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[[neo4j-gremlin]]
== Neo4j-Gremlin

[source,xml]
----
<dependency>
   <groupId>org.apache.tinkerpop</groupId>
   <artifactId>neo4j-gremlin</artifactId>
   <version>x.y.z</version>
</dependency>
<!-- neo4j-tinkerpop-api-impl is NOT Apache 2 licensed - more information below -->
<dependency>
  <groupId>org.neo4j</groupId>
  <artifactId>neo4j-tinkerpop-api-impl</artifactId>
  <version>0.3-2.3.3</version>
</dependency>
----

link:http://neo4j.com[Neo4j, Inc.] are the developers of the OLTP-based link:http://neo4j.com[Neo4j graph database].

WARNING: Unless under a commercial agreement with Neo4j, Inc., Neo4j is licensed
link:http://en.wikipedia.org/wiki/Affero_General_Public_License[AGPL]. The `neo4j-gremlin` module is licensed Apache2
because it only references the Apache2-licensed Neo4j API (not its implementation). Note that neither the
<<gremlin-console,Gremlin Console>> nor <<gremlin-server,Gremlin Server>> distribute with the Neo4j implementation
binaries. To access the binaries, use the `:install` command to download binaries from
link:http://search.maven.org/[Maven Central Repository].

TIP: For configuring Grape, the dependency resolver of Groovy, please refer to the <<gremlin-applications,Gremlin Applications>> section.

[source,groovy]
----
gremlin> :install org.apache.tinkerpop neo4j-gremlin x.y.z
==>Loaded: [org.apache.tinkerpop, neo4j-gremlin, x.y.z] - restart the console to use [tinkerpop.neo4j]
gremlin> :q
...
gremlin> :plugin use tinkerpop.neo4j
==>tinkerpop.neo4j activated
gremlin> graph = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[EmbeddedGraphDatabase [/tmp/neo4j]]
----

TIP: To host Neo4j in <<gremlin-server,Gremlin Server>>, the dependencies must first be "installed" or otherwise
copied to the Gremlin Server path. The automated method for doing this would be to execute
`bin/gremlin-server.sh -i org.apache.tinkerpop neo4j-gremlin x.y.z`.

=== Indices

Neo4j 2.x indices leverage vertex labels to partition the index space. TinkerPop3 does not provide method interfaces
for defining schemas/indices for the underlying graph system. Thus, in order to create indices, it is important to
call the Neo4j API directly.

NOTE: `Neo4jGraphStep` will attempt to discern which indices to use when executing a traversal of the form `g.V().has()`.

The Gremlin-Console session below demonstrates Neo4j indices. For more information, please refer to the Neo4j documentation:

* Manipulating indices with link:http://neo4j.com/docs/developer-manual/current/#query-schema-index[Cypher].
* Manipulating indices with the Neo4j link:http://neo4j.com/docs/stable/tutorials-java-embedded-new-index.html[Java API].

[gremlin-groovy]
----
graph = Neo4jGraph.open('/tmp/neo4j')
g = graph.traversal()
graph.cypher("CREATE INDEX ON :person(name)")
graph.tx().commit()  <1>
g.addV('person').property('name','marko')
g.addV('dog').property('name','puppy')
g.V().hasLabel('person').has('name','marko').values('name')
graph.close()
----

<1> Schema mutations must happen in a different transaction than graph mutations

Below demonstrates the runtime benefits of indices and demonstrates how if there is no defined index (only vertex
labels), a linear scan of the vertex-label partition is still faster than a linear scan of all vertices.

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

<1> Find all artists whose name is Garcia which does a linear scan of the artist vertex-label partition.
<2> Create an index for all artist vertices on their name property.
<3> Neo4j indices are eventually consistent so this stalls to give the index time to populate itself.
<4> Find all artists whose name is Garcia which uses the pre-defined schema index.
<5> Find all vertices whose name is Garcia which requires a linear scan of all the data in the graph.
<6> Drop the created index.

=== Multi/Meta-Properties

`Neo4jGraph` supports both multi- and meta-properties (see <<vertex-properties,vertex properties>>). These features
are not native to Neo4j and are implemented using "hidden" Neo4j nodes. For example, when a vertex has multiple
"name" properties, each property is a new node (multi-properties) which can have properties attached to it
(meta-properties). As such, the native, underlying representation may become difficult to query directly using
another graph language such as <<_cypher,Cypher>>. The default setting is to disable multi- and meta-properties.
However, if this feature is desired, then it can be activated via `gremlin.neo4j.metaProperties` and
`gremlin.neo4j.multiProperties` configurations being set to `true`. Once the configuration is set, it can not be
changed for the lifetime of the graph.

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

WARNING: `Neo4jGraph` without multi- and meta-properties is in 1-to-1 correspondence with the native, underlying Neo4j
representation. It is recommended that if the user does not require multi/meta-properties, then they should not
enable them. Without multi- and meta-properties enabled, Neo4j can be interacted with with other tools and technologies
that do not leverage TinkerPop.

IMPORTANT: When using a multi-property enabled `Neo4jGraph`, vertices may represent their properties on "hidden
nodes" adjacent to the vertex. If a vertex property key/value is required for indexing, then two indices are
required -- e.g. `CREATE INDEX ON :person(name)` and `CREATE INDEX ON :vertexProperty(name)`
(see <<_indices,Neo4j indices>>).

=== Cypher

image::gremlin-loves-cypher.png[width=400]

NeoTechnology are the creators of the graph pattern-match query language link:https://neo4j.com/developer/cypher-query-language/[Cypher].
It is possible to leverage Cypher from within Gremlin by using the `Neo4jGraph.cypher()` graph traversal method.

[gremlin-groovy]
----
graph = Neo4jGraph.open('/tmp/neo4j')
graph.io(gryo()).readGraph('data/tinkerpop-modern.kryo')
graph.cypher('MATCH (a {name:"marko"}) RETURN a')
graph.cypher('MATCH (a {name:"marko"}) RETURN a').select('a').out('knows').values('name')
graph.close()
----

Thus, like <<match-step,`match()`>>-step in Gremlin, it is possible to do a declarative pattern match and then move
back into imperative Gremlin.

TIP: For those developers using <<gremlin-server,Gremlin Server>> against Neo4j, it is possible to do Cypher queries
by simply placing the Cypher string in `graph.cypher(...)` before submission to the server.

=== Multi-Label

TinkerPop3 requires every `Element` to have a single, immutable string label (i.e. a `Vertex`, `Edge`, and
`VertexProperty`). In Neo4j, a `Node` (vertex) can have an
link:http://neo4j.com/docs/developer-manual/current/#graphdb-neo4j-labels[arbitrary number of labels] while a `Relationship`
(edge) can have one and only one. Furthermore, in Neo4j, `Node` labels are mutable while `Relationship` labels are
not. In order to handle this mismatch, three `Neo4jVertex` specific methods exist in Neo4j-Gremlin.

[source,java]
public Set<String> labels() // get all the labels of the vertex
public void addLabel(String label) // add a label to the vertex
public void removeLabel(String label) // remove a label from the vertex

An example use case is presented below.

[gremlin-groovy]
----
graph = Neo4jGraph.open('/tmp/neo4j')
g = graph.traversal()
vertex = (Neo4jVertex) g.addV('human::animal').next() <1>
vertex.label() <2>
vertex.labels() <3>
vertex.addLabel('organism') <4>
vertex.label()
vertex.removeLabel('human') <5>
vertex.labels()
vertex.addLabel('organism') <6>
vertex.labels()
vertex.removeLabel('human') <7>
vertex.label()
g.V().has(label,'organism') <8>
g.V().has(label,of('organism')) <9>
g.V().has(label,of('organism')).has(label,of('animal'))
g.V().has(label,of('organism').and(of('animal')))
graph.close()
----

<1> Typecasting to a `Neo4jVertex` is only required in Java.
<2> The standard `Vertex.label()` method returns all the labels in alphabetical order concatenated using `::`.
<3> `Neo4jVertex.labels()` method returns the individual labels as a set.
<4> `Neo4jVertex.addLabel()` method adds a single label.
<5> `Neo4jVertex.removeLabel()` method removes a single label.
<6> Labels are unique and thus duplicate labels don't exist.
<7> If a label that does not exist is removed, nothing happens.
<8> `P.eq()` does a full string match and should only be used if multi-labels are not leveraged.
<9> `LabelP.of()` is specific to `Neo4jGraph` and used for multi-label matching.

IMPORTANT: `LabelP.of()` is only required if multi-labels are leveraged. `LabelP.of()` is used when
filtering/looking-up vertices by their label(s) as the standard `P.eq()` does a direct match on the `::`-representation
of `vertex.label()`

=== Loading with BulkLoaderVertexProgram

The <<bulkloadervertexprogram, BulkLoaderVertexProgram>> is a generalized bulk loader that can be used to load
large amounts of data to and from Neo4j. The following code demonstrates how to load the modern graph from TinkerGraph
into Neo4j:

[gremlin-groovy]
----
wgConf = 'conf/neo4j-standalone.properties'
modern = TinkerFactory.createModern()
blvp = BulkLoaderVertexProgram.build().
           keepOriginalIds(false).
           writeGraph(wgConf).create(modern)
modern.compute().workers(1).program(blvp).submit().get()
graph = GraphFactory.open(wgConf)
g = graph.traversal()
g.V().valueMap()
graph.close()
----

[source,properties]
----
# neo4j-standalone.properties

gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j
gremlin.neo4j.conf.node_auto_indexing=true
gremlin.neo4j.conf.relationship_auto_indexing=true
----

=== High Availability Configuration

image:neo4j-ha.png[width=400,float=right] TinkerPop supports running Neo4j with its fault tolerant master-slave replication configuration, referred to as its
link:http://neo4j.com/docs/operations-manual/current/#_neo4j_cluster_install[High Availability (HA) cluster]. From the
TinkerPop perspective, configuring for HA is not that different than configuring for embedded mode as shown above. The
main difference is the usage of HA configuration options that enable the cluster. Once connected to a cluster, usage
from the TinkerPop perspective is largely the same.

In configuring for HA the most important thing to realize is that all Neo4j HA settings are simply passed through the
TinkerPop configuration settings given to the `GraphFactory.open()` or `Neo4j.open()` methods. For example, to
provide the all-important `ha.server_id` configuration option through TinkerPop, simply prefix that key with the
TinkerPop Neo4j key of `gremlin.neo4j.conf`.

The following properties demonstrates one of the three configuration files required to setup a simple three node HA
cluster on the same machine instance:

[source,properties]
----
gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j.server1
gremlin.neo4j.conf.ha.server_id=1
gremlin.neo4j.conf.ha.initial_hosts=localhost:5001\,localhost:5002\,localhost:5003
gremlin.neo4j.conf.ha.cluster_server=localhost:5001
gremlin.neo4j.conf.ha.server=localhost:6001
----

Assuming the intent is to configure this cluster completely within TinkerPop (perhaps within three separate Gremlin
Server instances), the other two configuration files will be quite similar. The second will be:

[source,properties]
----
gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j.server2
gremlin.neo4j.conf.ha.server_id=2
gremlin.neo4j.conf.ha.initial_hosts=localhost:5001\,localhost:5002\,localhost:5003
gremlin.neo4j.conf.ha.cluster_server=localhost:5002
gremlin.neo4j.conf.ha.server=localhost:6002
----

and the third will be:

[source,properties]
----
gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j.server3
gremlin.neo4j.conf.ha.server_id=3
gremlin.neo4j.conf.ha.initial_hosts=localhost:5001\,localhost:5002\,localhost:5003
gremlin.neo4j.conf.ha.cluster_server=localhost:5003
gremlin.neo4j.conf.ha.server=localhost:6003
----

IMPORTANT: The backslashes in the values provided to `gremlin.neo4j.conf.ha.initial_hosts` prevent that configuration
setting as being interpreted as a `List`.

Create three separate Gremlin Server configuration files and point each at one of these Neo4j files. Since these Gremlin
Server instances will be running on the same machine, ensure that each Gremlin Server instance has a unique `port`
setting in that Gremlin Server configuration file. Start each Gremlin Server instance to bring the HA cluster online.

NOTE: `Neo4jGraph` instances will block until all nodes join the cluster.

Neither Gremlin Server nor Neo4j will share transactions across the cluster. Be sure to either use Gremlin Server
managed transactions or, if using a session without that option, ensure that all requests are being routed to the
same server.

This example discussed use of Gremlin Server to demonstrate the HA configuration, but it is also easy to setup with
three Gremlin Console instances. Simply start three Gremlin Console instances and use `GraphFactory` to read those
configuration files to form the cluster. Furthermore, keep in mind that it is possible to have a Gremlin Console join
a cluster handled by two Gremlin Servers or Neo4j Enterprise. The only limits as to how the configuration can be
utilized are prescribed by Neo4j itself. Please refer to their
link:http://neo4j.com/docs/operations-manual/current/#ha-setup-tutorial[documentation] for more information on how
this feature works.
