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3.2.6

Provider Documentation

TinkerPop exposes a set of interfaces, protocols, and tests that make it possible for third-parties to build libraries and systems that plug-in to the TinkerPop stack. TinkerPop refers to those third-parties as "providers" and this documentation is designed to help providers understand what is involved in developing code on these lower levels of the TinkerPop API.

This document attempts to address the needs of the different providers that have been identified:

  • Graph System Provider

    • Graph Database Provider

    • Graph Processor Provider

  • Graph Driver Provider

  • Graph Language Provider

  • Graph Plugin Provider

Graph System Provider Requirements

tinkerpop enabled At the core of TinkerPop3 is a Java8 API. The implementation of this core API and its validation via the gremlin-test suite is all that is required of a graph system provider wishing to provide a TinkerPop3-enabled graph engine. Once a graph system has a valid implementation, then all the applications provided by TinkerPop (e.g. Gremlin Console, Gremlin Server, etc.) and 3rd-party developers (e.g. Gremlin-Scala, Gremlin-JS, etc.) will integrate properly. Finally, please feel free to use the logo on the left to promote your TinkerPop3 implementation.

Implementing Gremlin-Core

The classes that a graph system provider should focus on implementing are itemized below. It is a good idea to study the TinkerGraph (in-memory OLTP and OLAP in tinkergraph-gremlin), Neo4jGraph (OLTP w/ transactions in neo4j-gremlin) and/or HadoopGraph (OLAP in hadoop-gremlin) implementations for ideas and patterns.

  1. Online Transactional Processing Graph Systems (OLTP)

    1. Structure API: Graph, Element, Vertex, Edge, Property and Transaction (if transactions are supported).

    2. Process API: TraversalStrategy instances for optimizing Gremlin traversals to the provider’s graph system (i.e. TinkerGraphStepStrategy).

  2. Online Analytics Processing Graph Systems (OLAP)

    1. Everything required of OLTP is required of OLAP (but not vice versa).

    2. GraphComputer API: GraphComputer, Messenger, Memory.

Please consider the following implementation notes:

  • Be sure your Graph implementation is named as XXXGraph (e.g. TinkerGraph, Neo4jGraph, HadoopGraph, etc.).

  • Use StringHelper to ensuring that the toString() representation of classes are consistent with other implementations.

  • Ensure that your implementation’s Features (Graph, Vertex, etc.) are correct so that test cases handle particulars accordingly.

  • Use the numerous static method helper classes such as ElementHelper, GraphComputerHelper, VertexProgramHelper, etc.

  • There are a number of default methods on the provided interfaces that are semantically correct. However, if they are not efficient for the implementation, override them.

  • Implement the structure/ package interfaces first and then, if desired, interfaces in the process/ package interfaces.

  • ComputerGraph is a Wrapper system that ensure proper semantics during a GraphComputer computation.

OLTP Implementations

pipes character 1 The most important interfaces to implement are in the structure/ package. These include interfaces like Graph, Vertex, Edge, Property, Transaction, etc. The StructureStandardSuite will ensure that the semantics of the methods implemented are correct. Moreover, there are numerous Exceptions classes with static exceptions that should be thrown by the graph system so that all the exceptions and their messages are consistent amongst all TinkerPop3 implementations.

OLAP Implementations

furnace character 1 Implementing the OLAP interfaces may be a bit more complicated. Note that before OLAP interfaces are implemented, it is necessary for the OLTP interfaces to be, at minimal, implemented as specified in OLTP Implementations. A summary of each required interface implementation is presented below:

  1. GraphComputer: A fluent builder for specifying an isolation level, a VertexProgram, and any number of MapReduce jobs to be submitted.

  2. Memory: A global blackboard for ANDing, ORing, INCRing, and SETing values for specified keys.

  3. Messenger: The system that collects and distributes messages being propagated by vertices executing the VertexProgram application.

  4. MapReduce.MapEmitter: The system that collects key/value pairs being emitted by the MapReduce applications map-phase.

  5. MapReduce.ReduceEmitter: The system that collects key/value pairs being emitted by the MapReduce applications combine- and reduce-phases.

Note
The VertexProgram and MapReduce interfaces in the process/computer/ package are not required by the graph system. Instead, these are interfaces to be implemented by application developers writing VertexPrograms and MapReduce jobs.
Important
TinkerPop3 provides three OLAP implementations: TinkerGraphComputer (TinkerGraph), GiraphGraphComputer (HadoopGraph), and SparkGraphComputer (Hadoop). Given the complexity of the OLAP system, it is good to study and copy many of the patterns used in these reference implementations.
Implementing GraphComputer

furnace character 3 The most complex method in GraphComputer is the submit()-method. The method must do the following:

  1. Ensure the GraphComputer has not already been executed.

  2. Ensure that at least there is a VertexProgram or 1 MapReduce job.

  3. If there is a VertexProgram, validate that it can execute on the GraphComputer given the respectively defined features.

  4. Create the Memory to be used for the computation.

  5. Execute the VertexProgram.setup() method once and only once.

  6. Execute the VertexProgram.execute() method for each vertex.

  7. Execute the VertexProgram.terminate() method once and if true, repeat VertexProgram.execute().

  8. When VertexProgram.terminate() returns true, move to MapReduce job execution.

  9. MapReduce jobs are not required to be executed in any specified order.

  10. For each Vertex, execute MapReduce.map(). Then (if defined) execute MapReduce.combine() and MapReduce.reduce().

  11. Update Memory with runtime information.

  12. Construct a new ComputerResult containing the compute Graph and Memory.

Implementing Memory

gremlin brain The Memory object is initially defined by VertexProgram.setup(). The memory data is available in the first round of the VertexProgram.execute() method. Each Vertex, when executing the VertexProgram, can update the Memory in its round. However, the update is not seen by the other vertices until the next round. At the end of the first round, all the updates are aggregated and the new memory data is available on the second round. This process repeats until the VertexProgram terminates.

Implementing Messenger

The Messenger object is similar to the Memory object in that a vertex can read and write to the Messenger. However, the data it reads are the messages sent to the vertex in the previous step and the data it writes are the messages that will be readable by the receiving vertices in the subsequent round.

Implementing MapReduce Emitters

hadoop logo notext The MapReduce framework in TinkerPop3 is similar to the model popularized by Hadoop. The primary difference is that all Mappers process the vertices of the graph, not an arbitrary key/value pair. However, the vertices' edges can not be accessed — only their properties. This greatly reduces the amount of data needed to be pushed through the MapReduce engine as any edge information required, can be computed in the VertexProgram.execute() method. Moreover, at this stage, vertices can not be mutated, only their token and property data read. A Gremlin OLAP system needs to provide implementations for to particular classes: MapReduce.MapEmitter and MapReduce.ReduceEmitter. TinkerGraph’s implementation is provided below which demonstrates the simplicity of the algorithm (especially when the data is all within the same JVM).

public class TinkerMapEmitter<K, V> implements MapReduce.MapEmitter<K, V> {

    public Map<K, Queue<V>> reduceMap;
    public Queue<KeyValue<K, V>> mapQueue;
    private final boolean doReduce;

    public TinkerMapEmitter(final boolean doReduce) { 1
        this.doReduce = doReduce;
        if (this.doReduce)
            this.reduceMap = new ConcurrentHashMap<>();
        else
            this.mapQueue = new ConcurrentLinkedQueue<>();
    }

    @Override
    public void emit(K key, V value) {
        if (this.doReduce)
            this.reduceMap.computeIfAbsent(key, k -> new ConcurrentLinkedQueue<>()).add(value); 2
        else
            this.mapQueue.add(new KeyValue<>(key, value)); 3
    }

    protected void complete(final MapReduce<K, V, ?, ?, ?> mapReduce) {
        if (!this.doReduce && mapReduce.getMapKeySort().isPresent()) { 4
            final Comparator<K> comparator = mapReduce.getMapKeySort().get();
            final List<KeyValue<K, V>> list = new ArrayList<>(this.mapQueue);
            Collections.sort(list, Comparator.comparing(KeyValue::getKey, comparator));
            this.mapQueue.clear();
            this.mapQueue.addAll(list);
        } else if (mapReduce.getMapKeySort().isPresent()) {
            final Comparator<K> comparator = mapReduce.getMapKeySort().get();
            final List<Map.Entry<K, Queue<V>>> list = new ArrayList<>();
            list.addAll(this.reduceMap.entrySet());
            Collections.sort(list, Comparator.comparing(Map.Entry::getKey, comparator));
            this.reduceMap = new LinkedHashMap<>();
            list.forEach(entry -> this.reduceMap.put(entry.getKey(), entry.getValue()));
        }
    }
}
  1. If the MapReduce job has a reduce, then use one data structure (reduceMap), else use another (mapList). The difference being that a reduction requires a grouping by key and therefore, the Map<K,Queue<V>> definition. If no reduction/grouping is required, then a simple Queue<KeyValue<K,V>> can be leveraged.

  2. If reduce is to follow, then increment the Map with a new value for the key. MapHelper is a TinkerPop3 class with static methods for adding data to a Map.

  3. If no reduce is to follow, then simply append a KeyValue to the queue.

  4. When the map phase is complete, any map-result sorting required can be executed at this point.

public class TinkerReduceEmitter<OK, OV> implements MapReduce.ReduceEmitter<OK, OV> {

    protected Queue<KeyValue<OK, OV>> reduceQueue = new ConcurrentLinkedQueue<>();

    @Override
    public void emit(final OK key, final OV value) {
        this.reduceQueue.add(new KeyValue<>(key, value));
    }

    protected void complete(final MapReduce<?, ?, OK, OV, ?> mapReduce) {
        if (mapReduce.getReduceKeySort().isPresent()) {
            final Comparator<OK> comparator = mapReduce.getReduceKeySort().get();
            final List<KeyValue<OK, OV>> list = new ArrayList<>(this.reduceQueue);
            Collections.sort(list, Comparator.comparing(KeyValue::getKey, comparator));
            this.reduceQueue.clear();
            this.reduceQueue.addAll(list);
        }
    }
}

The method MapReduce.reduce() is defined as:

public void reduce(final OK key, final Iterator<OV> values, final ReduceEmitter<OK, OV> emitter) { ... }

In other words, for the TinkerGraph implementation, iterate through the entrySet of the reduceMap and call the reduce() method on each entry. The reduce() method can emit key/value pairs which are simply aggregated into a Queue<KeyValue<OK,OV>> in an analogous fashion to TinkerMapEmitter when no reduce is to follow. These two emitters are tied together in TinkerGraphComputer.submit().

...
for (final MapReduce mapReduce : mapReducers) {
    if (mapReduce.doStage(MapReduce.Stage.MAP)) {
        final TinkerMapEmitter<?, ?> mapEmitter = new TinkerMapEmitter<>(mapReduce.doStage(MapReduce.Stage.REDUCE));
        final SynchronizedIterator<Vertex> vertices = new SynchronizedIterator<>(this.graph.vertices());
        workers.setMapReduce(mapReduce);
        workers.mapReduceWorkerStart(MapReduce.Stage.MAP);
        workers.executeMapReduce(workerMapReduce -> {
            while (true) {
                final Vertex vertex = vertices.next();
                if (null == vertex) return;
                workerMapReduce.map(ComputerGraph.mapReduce(vertex), mapEmitter);
            }
        });
        workers.mapReduceWorkerEnd(MapReduce.Stage.MAP);

        // sort results if a map output sort is defined
        mapEmitter.complete(mapReduce);

        // no need to run combiners as this is single machine
        if (mapReduce.doStage(MapReduce.Stage.REDUCE)) {
            final TinkerReduceEmitter<?, ?> reduceEmitter = new TinkerReduceEmitter<>();
            final SynchronizedIterator<Map.Entry<?, Queue<?>>> keyValues = new SynchronizedIterator((Iterator) mapEmitter.reduceMap.entrySet().iterator());
            workers.mapReduceWorkerStart(MapReduce.Stage.REDUCE);
            workers.executeMapReduce(workerMapReduce -> {
                while (true) {
                    final Map.Entry<?, Queue<?>> entry = keyValues.next();
                    if (null == entry) return;
                        workerMapReduce.reduce(entry.getKey(), entry.getValue().iterator(), reduceEmitter);
                    }
                });
            workers.mapReduceWorkerEnd(MapReduce.Stage.REDUCE);
            reduceEmitter.complete(mapReduce); // sort results if a reduce output sort is defined
            mapReduce.addResultToMemory(this.memory, reduceEmitter.reduceQueue.iterator()); 1
        } else {
            mapReduce.addResultToMemory(this.memory, mapEmitter.mapQueue.iterator()); 2
        }
    }
}
...
  1. Note that the final results of the reducer are provided to the Memory as specified by the application developer’s MapReduce.addResultToMemory() implementation.

  2. If there is no reduce stage, the map-stage results are inserted into Memory as specified by the application developer’s MapReduce.addResultToMemory() implementation.

Hadoop-Gremlin Usage

Hadoop-Gremlin is centered around InputFormats and OutputFormats. If a 3rd-party graph system provider wishes to leverage Hadoop-Gremlin (and its respective GraphComputer engines), then they need to provide, at minimum, a Hadoop2 InputFormat<NullWritable,VertexWritable> for their graph system. If the provider wishes to persist computed results back to their graph system (and not just to HDFS via a FileOutputFormat), then a graph system specific OutputFormat<NullWritable,VertexWritable> must be developed as well.

Conceptually, HadoopGraph is a wrapper around a Configuration object. There is no "data" in the HadoopGraph as the InputFormat specifies where and how to get the graph data at OLAP (and OLTP) runtime. Thus, HadoopGraph is a small object with little overhead. Graph system providers should realize HadoopGraph as the gateway to the OLAP features offered by Hadoop-Gremlin. For example, a graph system specific Graph.compute(Class<? extends GraphComputer> graphComputerClass)-method may look as follows:

public <C extends GraphComputer> C compute(final Class<C> graphComputerClass) throws IllegalArgumentException {
  try {
    if (AbstractHadoopGraphComputer.class.isAssignableFrom(graphComputerClass))
      return graphComputerClass.getConstructor(HadoopGraph.class).newInstance(this);
    else
      throw Graph.Exceptions.graphDoesNotSupportProvidedGraphComputer(graphComputerClass);
  } catch (final Exception e) {
    throw new IllegalArgumentException(e.getMessage(),e);
  }
}

Note that the configurations for Hadoop are assumed to be in the Graph.configuration() object. If this is not the case, then the Configuration provided to HadoopGraph.open() should be dynamically created within the compute()-method. It is in the provided configuration that HadoopGraph gets the various properties which determine how to read and write data to and from Hadoop. For instance, gremlin.hadoop.graphReader and gremlin.hadoop.graphWriter.

GraphFilterAware Interface

Graph filters by OLAP processors to only pull a subgraph of the full graph from the graph data source. For instance, the example below constructs a GraphFilter that will only pull the "knows"-graph amongst people into the GraphComputer for processing.

graph.compute().vertices(hasLabel("person")).edges(bothE("knows"))

If the provider has a custom InputRDD, they can implement GraphFilterAware and that graph filter will be provided to their InputRDD at load time. For providers that use an InputFormat, state but the graph filter can be accessed from the configuration as such:

if (configuration.containsKey(Constants.GREMLIN_HADOOP_GRAPH_FILTER))
  this.graphFilter = VertexProgramHelper.deserialize(configuration, Constants.GREMLIN_HADOOP_GRAPH_FILTER);
PersistResultGraphAware Interface

A graph system provider’s OutputFormat should implement the PersistResultGraphAware interface which determines which persistence options are available to the user. For the standard file-based OutputFormats provided by Hadoop-Gremlin (e.g. GryoOutputFormat, GraphSONOutputFormat, and ScriptInputOutputFormat) ResultGraph.ORIGINAL is not supported as the original graph data files are not random access and are, in essence, immutable. Thus, these file-based OutputFormats only support ResultGraph.NEW which creates a copy of the data specified by the Persist enum.

IO Implementations

If a Graph requires custom serializers for IO to work properly, implement the Graph.io method. A typical example of where a Graph would require such a custom serializers is if their identifier system uses non-primitive values, such as OrientDB’s Rid class. From basic serialization of a single Vertex all the way up the stack to Gremlin Server, the need to know how to handle these complex identifiers is an important requirement.

The first step to implementing custom serializers is to first implement the IoRegistry interface and register the custom classes and serializers to it. Each Io implementation has different requirements for what it expects from the IoRegistry:

  • GraphML - No custom serializers expected/allowed.

  • GraphSON - Register a Jackson SimpleModule. The SimpleModule encapsulates specific classes to be serialized, so it does not need to be registered to a specific class in the IoRegistry (use null).

  • Gryo - Expects registration of one of three objects:

    • Register just the custom class with a null Kryo Serializer implementation - this class will use default "field-level" Kryo serialization.

    • Register the custom class with a specific Kryo `Serializer' implementation.

    • Register the custom class with a Function<Kryo, Serializer> for those cases where the Kryo Serializer requires the Kryo instance to get constructed.

This implementation should provide a zero-arg constructor as the stack may require instantiation via reflection. Consider extending AbstractIoRegistry for convenience as follows:

public class MyGraphIoRegistry extends AbstractIoRegistry {
    public MyGraphIoRegistry() {
        register(GraphSONIo.class, null, new MyGraphSimpleModule());
        register(GryoIo.class, MyGraphIdClass.class, new MyGraphIdSerializer());
    }
}

In the Graph.io method, provide the IoRegistry object to the supplied Builder and call the create method to return that Io instance as follows:

public <I extends Io> I io(final Io.Builder<I> builder) {
    return (I) builder.graph(this).registry(myGraphIoRegistry).create();
}}

In this way, Graph implementations can pre-configure custom serializers for IO interactions and users will not need to know about those details. Following this pattern will ensure proper execution of the test suite as well as simplified usage for end-users.

Important
Proper implementation of IO is critical to successful Graph operations in Gremlin Server. The Test Suite does have "serialization" tests that provide some assurance that an implementation is working properly, but those tests cannot make assertions against any specifics of a custom serializer. It is the responsibility of the implementer to test the specifics of their custom serializers.
Tip
Consider separating serializer code into its own module, if possible, so that clients that use the Graph implementation remotely don’t need a full dependency on the entire Graph - just the IO components and related classes being serialized.

RemoteConnection Implementations

A RemoteConnection is an interface that is important for usage on traversal sources configured using the withRemote() option. A Traversal that is generated from that source will apply a RemoteStrategy which will inject a RemoteStep to its end. That step will then send the Bytecode of the Traversal over the RemoteConnection to get the results that it will iterate.

There is one method to implement on RemoteConnection:

public <E> CompletableFuture<RemoteTraversal<?, E>> submitAsync(final Bytecode bytecode) throws RemoteConnectionException;

Note that it returns a RemoteTraversal. This interface should also be implemented and in most cases implementers can simply extend the AbstractRemoteTraversal, which provides default implementations for all the Traversal methods except for the important one getSideEffects().

public RemoteTraversalSideEffects getSideEffects();

This method has been overriden from Traversal to return RemoteTraversalSideEffects and this interface allows a user to do a retrieval of any side-effects produced by the Traversal. The assumption, of course, is that the RemoteConnection is communicating with a server that supports side-effect retreival (as Gremlin Server does).

TinkerPop provides the DriverRemoteConnection as a useful and example implementation. DriverRemoteConnection serializes the Traversal and then submits it for remote processing on Gremlin Server. Gremlin Server rebinds the Traversal to a configured Graph instance and then iterates the results back as it would normally do.

Implementing RemoteConnection is not something routinely done for those implementing gremlin-core. It is only something required if there is a need to exploit withRemote() in some way. If a graph provider has a "graph server" similar to Gremlin Server that can accept a serialized Traversal instance, then that would be one example of a reason to implement this interface.

Validating with Gremlin-Test

gremlin edumacated

<dependency>
  <groupId>org.apache.tinkerpop</groupId>
  <artifactId>gremlin-test</artifactId>
  <version>3.2.6</version>
</dependency>

The operational semantics of any OLTP or OLAP implementation are validated by gremlin-test. To implement these tests, provide test case implementations as shown below, where XXX below denotes the name of the graph implementation (e.g. TinkerGraph, Neo4jGraph, HadoopGraph, etc.).

// Structure API tests
@RunWith(StructureStandardSuite.class)
@GraphProviderClass(provider = XXXGraphProvider.class, graph = XXXGraph.class)
public class XXXStructureStandardTest {}

// Process API tests
@RunWith(ProcessComputerSuite.class)
@GraphProviderClass(provider = XXXGraphProvider.class, graph = XXXGraph.class)
public class XXXProcessComputerTest {}

@RunWith(ProcessStandardSuite.class)
@GraphProviderClass(provider = XXXGraphProvider.class, graph = XXXGraph.class)
public class XXXProcessStandardTest {}

The above set of tests represent the minimum test suite set to implement. There are other "integration" and "performance" tests that should be considered optional. Implementing those tests requires the same pattern as shown above.

Important
It is as important to look at "ignored" tests as it is to look at ones that fail. The gremlin-test suite utilizes the Feature implementation exposed by the Graph to determine which tests to execute. If a test utilizes features that are not supported by the graph, it will ignore them. While that may be fine, implementers should validate that the ignored tests are appropriately bypassed and that there are no mistakes in their feature definitions. Moreover, implementers should consider filling gaps in their own test suites, especially when IO-related tests are being ignored.

The only test-class that requires any code investment is the GraphProvider implementation class. This class is a used by the test suite to construct Graph configurations and instances and provides information about the implementation itself. In most cases, it is best to simply extend AbstractGraphProvider as it provides many default implementations of the GraphProvider interface.

Finally, specify the test suites that will be supported by the Graph implementation using the @Graph.OptIn annotation. See the TinkerGraph implementation below as an example:

@Graph.OptIn(Graph.OptIn.SUITE_STRUCTURE_STANDARD)
@Graph.OptIn(Graph.OptIn.SUITE_PROCESS_STANDARD)
@Graph.OptIn(Graph.OptIn.SUITE_PROCESS_COMPUTER)
public class TinkerGraph implements Graph {

Only include annotations for the suites the implementation will support. Note that implementing the suite, but not specifying the appropriate annotation will prevent the suite from running (an obvious error message will appear in this case when running the mis-configured suite).

There are times when there may be a specific test in the suite that the implementation cannot support (despite the features it implements) or should not otherwise be executed. It is possible for implementers to "opt-out" of a test by using the @Graph.OptOut annotation. The following is an example of this annotation usage as taken from HadoopGraph:

@Graph.OptIn(Graph.OptIn.SUITE_PROCESS_STANDARD)
@Graph.OptIn(Graph.OptIn.SUITE_PROCESS_COMPUTER)
@Graph.OptOut(
        test = "org.apache.tinkerpop.gremlin.process.graph.step.map.MatchTest$Traversals",
        method = "g_V_matchXa_hasXname_GarciaX__a_inXwrittenByX_b__a_inXsungByX_bX",
        reason = "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute.")
@Graph.OptOut(
        test = "org.apache.tinkerpop.gremlin.process.graph.step.map.MatchTest$Traversals",
        method = "g_V_matchXa_inXsungByX_b__a_inXsungByX_c__b_outXwrittenByX_d__c_outXwrittenByX_e__d_hasXname_George_HarisonX__e_hasXname_Bob_MarleyXX",
        reason = "Hadoop-Gremlin is OLAP-oriented and for OLTP operations, linear-scan joins are required. This particular tests takes many minutes to execute.")
@Graph.OptOut(
        test = "org.apache.tinkerpop.gremlin.process.computer.GraphComputerTest",
        method = "shouldNotAllowBadMemoryKeys",
        reason = "Hadoop does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though.")
@Graph.OptOut(
        test = "org.apache.tinkerpop.gremlin.process.computer.GraphComputerTest",
        method = "shouldRequireRegisteringMemoryKeys",
        reason = "Hadoop does a hard kill on failure and stops threads which stops test cases. Exception handling semantics are correct though.")
public class HadoopGraph implements Graph {

The above examples show how to ignore individual tests. It is also possible to:

  • Ignore an entire test case (i.e. all the methods within the test) by setting the method to "*".

  • Ignore a "base" test class such that test that extend from those classes will all be ignored.

  • Ignore a GraphComputer test based on the type of GraphComputer being used. Specify the "computer" attribute on the OptOut (which is an array specification) which should have a value of the GraphComputer implementation class that should ignore that test. This attribute should be left empty for "standard" execution and by default all GraphComputer implementations will be included in the OptOut so if there are multiple implementations, explicitly specify the ones that should be excluded.

Also note that some of the tests in the Gremlin Test Suite are parameterized tests and require an additional level of specificity to be properly ignored. To ignore these types of tests, examine the name template of the parameterized tests. It is defined by a Java annotation that looks like this:

@Parameterized.Parameters(name = "expect({0})")

The annotation above shows that the name of each parameterized test will be prefixed with "expect" and have parentheses wrapped around the first parameter (at index 0) value supplied to each test. This information can only be garnered by studying the test set up itself. Once the pattern is determined and the specific unique name of the parameterized test is identified, add it to the specific property on the OptOut annotation in addition to the other arguments.

These annotations help provide users a level of transparency into test suite compliance (via the describeGraph() utility function). It also allows implementers to have a lot of flexibility in terms of how they wish to support TinkerPop. For example, maybe there is a single test case that prevents an implementer from claiming support of a Feature. The implementer could choose to either not support the Feature or to support it but "opt-out" of the test with a "reason" as to why so that users understand the limitation.

Important
Before using OptOut be sure that the reason for using it is sound and it is more of a last resort. It is possible that a test from the suite doesn’t properly represent the expectations of a feature, is too broad or narrow for the semantics it is trying to enforce or simply contains a bug. Please consider raising issues in the developer mailing list with such concerns before assuming OptOut is the only answer.
Important
There are no tests that specifically validate complete compliance with Gremlin Server. Generally speaking, a Graph that passes the full Test Suite, should be compliant with Gremlin Server. The one area where problems can occur is in serialization. Always ensure that IO is properly implemented, that custom serializers are tested fully and ultimately integration test the Graph with an actual Gremlin Server instance.
Warning
Configuring tests to run in parallel might result in errors that are difficult to debug as there is some shared state in test execution around graph configuration. It is therefore recommended that parallelism be turned off for the test suite (the Maven SureFire Plugin is configured this way by default). It may also be important to include this setting, <reuseForks>false</reuseForks>, in the SureFire configuration if tests are failing in an unexplainable way.
Tip
When running the gremlin-test suite against your implementation, you may need to set build.dir as an environment variable, depending on your project layout. Some tests require this to find a writable directory for creating temporary files. The value is typically set to the project build directory. For example using the Maven SureFire Plugin, this is done via the configuration argLine with -Dbuild.dir=${project.build.directory}.

Accessibility via GremlinPlugin

gremlin plugin The applications distributed with TinkerPop3 do not distribute with any graph system implementations besides TinkerGraph. If your implementation is stored in a Maven repository (e.g. Maven Central Repository), then it is best to provide a GremlinPlugin implementation so the respective jars can be downloaded according and when required by the user. Neo4j’s GremlinPlugin is provided below for reference.

public class Neo4jGremlinPlugin implements GremlinPlugin {

    private static final String IMPORT = "import ";
    private static final String DOT_STAR = ".*";

    private static final Set<String> IMPORTS = new HashSet<String>() {{
        add(IMPORT + Neo4jGraph.class.getPackage().getName() + DOT_STAR);
    }};

    @Override
    public String getName() {
        return "neo4j";
    }

    @Override
    public void pluginTo(final PluginAcceptor pluginAcceptor) {
        pluginAcceptor.addImports(IMPORTS);
    }
}

With the above plugin implementations, users can now download respective binaries for Gremlin Console, Gremlin Server, etc.

gremlin> g = Neo4jGraph.open('/tmp/neo4j')
No such property: Neo4jGraph for class: groovysh_evaluate
Display stack trace? [yN]
gremlin> :install org.apache.tinkerpop neo4j-gremlin 3.2.6
==>loaded: [org.apache.tinkerpop, neo4j-gremlin, ]
gremlin> :plugin use tinkerpop.neo4j
==>tinkerpop.neo4j activated
gremlin> g = Neo4jGraph.open('/tmp/neo4j')
==>neo4jgraph[EmbeddedGraphDatabase [/tmp/neo4j]]

In-Depth Implementations

gremlin painting The graph system implementation details presented thus far are minimum requirements necessary to yield a valid TinkerPop3 implementation. However, there are other areas that a graph system provider can tweak to provide an implementation more optimized for their underlying graph engine. Typical areas of focus include:

  • Traversal Strategies: A TraversalStrategy can be used to alter a traversal prior to its execution. A typical example is converting a pattern of g.V().has('name','marko') into a global index lookup for all vertices with name "marko". In this way, a O(|V|) lookup becomes an O(log(|V|)). Please review TinkerGraphStepStrategy for ideas.

  • Step Implementations: Every step is ultimately referenced by the GraphTraversal interface. It is possible to extend GraphTraversal to use a graph system specific step implementation.

Graph Driver Provider Requirements

gremlin server protocol

One of the roles for Gremlin Server is to provide a bridge from TinkerPop to non-JVM languages (e.g. Go, Python, etc.). Developers can build language bindings (or driver) that provide a way to submit Gremlin scripts to Gremlin Server and get back results. Given the extensible nature of Gremlin Server, it is difficult to provide an authoritative guide to developing a driver. It is however possible to describe the core communication protocol using the standard out-of-the-box configuration which should provide enough information to develop a driver for a specific language.

gremlin server flow

Gremlin Server is distributed with a configuration that utilizes WebSocket with a custom sub-protocol. Under this configuration, Gremlin Server accepts requests containing a Gremlin script, evaluates that script and then streams back the results. The notion of "streaming" is depicted in the diagram to the right.

The diagram shows an incoming request to process the Gremlin script of g.V(). Gremlin Server evaluates that script, getting an Iterator of vertices as a result, and steps through each Vertex within it. The vertices are batched together given the resultIterationBatchSize configuration. In this case, that value must be 2 given that each "response" contains two vertices. Each response is serialized given the requested serializer type (JSON is likely best for non-JVM languages) and written back to the requesting client immediately. Gremlin Server does not wait for the entire result to be iterated, before sending back a response. It will send the responses as they are realized.

This approach allows for the processing of large result sets without having to serialize the entire result into memory for the response. It places a bit of a burden on the developer of the driver however, because it becomes necessary to provide a way to reconstruct the entire result on the client side from all of the individual responses that Gremlin Server returns for a single request. Again, this description of Gremlin Server’s "flow" is related to the out-of-the-box configuration. It is quite possible to construct other flows, that might be more amenable to a particular language or style of processing.

To formulate a request to Gremlin Server, a RequestMessage needs to be constructed. The RequestMessage is a generalized representation of a request that carries a set of "standard" values in addition to optional ones that are dependent on the operation being performed. A RequestMessage has these fields:

Key Description

requestId

A UUID representing the unique identification for the request.

op

The name of the "operation" to execute based on the available OpProcessor configured in the Gremlin Server. To evaluate a script, use eval.

processor

The name of the OpProcessor to utilize. The default OpProcessor for evaluating scripts is unnamed and therefore script evaluation purposes, this value can be an empty string.

args

A Map of arbitrary parameters to pass to Gremlin Server. The requirements for the contents of this Map are dependent on the op selected.

This message can be serialized in any fashion that is supported by Gremlin Server. New serialization methods can be plugged in by implementing a ServiceLoader enabled MessageSerializer, however Gremlin Server provides for JSON serialization by default which will be good enough for purposes of most developers building drivers. A RequestMessage to evaluate a script with variable bindings looks like this in JSON:

{ "requestId":"1d6d02bd-8e56-421d-9438-3bd6d0079ff1",
  "op":"eval",
  "processor":"",
  "args":{"gremlin":"g.traversal().V(x).out()",
          "bindings":{"x":1},
          "language":"gremlin-groovy"}}

The above JSON represents the "body" of the request to send to Gremlin Server. When sending this "body" over WebSocket, Gremlin Server can accept a packet frame using a "text" (1) or a "binary" (2) opcode. Using "text" is a bit more limited in that Gremlin Server will always process the body of that request as JSON. Generally speaking "text" is just for testing purposes.

The preferred method for sending requests to Gremlin Server is to use the "binary" opcode. In this case, a "header" will need be sent in addition to to the "body". The "header" basically consists of a "mime type" so that Gremlin Server knows how to deserialize the RequestMessage. So, the actual byte array sent to Gremlin Server would be formatted as follows:

gremlin server request

The first byte represents the length of the "mime type" string value that follows. Given the default configuration of Gremlin Server, this value should be set to application/json. The "payload" represents the JSON message above encoded as bytes.

Note
Gremlin Server will only accept masked packets as it pertains to a WebSocket packet header construction.

When Gremlin Server receives that request, it will decode it given the "mime type", pass it to the requested OpProcessor which will execute the op defined in the message. In this case, it will evaluate the script g.traversal().V(x).out() using the bindings supplied in the args and stream back the results in a series of ResponseMessages. A ResponseMessage looks like this:

Key Description

requestId

The identifier of the RequestMessage that generated this ResponseMessage.

status

The status contains a Map of three keys: code which refers to a ResultCode that is somewhat analogous to an HTTP status code, attributes that represent a Map of protocol-level information, and message which is just a human-readable String usually associated with errors.

result

The result contains a Map of two keys: data which refers to the actual data returned from the server (the type of data is determined by the operation requested) and meta which is a Map of meta-data related to the response.

In this case the ResponseMessage returned to the client would look something like this:

{"result":{"data":[{"id": 2,"label": "person","type": "vertex","properties": [
  {"id": 2, "value": "vadas", "label": "name"},
  {"id": 3, "value": 27, "label": "age"}]},
  ], "meta":{}},
 "requestId":"1d6d02bd-8e56-421d-9438-3bd6d0079ff1",
 "status":{"code":206,"attributes":{},"message":""}}

Gremlin Server is capable of streaming results such that additional responses will arrive over the WebSocket connection until the iteration of the result on the server is complete. Each successful incremental message will have a ResultCode of 206. Termination of the stream will be marked by a final 200 status code. Note that all messages without a 206 represent terminating conditions for a request. The following table details the various status codes that Gremlin Server will send:

Code Name Description

200

SUCCESS

The server successfully processed a request to completion - there are no messages remaining in this stream.

204

NO CONTENT

The server processed the request but there is no result to return (e.g. an Iterator with no elements) - there are no messages remaining in this stream.

206

PARTIAL CONTENT

The server successfully returned some content, but there is more in the stream to arrive - wait for a SUCCESS to signify the end of the stream.

401

UNAUTHORIZED

The request attempted to access resources that the requesting user did not have access to.

407

AUTHENTICATE

A challenge from the server for the client to authenticate its request.

498

MALFORMED REQUEST

The request message was not properly formatted which means it could not be parsed at all or the "op" code was not recognized such that Gremlin Server could properly route it for processing. Check the message format and retry the request.

499

INVALID REQUEST ARGUMENTS

The request message was parseable, but the arguments supplied in the message were in conflict or incomplete. Check the message format and retry the request.

500

SERVER ERROR

A general server error occurred that prevented the request from being processed.

597

SCRIPT EVALUATION ERROR

The script submitted for processing evaluated in the ScriptEngine with errors and could not be processed. Check the script submitted for syntax errors or other problems and then resubmit.

598

SERVER TIMEOUT

The server exceeded one of the timeout settings for the request and could therefore only partially responded or did not respond at all.

599

SERVER SERIALIZATION ERROR

The server was not capable of serializing an object that was returned from the script supplied on the request. Either transform the object into something Gremlin Server can process within the script or install mapper serialization classes to Gremlin Server.

Note
Please refer to the IO Reference Documentation for more examples of RequestMessage and ResponseMessage instances.

OpProcessors Arguments

The following sections define a non-exhaustive list of available operations and arguments for embedded OpProcessors (i.e. ones packaged with Gremlin Server).

Common

All OpProcessor instances support these arguments.

Key Type Description

batchSize

Int

When the result is an iterator this value defines the number of iterations each ResponseMessage should contain - overrides the resultIterationBatchSize server setting.

Standard OpProcessor

The "standard" OpProcessor handles requests for the primary function of Gremlin Server - executing Gremlin. Requests made to this OpProcessor are "sessionless" in the sense that a request must encapsulate the entirety of a transaction. There is no state maintained between requests. A transaction is started when the script is first evaluated and is committed when the script completes (or rolled back if an error occurred).

Key Description

processor

As this is the default OpProcessor this value can be set to an empty string.

op

Key Description

authentication

A request that contains the response to a server challenge for authentication.

eval

Evaluate a Gremlin script provided as a String.

'`authentication` operation arguments'

Key Type Description

sasl

String

Required The response to the server authentication challenge. This value is dependent on the SASL authentication mechanism required by the server and is Base64 encoded.

saslMechanism

String

The SASL mechanism: PLAIN or GSSAPI. Note that it is up to the server implementation to use or disregard this setting (default implementation in Gremlin Server ignores it).

'`eval` operation arguments'

Key Type Description

gremlin

String

Required The Gremlin script to evaluate.

bindings

Map

A map of key/value pairs to apply as variables in the context of the Gremlin script.

language

String

The flavor of Gremlin used (e.g. gremlin-groovy).

aliases

Map

A map of key/value pairs that allow globally bound Graph and TraversalSource objects to be aliased to different variable names for purposes of the current request. The value represents the name of the global variable and its key represents the new binding name as it will be referenced in the Gremlin query. For example, if the Gremlin Server defines two TraversalSource instances named g1 and g2, it would be possible to send an alias pair with key of "g" and value of "g2" and thus allow the script to refer to "g2" simply as "g".

scriptEvaluationTimeout

Long

An override for the server setting that determines the maximum time to wait for a script to execute on the server.

Session OpProcessor

The "session" OpProcessor handles requests for the primary function of Gremlin Server - executing Gremlin. It is like the "standard" OpProcessor, but instead maintains state between sessions and allows the option to leave all transaction management up to the calling client. It is important that clients that open sessions, commit or roll them back, however Gremlin Server will try to clean up such things when a session is killed that has been abandoned. It is important to consider that a session can only be maintained with a single machine. In the event that multiple Gremlin Server are deployed, session state is not shared among them.

Key Description

processor

This value should be set to session

op

Key Description

authentication

A request that contains the response to a server challenge for authentication.

eval

Evaluate a Gremlin script provided as a String.

close

Close the specified session. Will return a NO CONTENT message as confirmation of the close being completed.

'`authentication` operation arguments'

Key Type Description

saslMechanism

String

The SASL mechanism: PLAIN or GSSAPI. Note that it is up to the server implementation to use or disregard this setting (default implementation in Gremlin Server ignores it).

sasl

String

Required The response to the server authentication challenge. This value is dependent on the SASL authentication mechanism required by the server and is Base64 encoded.

'`eval` operation arguments'

Key Type Description

gremlin

String

Required The Gremlin script to evaluate.

session

String

Required The session identifier for the current session - typically this value should be a UUID (the session will be created if it doesn’t exist).

manageTransaction

Boolean

When set to true the transaction for the current request is auto-committed or rolled-back as are done with sessionless requests - defaulted to false.

bindings

Map

A map of key/value pairs to apply as variables in the context of the Gremlin script.

scriptEvaluationTimeout

Long

An override for the server setting that determines the maximum time to wait for a script to execute on the server.

language

String

The flavor of Gremlin used (e.g. gremlin-groovy)

aliases

Map

A map of key/value pairs that allow globally bound Graph and TraversalSource objects to be aliased to different variable names for purposes of the current request. The value represents the name the global variable and its key represents the new binding name as it will be referenced in the Gremlin query. For example, if the Gremlin Server defines two TraversalSource instances named g1 and g2, it would be possible to send an alias pair with key of "g" and value of "g2" and thus allow the script to refer to "g2" simply as "g".

'`close` operation arguments'

Key Type Description

session

String

Required The session identifier for the session to close.

force

Boolean

Determines if the session should be force closed when the client is closed. Force closing will not attempt to close open transactions from existing running jobs and leave it to the underlying graph to decided how to proceed with those orphaned transactions. Setting this to true tends to lead to faster close operation and release of resources which can be desirable if Gremlin Server has a long session timeout and a long script evaluation timeout as attempts to close long run jobs can occur more rapidly. If not provided, this value is false.

Traversal OpProcessor

Both the Standard and Session OpProcessors allow for Gremlin scripts to be submitted to the server. The TraversalOpProcessor however allows Gremlin Bytecode to be submitted to the server. Supporting this OpProcessor makes it possible for a Gremlin Language Variant to submit a Traversal directly to Gremlin Server in the native language of the GLV without having to use a script in a different language.

Unlike Standard and Session OpProcessors, the Traversal OpProcessor does not simply return the results of the Traversal. It instead returns Traverser objects which allows the client to take advantage of bulking. To describe this interaction more directly, the returned Traverser will represent some value from the Traversal result and the number of times it is represented in the full stream of results. So, if a Traversal happens to return the same vertex twenty times it won’t return twenty instances of the same object. It will return one in Traverser with the bulk value set to twenty. Under this model, the amount of processing and network overhead can be reduced considerably.

To demonstrate consider this example:

gremlin> cluster = Cluster.open()
==>localhost/127.0.0.1:8182
gremlin> client = cluster.connect()
==>org.apache.tinkerpop.gremlin.driver.Client$ClusteredClient@579f3c8e
gremlin> aliased = client.alias("g")
==>org.apache.tinkerpop.gremlin.driver.Client$AliasClusteredClient@42503099
gremlin> g = org.apache.tinkerpop.gremlin.structure.util.empty.EmptyGraph.INSTANCE.traversal() 1
==>graphtraversalsource[emptygraph[empty], standard]
gremlin> rs = aliased.submit(g.V().both().barrier().both().barrier()).all().get() 2
==>result{object=v[1] class=org.apache.tinkerpop.gremlin.process.remote.traversal.DefaultRemoteTraverser}
==>result{object=v[4] class=org.apache.tinkerpop.gremlin.process.remote.traversal.DefaultRemoteTraverser}
==>result{object=v[6] class=org.apache.tinkerpop.gremlin.process.remote.traversal.DefaultRemoteTraverser}
==>result{object=v[5] class=org.apache.tinkerpop.gremlin.process.remote.traversal.DefaultRemoteTraverser}
==>result{object=v[3] class=org.apache.tinkerpop.gremlin.process.remote.traversal.DefaultRemoteTraverser}
==>result{object=v[2] class=org.apache.tinkerpop.gremlin.process.remote.traversal.DefaultRemoteTraverser}
gremlin> aliased.submit(g.V().both().barrier().both().barrier().count()).all().get().get(0).getInt() 3
==>30
gremlin> rs.collect{[value: it.getObject().get(), bulk: it.getObject().bulk()]} 4
==>[value:v[1],bulk:7]
==>[value:v[4],bulk:7]
==>[value:v[6],bulk:3]
==>[value:v[5],bulk:3]
==>[value:v[3],bulk:7]
==>[value:v[2],bulk:3]
  1. All commands through this step are just designed to demonstrate bulking with Gremlin Server and don’t represent a real-world way that this feature would be used.

  2. Submit a Traversal that happens to ensure that the server uses bulking. Note that a Traverser is returned and that there are only six results.

  3. In actuality, however, if this same Traversal is iterated there are thirty results. Without bulking, the previous request would have sent back thirty traversers.

  4. Note that the sum of the bulk of each Traverser is thirty.

The full iteration of aa Traversal is thus left to the client. It must interpret the bulk on the Traverser and unroll it to represent the actual number of times it exists when iterated. The unrolling is typically handled directly within TinkerPop’s RemoteGraph implementations.

One of the important aspects of the Traversal OpProcessor is that it enables the user to not only get the results of the Traversal but also the side-effects that it produces. When the Traversal is submitted with the "bytecode" operation, the results are iterated back as usual, but any side-effects are retained on the server in a cache keyed by the identifier of the request that originally submitted the Traversal. The client will want to retain that identifier if it intends to later get side-effects. The Traversal OpProcessor supplies the "keys" and "gather" operations to get the keys stored in the side-effect and to get the value of a specific side-effect respectively. Finally, a "close" operation is available to clear the cache of a specific side-effect.

Key Description

processor

This value should be set to traversal

op

Key Description

authentication

A request that contains the response to a server challenge for authentication.

bytecode

A request that contains the Bytecode representation of a Traversal.

close

Releases side-effects held in cache for a particular Traversal.

gather

Gets a particular side-effect as produced by a previously executed Traversal.

keys

Gets all the keys of all side-effects as produced by a previously executed Traversal.

'`authentication` operation arguments'

Key Type Description

sasl

String

Required The response to the server authentication challenge. This value is dependent on the SASL authentication mechanism required by the server and is Base64 encoded.

'`bytecode` operation arguments'

Key Type Description

gremlin

String

Required The Bytecode representation of a Traversal.

aliases

Map

Required A map with a single key/value pair that refers to a globally bound TraversalSource object to be aliased to different variable names for purposes of the current request. The value represents the name of the global variable and its key represents the new binding name as it will be referenced in the Gremlin query. For example, if the Gremlin Server defines two TraversalSource instances named g1, it would be possible to send an alias pair with key of "g" and value of "g1" and thus allow the script to refer to "g1" simply as "g". Note that unlike users of alias in other contexts, in this case, the key can only be set to "g" and there can be only one key value pair present (since only one Traversal is being submitted, there is no sense to having more than a single alias).

'`close` operation arguments'

Key Type Description

sideEffect

UUID

Required The unique identifier for the request that original submitted the traversal (side-effects are keyed by that value)

'`gather` operation arguments'

Key Type Description

sideEffect

UUID

Required The unique identifier for the request that original submitted the traversal (side-effects are keyed by that value)

sideEffectKey

String

Required The key for a specific side-effect.

aliases

Map

Required A map with a single key/value pair that refers to a globally bound TraversalSource object to be aliased to different variable names for purposes of the current request. The value represents the name of the global variable and its key represents the new binding name as it will be referenced in the Gremlin query. For example, if the Gremlin Server defines two TraversalSource instances named g1, it would be possible to send an alias pair with key of "g" and value of "g1" and thus allow the script to refer to "g1" simply as "g". Note that unlike users of alias in other contexts, in this case, the key can only be set to "g" and there can be only one key value pair present (since only one Traversal is being submitted, there is no sense to having more than a single alias).

When using "gather" it is important to note the metadata that is returned on the ResponseMessage. It returns both the "sideEffectKey" that was requested as well as a value keyed as "aggregateTo". The "aggregateTo" field describes how the streamed side-effect data should be treated on the client. It provides a hint as to whether or not the data should be rolled back up into a single object or simply left as-is. There are four values for "aggregateTo": bulkset, list, map, set and none.

'`keys` operation arguments'

Key Type Description

sideEffect

UUID

Required The unique identifier for the request that original submitted the traversal (side-effects are keyed by that value)

Authentication

Gremlin Server supports SASL-based authentication. A SASL implementation provides a series of challenges and responses that a driver must comply with in order to authenticate. By default, Gremlin Server only supports the "PLAIN" SASL mechanism, which is a cleartext password system. When authentication is enabled, an incoming request is intercepted before it is evaluated by the ScriptEngine. The request is saved on the server and a AUTHENTICATE challenge response (status code 407) is returned to the client.

The client will detect the AUTHENTICATE and respond with an authentication for the op and an arg named sasl that contains the password. The password should be either, an encoded sequence of UTF-8 bytes, delimited by 0 (US-ASCII NUL), where the form is : <NUL>username<NUL>password, or a Base64 encoded string of the former (which in this instance would be AHVzZXJuYW1lAHBhc3N3b3Jk). Should Gremlin Server be able to authenticate with the provided credentials, the server will return the results of the original request as it normally does without authentication. If it cannot authenticate given the challenge response from the client, it will return UNAUTHORIZED (status code 401).

Note
Gremlin Server does not support the "authorization identity" as described in RFC4616.

Gremlin Plugins

gremlin plugin

Plugins provide a way to expand the features of Gremlin Console and Gremlin Server. The first step to developing a plugin is to implement the GremlinPlugin interface:


package org.apache.tinkerpop.gremlin.groovy.plugin;

import java.util.Optional;

/**
 * Those wanting to extend Gremlin can implement this interface to provide mapper imports and extension
 * methods to the language itself.  Gremlin uses {@code ServiceLoader} to install plugins.  It is necessary for
 * projects to include a {@code org.apache.tinkerpop.gremlin.groovy.plugin.GremlinPlugin} file in
 * {@code META-INF/services} of their packaged project which includes the full class names of the implementations
 * of this interface to install.
 *
 * @author Stephen Mallette (http://stephen.genoprime.com)
 * @deprecated As for 3.2.4, replaced by {@link org.apache.tinkerpop.gremlin.jsr223.GremlinPlugin}
 */
@Deprecated
public interface GremlinPlugin {
    public static final String ENVIRONMENT = "GremlinPlugin.env";

    /**
     * The name of the plugin.  This name should be unique (use a namespaced approach) as naming clashes will
     * prevent proper plugin operations. Plugins developed by TinkerPop will be prefixed with "tinkerpop."
     * For example, TinkerPop's implementation of Giraph would be named "tinkerpop.giraph".  If Facebook were
     * to do their own implementation the implementation might be called "facebook.giraph".
     */
    public String getName();

    /**
     * Implementers will typically execute imports of classes within their project that they want available in the
     * console or they may use meta programming to introduce new extensions to the Gremlin.
     *
     * @throws IllegalEnvironmentException   if there are missing environment properties required by the plugin as
     *                                       provided from {@link PluginAcceptor#environment()}.
     * @throws PluginInitializationException if there is a failure in the plugin iniitalization process
     */
    public void pluginTo(final PluginAcceptor pluginAcceptor) throws IllegalEnvironmentException, PluginInitializationException;

    /**
     * Some plugins may require a restart of the plugin host for the classloader to pick up the features.  This is
     * typically true of plugins that rely on {@code Class.forName()} to dynamically instantiate classes from the
     * root classloader (e.g. JDBC drivers that instantiate via @{code DriverManager}).
     */
    public default boolean requireRestart() {
        return false;
    }

    /**
     * Allows a plugin to utilize features of the {@code :remote} and {@code :submit} commands of the Gremlin Console.
     * This method does not need to be implemented if the plugin is not meant for the Console for some reason or
     * if it does not intend to take advantage of those commands.
     */
    public default Optional<RemoteAcceptor> remoteAcceptor() {
        return Optional.empty();
    }
}

The most simple plugin and the one most commonly implemented will likely be one that just provides a list of classes to import to the Gremlin Console. This type of plugin is the easiest way for implementers of the TinkerPop Structure and Process APIs to make their implementations available to users. The TinkerGraph implementation has just such a plugin:


package org.apache.tinkerpop.gremlin.tinkergraph.groovy.plugin;

import org.apache.tinkerpop.gremlin.groovy.plugin.AbstractGremlinPlugin;
import org.apache.tinkerpop.gremlin.groovy.plugin.IllegalEnvironmentException;
import org.apache.tinkerpop.gremlin.groovy.plugin.PluginAcceptor;
import org.apache.tinkerpop.gremlin.groovy.plugin.PluginInitializationException;
import org.apache.tinkerpop.gremlin.tinkergraph.process.computer.TinkerGraphComputer;
import org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph;

import java.util.HashSet;
import java.util.Set;

/**
 * @author Stephen Mallette (http://stephen.genoprime.com)
 * @deprecated As of release 3.2.4, replaced by {@link org.apache.tinkerpop.gremlin.tinkergraph.jsr223.TinkerGraphGremlinPlugin}.
 */
@Deprecated
public final class TinkerGraphGremlinPlugin extends AbstractGremlinPlugin {

    private static final Set<String> IMPORTS = new HashSet<String>() {{
        add(IMPORT_SPACE + TinkerGraph.class.getPackage().getName() + DOT_STAR);
        add(IMPORT_SPACE + TinkerGraphComputer.class.getPackage().getName() + DOT_STAR);
    }};

    @Override
    public String getName() {
        return "tinkerpop.tinkergraph";
    }

    @Override
    public void pluginTo(final PluginAcceptor pluginAcceptor) throws PluginInitializationException, IllegalEnvironmentException {
        pluginAcceptor.addImports(IMPORTS);
    }

    @Override
    public void afterPluginTo(final PluginAcceptor pluginAcceptor) throws IllegalEnvironmentException, PluginInitializationException {

    }
}

Note that the plugin provides a unique name for the plugin which follows a namespaced pattern as namespace.plugin-name (e.g. "tinkerpop.hadoop" - "tinkerpop" is the reserved namespace for TinkerPop maintained plugins). To make TinkerGraph classes available to the Console, the PluginAcceptor is given a Set of imports to provide to the plugin host. The PluginAcceptor essentially behaves as an abstraction to the "host" that is handling the GremlinPlugin. GremlinPlugin implementations maybe hosted by the Console as well as the ScriptEngine in Gremlin Server. Obviously, registering new commands and other operations that are specific to the Groovy Shell don’t make sense there. Write the code for the plugin defensively by checking the GremlinPlugin.env key in the PluginAcceptor.environment() to understand which environment the plugin is being used in.

There is one other step to follow to ensure that the GremlinPlugin is visible to its hosts. GremlinPlugin implementations are loaded via ServiceLoader and therefore need a resource file added to the jar file where the plugin exists. Add a file called org.apache.tinkerpop.gremlin.groovy.plugin.GremlinPlugin to META-INF.services. In the case of the TinkerGraph plugin above, that file will have this line in it:

org.apache.tinkerpop.gremlin.tinkergraph.groovy.plugin.TinkerGraphGremlinPlugin

Once the plugin is packaged, there are two ways to test it out:

  1. Copy the jar and its dependencies to the Gremlin Console path and start it.

  2. Start Gremlin Console and try the :install command: :install com.company my-plugin 1.0.0.

In either case, once one of these two approaches is taken, the jars and their dependencies are available to the Console. The next step is to "activate" the plugin by doing :plugin use my-plugin, where "my-plugin" refers to the name of the plugin to activate.

Note
When :install is used logging dependencies related to SLF4J are filtered out so as not to introduce multiple logger bindings (which generates warning messages to the logs).

A plugin can do much more than just import classes. One can expand the Gremlin language with new functions or steps, provide useful commands to make repetitive or complex tasks easier to execute, or do helpful integrations with other systems. The secret to doing so lies in the PluginAcceptor. As mentioned earlier, the PluginAcceptor provides access to the host of the plugin. It provides several important methods for doing so:

  1. addBinding - These two function allow the plugin to inject whatever context it wants to the host. For example, doing addBinding('x',1) would place a variable of x with a value of 1 into the console at the time of the plugin load.

  2. eval - Evaluates a script in the context of the host at the time of plugin startup. For example, doing eval("sum={x,y→x+y}") would create a sum function that would be available to the user of the Console after the load of the plugin.

  3. environment - Provides context from the host environment. For the console, the environment will return a Map containing a reference to the IO stream and the Groovysh instance. These classes represent very low-level access to the underpinnings of the console. Access to Groovysh allows for advanced features such as registering new commands (e.g. like the :plugin or :remote commands).

Plugins can also tie into the :remote and :submit commands. Recall that a :remote represents a different context within which Gremlin is executed, when issued with :submit. It is encouraged to use this integration point when possible, as opposed to registering new commands that can otherwise follow the :remote and :submit pattern. To expose this integration point as part of a plugin, implement the RemoteAcceptor interface:

Tip
Be good to the users of plugins and prevent dependency conflicts. Maintaining a conflict free plugin is most easily done by using the Maven Enforcer Plugin.
Tip
Consider binding the plugin’s minor version to the TinkerPop minor version so that it’s easy for users to figure out plugin compatibility. Otherwise, clearly document a compatibility matrix for the plugin somewhere that users can find it.

package org.apache.tinkerpop.gremlin.groovy.plugin;

import org.codehaus.groovy.tools.shell.Groovysh;

import java.io.Closeable;
import java.util.List;

/**
 * The Gremlin Console supports the {@code :remote} and {@code :submit} commands which provide standardized ways
 * for plugins to provide "remote connections" to resources and a way to "submit" a command to those resources.
 * A "remote connection" does not necessarily have to be a remote server.  It simply refers to a resource that is
 * external to the console.
 * <p/>
 * By implementing this interface and returning an instance of it through {@link GremlinPlugin#remoteAcceptor()} a
 * plugin can hook into those commands and provide remoting features.
 *
 * @author Stephen Mallette (http://stephen.genoprime.com)
 * @deprecated As of release 3.2.4, replaced by {@link org.apache.tinkerpop.gremlin.jsr223.console.RemoteAcceptor};
 */
@Deprecated
public interface RemoteAcceptor extends Closeable {

    public static final String RESULT = "result";

    /**
     * Gets called when {@code :remote} is used in conjunction with the "connect" option.  It is up to the
     * implementation to decide how additional arguments on the line should be treated after "connect".
     *
     * @return an object to display as output to the user
     * @throws org.apache.tinkerpop.gremlin.groovy.plugin.RemoteException if there is a problem with connecting
     */
    public Object connect(final List<String> args) throws RemoteException;

    /**
     * Gets called when {@code :remote} is used in conjunction with the {@code config} option.  It is up to the
     * implementation to decide how additional arguments on the line should be treated after {@code config}.
     *
     * @return an object to display as output to the user
     * @throws org.apache.tinkerpop.gremlin.groovy.plugin.RemoteException if there is a problem with configuration
     */
    public Object configure(final List<String> args) throws RemoteException;

    /**
     * Gets called when {@code :submit} is executed.  It is up to the implementation to decide how additional
     * arguments on the line should be treated after {@code :submit}.
     *
     * @return an object to display as output to the user
     * @throws org.apache.tinkerpop.gremlin.groovy.plugin.RemoteException if there is a problem with submission
     */
    public Object submit(final List<String> args) throws RemoteException;

    /**
     * If the {@code RemoteAcceptor} is used in the Gremlin Console, then this method might be called to determine
     * if it can be used in a fashion that supports the {@code :remote console} command.  By default, this value is
     * set to {@code false}.
     * <p/>
     * A {@code RemoteAcceptor} should only return {@code true} for this method if it expects that all activities it
     * supports are executed through the {@code :sumbit} command. If the users interaction with the remote requires
     * working with both local and remote evaluation at the same time, it is likely best to keep this method return
     * {@code false}. A good example of this type of plugin would be the Gephi Plugin which uses {@code :remote config}
     * to configure a local {@code TraversalSource} to be used and expects calls to {@code :submit} for the same body
     * of analysis.
     */
    public default boolean allowRemoteConsole() {
        return false;
    }

    /**
     * Retrieve a script as defined in the shell context.  This allows for multi-line scripts to be submitted.
     */
    public static String getScript(final String submittedScript, final Groovysh shell) {
        return submittedScript.startsWith("@") ? shell.getInterp().getContext().getProperty(submittedScript.substring(1)).toString() : submittedScript;
    }
}

The RemoteAcceptor implementation ties to a GremlinPlugin and will only be executed when in use with the Gremlin Console plugin host. Simply instantiate and return a RemoteAcceptor in the GremlinPlugin.remoteAcceptor() method of the plugin implementation. Generally speaking, each call to remoteAcceptor() should produce a new instance of a RemoteAcceptor. It will likely be necessary that you provide context from the GremlinPlugin to the RemoteAcceptor plugin. For example, the RemoteAcceptor implementation might require an instance of Groovysh to provide a way to dynamically evaluate a script provided to it so that it can process the results in a different way.