-08-02T00:00:00Z", "2023-08-03T00:00:00Z"]).unfold().asDate().dateAdd(DT.minute, 1) //2

Add 7 days to Date
Add 1 minute to incoming dates

Additional References

dateAdd(DT,int)

DateDiff Step

The dateDiff()-step (map) returns the difference between two Dates in epoch time. If the incoming traverser is not a Date, then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.inject("2023-08-02T00:00:00Z").asDate().dateDiff(constant("2023-08-03T00:00:00Z").asDate()) //// (1)
==>-86400

g.inject("2023-08-02T00:00:00Z").asDate().dateDiff(constant("2023-08-03T00:00:00Z").asDate()) //1

Find difference between two dates

Additional References

dateDiff(Date), dateDiff(Traversal)

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]]
gremlin> g.V().bothE().properties().dedup() //// (2)
==>p[weight->0.4]
==>p[weight->0.5]
==>p[weight->1.0]
==>p[weight->0.2]

g.V().values('lang')
g.V().values('lang').dedup()
g.V(1).repeat(bothE('created').dedup().otherV()).emit().path() //// (1)
g.V().bothE().properties().dedup() //2

Traverse all created edges, but don’t touch any edge twice.
Note that Property instances will compare on key and value, whereas a VertexProperty will also include its element as it is a first-class citizen.

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

g.V().elementMap('name')
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]]
gremlin> g.V().as('a').both().as('b').both().as('c').
           dedup('a','b').by('age'). //// (2)
           select('a','b','c').by('name')
==>[a:marko,b:vadas,c:marko]
==>[a:marko,b:josh,c:ripple]
==>[a:vadas,b:marko,c:lop]
==>[a:josh,b:marko,c:lop]

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)
g.V().as('a').both().as('b').both().as('c').
  dedup('a','b').by('age'). //// (2)
  select('a','b','c').by('name')

If the current a and b combination has been seen previously, then filter the traverser.
The "age" property is not productive for all vertices and therefore those values are filtered.

Additional References

dedup(Scope,String…), dedup(String…), Scope, Semantics

Difference Step

The difference()-step (map) calculates the difference between the incoming list traverser and the provided list argument. More specifically, this provides the set operation A-B where A is the traverser and B is the argument. This step only expects list data (array or Iterable) and will throw an IllegalArgumentException if any other type is encountered (including null).

console (groovy) groovy

gremlin> g.V().values("name").fold().difference(["lop","ripple"])
==>[peter,vadas,josh,marko]
gremlin> g.V().values("name").fold().difference(__.V().limit(2).values("name").fold())
==>[ripple,peter,josh,lop]

g.V().values("name").fold().difference(["lop","ripple"])
g.V().values("name").fold().difference(__.V().limit(2).values("name").fold())

Additional References

difference(Object) Semantics

Disjunct Step

The disjunct()-step (map) calculates the disjunct set between the incoming list traverser and the provided list argument. This step only expects list data (array or Iterable) and will throw an IllegalArgumentException if any other type is encountered (including null).

console (groovy) groovy

gremlin> g.V().values("name").fold().disjunct(["lop","peter","sam"]) //// (1)
==>[ripple,vadas,josh,sam,marko]
gremlin> g.V().values("name").fold().disjunct(__.V().limit(3).values("name").fold())
==>[ripple,peter,josh]

g.V().values("name").fold().disjunct(["lop","peter","sam"]) //// (1)
g.V().values("name").fold().disjunct(__.V().limit(3).values("name").fold())

Find the unique names between two group of names

Additional References

disjunct(Object) Semantics

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().elementMap()
==>[id:1,label:person,age:29]
==>[id:2,label:person,age:27]
==>[id:3,label:software,lang:java]
==>[id:4,label:person,age:32]
==>[id:5,label:software,lang:java]
==>[id:6,label:person,age:35]
gremlin> g.V().drop()
gremlin> g.V()

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

Additional References

drop()

E Step

The E()-step is meant to read edges from the graph and is usually used to start a GraphTraversal, but can also be used mid-traversal.

console (groovy) groovy

gremlin> g.E(11) //// (1)
==>e[11][4-created->3]
gremlin> g.E().hasLabel('knows').has('weight', gt(0.75))
==>e[8][1-knows->4]
gremlin> g.inject(1).coalesce(E().hasLabel("knows"), addE("knows").from(V().has("name","josh")).to(V().has("name","vadas"))) //// (2)
==>e[7][1-knows->2]
==>e[8][1-knows->4]

g.E(11) //// (1)
g.E().hasLabel('knows').has('weight', gt(0.75))
g.inject(1).coalesce(E().hasLabel("knows"), addE("knows").from(V().has("name","josh")).to(V().has("name","vadas"))) //2

Find the edge by its unique identifier (i.e. T.id) - not all graphs will use a numeric value for their identifier.
Get edges with label knows, if there is none then add new one between josh and vadas.

Additional References

E(Object…)

Element Step

The element() step is a no-argument step that traverses from a Property to the Element that owns it.

console (groovy) groovy

gremlin> g.V().properties().element() //// (1)
==>v[1]
==>v[1]
==>v[1]
==>v[1]
==>v[1]
==>v[7]
==>v[7]
==>v[7]
==>v[7]
==>v[8]
==>v[8]
==>v[8]
==>v[8]
==>v[8]
==>v[9]
==>v[9]
==>v[9]
==>v[9]
==>v[10]
==>v[11]
gremlin> g.E().properties().element() //// (2)
==>e[13][1-develops->10]
==>e[14][1-develops->11]
==>e[15][1-uses->10]
==>e[16][1-uses->11]
==>e[17][7-develops->10]
==>e[18][7-develops->11]
==>e[19][7-uses->10]
==>e[20][7-uses->11]
==>e[21][8-develops->10]
==>e[22][8-uses->10]
==>e[23][8-uses->11]
==>e[24][9-uses->10]
==>e[25][9-uses->11]
gremlin> g.V().properties().properties().element() //// (3)
==>vp[location->san diego]
==>vp[location->san diego]
==>vp[location->santa cruz]
==>vp[location->santa cruz]
==>vp[location->brussels]
==>vp[location->brussels]
==>vp[location->santa fe]
==>vp[location->centreville]
==>vp[location->centreville]
==>vp[location->dulles]
==>vp[location->dulles]
==>vp[location->purcellville]
==>vp[location->bremen]
==>vp[location->bremen]
==>vp[location->baltimore]
==>vp[location->baltimore]
==>vp[location->oakland]
==>vp[location->oakland]
==>vp[location->seattle]
==>vp[location->spremberg]
==>vp[location->spremberg]
==>vp[location->kaiserslautern]
==>vp[location->kaiserslautern]
==>vp[location->aachen]

g.V().properties().element() //// (1)
g.E().properties().element() //// (2)
g.V().properties().properties().element() //3

Traverse from VertexProperty to Vertex
Traverse from Property (edge property) to Edge
Traverse from Property (meta property) to VertexProperty

Additional References

element()

ElementMap Step

The elementMap()-step yields a Map representation of the structure of an element.

console (groovy) groovy

gremlin> g.V().elementMap()
==>[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.V().elementMap('age')
==>[id:1,label:person,age:29]
==>[id:2,label:person,age:27]
==>[id:3,label:software]
==>[id:4,label:person,age:32]
==>[id:5,label:software]
==>[id:6,label:person,age:35]
gremlin> g.V().elementMap('age','blah')
==>[id:1,label:person,age:29]
==>[id:2,label:person,age:27]
==>[id:3,label:software]
==>[id:4,label:person,age:32]
==>[id:5,label:software]
==>[id:6,label:person,age:35]
gremlin> g.E().elementMap()
==>[id:7,label:knows,IN:[id:2,label:person],OUT:[id:1,label:person],weight:0.5]
==>[id:8,label:knows,IN:[id:4,label:person],OUT:[id:1,label:person],weight:1.0]
==>[id:9,label:created,IN:[id:3,label:software],OUT:[id:1,label:person],weight:0.4]
==>[id:10,label:created,IN:[id:5,label:software],OUT:[id:4,label:person],weight:1.0]
==>[id:11,label:created,IN:[id:3,label:software],OUT:[id:4,label:person],weight:0.4]
==>[id:12,label:created,IN:[id:3,label:software],OUT:[id:6,label:person],weight:0.2]

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

It is important to note that the map of a vertex assumes that cardinality for each key is single and if it is list then only the first item encountered will be returned. As single is the more common cardinality for properties this assumption should serve the greatest number of use cases.

console (groovy) groovy

gremlin> g.V().elementMap()
==>[id:1,label:person,name:marko,location:santa fe]
==>[id:7,label:person,name:stephen,location:purcellville]
==>[id:8,label:person,name:matthias,location:seattle]
==>[id:9,label:person,name:daniel,location:aachen]
==>[id:10,label:software,name:gremlin]
==>[id:11,label:software,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').elementMap()
==>[id:6,key:location,value:san diego,startTime:1997,endTime:2001]
==>[id:7,key:location,value:santa cruz,startTime:2001,endTime:2004]
==>[id:8,key:location,value:brussels,startTime:2004,endTime:2005]
==>[id:9,key:location,value:santa fe,startTime:2005]

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

Important

The elementMap()-step does not return the vertex labels for incident vertices when using GraphComputer as the id is the only available data to the star graph.

Additional References

elementMap(String…)

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

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

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

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

Fail Step

The fail()-step provides a way to force a traversal to immediately fail with an exception. This feature is often helpful during debugging purposes and for validating certain conditions prior to continuing with traversal execution.

gremlin> g.V().has('person','name','peter').fold().
......1>   coalesce(unfold(),
......2>            fail('peter should exist')).
......3>   property('k',100)
==>v[6]
gremlin> g.V().has('person','name','stephen').fold().
......1>   coalesce(unfold(),
......2>            fail('stephen should exist')).
......3>   property('k',100)
fail() Step Triggered
===========================================================================================================================
Message > stephen should exist
Traverser> []
  Bulk   > 1
Traversal> fail()
Parent   > CoalesceStep [V().has("person","name","stephen").fold().coalesce(__.unfold(),__.fail()).property("k",(int) 100)]
Metadata > {}
===========================================================================================================================

The code example above exemplifies the latter use case where there is essentially an assertion that there is a vertex with a particular "name" value prior to updating the property "k" and explicitly failing when that vertex is not found.

Additional References

fail(), fail(String)

Filter Step

The filter() step maps the traverser from the current object to either true or false where the latter will not pass the traverser to the next step in the process. Please see the General Steps section for more information.

Additional References

map(Traversal)

FlatMap Step

The flatMap() step maps the traverser from the current object to an Iterator of objects for the next step in the process. Please see the General Steps section for more information.

Additional References

map(Traversal)

Format Step

This step is designed to simplify some string operations. In general, it is similar to the string formatting function available in many programming languages. Variable values can be picked up from Element properties, maps and scope variables.

console (groovy) groovy

gremlin> g.V().format("%{name} is %{age} years old") //// (1)
==>marko is 29 years old
==>vadas is 27 years old
==>josh is 32 years old
==>peter is 35 years old
gremlin> g.V().hasLabel("person").as("a").values("name").as("p1").select("a").in("knows").format("%{p1} knows %{name}") //// (2)
==>vadas knows marko
==>josh knows marko
gremlin> g.V().format("%{name} has %{_} connections").by(bothE().count()) //// (3)
==>marko has 3 connections
==>vadas has 1 connections
==>lop has 3 connections
==>josh has 3 connections
==>ripple has 1 connections
==>peter has 1 connections
gremlin> g.V().project("name","count").by(values("name")).by(bothE().count()).format("%{name} has %{count} connections") //// (4)
==>marko has 3 connections
==>vadas has 1 connections
==>lop has 3 connections
==>josh has 3 connections
==>ripple has 1 connections
==>peter has 1 connections

g.V().format("%{name} is %{age} years old") //// (1)
g.V().hasLabel("person").as("a").values("name").as("p1").select("a").in("knows").format("%{p1} knows %{name}") //// (2)
g.V().format("%{name} has %{_} connections").by(bothE().count()) //// (3)
g.V().project("name","count").by(values("name")).by(bothE().count()).format("%{name} has %{count} connections") //4

A format() will use property values from incoming Element to produce String result.
A format() will use scope variable p1 and property name to resolve variable values.
A format() will use property name and traversal product for positional argument to resolve variable values.
A format() will use map produced by project step to resolve variable values.

Additional References

format(String),

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
gremlin> g.inject(["a":1],["b":2]).fold([], addAll) //// (7)
==>[[a:1],[b:2]]

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)
g.inject(["a":1],["b":2]).fold([], addAll) //7

A parameterless fold() will aggregate all the objects into a list and then emit the list.
A verification of the type of list returned.
fold() can be provided two arguments — a seed value and a reduce bi-function ("vadas" is 5 characters + "josh" with 4 characters).
What is the total age of the people in the graph?
The same as before, but using a built-in bi-function.
The same as before, but using the sum()-step.
A mechanism for merging Map instances. If a key occurs in more than a single Map, the later occurrence will replace the earlier.

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)

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

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

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

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

console (groovy) groovy

gremlin> g.V().group().by('age').by('name') //// (1)
==>[32:[josh],35:[peter],27:[vadas],29:[marko]]
gremlin> g.V().group().by('name').by('age') //// (2)
==>[ripple:[],peter:[35],vadas:[27],josh:[32],lop:[],marko:[29]]

g.V().group().by('age').by('name') //// (1)
g.V().group().by('name').by('age') //2

The "age" property is not productive for all vertices and therefore those keys are filtered.
The "age" property is not productive for all vertices and therefore those values are filtered.

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]
gremlin> g.V().groupCount().by('age') //// (2)
==>[32:1,35:1,27:1,29:1]

g.V().hasLabel('person').values('age').groupCount()
g.V().hasLabel('person').groupCount().by('age') //// (1)
g.V().groupCount().by('age') //2

You can also supply a pre-group projection, where the provided by()-modulation determines what to group the incoming object by.
The "age" property is not productive for all vertices and therefore those values are filtered.

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 Property traverser if it does not match one of the provided keys.
hasValue(values…): Remove the Property traverser if it does not match one 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','name','marko')
==>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')).elementMap() //// (3)
==>[id:1,label:person,name:marko,age:29]
==>[id:4,label:person,name:josh,age:32]
gremlin> g.V().has('name',without('josh','marko')).elementMap() //// (4)
==>[id:2,label:person,name:vadas,age:27]
==>[id:3,label:software,name:lop,lang:java]
==>[id:5,label:software,name:ripple,lang:java]
==>[id:6,label:person,name:peter,age:35]
gremlin> g.V().has('name',not(within('josh','marko'))).elementMap() //// (5)
==>[id:2,label:person,name:vadas,age:27]
==>[id:3,label:software,name:lop,lang:java]
==>[id:5,label:software,name:ripple,lang:java]
==>[id:6,label:person,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
gremlin> g.V().has('person','name', startingWith('m')) //// (8)
==>v[1]
gremlin> g.V().has(null, 'vadas') //// (9)
gremlin> g.V().has(label, __.is('person')) //// (10)
==>v[1]
==>v[2]
==>v[4]
==>v[6]

g.V().hasLabel('person')
g.V().hasLabel('person','name','marko')
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')).elementMap() //// (3)
g.V().has('name',without('josh','marko')).elementMap() //// (4)
g.V().has('name',not(within('josh','marko'))).elementMap() //// (5)
g.V().properties().hasKey('age').value() //// (6)
g.V().hasNot('age').values('name') //// (7)
g.V().has('person','name', startingWith('m')) //// (8)
g.V().has(null, 'vadas') //// (9)
g.V().has(label, __.is('person')) //10

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.
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.
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.
Find all vertices whose names are not in the collection [josh,marko], display all the key,value pairs for those vertices.
Same as the prior example save using not on within to yield without.
Find all age-properties and emit their value.
Find all vertices that do not have an age-property and emit their name.
Find all "person" vertices that have a name property that starts with the letter "m".
Property key is always stored as String and therefore an equality check with null will produce no result.
An example of has() where the argument is a Traversal and does not quite behave the way most expect.

Item 10 in the above set of examples bears some discussion. The behavior is not such that the result of the Traversal is used as the comparing value for has(), but the current Traverser, which in this case is the vertex label, is given to the Traversal to behave as a filter itself. In other words, if the Traversal (i.e. is('person')) returns a value then the has() is effectively true. A common mistake is to try to use select() in this context where one would do has('name', select('n')) to try to inject the value of "n" into the step to get has('name', <value-of-n>), but this would instead simply produce an always true filter for has().

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

Additional References

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

Indexing non-collection items results in multiple indexed single-item collections.
Index all software names in their alphabetical order.
Same as statement 1, but with an explicitely specified list indexer.
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)

Intersect Step

The intersect()-step (map) calculates the intersection between the incoming list traverser and the provided list argument. This step only expects list data (array or Iterable) and will throw an IllegalArgumentException if any other type is encountered (including null).

console (groovy) groovy

gremlin> g.V().values("name").fold().intersect(["marko","josh","james","jen"])
==>[josh,marko]
gremlin> g.V().values("name").fold().intersect(__.V().limit(2).values("name").fold())
==>[vadas,marko]

g.V().values("name").fold().intersect(["marko","josh","james","jen"])
g.V().values("name").fold().intersect(__.V().limit(2).values("name").fold())

Additional References

intersect(Object) Semantics

IO Step

gremlin io 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.

Important

Default graph formats don’t contain information about property cardinality, so it is up to the graph provider to choose the appropriate one. You will see a warning message if the chosen cardinality is SINGLE while your graph input contains multiple values for that property.

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.

Note	The `io()` step will try to automatically detect the appropriate `GraphReader` or `GraphWriter` to use based on the file extension. If the file has a different extension than the ones expected, use `with()` as shown above to set the `reader` or `writer` explicitly.

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

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

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

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.

// expects a file extension of .xml or .graphml to determine that
// a GraphML reader/writer should be used.
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

gremlin 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.)

// expects a file extension of .json to interpret that
// a GraphSON reader/writer should be used
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

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

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

Warning

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

// expects a file extension of .kryo to interpret that
// a GraphSON reader/writer should be used
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

Find projects having exactly one contributor.
Find projects having two or more contributors.
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()

Length Step

The length()-step (map) returns the length incoming string or list of string traverser. Null values are not processed and remain as null when returned. If the incoming traverser is a non-String value then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.V().values('name').length() //// (1)
==>5
==>5
==>3
==>4
==>6
==>5
gremlin> g.V().values('name').fold().length(local) //// (2)
==>[5,5,3,4,6,5]

g.V().values('name').length() //// (1)
g.V().values('name').fold().length(local) //2

Return the string length of all vertex names.
Use Scope.local to operate on individual string elements inside incoming list, which will return a list.

Additional References

length() length(Scope)

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

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

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

The two traversals above look nearly identical save the inclusion of local() which wraps a section of the traversal in an 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"))

Use of local() is often a mistake. This is especially true when its argument contains a reducing step. For example, let’s say the requirement was to count the number of properties per Vertex in:

console (groovy) groovy

gremlin> g.V().both().local(properties('name','age').count()) //// (1)
==>3
==>2
==>6
==>6
==>2
==>1
gremlin> g.V().both().map(properties('name','age').count()) //// (2)
==>1
==>1
==>1
==>2
==>2
==>2
==>2
==>2
==>2
==>2
==>2
==>1

g.V().both().local(properties('name','age').count()) //// (1)
g.V().both().map(properties('name','age').count()) //2

The output here seems impossible because no single vertex in the "modern" graph can have more than two properties given the "name" and "age" filters, but because the counting is happening object-local the counting is occurring unique to each object rather than each global traverser.
Replacing local() with map() returns the result desired by the requirement.

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(), Looping Recipes

LTrim Step

The lTrim()-step (map) returns a string with leading whitespace removed. Null values are not processed and remain as null when returned. If the incoming traverser is a non-String value then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.inject("   hello   ", " world ", null).lTrim()
==>hello
==>world
==>null
gremlin> g.inject(["   hello   ", " world ", null]).lTrim(local) //// (1)
==>[hello   ,world ,null]

g.inject("   hello   ", " world ", null).lTrim()
g.inject(["   hello   ", " world ", null]).lTrim(local) //1

Use Scope.local to operate on individual string elements inside incoming list, which will return a list.

lTrim() lTrim(Scope)

Map Step

The map() step maps the traverser from the current object to the next step in the process. Please see the General Steps section for more information.

Additional References

map(Traversal)

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

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

Figure 4. Grateful Dead

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

console (groovy) groovy

gremlin> g = traversal().withEmbedded(graph)
==>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 = traversal().withEmbedded(graph)
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

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

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

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.

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

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

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

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

Using Where with Match

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

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)
==>null
gremlin> traversal.toString() //// (4)
==>[GraphStep(vertex,[]), MatchStep(null,AND,[[MatchStartStep(a), HasStep([~label.eq(person)]), MatchEndStep(null)], [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(null,AND,[[MatchStartStep(a), VertexStep(OUT,[created],vertex), MatchEndStep(b)], [MatchStartStep(b), VertexStep(IN,[created],vertex), MatchEndStep(c)], [MatchStartStep(a), WhereTraversalStep([WhereStartStep(null), VertexStep(OUT,[knows],vertex), WhereEndStep(c)]), MatchEndStep(null)]]), 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

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

Important

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

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
gremlin> g.V().math('_+1').by('age') //// (1)
==>30.0
==>28.0
==>33.0
==>36.0

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 _')
g.V().math('_+1').by('age') //1

The "age" property is not productive for all vertices and therefore those values are filtered.

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()

When called as max(local) it determines the maximum value of the current, local object (not the objects in the traversal stream). This works for Collection and Comparable-type objects.

console (groovy) groovy

gremlin> g.V().values('age').fold().max(local)
==>35

g.V().values('age').fold().max(local)

When there are null values being evaluated the null objects are ignored, but if all values are recognized as null the return value is null.

console (groovy) groovy

gremlin> g.inject(null,10, 9, null).max()
==>10
gremlin> g.inject([null,null,null]).max(local)
==>null

g.inject(null,10, 9, null).max()
g.inject([null,null,null]).max(local)

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()

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

When called as mean(local) it determines the mean of the current, local object (not the objects in the traversal stream). This works for Collection and Number-type objects.

console (groovy) groovy

gremlin> g.V().values('age').fold().mean(local)
==>30.75

g.V().values('age').fold().mean(local)

If mean() encounters null values, they will be ignored (i.e. their traversers not counted toward toward the divisor). If all traversers are null then the stream will return null.

console (groovy) groovy

gremlin> g.inject(null,10, 9, null).mean()
==>9.5
gremlin> g.inject([null,null,null]).mean(local)
==>null

g.inject(null,10, 9, null).mean()
g.inject([null,null,null]).mean(local)

Additional References

mean(), mean(Scope), Scope

Merge Step

The merge()-step (map) combines collections like lists and maps. It expects an incoming traverser to contain a collection objection and will combine that object with its specified argument which must be of a matching type. This is also known as the union operation. If the incoming traverser or its associated argument do not meet the expected type, the step will throw an IllegalArgumentException if any other type is encountered (including null). This step differs from the combine()-step in that it doesn’t allow duplicates.

console (groovy) groovy

gremlin> g.V().values("name").fold().merge(["james","jen","marko","vadas"])
==>[jen,ripple,peter,vadas,james,josh,lop,marko]
gremlin> g.V().values("name").fold().merge(__.constant("james").fold())
==>[ripple,peter,vadas,james,josh,lop,marko]
gremlin> g.V().hasLabel('software').elementMap().merge([year:2009])
==>[name:lop,id:3,label:software,lang:java,year:2009]
==>[name:ripple,id:5,label:software,lang:java,year:2009]

g.V().values("name").fold().merge(["james","jen","marko","vadas"])
g.V().values("name").fold().merge(__.constant("james").fold())
g.V().hasLabel('software').elementMap().merge([year:2009])

Additional References

merge(Object) Semantics

MergeEdge Step

The mergeE() step is used to add edges and their properties to a graph in a "create if not exist" fashion. The mergeE() step can also be used to find edges matching a given pattern. The input passed to mergeE() can be either a Map, or a child traversal that produces a Map.

Note	There is a corresponding `mergeV()` step that can be used when creating vertices.

Additionally, option() modulators may be combined with mergeE() to take action depending on whether a vertex was created, or already existed. There are various ways that mergeE() can be used. The simplest being to provide a single Map of keys and values, along with the source and target vertex IDs, as a parameter. A T.id and a T.label may also be provided but this is optional. The mergeE() step can be used directly from the GraphTraversalSource - g, or in the middle of a traversal. For a match with an existing vertex to occur, all values in the Map must exist on a vertex; otherwise, a new vertex will be created. The examples that follow show how mergeE() can be used to add relationships between dogs in the graph.

console (groovy) groovy

gremlin> g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby'])
==>v[1]
gremlin> g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy']) //// (1)
==>v[2]
gremlin> g.mergeE([(T.label):'Sibling',created:'2022-02-07',(Direction.from):1,(Direction.to):2]) //// (2)
==>e[2][1-Sibling->2]
gremlin> g.E().elementMap()
==>[id:2,label:Sibling,IN:[id:2,label:Dog],OUT:[id:1,label:Dog],created:2022-02-07]

g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby'])
g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy']) //// (1)
g.mergeE([(T.label):'Sibling',created:'2022-02-07',(Direction.from):1,(Direction.to):2]) //// (2)
g.E().elementMap()

Create two vertices with ID values of 1 and 2.
Create a "Sibling" relationship between the vertices.

Note	The example above is written with `gremlin-groovy` and evaluated in Gremlin Console as a Groovy script thus allowing Groovy syntax for initializing a `Map`.

For a mergeE() step to succeed, both the from and to vertices must already exist. It is not possible to create new vertices directly using mergeE(), but mergeV() and mergeE() steps can be combined, in a single query, to achieve that goal.

Note	The `mergeE()` step will not create vertices that do not exist. In those cases an error will be returned.

If the Direction enum has been statically included, its explicit use can be omitted from the query.

console (groovy) groovy

gremlin> g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby'])
==>v[1]
gremlin> g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy'])
==>v[2]
gremlin> g.mergeE([(T.label):'Sibling',created:'2022-02-07',(from):1,(to):2])
==>e[2][1-Sibling->2]
gremlin> g.E().elementMap()
==>[id:2,label:Sibling,IN:[id:2,label:Dog],OUT:[id:1,label:Dog],created:2022-02-07]

g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby'])
g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy'])
g.mergeE([(T.label):'Sibling',created:'2022-02-07',(from):1,(to):2])
g.E().elementMap()

One or more option() steps can be used to control the behavior when an edge is created or updated. Similar to mergeV(), the onCreate Map inherits from the main merge argument - any existence criteria in the main merge argument (T.id, T.label, Direction.OUT, Direction.IN) will be automatically carried over to the onCreate action, and these existence criteria cannot be overriden in the onCreate Map.

console (groovy) groovy

gremlin> g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby'])
==>v[1]
gremlin> g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy'])
==>v[2]
gremlin> g.withSideEffect('map',[(T.label):'Sibling',(from):1,(to):2]).
           mergeE(select('map')).
             option(Merge.onCreate,[created:'2022-02-07']). //// (1)
             option(Merge.onMatch,[updated:'2022-02-07'])
==>e[2][1-Sibling->2]
gremlin> g.E().elementMap()
==>[id:2,label:Sibling,IN:[id:2,label:Dog],OUT:[id:1,label:Dog],created:2022-02-07]
gremlin> g.withSideEffect('map',[(T.label):'Sibling',(from):1,(to):2]).
           mergeE(select('map')).
             option(Merge.onCreate,[created:'2022-02-07']).
             option(Merge.onMatch,[updated:'2022-02-07']) //// (2)
==>e[2][1-Sibling->2]
gremlin> g.E().elementMap()
==>[id:2,label:Sibling,IN:[id:2,label:Dog],OUT:[id:1,label:Dog],created:2022-02-07,updated:2022-02-07]

g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby'])
g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy'])
g.withSideEffect('map',[(T.label):'Sibling',(from):1,(to):2]).
  mergeE(select('map')).
    option(Merge.onCreate,[created:'2022-02-07']). //// (1)
    option(Merge.onMatch,[updated:'2022-02-07'])
g.E().elementMap()
g.withSideEffect('map',[(T.label):'Sibling',(from):1,(to):2]).
  mergeE(select('map')).
    option(Merge.onCreate,[created:'2022-02-07']).
    option(Merge.onMatch,[updated:'2022-02-07']) //// (2)
g.E().elementMap()

The edge did not exist - set the created date.
The edge did exist - set the updated date.

More than one edge can be created by a single mergeE() operation. This is done by injecting a list of maps into the traversal and letting them stream into the mergeE() step.

console (groovy) groovy

gremlin> maps = [[(T.label):'Siblings',(from):1,(to):2],
                 [(T.label):'Siblings',(from):1,(to):3]]
==>[label:Siblings,OUT:1,IN:2]
==>[label:Siblings,OUT:1,IN:3]
gremlin> g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby']) //// (1)
==>v[1]
gremlin> g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy'])
==>v[2]
gremlin> g.mergeV([(T.id):3,(T.label):'Dog',name:'Dax'])
==>v[3]
gremlin> g.inject(maps).unfold().mergeE() //// (2)
==>e[3][1-Siblings->2]
==>e[4][1-Siblings->3]
gremlin> g.E().elementMap()
==>[id:3,label:Siblings,IN:[id:2,label:Dog],OUT:[id:1,label:Dog]]
==>[id:4,label:Siblings,IN:[id:3,label:Dog],OUT:[id:1,label:Dog]]

maps = [[(T.label):'Siblings',(from):1,(to):2],
        [(T.label):'Siblings',(from):1,(to):3]]
g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby']) //// (1)
g.mergeV([(T.id):2,(T.label):'Dog',name:'Brandy'])
g.mergeV([(T.id):3,(T.label):'Dog',name:'Dax'])
g.inject(maps).unfold().mergeE() //// (2)
g.E().elementMap()

Create three dogs.
Stream the edge maps into mergeE() steps.

Warning

There is a bit of an inconsistency present when mergeE() is used as a start step versus when it is used mid-traversal. As a start step, mergeE() will promote the currently created or matched Edge to the child traversal, allowing you to directly update it like option(onMatch, property('k', 'v').constant([:])). However, when mergeE() is used mid-traversal, the Edge is not promoted to the child traversal and the incoming traverser is used instead. Such behavior is essentially blocked to prevent accidental misuse and will result in an exception at execution time that will have a message like, "The incoming traverser for MergeEdgeStep cannot be an Element".

The mergeE step can be combined with the mergeV step (or any other step producing a Vertex) using the Merge.outV and Merge.inV option modulators. These options can be used to "late-bind" the OUT and IN vertices in the main merge argument and in the onCreate argument:

console (groovy) groovy

gremlin> g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby']).as('Toby').
           mergeV([(T.id):2,(T.label):'Dog',name:'Brandy']).as('Brandy').
           mergeE([(T.label):'Sibling',created:'2022-02-07',(from):Merge.outV,(to):Merge.inV]).
             option(Merge.outV, select('Toby')).
             option(Merge.inV, select('Brandy'))
==>e[2][1-Sibling->2]
gremlin> g.E().elementMap()
==>[id:2,label:Sibling,IN:[id:2,label:Dog],OUT:[id:1,label:Dog],created:2022-02-07]

g.mergeV([(T.id):1,(T.label):'Dog',name:'Toby']).as('Toby').
  mergeV([(T.id):2,(T.label):'Dog',name:'Brandy']).as('Brandy').
  mergeE([(T.label):'Sibling',created:'2022-02-07',(from):Merge.outV,(to):Merge.inV]).
    option(Merge.outV, select('Toby')).
    option(Merge.inV, select('Brandy'))
g.E().elementMap()

The Merge.outV and Merge.inV tokens can be used as placeholders for values for Direction.OUT and Direction.IN respectively in the mergeE arguments. These options can produce Vertices, as in the example above, or they can specify Maps, which will be used to search for Vertices in the graph. This is useful when the exact T.id of the from/to vertices is not known in advance:

console (groovy) groovy

gremlin> g.mergeV([(T.label):'Dog',name:'Toby'])
==>v[0]
gremlin> g.mergeV([(T.label):'Dog',name:'Brandy'])
==>v[2]
gremlin> g.mergeE([(T.label):'Sibling',created:'2022-02-07',(from):Merge.outV,(to):Merge.inV]).
           option(Merge.outV, [(T.label):'Dog',name:'Toby']).
           option(Merge.inV, [(T.label):'Dog',name:'Brandy'])
==>e[4][0-Sibling->2]
gremlin> g.E().elementMap()
==>[id:4,label:Sibling,IN:[id:2,label:Dog],OUT:[id:0,label:Dog],created:2022-02-07]

g.mergeV([(T.label):'Dog',name:'Toby'])
g.mergeV([(T.label):'Dog',name:'Brandy'])
g.mergeE([(T.label):'Sibling',created:'2022-02-07',(from):Merge.outV,(to):Merge.inV]).
  option(Merge.outV, [(T.label):'Dog',name:'Toby']).
  option(Merge.inV, [(T.label):'Dog',name:'Brandy'])
g.E().elementMap()

Additional References

mergeE(), mergeE(Map), mergeE(Traversal), Merge, Semantics

MergeVertex Step

The mergeV() -step is used to add vertices and their properties to a graph in a "create if not exist" fashion. The mergeV() step can also be used to find vertices matching a given pattern. The input passed to mergeV() can be either a Map, or a child Traversal that produces a Map.

Note	There is a corresponding `mergeE()` step that can be used when creating edges.

Additionally, option() modulators may be combined with mergeV() to take action depending on whether a vertex was created, or already existed. There are various ways mergeV() can be used. The simplest being to provide a single Map of keys and values as a parameter. A T.id and a T.label may also be provided but this is optional. The mergeV() step can be used directly from the GraphTraversalSource - g, or in the middle of a traversal. For a match with an existing vertex to occur, all values in the Map must exist on a vertex; otherwise, a new vertex will be created. The examples that follow show how mergeV() can be used to add some dogs to the graph.

console (groovy) groovy

gremlin> g.mergeV([name: 'Brandy']) //// (1)
==>v[0]
gremlin> g.V().has('name','Brandy')
==>v[0]
gremlin> g.mergeV([(T.label):'Dog',name:'Scamp', age:12]) //// (2)
==>v[2]
gremlin> g.V().hasLabel('Dog').valueMap()
==>[name:[Scamp],age:[12]]
gremlin> g.mergeV([(T.id):300, (T.label):'Dog', name:'Toby', age:10]) //// (3)
==>v[300]
gremlin> g.V().hasLabel('Dog').valueMap().with(WithOptions.tokens)
==>[id:2,label:Dog,name:[Scamp],age:[12]]
==>[id:300,label:Dog,name:[Toby],age:[10]]

g.mergeV([name: 'Brandy']) //// (1)
g.V().has('name','Brandy')
g.mergeV([(T.label):'Dog',name:'Scamp', age:12]) //// (2)
g.V().hasLabel('Dog').valueMap()
g.mergeV([(T.id):300, (T.label):'Dog', name:'Toby', age:10]) //// (3)
g.V().hasLabel('Dog').valueMap().with(WithOptions.tokens)

Create a vertex for Brandy as no other matching ones exist yet.
Create a vertex for Scamp and also add a Dog label his age.
Create a vertex for Toby with an T.id of 300.

Note	The example above is written with `gremlin-groovy` and evaluated in Gremlin Console as a Groovy script thus allowing Groovy syntax for initializing a `Map`.

If a vertex already exists that matches the map passed to mergeV(), the existing vertex will be returned, otherwise a new one will be created. In this way, mergeV() provides "get or create" semantics.

console (groovy) groovy

gremlin> g.mergeV([name: 'Brandy']) //// (1)
==>v[0]

g.mergeV([name: 'Brandy']) //1

A vertex for Brandy already exists so return it. A new one is not created.

It’s important to note that every key/value pair passed to mergeV() must already exist on one or more vertices for there to be a match. If a match is found, the vertex, or vertices, representing that match will be returned. If a vertex representing a dog called Brandy already exists, but it does not have an "age" property, the mergeV() below will not find a match and a new vertex will be created.

console (groovy) groovy

gremlin> g.addV('Dog').property('name','Brandy') //// (1)
==>v[0]
gremlin> g.mergeV([(T.label):'Dog',name:'Brandy',age:13]) //// (2)
==>v[2]

g.addV('Dog').property('name','Brandy') //// (1)
g.mergeV([(T.label):'Dog',name:'Brandy',age:13]) //2

Create a vertex for Brandy with no age property.
A new vertex is created as there is no exact match to any existing vertices.

A common scenario is to search for a vertex with a known T.id and if it exists return that vertex. If it does not exist, create it. As we have seen, one way to do this is to pass the T.id and all properties directly to mergeV(). Another is to use Merge.onCreate. Note that the Map specified for Match.onCreate does not need to include the T.id already present in the original search. The values provided to the mergeV() Map are inherited by the onCreate action and combined with the Map provided to Merge.onCreate. Overrides of the T.id or T.label in the onCreate Map are prohibited.

console (groovy) groovy

gremlin> g.mergeV([(T.id):300]).
           option(Merge.onCreate,[(T.label):'Dog', name:'Toby', age:10])
==>v[300]

g.mergeV([(T.id):300]).
  option(Merge.onCreate,[(T.label):'Dog', name:'Toby', age:10])

To take specific action when the vertex already exists, Merge.onMatch can be used. The second parameter to the option step can be either a Map whose values are used to update the vertex or another Gremlin traversal that generates a Map.

Note	If `mergeV()` is given an empty `Map`; such as `mergeV([:])`, it will match, and return, every vertex in the graph. This is the same behavior seen with `V([])`.

console (groovy) groovy

gremlin> g.mergeV([(T.id):300]).
           option(Merge.onCreate,[(T.label):'Dog', name:'Toby', age:10]). //// (1)
           option(Merge.onMatch,[age:11]) //// (2)
==>v[300]
gremlin> g.withSideEffect('new-data',[age:11]).
           mergeV([(T.id):300]).
           option(Merge.onCreate,[(T.label):'Dog', name:'Toby', age:10]).
           option(Merge.onMatch,select('new-data')) //// (3)
==>v[300]
gremlin> g.V(300).valueMap().with(WithOptions.tokens)
==>[id:300,label:Dog,name:[Toby],age:[11]]

g.mergeV([(T.id):300]).
  option(Merge.onCreate,[(T.label):'Dog', name:'Toby', age:10]). //// (1)
  option(Merge.onMatch,[age:11]) //// (2)
g.withSideEffect('new-data',[age:11]).
  mergeV([(T.id):300]).
  option(Merge.onCreate,[(T.label):'Dog', name:'Toby', age:10]).
  option(Merge.onMatch,select('new-data')) //// (3)
g.V(300).valueMap().with(WithOptions.tokens)

If no match found create the vertex using these values.
If a match is found, change the age property value.
Change the age property by selecting from the new-data map.

It is sometimes helpful to incorporate fail() step into scenarios where there is a need to stop the traversal for one event or the other:

gremlin> g.mergeV([(T.id): 1]).
......1>     option(onCreate, fail("vertex did not exist")).
......2>     option(onMatch, [modified: 2022])
fail() Step Triggered
======================================================================================================================================================================
Message  > vertex did not exist
Traverser> false
  Bulk   > 1
Traversal> fail("vertex did not exist")
Parent   > TinkerMergeVertexStep [mergeV([(T.id):((int) 1)]).option(Merge.onCreate,__.fail("vertex did not exist")).option(Merge.onMatch,[("modified"):((int) 2022)])]
Metadata > {}
======================================================================================================================================================================

When working with multi-properties, there are two ways to specify them for mergeV(). First, you can specify them individually using a CardinalityValue as the value in the Map. The CardinalityValue allows you to specify the value as well as the Cardinality for that value. Note that it is only possible to specify one value with this syntax even if you are using set or list.

console (groovy) groovy

gremlin> g.mergeV([(T.label):'Dog', name:'Max']). //// (1)
             option(onCreate, [alias: set('Maximus')]). //// (2)
           property(set,'alias','Maxamillion') //// (3)
==>v[0]
gremlin> g.V().has('name','Max').valueMap().with(WithOptions.tokens)
==>[id:0,label:Dog,name:[Max],alias:[Maximus,Maxamillion]]

g.mergeV([(T.label):'Dog', name:'Max']). //// (1)
    option(onCreate, [alias: set('Maximus')]). //// (2)
  property(set,'alias','Maxamillion') //// (3)
g.V().has('name','Max').valueMap().with(WithOptions.tokens)

Find or create a vertex for Max.
If Max is not found then add an alias of set cardinality.
Whether Max was found or created, add another alias with set cardinality.

The second option is to specify Cardinality for the entire range of values as follows:

console (groovy) groovy

gremlin> g.mergeV([(T.label):'Dog', name:'Max']).
             option(onCreate, [alias: 'Maximus', city: 'Boston'], set) //// (1)
==>v[0]
gremlin> g.mergeV([(T.label):'Dog', name:'Max']).
             option(onCreate, [alias: 'Maximus', city: single('Boston')], set) //// (2)
==>v[0]

g.mergeV([(T.label):'Dog', name:'Max']).
    option(onCreate, [alias: 'Maximus', city: 'Boston'], set) //// (1)
g.mergeV([(T.label):'Dog', name:'Max']).
    option(onCreate, [alias: 'Maximus', city: single('Boston')], set) //2

If Max is created then set the alias and city with cardinality of set.
If Max is created then set the alias with cardinality of set and city with cardinality single.

More than one vertex can be created by a single mergeV() operation. This is done by injecting a List of Map objects into the traversal and letting them stream into the mergeV() step.

console (groovy) groovy

gremlin> maps = [[(T.label) : 'Dog', name: 'Toby'  , breed: 'Golden Retriever'],
                 [(T.label) : 'Dog', name: 'Brandy', breed: 'Golden Retriever'],
                 [(T.label) : 'Dog', name: 'Scamp' , breed: 'King Charles Spaniel'],
                 [(T.label) : 'Dog', name: 'Shadow', breed: 'Mixed'],
                 [(T.label) : 'Dog', name: 'Rocket', breed: 'Golden Retriever'],
                 [(T.label) : 'Dog', name: 'Dax'   , breed: 'Mixed'],
                 [(T.label) : 'Dog', name: 'Baxter', breed: 'Mixed'],
                 [(T.label) : 'Dog', name: 'Zoe'   , breed: 'Corgi'],
                 [(T.label) : 'Dog', name: 'Pixel' , breed: 'Mixed']]
==>[label:Dog,name:Toby,breed:Golden Retriever]
==>[label:Dog,name:Brandy,breed:Golden Retriever]
==>[label:Dog,name:Scamp,breed:King Charles Spaniel]
==>[label:Dog,name:Shadow,breed:Mixed]
==>[label:Dog,name:Rocket,breed:Golden Retriever]
==>[label:Dog,name:Dax,breed:Mixed]
==>[label:Dog,name:Baxter,breed:Mixed]
==>[label:Dog,name:Zoe,breed:Corgi]
==>[label:Dog,name:Pixel,breed:Mixed]
gremlin> g.inject(maps).unfold().mergeV()
==>v[0]
==>v[3]
==>v[6]
==>v[9]
==>v[12]
==>v[15]
==>v[18]
==>v[21]
==>v[24]
gremlin> g.V().hasLabel('Dog').valueMap().with(WithOptions.tokens)
==>[id:0,label:Dog,name:[Toby],breed:[Golden Retriever]]
==>[id:18,label:Dog,name:[Baxter],breed:[Mixed]]
==>[id:3,label:Dog,name:[Brandy],breed:[Golden Retriever]]
==>[id:21,label:Dog,name:[Zoe],breed:[Corgi]]
==>[id:6,label:Dog,name:[Scamp],breed:[King Charles Spaniel]]
==>[id:24,label:Dog,name:[Pixel],breed:[Mixed]]
==>[id:9,label:Dog,name:[Shadow],breed:[Mixed]]
==>[id:12,label:Dog,name:[Rocket],breed:[Golden Retriever]]
==>[id:15,label:Dog,name:[Dax],breed:[Mixed]]

maps = [[(T.label) : 'Dog', name: 'Toby'  , breed: 'Golden Retriever'],
        [(T.label) : 'Dog', name: 'Brandy', breed: 'Golden Retriever'],
        [(T.label) : 'Dog', name: 'Scamp' , breed: 'King Charles Spaniel'],
        [(T.label) : 'Dog', name: 'Shadow', breed: 'Mixed'],
        [(T.label) : 'Dog', name: 'Rocket', breed: 'Golden Retriever'],
        [(T.label) : 'Dog', name: 'Dax'   , breed: 'Mixed'],
        [(T.label) : 'Dog', name: 'Baxter', breed: 'Mixed'],
        [(T.label) : 'Dog', name: 'Zoe'   , breed: 'Corgi'],
        [(T.label) : 'Dog', name: 'Pixel' , breed: 'Mixed']]
g.inject(maps).unfold().mergeV()
g.V().hasLabel('Dog').valueMap().with(WithOptions.tokens)

Another useful pattern that can be used with mergeV() involves putting multiple maps in a list and selecting different maps based on the action being taken. The examples below use a list containing three maps. The first containing just the ID to be searched for. The second map contains all the information to use when the vertex is created. The third map contains additional information that will be applied if an existing vertex is found.

console (groovy) groovy

gremlin> g.inject([[(T.id):400],[(T.label):'Dog',name:'Pixel',age:1],[updated:'2022-02-1']]).
           mergeV(limit(local,1)). //// (1)
           option(Merge.onCreate,range(local,1,2)). //// (2)
           option(Merge.onMatch,tail(local)) //// (3)
==>v[400]
gremlin> g.V(400).valueMap().with(WithOptions.tokens)
==>[id:400,label:Dog,name:[Pixel],age:[1]]
gremlin> g.inject([[(T.id):400],[(T.label):'Dog',name:'Pixel',age:1],[updated:'2022-02-1']]).
           mergeV(limit(local,1)).
           option(Merge.onCreate,range(local,1,2)).
           option(Merge.onMatch,tail(local)) //// (4)
==>v[400]
gremlin> g.V(400).valueMap().with(WithOptions.tokens) //// (5)
==>[id:400,label:Dog,name:[Pixel],updated:[2022-02-1],age:[1]]

g.inject([[(T.id):400],[(T.label):'Dog',name:'Pixel',age:1],[updated:'2022-02-1']]).
  mergeV(limit(local,1)). //// (1)
  option(Merge.onCreate,range(local,1,2)). //// (2)
  option(Merge.onMatch,tail(local)) //// (3)
g.V(400).valueMap().with(WithOptions.tokens)
g.inject([[(T.id):400],[(T.label):'Dog',name:'Pixel',age:1],[updated:'2022-02-1']]).
  mergeV(limit(local,1)).
  option(Merge.onCreate,range(local,1,2)).
  option(Merge.onMatch,tail(local)) //// (4)
g.V(400).valueMap().with(WithOptions.tokens)  //5

Use the first map to search for a vertex with an ID of 400.
If the vertex was not found, use the second map to create it.
If the vertex was found, add an updated property.
Pixel exists now, so we will take this option.
The updated property has now been added.

Warning

There is a bit of an inconsistency present when mergeV() is used as a start step versus when it is used mid-traversal. As a start step, mergeV() will promote the currently created or matched Vertex to the child traversal, allowing you to directly update it like option(onMatch, property('k', 'v').constant([:])). However, when mergeV() is used mid-traversal, the Vertex is not promoted to the child traversal and the incoming traverser is used instead. Such behavior is essentially blocked to prevent accidental misuse and will result in an exception at execution time that will have a message like, "The incoming traverser for MergeVertexStep cannot be an Element".

Additional References

mergeV(), mergeV(Map), mergeV(Traversal), Merge, Semantics

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()

When called as min(local) it determines the minimum value of the current, local object (not the objects in the traversal stream). This works for Collection and Comparable-type objects.

console (groovy) groovy

gremlin> g.V().values('age').fold().min(local)
==>27

g.V().values('age').fold().min(local)

When there are null values being evaluated the null objects are ignored, but if all values are recognized as null the return value is null.

console (groovy) groovy

gremlin> g.inject(null,10, 9, null).min()
==>9
gremlin> g.inject([null,null,null]).min(local)
==>null

g.inject(null,10, 9, null).min()
g.inject([null,null,null]).min(local)

Additional References

min(), min(Scope), Scope

None Step

The none()-step (filter) filters all objects from a traversal stream. It is especially useful for 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.

Note	As of release 4.0.0, `none()` will be renamed to `discard()`.

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')).elementMap()
==>[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')).elementMap()
g.V().hasLabel('person').
  not(out('created').count().is(gt(1))).values('name')   //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

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

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
gremlin> g.V().both().order().by('age') //// (1)
==>v[2]
==>v[1]
==>v[1]
==>v[1]
==>v[4]
==>v[4]
==>v[4]
==>v[6]

g.V().values('name')
g.V().order().by('name',asc).values('name')
g.V().order().by('name',desc).values('name')
g.V().both().order().by('age') //1

The "age" property is not productive for all vertices and therefore those values are filtered.

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

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

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

If a property key does not exist, then it will be treated as null which will sort it first for Order.asc and last for Order.desc.

console (groovy) groovy

gremlin> g.V().order().by("age").elementMap()
==>[id:2,label:person,name:vadas,age:27]
==>[id:1,label:person,name:marko,age:29]
==>[id:4,label:person,name:josh,age:32]
==>[id:6,label:person,name:peter,age:35]

g.V().order().by("age").elementMap()

Note	Prior to version 3.3.4, ordering was defined by `Order.incr` for ascending order and `Order.decr` for descending order. Those tokens were deprecated and eventually removed in 3.5.0.

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 = traversal().withEmbedded(graph).withComputer()
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
gremlin> g.V().pageRank().with(PageRank.propertyName, 'friendRank').values('pageRank')
gremlin> g.V().hasLabel('person').
           pageRank().
             with(PageRank.edges, __.outE('knows')).
             with(PageRank.propertyName, 'friendRank').
           order().by('friendRank',desc).
           elementMap('name','friendRank')
==>[id:4,label:person,friendRank:0.8321166533236799,name:josh]
==>[id:2,label:person,friendRank:0.8321166533236799,name:vadas]
==>[id:6,label:person,friendRank:0.5839416733381598,name:peter]
==>[id:1,label:person,friendRank:0.5839416733381598,name:marko]

g = traversal().withEmbedded(graph).withComputer()
g.V().pageRank().with(PageRank.propertyName, 'friendRank').values('pageRank')
g.V().hasLabel('person').
  pageRank().
    with(PageRank.edges, __.outE('knows')).
    with(PageRank.propertyName, 'friendRank').
  order().by('friendRank',desc).
  elementMap('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 = traversal().withEmbedded(graph).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).
           elementMap('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]]), ElementMa
                                         pStep([name, friendRank])]

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]]), ElementMapStep([name, friendRank])],graphfilter[none]), ComputerResultStep]
ConnectiveStrategy              [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]]), ElementMapStep([name, friendRank])],graphfilter[none]), ComputerResultStep]
IdentityRemovalStrategy         [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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],graphfilter[none]), ComputerResultStep]
ByModulatorOptimizationStrategy [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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],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]]), ElementMapStep([name, friendRank])],graphfilter[none]), ComputerResultStep]

g = traversal().withEmbedded(graph).withComputer()
g.V().hasLabel('person').
  pageRank().
    with(PageRank.edges, __.outE('knows')).
    with(PageRank.propertyName, 'friendRank').
  order().by('friendRank',desc).
  elementMap('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]
gremlin> g.V().both().path().by('age') //// (1)
==>[29,27]
==>[29,32]
==>[27,29]
==>[32,29]

g.V().out().out().values('name')
g.V().out().out().values('name').path()
g.V().both().path().by('age') //1

The "age" property is not productive for all vertices and therefore those values are filtered.

If edges are required in the path, then be sure to traverse 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())

It’s possible to limit the path using the to() or from() step modulators.

console (groovy) groovy

gremlin> g.V().has('person','name','vadas').as('e').
               in('knows').
               out('knows').where(neq('e')).
               path().by('name') //// (1)
==>[vadas,marko,josh]
gremlin> g.V().has('person','name','vadas').as('e').
                in('knows').as('m').
                out('knows').where(neq('e')).
                path().to('m').by('name') //// (2)
==>[vadas,marko]
gremlin> g.V().has('person','name','vadas').as('e').
                in('knows').as('m').
                out('knows').where(neq('e')).
                path().from('m').by('name') //// (3)
==>[marko,josh]

g.V().has('person','name','vadas').as('e').
      in('knows').
      out('knows').where(neq('e')).
      path().by('name') //// (1)
g.V().has('person','name','vadas').as('e').
       in('knows').as('m').
       out('knows').where(neq('e')).
       path().to('m').by('name') //// (2)
g.V().has('person','name','vadas').as('e').
       in('knows').as('m').
       out('knows').where(neq('e')).
       path().from('m').by('name') //3

Obtain the full path from vadas to josh.
Save the middle node, marko, and use the to() modulator to show only the path from vadas to marko
Use the from() mdoulator to show only the path from marko to josh

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 = traversal().withEmbedded(graph).withComputer()
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
gremlin> g.V().peerPressure().with(PeerPressure.propertyName, '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 = traversal().withEmbedded(graph).withComputer()
g.V().peerPressure().with(PeerPressure.propertyName, '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()

Product Step

The product()-step (map) calculates the cartesian product between the incoming list traverser and the provided list argument. This step only expects list data (array or Iterable) and will throw an IllegalArgumentException if any other type is encountered (including null).

console (groovy) groovy

gremlin> g.V().values("name").fold().product(["james","jen"])
==>[[marko,james],[marko,jen],[vadas,james],[vadas,jen],[lop,james],[lop,jen],[josh,james],[josh,jen],[ripple,james],[ripple,jen],[peter,james],[peter,jen]]
gremlin> g.V().values("name").fold().product(__.V().has("age").limit(1).values("age").fold())
==>[[marko,29],[vadas,29],[lop,29],[josh,29],[ripple,29],[peter,29]]

g.V().values("name").fold().product(["james","jen"])
g.V().values("name").fold().product(__.V().has("age").limit(1).values("age").fold())

Additional References

product(Object) Semantics

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           0.084    22.51
VertexStep(OUT,[created],vertex)                                       4           4           0.044    11.97
NoOpBarrierStep(2500)                                                  4           2           0.026     6.94
VertexStep(BOTH,vertex)                                               10           4           0.012     3.44
NoOpBarrierStep(2500)                                                 10           3           0.013     3.68
VertexStep(BOTH,vertex)                                               24           7           0.016     4.40
NoOpBarrierStep(2500)                                                 24           5           0.018     4.92
VertexStep(BOTH,vertex)                                               58          11           0.022     5.88
NoOpBarrierStep(2500)                                                 58           6           0.030     8.03
HasStep([~label.eq(person)])                                          48           4           0.027     7.34
PropertiesStep([age],value)                                           48           4           0.024     6.41
SumGlobalStep                                                          1           1           0.054    14.48
                                            >TOTAL                     -           -           0.375        -

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.

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

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

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().has('name','marko').
           project('id', 'name', 'out', 'in').
             by(id).
             by('name').
             by(outE().count()).
             by(inE().count())
==>[id:1,name:marko,out:3,in:0]
gremlin> g.V().has('name','marko').
           project('name', 'friendsNames').
             by('name').
             by(out('knows').values('name').fold())
==>[name:marko,friendsNames:[vadas,josh]]
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().project('n','a').by('name').by('age') //// (1)
==>[n:marko,a:29]
==>[n:vadas,a:27]
==>[n:lop]
==>[n:josh,a:32]
==>[n:ripple]
==>[n:peter,a:35]

g.V().has('name','marko').
  project('id', 'name', 'out', 'in').
    by(id).
    by('name').
    by(outE().count()).
    by(inE().count())
g.V().has('name','marko').
  project('name', 'friendsNames').
    by('name').
    by(out('knows').values('name').fold())
g.V().out('created').
  project('a','b').
    by('name').
    by(__.in('created').count()).
  order().by(select('b'),desc).
  select('a')
g.V().project('n','a').by('name').by('age') //1

The "age" property is not productive for all vertices and therefore those values are filtered and the key not present in the Map.

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 = traversal().withEmbedded(graph).withComputer()
==>graphtraversalsource[tinkergraph[vertices:6 edges:6], graphcomputer]
gremlin> g.V().hasLabel('person').
           program(PageRankVertexProgram.build().property('rank').create(graph)).
             order().by('rank', asc).
           elementMap('name', 'rank')
==>[id:4,label:person,name:josh,rank:0.14598540152719103]
==>[id:1,label:person,name:marko,rank:0.11375510357865537]
==>[id:2,label:person,name:vadas,rank:0.14598540152719103]
==>[id:6,label:person,name:peter,rank:0.11375510357865537]

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

Property 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(['city': 'santa fe', 'state': 'new mexico']) //// (1)
==>v[1]
gremlin> g.V(1).property(list,'age',35) //// (2)
==>v[1]
gremlin> g.V(1).property(list, ['city': 'santa fe', 'state': 'new mexico']) //// (3)
==>v[1]
gremlin> g.V(1).valueMap()
==>[country:[usa],city:[santa fe,santa fe],name:[marko],state:[new mexico,new mexico],age:[29,35]]
gremlin> g.V(1).property(list, ['age': single(36), 'city': 'wilmington', 'state': 'delaware']) //// (4)
==>v[1]
gremlin> g.V(1).valueMap()
==>[country:[usa],city:[santa fe,santa fe,wilmington],name:[marko],state:[new mexico,new mexico,delaware],age:[36]]
gremlin> g.V(1).property('friendWeight',outE('knows').values('weight').sum(),'acl','private') //// (5)
==>v[1]
gremlin> g.V(1).properties('friendWeight').valueMap() //// (6)
==>[acl:private]
gremlin> g.addV().property(T.label,'person').valueMap().with(WithOptions.tokens) //// (7)
==>[id:13,label:person]
gremlin> g.addV().property(null) //// (8)
==>v[14]
gremlin> g.addV().property(set, null)
==>v[15]

g.V(1).property('country','usa')
g.V(1).property('city','santa fe').property('state','new mexico').valueMap()
g.V(1).property(['city': 'santa fe', 'state': 'new mexico']) //// (1)
g.V(1).property(list,'age',35) //// (2)
g.V(1).property(list, ['city': 'santa fe', 'state': 'new mexico']) //// (3)
g.V(1).valueMap()
g.V(1).property(list, ['age': single(36), 'city': 'wilmington', 'state': 'delaware']) //// (4)
g.V(1).valueMap()
g.V(1).property('friendWeight',outE('knows').values('weight').sum(),'acl','private') //// (5)
g.V(1).properties('friendWeight').valueMap() //// (6)
g.addV().property(T.label,'person').valueMap().with(WithOptions.tokens) //// (7)
g.addV().property(null) //// (8)
g.addV().property(set, null)

Properties can also take a Map as an argument.
For vertices, a cardinality can be provided for vertex properties.
If a cardinality is specified for a Map then that cardinality will be used for all properties in the map.
Assign the Cardinality individually to override the specified list or the default cardinality if not specified.
It is possible to select the property value (as well as key) via a traversal.
For vertices, the property()-step can add meta-properties.
The label value can be specified as a property only at the time a vertex is added and if one is not specified in the addV()
If you pass a null value for the Map this will be treated as a no-op and the input will be returned

Additional References

property(Object, Object, Object…), property(Cardinality, Object, Object, Object…), Cardinality

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

Read Step

The read()-step is not really a "step" but a step modulator in that it modifies the functionality of the io()-step. More specifically, it tells the io()-step that it is expected to use its configuration to read data from some location. Please see the documentation for io()-step for more complete details on usage.

Additional References

read()

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

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

Important

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

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

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

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

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

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

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

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

Warning

The anonymous traversal of emit() and until() (not repeat()) process their current objects "locally." In OLAP, where the atomic unit of computing is the 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)

Looping Recipes

Replace Step

The replace()-step (map) returns a string with the specified characters in the original string replaced with the new characters. Any null arguments will be a no-op and the original string is returned. Null values from the incoming traversers are not processed and remain as null when returned. If the incoming traverser is a non-String value then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.inject('that', 'this', 'test', null).replace('h', 'j') //// (1)
==>tjat
==>tjis
==>test
==>null
gremlin> g.inject('hello world').replace(null, 'j') //// (2)
==>hello world
gremlin> g.V().hasLabel("software").values("name").replace("p", "g") //// (3)
==>log
==>riggle
gremlin> g.V().hasLabel("software").values("name").fold().replace(local, "p", "g") //// (4)
==>[log,riggle]

g.inject('that', 'this', 'test', null).replace('h', 'j') //// (1)
g.inject('hello world').replace(null, 'j') //// (2)
g.V().hasLabel("software").values("name").replace("p", "g") //// (3)
g.V().hasLabel("software").values("name").fold().replace(local, "p", "g") //4

Replace "h" in the strings with "j".
Null inputs are ignored and the original string is returned.
Return software names with "p" replaced by "g".
Use Scope.local to operate on individual string elements inside incoming list, which will return a list.

Additional References replace(String,String) replace(Scope,String,String)

Reverse Step

The reverse()-step (map) returns the reverse of the incoming list traverser. Single values (including null) are not processed and are added back to the Traversal Stream unchanged. If the incoming traverser is a String value then the reversed String will be returned.

console (groovy) groovy

gremlin> g.V().values("name").reverse() //// (1)
==>okram
==>sadav
==>pol
==>hsoj
==>elppir
==>retep
gremlin> g.V().values("name").order().fold().reverse() //// (2)
==>[vadas,ripple,peter,marko,lop,josh]

g.V().values("name").reverse() //// (1)
g.V().values("name").order().fold().reverse() //2

Reverse the order of the characters in each name.
Fold all the names into a list in ascending order and then reverse the list’s ordering (into descending).

reverse()

RTrim Step

The rTrim()-step (map) returns a string with trailing whitespace removed. Null values are not processed and remain as null when returned. If the incoming traverser is a non-String value then an IllegalArgumentException will be thrown.

console (groovy) groovy

gremlin> g.inject("   hello   ", " world ", null).rTrim()
==>   hello
==> world
==>null
gremlin> g.inject(["   hello   ", " world ", null]).rTrim(local) //// (1)
==>[   hello, world,null]

g.inject("   hello   ", " world ", null).rTrim()
g.inject(["   hello   ", " world ", null]).rTrim(local) //1

Use Scope.local to operate on individual string elements inside incoming list, which will return a list.

rTrim() rTrim(Scope)

Sack Step

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

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

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

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@5d6125d8
gremlin> g.withSack {rand.nextFloat()}.V().sack()
==>0.30364954
==>0.6187649
==>0.046979964
==>0.2031619
==>0.33781552
==>0.66723937

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]]
gremlin> g.V().sack(assign).by('age').sack() //// (1)
==>29
==>27
==>32
==>35

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')
g.V().sack(assign).by('age').sack() //1

The "age" property is not productive for all vertices and therefore those values are filtered during the assignment.

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

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(