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From dkupp...@apache.org
Subject [2/8] incubator-tinkerpop git commit: splitted implementations.asciidoc into implementations-hadoop.asciidoc and implementations-neo4j.asciidoc
Date Fri, 19 Feb 2016 18:38:45 GMT
http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/blob/bbf5b3f4/docs/src/reference/implementations.asciidoc
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diff --git a/docs/src/reference/implementations.asciidoc b/docs/src/reference/implementations.asciidoc
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-////
-Licensed to the Apache Software Foundation (ASF) under one or more
-contributor license agreements.  See the NOTICE file distributed with
-this work for additional information regarding copyright ownership.
-The ASF licenses this file to You under the Apache License, Version 2.0
-(the "License"); you may not use this file except in compliance with
-the License.  You may obtain a copy of the License at
-
-  http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-////
-[[implementations]]
-Implementations
-===============
-
-image::gremlin-racecar.png[width=325]
-
-[[graph-system-provider-requirements]]
-Graph System Provider Requirements
-----------------------------------
-
-image:tinkerpop-enabled.png[width=140,float=left] 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-gremlin,TinkerGraph>> (in-memory OLTP and OLAP in `tinkergraph-gremlin`), <<neo4j-gremlin,Neo4jGraph>>
-(OTLP w/ transactions in `neo4j-gremlin`) and/or <<hadoop-gremlin,HadoopGraph>> (OLAP in `hadoop-gremlin`)
-implementations for ideas and patterns.
-
-. Online Transactional Processing Graph Systems (*OLTP*)
- .. Structure API: `Graph`, `Element`, `Vertex`, `Edge`, `Property` and `Transaction` (if transactions are supported).
- .. Process API: `TraversalStrategy` instances for optimizing Gremlin traversals to the provider's graph system (i.e. `TinkerGraphStepStrategy`).
-. Online Analytics Processing Graph Systems (*OLAP*)
- .. Everything required of OTLP is required of OLAP (but not vice versa).
- .. 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]]
-OLTP Implementations
-^^^^^^^^^^^^^^^^^^^^
-
-image:pipes-character-1.png[width=110,float=right] 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]]
-OLAP Implementations
-^^^^^^^^^^^^^^^^^^^^
-
-image:furnace-character-1.png[width=110,float=right] 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,OLTP Implementations>>. A summary of each required interface
-implementation is presented below:
-
-. `GraphComputer`: A fluent builder for specifying an isolation level, a VertexProgram, and any number of MapReduce jobs to be submitted.
-. `Memory`: A global blackboard for ANDing, ORing, INCRing, and SETing values for specified keys.
-. `Messenger`: The system that collects and distributes messages being propagated by vertices executing the VertexProgram application.
-. `MapReduce.MapEmitter`: The system that collects key/value pairs being emitted by the MapReduce applications map-phase.
-. `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: <<tinkergraph-gremlin,TinkerGraphComputer>> (TinkerGraph),
-<<giraphgraphcomputer,GiraphGraphComputer>> (HadoopGraph), and <<sparkgraphcomputer,`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
-++++++++++++++++++++++++++
-
-image:furnace-character-3.png[width=150,float=right] The most complex method in GraphComputer is the `submit()`-method. The method must do the following:
-
-. Ensure the the GraphComputer has not already been executed.
-. Ensure that at least there is a VertexProgram or 1 MapReduce job.
-. If there is a VertexProgram, validate that it can execute on the GraphComputer given the respectively defined features.
-. Create the Memory to be used for the computation.
-. Execute the VertexProgram.setup() method once and only once.
-. Execute the VertexProgram.execute() method for each vertex.
-. Execute the VertexProgram.terminate() method once and if true, repeat VertexProgram.execute().
-. When VertexProgram.terminate() returns true, move to MapReduce job execution.
-. MapReduce jobs are not required to be executed in any specified order.
-. For each Vertex, execute MapReduce.map(). Then (if defined) execute MapReduce.combine() and MapReduce.reduce().
-. Update Memory with runtime information.
-. Construct a new `ComputerResult` containing the compute Graph and Memory.
-
-Implementing Memory
-+++++++++++++++++++
-
-image:gremlin-brain.png[width=175,float=left] 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
-+++++++++++++++++++++++++++++++
-
-image:hadoop-logo-notext.png[width=150,float=left] The MapReduce framework in TinkerPop3 is similar to the model
-popularized by link:http://apache.hadoop.org[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).
-
-[source,java]
-----
-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.
-
-[source,java]
-----
-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:
-
-[source,java]
-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()`.
-
-[source,java]
-----
-...
-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 the map-stage results are inserted into Memory as specified by the application
-developer's `MapReduce.addResultToMemory()` implementation.
-
-[[io-implementations]]
-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:
-
-[source,java]
-----
-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:
-
-[source,java]
-----
-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.
-
-[[validating-with-gremlin-test]]
-Validating with Gremlin-Test
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-image:gremlin-edumacated.png[width=225]
-
-[source,xml]
-<dependency>
-  <groupId>org.apache.tinkerpop</groupId>
-  <artifactId>gremlin-test</artifactId>
-  <version>x.y.z</version>
-</dependency>
-<dependency>
-  <groupId>org.apache.tinkerpop</groupId>
-  <artifactId>gremlin-groovy-test</artifactId>
-  <version>x.y.z</version>
-</dependency>
-
-The operational semantics of any OLTP or OLAP implementation are validated by `gremlin-test` and functional
-interoperability with the Groovy environment is ensured by `gremlin-groovy-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.).
-
-[source,java]
-----
-// 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 {}
-
-@RunWith(GroovyEnvironmentSuite.class)
-@GraphProviderClass(provider = XXXProvider.class, graph = TinkerGraph.class)
-public class XXXGroovyEnvironmentTest {}
-
-@RunWith(GroovyProcessStandardSuite.class)
-@GraphProviderClass(provider = XXXGraphProvider.class, graph = TinkerGraph.class)
-public class XXXGroovyProcessStandardTest {}
-
-@RunWith(GroovyProcessComputerSuite.class)
-@GraphProviderClass(provider = XXXGraphComputerProvider.class, graph = TinkerGraph.class)
-public class XXXGroovyProcessComputerTest {}
-----
-
-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:
-
-[source,java]
-----
-@Graph.OptIn(Graph.OptIn.SUITE_STRUCTURE_STANDARD)
-@Graph.OptIn(Graph.OptIn.SUITE_PROCESS_STANDARD)
-@Graph.OptIn(Graph.OptIn.SUITE_PROCESS_COMPUTER)
-@Graph.OptIn(Graph.OptIn.SUITE_GROOVY_PROCESS_STANDARD)
-@Graph.OptIn(Graph.OptIn.SUITE_GROOVY_PROCESS_COMPUTER)
-@Graph.OptIn(Graph.OptIn.SUITE_GROOVY_ENVIRONMENT)
-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`:
-
-[source, java]
-----
-@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.  This style of
-ignoring is useful for Gremlin "process" tests that have bases classes that are extended by various Gremlin flavors (e.g. groovy).
-* 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:
-
-[source, java]
-@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
-xref:describe-graph[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.
-
-CAUTION: 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.
-
-Accessibility via GremlinPlugin
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-image:gremlin-plugin.png[width=100,float=left] 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.
-
-[source,java]
-----
-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.
-
-[source,groovy]
-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 x.y.z
-==>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
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-image:gremlin-painting.png[width=200,float=right] 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,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 <<graph-traversal-steps,step>> is ultimately referenced by the `GraphTraversal`
-interface. It is possible to extend `GraphTraversal` to use a graph system specific step implementation.
-
-
-[[tinkergraph-gremlin]]
-TinkerGraph-Gremlin
--------------------
-
-[source,xml]
-----
-<dependency>
-   <groupId>org.apache.tinkerpop</groupId>
-   <artifactId>tinkergraph-gremlin</artifactId>
-   <version>x.y.z</version>
-</dependency>
-----
-
-image:tinkerpop-character.png[width=100,float=left] TinkerGraph is a single machine, in-memory (with optional
-persistence), non-transactional graph engine that provides both OLTP and OLAP functionality. It is deployed with
-TinkerPop3 and serves as the reference implementation for other providers to study in order to understand the
-semantics of the various methods of the TinkerPop3 API. Constructing a simple graph in Java8 is presented below.
-
-[source,java]
-Graph g = TinkerGraph.open();
-Vertex marko = g.addVertex("name","marko","age",29);
-Vertex lop = g.addVertex("name","lop","lang","java");
-marko.addEdge("created",lop,"weight",0.6d);
-
-The above graph creates two vertices named "marko" and "lop" and connects them via a created-edge with a weight=0.6
-property. Next, the graph can be queried as such.
-
-[source,java]
-g.V().has("name","marko").out("created").values("name")
-
-The `g.V().has("name","marko")` part of the query can be executed in two ways.
-
- * A linear scan of all vertices filtering out those vertices that don't have the name "marko"
- * A `O(log(|V|))` index lookup for all vertices with the name "marko"
-
-Given the initial graph construction in the first code block, no index was defined and thus, a linear scan is executed.
-However, if the graph was constructed as such, then an index lookup would be used.
-
-[source,java]
-Graph g = TinkerGraph.open();
-g.createIndex("name",Vertex.class)
-
-The execution times for a vertex lookup by property is provided below for both no-index and indexed version of
-TinkerGraph over the Grateful Dead graph.
-
-[gremlin-groovy]
-----
-graph = TinkerGraph.open()
-g = graph.traversal()
-graph.io(graphml()).readGraph('data/grateful-dead.xml')
-clock(1000) {g.V().has('name','Garcia').iterate()} <1>
-graph = TinkerGraph.open()
-g = graph.traversal()
-graph.createIndex('name',Vertex.class)
-graph.io(graphml()).readGraph('data/grateful-dead.xml')
-clock(1000){g.V().has('name','Garcia').iterate()} <2>
-----
-
-<1> Determine the average runtime of 1000 vertex lookups when no `name`-index is defined.
-<2> Determine the average runtime of 1000 vertex lookups when a `name`-index is defined.
-
-IMPORTANT: Each graph system will have different mechanism by which indices and schemas are defined. TinkerPop3
-does not require any conformance in this area. In TinkerGraph, the only definitions are around indices. With other
-graph systems, property value types, indices, edge labels, etc. may be required to be defined _a priori_ to adding
-data to the graph.
-
-NOTE: TinkerGraph is distributed with Gremlin Server and is therefore automatically available to it for configuration.
-
-Configuration
-~~~~~~~~~~~~~
-
-TinkerGraph has several settings that can be provided on creation via `Configuration` object:
-
-[width="100%",cols="2,10",options="header"]
-|=========================================================
-|Property |Description
-|gremlin.graph |`org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph`
-|gremlin.tinkergraph.vertexIdManager |The `IdManager` implementation to use for vertices.
-|gremlin.tinkergraph.edgeIdManager |The `IdManager` implementation to use for edges.
-|gremlin.tinkergraph.vertexPropertyIdManager |The `IdManager` implementation to use for vertex properties.
-|gremlin.tinkergraph.defaultVertexPropertyCardinality |The default `VertexProperty.Cardinality` to use when `Vertex.property(k,v)` is called.
-|gremlin.tinkergraph.graphLocation |The path and file name for where TinkerGraph should persist the graph data. If a
-value is specified here, the the `gremlin.tinkergraph.graphFormat` should also be specified.  If this value is not
-included (default), then the graph will stay in-memory and not be loaded/persisted to disk.
-|gremlin.tinkergraph.graphFormat |The format to use to serialize the graph which may be one of the following:
-`graphml`, `graphson`, `gryo`, or a fully qualified class name that implements Io.Builder interface (which allows for
-external third party graph reader/writer formats to be used for persistence).
-If a value is specified here, then the `gremlin.tinkergraph.graphLocation` should
-also be specified.  If this value is not included (default), then the graph will stay in-memory and not be
-loaded/persisted to disk.
-|=========================================================
-
-The `IdManager` settings above refer to how TinkerGraph will control identifiers for vertices, edges and vertex
-properties.  There are several options for each of these settings: `ANY`, `LONG`, `INTEGER`, `UUID`, or the fully
-qualified class name of an `IdManager` implementation on the classpath.  When not specified, the default values
-for all settings is `ANY`, meaning that the graph will work with any object on the JVM as the identifier and will
-generate new identifiers from `Long` when the identifier is not user supplied.  TinkerGraph will also expect the
-user to understand the types used for identifiers when querying, meaning that `g.V(1)` and `g.V(1L)` could return
-two different vertices.  `LONG`, `INTEGER` and `UUID` settings will try to coerce identifier values to the expected
-type as well as generate new identifiers with that specified type.
-
-If the TinkerGraph is configured for persistence with `gremlin.tinkergraph.graphLocation` and
-`gremlin.tinkergraph.graphFormat`, then the graph will be written to the specified location with the specified
-format when `Graph.close()` is called.  In addition, if these settings are present, TinkerGraph will attempt to
-load the graph from the specified location.
-
-IMPORTANT: If choosing `graphson` as the `gremlin.tinkergraph.graphFormat`, be sure to also establish the  various
-`IdManager` settings as well to ensure that identifiers are properly coerced to the appropriate types as GraphSON
-can lose the identifier's type during serialization (i.e. it will assume `Integer` when the default for TinkerGraph
-is `Long`, which could lead to load errors that result in a message like, "Vertex with id already exists").
-
-It is important to consider the data being imported to TinkerGraph with respect to `defaultVertexPropertyCardinality`
-setting.  For example, if a `.gryo` file is known to contain multi-property data, be sure to set the default
-cardinality to `list` or else the data will import as `single`.  Consider the following:
-
-[gremlin-groovy]
-----
-graph = TinkerGraph.open()
-graph.io(gryo()).readGraph("data/tinkerpop-crew.kryo")
-g = graph.traversal()
-g.V().properties()
-conf = new BaseConfiguration()
-conf.setProperty("gremlin.tinkergraph.defaultVertexPropertyCardinality","list")
-graph = TinkerGraph.open(conf)
-graph.io(gryo()).readGraph("data/tinkerpop-crew.kryo")
-g = graph.traversal()
-g.V().properties()
-----
-
-[[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>0.1-2.2</version>
-</dependency>
-----
-
-link:http://neotechnology.com[Neo Technology] are the developers of the OLTP-based link:http://neo4j.org[Neo4j graph database].
-
-CAUTION: Unless under a commercial agreement with Neo Technology, 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].
-
-[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]]
-----
-
-NOTE: Neo4j link:http://docs.neo4j.org/chunked/stable/ha.html[High Availability] is currently not supported by
-Neo4j-Gremlin.
-
-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://docs.neo4j.org/chunked/stable/query-schema-index.html[Cypher].
-* Manipulating indices with the Neo4j link:http://docs.neo4j.org/chunked/stable/tutorials-java-embedded-new-index.html[Java API].
-
-[gremlin-groovy]
-----
-graph = Neo4jGraph.open('/tmp/neo4j')
-graph.cypher("CREATE INDEX ON :person(name)")
-graph.tx().commit()  <1>
-graph.addVertex(label,'person','name','marko')
-graph.addVertex(label,'dog','name','puppy')
-g = graph.traversal()
-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('name','michael','name','michael hunger','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:http://www.neo4j.org/learn/cypher[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/stable/graphdb-neo4j-labels.html[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')
-vertex = (Neo4jVertex) graph.addVertex('human::animal') <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 = graph.traversal()
-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
-----
-
-[[hadoop-gremlin]]
-Hadoop-Gremlin
---------------
-
-[source,xml]
-----
-<dependency>
-   <groupId>org.apache.tinkerpop</groupId>
-   <artifactId>hadoop-gremlin</artifactId>
-   <version>x.y.z</version>
-</dependency>
-----
-
-image:hadoop-logo-notext.png[width=100,float=left] link:http://hadoop.apache.org/[Hadoop] is a distributed
-computing framework that is used to process data represented across a multi-machine compute cluster. When the
-data in the Hadoop cluster represents a TinkerPop3 graph, then Hadoop-Gremlin can be used to process the graph
-using both TinkerPop3's OLTP and OLAP graph computing models.
-
-IMPORTANT: This section assumes that the user has a Hadoop 2.x cluster functioning. For more information on getting
-started with Hadoop, please see the
-link:http://hadoop.apache.org/docs/r2.7.1/hadoop-project-dist/hadoop-common/SingleCluster.html[Single Node Setup]
-tutorial. Moreover, if using `GiraphGraphComputer` or `SparkGraphComputer` it is advisable that the reader also
-familiarize their self with Giraph (link:http://giraph.apache.org/quick_start.html[Getting Started]) and Spark
-(link:http://spark.apache.org/docs/latest/quick-start.html[Quick Start]).
-
-Installing Hadoop-Gremlin
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The `HADOOP_GREMLIN_LIBS` references locations that contains jars that should be uploaded to a respective
-distributed cache (link:http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html[YARN] or SparkServer).
-Note that the locations in `HADOOP_GREMLIN_LIBS` can be a colon-separated (`:`) and all jars from all locations will
-be loaded into the cluster. Typically, only the jars of the respective GraphComputer are required to be loaded (e.g.
-`GiraphGraphComputer` plugin lib directory).
-
-[source,shell]
-export HADOOP_GREMLIN_LIBS=/usr/local/gremlin-console/ext/giraph-gremlin/lib
-
-If using <<gremlin-console,Gremlin Console>>, it is important to install the Hadoop-Gremlin plugin. Note that
-Hadoop-Gremlin requires a Gremlin Console restart after installing.
-
-[source,text]
-----
-$ bin/gremlin.sh
-
-         \,,,/
-         (o o)
------oOOo-(3)-oOOo-----
-plugin activated: tinkerpop.server
-plugin activated: tinkerpop.utilities
-plugin activated: tinkerpop.tinkergraph
-gremlin> :install org.apache.tinkerpop hadoop-gremlin x.y.z
-==>loaded: [org.apache.tinkerpop, hadoop-gremlin, x.y.z] - restart the console to use [tinkerpop.hadoop]
-gremlin> :q
-$ bin/gremlin.sh
-
-         \,,,/
-         (o o)
------oOOo-(3)-oOOo-----
-plugin activated: tinkerpop.server
-plugin activated: tinkerpop.utilities
-plugin activated: tinkerpop.tinkergraph
-gremlin> :plugin use tinkerpop.hadoop
-==>tinkerpop.hadoop activated
-gremlin>
-----
-
-Properties Files
-~~~~~~~~~~~~~~~~
-
-`HadoopGraph` makes use of properties files which ultimately get turned into Apache configurations and/or
-Hadoop configurations. The example properties file presented below is located at `conf/hadoop/hadoop-gryo.properties`.
-
-[source,text]
-gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
-gremlin.hadoop.inputLocation=tinkerpop-modern.kryo
-gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
-gremlin.hadoop.outputLocation=output
-gremlin.hadoop.graphOutputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat
-gremlin.hadoop.jarsInDistributedCache=true
-####################################
-# Spark Configuration              #
-####################################
-spark.master=local[4]
-spark.executor.memory=1g
-spark.serializer=org.apache.tinkerpop.gremlin.spark.structure.io.gryo.GryoSerializer
-####################################
-# SparkGraphComputer Configuration #
-####################################
-gremlin.spark.graphInputRDD=org.apache.tinkerpop.gremlin.spark.structure.io.InputRDDFormat
-gremlin.spark.graphOutputRDD=org.apache.tinkerpop.gremlin.spark.structure.io.OutputRDDFormat
-gremlin.spark.persistContext=true
-#####################################
-# GiraphGraphComputer Configuration #
-#####################################
-giraph.minWorkers=2
-giraph.maxWorkers=2
-giraph.useOutOfCoreGraph=true
-giraph.useOutOfCoreMessages=true
-mapreduce.map.java.opts=-Xmx1024m
-mapreduce.reduce.java.opts=-Xmx1024m
-giraph.numInputThreads=2
-giraph.numComputeThreads=2
-
-A review of the Hadoop-Gremlin specific properties are provided in the table below. For the respective OLAP
-engines (<<sparkgraphcomputer,`SparkGraphComputer`>> or <<giraphgraphcomputer,`GiraphGraphComputer`>>) refer
-to their respective documentation for configuration options.
-
-[width="100%",cols="2,10",options="header"]
-|=========================================================
-|Property |Description
-|gremlin.graph |The class of the graph to construct using GraphFactory.
-|gremlin.hadoop.inputLocation |The location of the input file(s) for Hadoop-Gremlin to read the graph from.
-|gremlin.hadoop.graphInputFormat |The format that the graph input file(s) are represented in.
-|gremlin.hadoop.outputLocation |The location to write the computed HadoopGraph to.
-|gremlin.hadoop.graphOutputFormat |The format that the output file(s) should be represented in.
-|gremlin.hadoop.jarsInDistributedCache |Whether to upload the Hadoop-Gremlin jars to a distributed cache (necessary if jars are not on the machines' classpaths).
-|=========================================================
-
-
-
-Along with the properties above, the numerous link:http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/core-default.xml[Hadoop specific properties]
-can be added as needed to tune and parameterize the executed Hadoop-Gremlin job on the respective Hadoop cluster.
-
-IMPORTANT: As the size of the graphs being processed becomes large, it is important to fully understand how the
-underlying OLAP engine (e.g. Spark, Giraph, etc.) works and understand the numerous parameterizations offered by
-these systems. Such knowledge can help alleviate out of memory exceptions, slow load times, slow processing times,
-garbage collection issues, etc.
-
-OLTP Hadoop-Gremlin
-~~~~~~~~~~~~~~~~~~~
-
-image:hadoop-pipes.png[width=180,float=left] It is possible to execute OLTP operations over a `HadoopGraph`.
-However, realize that the underlying HDFS files are not random access and thus, to retrieve a vertex, a linear scan
-is required. OLTP operations are useful for peeking into the graph prior to executing a long running OLAP job -- e.g.
-`g.V().valueMap().limit(10)`.
-
-CAUTION: OLTP operations on `HadoopGraph` are not efficient. They require linear scans to execute and are unreasonable
-for large graphs. In such large graph situations, make use of <<traversalvertexprogram,TraversalVertexProgram>>
-which is the OLAP Gremlin machine.
-
-[gremlin-groovy]
-----
-hdfs.copyFromLocal('data/tinkerpop-modern.kryo', 'tinkerpop-modern.kryo')
-hdfs.ls()
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-g = graph.traversal()
-g.V().count()
-g.V().out().out().values('name')
-g.V().group().by{it.value('name')[1]}.by('name').next()
-----
-
-OLAP Hadoop-Gremlin
-~~~~~~~~~~~~~~~~~~~
-
-image:hadoop-furnace.png[width=180,float=left] Hadoop-Gremlin was designed to execute OLAP operations via
-`GraphComputer`. The OLTP examples presented previously are reproduced below, but using `TraversalVertexProgram`
-for the execution of the Gremlin traversal.
-
-A `Graph` in TinkerPop3 can support any number of `GraphComputer` implementations. Out of the box, Hadoop-Gremlin
-supports the following three implementations.
-
-* <<mapreducegraphcomputer,`MapReduceGraphComputer`>>: Leverages Hadoop's MapReduce engine to execute TinkerPop3 OLAP
-computations. (*coming soon*)
-** The graph must fit within the total disk space of the Hadoop cluster (supports massive graphs). Message passing is
-coordinated via MapReduce jobs over the on-disk graph (slow traversals).
-* <<sparkgraphcomputer,`SparkGraphComputer`>>: Leverages Apache Spark to execute TinkerPop3 OLAP computations.
-** The graph may fit within the total RAM of the cluster (supports larger graphs). Message passing is coordinated via
-Spark map/reduce/join operations on in-memory and disk-cached data (average speed traversals).
-* <<giraphgraphcomputer,`GiraphGraphComputer`>>: Leverages Apache Giraph to execute TinkerPop3 OLAP computations.
-** The graph should fit within the total RAM of the Hadoop cluster (graph size restriction), though "out-of-core"
-processing is possible. Message passing is coordinated via ZooKeeper for the in-memory graph (speedy traversals).
-
-TIP: image:gremlin-sugar.png[width=50,float=left] For those wanting to use the <<sugar-plugin,SugarPlugin>> with
-their submitted traversal, do `:remote config useSugar true` as well as `:plugin use tinkerpop.sugar` at the start of
-the Gremlin Console session if it is not already activated.
-
-Note that `SparkGraphComputer` and `GiraphGraphComputer` are loaded via their respective plugins. Typically only
-one plugin or the other is loaded depending on the desired `GraphComputer` to use.
-
-[source,text]
-----
-$ bin/gremlin.sh
-
-         \,,,/
-         (o o)
------oOOo-(3)-oOOo-----
-plugin activated: tinkerpop.server
-plugin activated: tinkerpop.utilities
-plugin activated: tinkerpop.tinkergraph
-plugin activated: tinkerpop.hadoop
-gremlin> :install org.apache.tinkerpop giraph-gremlin x.y.z
-==>loaded: [org.apache.tinkerpop, giraph-gremlin, x.y.z] - restart the console to use [tinkerpop.giraph]
-gremlin> :install org.apache.tinkerpop spark-gremlin x.y.z
-==>loaded: [org.apache.tinkerpop, spark-gremlin, x.y.z] - restart the console to use [tinkerpop.spark]
-gremlin> :q
-$ bin/gremlin.sh
-
-         \,,,/
-         (o o)
------oOOo-(3)-oOOo-----
-plugin activated: tinkerpop.server
-plugin activated: tinkerpop.utilities
-plugin activated: tinkerpop.tinkergraph
-plugin activated: tinkerpop.hadoop
-gremlin> :plugin use tinkerpop.giraph
-==>tinkerpop.giraph activated
-gremlin> :plugin use tinkerpop.spark
-==>tinkerpop.spark activated
-----
-
-WARNING: Hadoop, Spark, and Giraph all depend on many of the same libraries (e.g. ZooKeeper, Snappy, Netty, Guava,
-etc.). Unfortunately, typically these dependencies are not to the same versions of the respective libraries. As such,
-it is best to *not* have both Spark and Giraph plugins loaded in the same console session nor in the same Java
-project (though intelligent `<exclusion>`-usage can help alleviate conflicts in a Java project).
-
-CAUTION: It is important to note that when doing an OLAP traversal, any resulting vertices, edges, or properties will be
-attached to the source graph. For Hadoop-based graphs, this may lead to linear search times on massive graphs. Thus,
-if vertex, edge, or property objects are to be returns (as a final result), it is best to `.id()` to get the id
-of the object and not the actual attached object.
-
-[[mapreducegraphcomputer]]
-MapReduceGraphComputer
-^^^^^^^^^^^^^^^^^^^^^^
-
-*COMING SOON*
-
-[[sparkgraphcomputer]]
-SparkGraphComputer
-^^^^^^^^^^^^^^^^^^
-
-[source,xml]
-----
-<dependency>
-   <groupId>org.apache.tinkerpop</groupId>
-   <artifactId>spark-gremlin</artifactId>
-   <version>x.y.z</version>
-</dependency>
-----
-
-image:spark-logo.png[width=175,float=left] link:http://spark.apache.org[Spark] is an Apache Software Foundation
-project focused on general-purpose OLAP data processing. Spark provides a hybrid in-memory/disk-based distributed
-computing model that is similar to Hadoop's MapReduce model. Spark maintains a fluent function chaining DSL that is
-arguably easier for developers to work with than native Hadoop MapReduce. Spark-Gremlin provides an implementation of
-the bulk-synchronous parallel, distributed message passing algorithm within Spark and thus, any `VertexProgram` can be
-executed over `SparkGraphComputer`.
-
-If `SparkGraphComputer` will be used as the `GraphComputer` for `HadoopGraph` then its `lib` directory should be
-specified in `HADOOP_GREMLIN_LIBS`.
-
-[source,shell]
-export HADOOP_GREMLIN_LIBS=$HADOOP_GREMLIN_LIBS:/usr/local/gremlin-console/ext/spark-gremlin/lib
-
-Furthermore the `lib/` directory should be distributed across all machines in the SparkServer cluster. For this purpose TinkerPop
-provides a helper script, which takes the Spark installation directory and the the Spark machines as input:
-
-[source,shell]
-bin/init-tp-spark.sh /usr/local/spark spark@10.0.0.1 spark@10.0.0.2 spark@10.0.0.3
-
-Once the `lib/` directory is distributed, `SparkGraphComputer` can be used as follows.
-
-[gremlin-groovy]
-----
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-g = graph.traversal(computer(SparkGraphComputer))
-g.V().count()
-g.V().out().out().values('name')
-----
-
-For using lambdas in Gremlin-Groovy, simply provide `:remote connect` a `TraversalSource` which leverages SparkGraphComputer.
-
-[gremlin-groovy]
-----
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-g = graph.traversal(computer(SparkGraphComputer))
-:remote connect tinkerpop.hadoop graph g
-:> g.V().group().by{it.value('name')[1]}.by('name')
-----
-
-The `SparkGraphComputer` algorithm leverages Spark's caching abilities to reduce the amount of data shuffled across
-the wire on each iteration of the <<vertexprogram,`VertexProgram`>>. When the graph is loaded as a Spark RDD
-(Resilient Distributed Dataset) it is immediately cached as `graphRDD`. The `graphRDD` is a distributed adjacency
-list which encodes the vertex, its properties, and all its incident edges. On the first iteration, each vertex
-(in parallel) is passed through `VertexProgram.execute()`. This yields an output of the vertex's mutated state
-(i.e. updated compute keys -- `propertyX`) and its outgoing messages. This `viewOutgoingRDD` is then reduced to
-`viewIncomingRDD` where the outgoing messages are sent to their respective vertices. If a `MessageCombiner` exists
-for the vertex program, then messages are aggregated locally and globally to ultimately yield one incoming message
-for the vertex. This reduce sequence is the "message pass." If the vertex program does not terminate on this
-iteration, then the `viewIncomingRDD` is joined with the cached `graphRDD` and the process continues. When there
-are no more iterations, there is a final join and the resultant RDD is stripped of its edges and messages. This
-`mapReduceRDD` is cached and is processed by each <<mapreduce,`MapReduce`>> job in the
-<<graphcomputer,`GraphComputer`>> computation.
-
-image::spark-algorithm.png[width=775]
-
-[width="100%",cols="2,10",options="header"]
-|========================================================
-|Property |Description
-|gremlin.spark.graphInputRDD |A class for creating RDD's from underlying graph data, defaults to Hadoop `InputFormat`.
-|gremlin.spark.graphOutputRDD |A class for output RDD's, defaults to Hadoop `OutputFormat`.
-|gremlin.spark.graphStorageLevel |What `StorageLevel` to use for the cached graph during job execution (default `MEMORY_ONLY`).
-|gremlin.spark.persistContext |Whether to create a new `SparkContext` for every `SparkGraphComputer` or to reuse an existing one.
-|gremlin.spark.persistStorageLevel |What `StorageLevel` to use when persisted RDDs via `PersistedOutputRDD` (default `MEMORY_ONLY`).
-|========================================================
-
-InputRDD and OutputRDD
-++++++++++++++++++++++
-
-If the provider/user does not want to use Hadoop `InputFormats`, it is possible to leverage Spark's RDD
-constructs directly. There is a `gremlin.spark.graphInputRDD` configuration that references a `Class<? extends
-InputRDD>`. An `InputRDD` provides a read method that takes a `SparkContext` and returns a graphRDD. Likewise, use
-`gremlin.spark.graphOutputRDD` and the respective `OutputRDD`.
-
-If the graph system provider uses an `InputRDD`, the RDD should maintain an associated `org.apache.spark.Partitioner`. By doing so,
-`SparkGraphComputer` will not partition the loaded graph across the cluster as it has already been partitioned by the graph system provider.
-This can save a significant amount of time and space resources.
-If the `InputRDD` does not have a registered partitioner, `SparkGraphComputer` will partition the graph using
-a `org.apache.spark.HashPartitioner` with the number of partitions being either the number of existing partitions in the input (e.g. input splits)
-or the user specified number of `GraphComputer.workers()`.
-
-Using a Persisted Context
-+++++++++++++++++++++++++
-
-It is possible to persist the graph RDD between jobs within the `SparkContext` (e.g. SparkServer) by leveraging `PersistedOutputRDD`.
-Note that `gremlin.spark.persistContext` should be set to `true` or else the persisted RDD will be destroyed when the `SparkContext` closes.
-The persisted RDD is named by the `gremlin.hadoop.outputLocation` configuration. Similarly, `PersistedInputRDD` is used with respective
-`gremlin.hadoop.inputLocation` to retrieve the persisted RDD from the `SparkContext`.
-
-When using a persistent `SparkContext` the configuration used by the original Spark Configuration will be inherited by all threaded
-references to that Spark Context. The exception to this rule are those properties which have a specific thread local effect.
-
-.Thread Local Properties
-. spark.jobGroup.id
-. spark.job.description
-. spark.job.interruptOnCancel
-. spark.scheduler.pool
-
-Finally, there is a `spark` object that can be used to manage persisted RDDs (see <<interacting-with-spark, Interacting with Spark>>).
-
-[[bulkdumpervertexprogramusingspark]]
-Exporting with BulkDumperVertexProgram
-++++++++++++++++++++++++++++++++++++++
-
-The <<bulkdumpervertexprogram, BulkDumperVertexProgram>> exports a whole graph in any of the supported Hadoop GraphOutputFormats (`GraphSONOutputFormat`,
-`GryoOutputFormat` or `ScriptOutputFormat`). The example below takes a Hadoop graph as the input (in `GryoInputFormat`) and exports it as a GraphSON file
-(`GraphSONOutputFormat`).
-
-[gremlin-groovy]
-----
-hdfs.copyFromLocal('data/tinkerpop-modern.kryo', 'tinkerpop-modern.kryo')
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-graph.configuration().setProperty('gremlin.hadoop.graphOutputFormat', 'org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONOutputFormat')
-graph.compute(SparkGraphComputer).program(BulkDumperVertexProgram.build().create()).submit().get()
-hdfs.ls('output')
-hdfs.head('output/~g')
-----
-
-Loading with BulkLoaderVertexProgram
-++++++++++++++++++++++++++++++++++++
-
-The <<bulkloadervertexprogram, BulkLoaderVertexProgram>> is a generalized bulk loader that can be used to load large
-amounts of data to and from different `Graph` implementations. The following code demonstrates how to load the
-Grateful Dead graph from HadoopGraph into TinkerGraph over Spark:
-
-[gremlin-groovy]
-----
-hdfs.copyFromLocal('data/grateful-dead.kryo', 'grateful-dead.kryo')
-readGraph = GraphFactory.open('conf/hadoop/hadoop-grateful-gryo.properties')
-writeGraph = 'conf/tinkergraph-gryo.properties'
-blvp = BulkLoaderVertexProgram.build().
-           keepOriginalIds(false).
-           writeGraph(writeGraph).create(readGraph)
-readGraph.compute(SparkGraphComputer).workers(1).program(blvp).submit().get()
-:set max-iteration 10
-graph = GraphFactory.open(writeGraph)
-g = graph.traversal()
-g.V().valueMap()
-graph.close()
-----
-
-[source,properties]
-----
-# hadoop-grateful-gryo.properties
-
-#
-# Hadoop Graph Configuration
-#
-gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
-gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
-gremlin.hadoop.inputLocation=grateful-dead.kryo
-gremlin.hadoop.outputLocation=output
-gremlin.hadoop.jarsInDistributedCache=true
-
-#
-# SparkGraphComputer Configuration
-#
-spark.master=local[1]
-spark.executor.memory=1g
-spark.serializer=org.apache.tinkerpop.gremlin.spark.structure.io.gryo.GryoSerializer
-----
-
-[source,properties]
-----
-# tinkergraph-gryo.properties
-
-gremlin.graph=org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph
-gremlin.tinkergraph.graphFormat=gryo
-gremlin.tinkergraph.graphLocation=/tmp/tinkergraph.kryo
-----
-
-IMPORTANT: The path to TinkerGraph jars needs to be included in the `HADOOP_GREMLIN_LIBS` for the above example to work.
-
-[[giraphgraphcomputer]]
-GiraphGraphComputer
-^^^^^^^^^^^^^^^^^^^
-
-[source,xml]
-----
-<dependency>
-   <groupId>org.apache.tinkerpop</groupId>
-   <artifactId>giraph-gremlin</artifactId>
-   <version>x.y.z</version>
-</dependency>
-----
-
-image:giraph-logo.png[width=100,float=left] link:http://giraph.apache.org[Giraph] is an Apache Software Foundation
-project focused on OLAP-based graph processing. Giraph makes use of the distributed graph computing paradigm made
-popular by Google's Pregel. In Giraph, developers write "vertex programs" that get executed at each vertex in
-parallel. These programs communicate with one another in a bulk synchronous parallel (BSP) manner. This model aligns
-with TinkerPop3's `GraphComputer` API. TinkerPop3 provides an implementation of `GraphComputer` that works for Giraph
-called `GiraphGraphComputer`. Moreover, with TinkerPop3's <<mapreduce,MapReduce>>-framework, the standard
-Giraph/Pregel model is extended to support an arbitrary number of MapReduce phases to aggregate and yield results
-from the graph. Below are examples using `GiraphGraphComputer` from the <<gremlin-console,Gremlin-Console>>.
-
-WARNING: Giraph uses a large number of Hadoop counters. The default for Hadoop is 120. In `mapred-site.xml` it is
-possible to increase the limit it via the `mapreduce.job.counters.max` property. A good value to use is 1000. This
-is a cluster-wide property so be sure to restart the cluster after updating.
-
-WARNING: The maximum number of workers can be no larger than the number of map-slots in the Hadoop cluster minus 1.
-For example, if the Hadoop cluster has 4 map slots, then `giraph.maxWorkers` can not be larger than 3. One map-slot
-is reserved for the master compute node and all other slots can be allocated as workers to execute the VertexPrograms
-on the vertices of the graph.
-
-If `GiraphGraphComputer` will be used as the `GraphComputer` for `HadoopGraph` then its `lib` directory should be
-specified in `HADOOP_GREMLIN_LIBS`.
-
-[source,shell]
-export HADOOP_GREMLIN_LIBS=$HADOOP_GREMLIN_LIBS:/usr/local/gremlin-console/ext/giraph-gremlin/lib
-
-Or, the user can specify the directory in the Gremlin Console.
-
-[source,groovy]
-System.setProperty('HADOOP_GREMLIN_LIBS',System.getProperty('HADOOP_GREMLIN_LIBS') + ':' + '/usr/local/gremlin-console/ext/giraph-gremlin/lib')
-
-[gremlin-groovy]
-----
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-g = graph.traversal(computer(GiraphGraphComputer))
-g.V().count()
-g.V().out().out().values('name')
-----
-
-IMPORTANT: The examples above do not use lambdas (i.e. closures in Gremlin-Groovy). This makes the traversal
-serializable and thus, able to be distributed to all machines in the Hadoop cluster. If a lambda is required in a
-traversal, then the traversal must be sent as a `String` and compiled locally at each machine in the cluster. The
-following example demonstrates the `:remote` command which allows for submitting Gremlin traversals as a `String`.
-
-[gremlin-groovy]
-----
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-g = graph.traversal(computer(GiraphGraphComputer))
-:remote connect tinkerpop.hadoop graph g
-:> g.V().group().by{it.value('name')[1]}.by('name')
-result
-result.memory.runtime
-result.memory.keys()
-result.memory.get('~reducing')
-----
-
-NOTE: If the user explicitly specifies `giraph.maxWorkers` and/or `giraph.numComputeThreads` in the configuration,
-then these values will be used by Giraph. However, if these are not specified and the user never calls
-`GraphComputer.workers()` then `GiraphGraphComputer` will try to compute the number of workers/threads to use based
-on the cluster's profile.
-
-Loading with BulkLoaderVertexProgram
-++++++++++++++++++++++++++++++++++++
-
-The <<bulkloadervertexprogram, BulkLoaderVertexProgram>> is a generalized bulk loader that can be used to load
-large amounts of data to and from different `Graph` implementations. The following code demonstrates how to load
-the Grateful Dead graph from HadoopGraph into TinkerGraph over Giraph:
-
-[gremlin-groovy]
-----
-hdfs.copyFromLocal('data/grateful-dead.kryo', 'grateful-dead.kryo')
-readGraph = GraphFactory.open('conf/hadoop/hadoop-grateful-gryo.properties')
-writeGraph = 'conf/tinkergraph-gryo.properties'
-blvp = BulkLoaderVertexProgram.build().
-           keepOriginalIds(false).
-           writeGraph(writeGraph).create(readGraph)
-readGraph.compute(GiraphGraphComputer).workers(1).program(blvp).submit().get()
-:set max-iteration 10
-graph = GraphFactory.open(writeGraph)
-g = graph.traversal()
-g.V().valueMap()
-graph.close()
-----
-
-[source,properties]
-----
-# hadoop-grateful-gryo.properties
-
-#
-# Hadoop Graph Configuration
-#
-gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
-gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat
-gremlin.hadoop.graphOutputFormat=org.apache.hadoop.mapreduce.lib.output.NullOutputFormat
-gremlin.hadoop.inputLocation=grateful-dead.kryo
-gremlin.hadoop.outputLocation=output
-gremlin.hadoop.jarsInDistributedCache=true
-
-#
-# GiraphGraphComputer Configuration
-#
-giraph.minWorkers=1
-giraph.maxWorkers=1
-giraph.useOutOfCoreGraph=true
-giraph.useOutOfCoreMessages=true
-mapred.map.child.java.opts=-Xmx1024m
-mapred.reduce.child.java.opts=-Xmx1024m
-giraph.numInputThreads=4
-giraph.numComputeThreads=4
-giraph.maxMessagesInMemory=100000
-----
-
-[source,properties]
-----
-# tinkergraph-gryo.properties
-
-gremlin.graph=org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph
-gremlin.tinkergraph.graphFormat=gryo
-gremlin.tinkergraph.graphLocation=/tmp/tinkergraph.kryo
-----
-
-NOTE: The path to TinkerGraph needs to be included in the `HADOOP_GREMLIN_LIBS` for the above example to work.
-
-Input/Output Formats
-~~~~~~~~~~~~~~~~~~~~
-
-image:adjacency-list.png[width=300,float=right] Hadoop-Gremlin provides various I/O formats -- i.e. Hadoop
-`InputFormat` and `OutputFormat`. All of the formats make use of an link:http://en.wikipedia.org/wiki/Adjacency_list[adjacency list]
-representation of the graph where each "row" represents a single vertex, its properties, and its incoming and
-outgoing edges.
-
-{empty} +
-
-[[gryo-io-format]]
-Gryo I/O Format
-^^^^^^^^^^^^^^^
-
-* **InputFormat**: `org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat`
-* **OutputFormat**: `org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat`
-
-<<gryo-reader-writer,Gryo>> is a binary graph format that leverages link:https://github.com/EsotericSoftware/kryo[Kryo]
-to make a compact, binary representation of a vertex. It is recommended that users leverage Gryo given its space/time
-savings over text-based representations.
-
-NOTE: The `GryoInputFormat` is splittable.
-
-[[graphson-io-format]]
-GraphSON I/O Format
-^^^^^^^^^^^^^^^^^^^
-
-* **InputFormat**: `org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONInputFormat`
-* **OutputFormat**: `org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONOutputFormat`
-
-<<graphson-reader-writer,GraphSON>> is a JSON based graph format. GraphSON is a space-expensive graph format in that
-it is a text-based markup language. However, it is convenient for many developers to work with as its structure is
-simple (easy to create and parse).
-
-The data below represents an adjacency list representation of the classic TinkerGraph toy graph in GraphSON format.
-
-[source,json]
-----
-{"id":1,"label":"person","outE":{"created":[{"id":9,"inV":3,"properties":{"weight":0.4}}],"knows":[{"id":7,"inV":2,"properties":{"weight":0.5}},{"id":8,"inV":4,"properties":{"weight":1.0}}]},"properties":{"name":[{"id":0,"value":"marko"}],"age":[{"id":1,"value":29}]}}
-{"id":2,"label":"person","inE":{"knows":[{"id":7,"outV":1,"properties":{"weight":0.5}}]},"properties":{"name":[{"id":2,"value":"vadas"}],"age":[{"id":3,"value":27}]}}
-{"id":3,"label":"software","inE":{"created":[{"id":9,"outV":1,"properties":{"weight":0.4}},{"id":11,"outV":4,"properties":{"weight":0.4}},{"id":12,"outV":6,"properties":{"weight":0.2}}]},"properties":{"name":[{"id":4,"value":"lop"}],"lang":[{"id":5,"value":"java"}]}}
-{"id":4,"label":"person","inE":{"knows":[{"id":8,"outV":1,"properties":{"weight":1.0}}]},"outE":{"created":[{"id":10,"inV":5,"properties":{"weight":1.0}},{"id":11,"inV":3,"properties":{"weight":0.4}}]},"properties":{"name":[{"id":6,"value":"josh"}],"age":[{"id":7,"value":32}]}}
-{"id":5,"label":"software","inE":{"created":[{"id":10,"outV":4,"properties":{"weight":1.0}}]},"properties":{"name":[{"id":8,"value":"ripple"}],"lang":[{"id":9,"value":"java"}]}}
-{"id":6,"label":"person","outE":{"created":[{"id":12,"inV":3,"properties":{"weight":0.2}}]},"properties":{"name":[{"id":10,"value":"peter"}],"age":[{"id":11,"value":35}]}}
-----
-
-[[script-io-format]]
-Script I/O Format
-^^^^^^^^^^^^^^^^^
-
-* **InputFormat**: `org.apache.tinkerpop.gremlin.hadoop.structure.io.script.ScriptInputFormat`
-* **OutputFormat**: `org.apache.tinkerpop.gremlin.hadoop.structure.io.script.ScriptOutputFormat`
-
-`ScriptInputFormat` and `ScriptOutputFormat` take an arbitrary script and use that script to either read or write
-`Vertex` objects, respectively. This can be considered the most general `InputFormat`/`OutputFormat` possible in that
-Hadoop-Gremlin uses the user provided script for all reading/writing.
-
-ScriptInputFormat
-+++++++++++++++++
-
-The data below represents an adjacency list representation of the classic TinkerGraph toy graph. First line reads,
-"vertex `1`, labeled `person` having 2 property values (`marko` and `29`) has 3 outgoing edges; the first edge is
-labeled `knows`, connects the current vertex `1` with vertex `2` and has a property value `0.4`, and so on."
-
-[source]
-1:person:marko:29 knows:2:0.5,knows:4:1.0,created:3:0.4
-2:person:vadas:27
-3:project:lop:java
-4:person:josh:32 created:3:0.4,created:5:1.0
-5:project:ripple:java
-6:person:peter:35 created:3:0.2
-
-There is no corresponding `InputFormat` that can parse this particular file (or some adjacency list variant of it).
-As such, `ScriptInputFormat` can be used. With `ScriptInputFormat` a script is stored in HDFS and leveraged by each
-mapper in the Hadoop job. The script must have the following method defined:
-
-[source,groovy]
-def parse(String line, ScriptElementFactory factory) { ... }
-
-`ScriptElementFactory` is a legacy from previous versions and, although it's still functional, it should no longer be used.
-In order to create vertices and edges, the `parse()` method gets access to a global variable named `graph`, which holds
-the local `StarGraph` for the current line/vertex.
-
-An appropriate `parse()` for the above adjacency list file is:
-
-[source,groovy]
-def parse(line, factory) {
-    def parts = line.split(/ /)
-    def (id, label, name, x) = parts[0].split(/:/).toList()
-    def v1 = graph.addVertex(T.id, id, T.label, label)
-    if (name != null) v1.property('name', name) // first value is always the name
-    if (x != null) {
-        // second value depends on the vertex label; it's either
-        // the age of a person or the language of a project
-        if (label.equals('project')) v1.property('lang', x)
-        else v1.property('age', Integer.valueOf(x))
-    }
-    if (parts.length == 2) {
-        parts[1].split(/,/).grep { !it.isEmpty() }.each {
-            def (eLabel, refId, weight) = it.split(/:/).toList()
-            def v2 = graph.addVertex(T.id, refId)
-            v1.addOutEdge(eLabel, v2, 'weight', Double.valueOf(weight))
-        }
-    }
-    return v1
-}
-
-The resultant `Vertex` denotes whether the line parsed yielded a valid Vertex. As such, if the line is not valid
-(e.g. a comment line, a skip line, etc.), then simply return `null`.
-
-ScriptOutputFormat Support
-++++++++++++++++++++++++++
-
-The principle above can also be used to convert a vertex to an arbitrary `String` representation that is ultimately
-streamed back to a file in HDFS. This is the role of `ScriptOutputFormat`. `ScriptOutputFormat` requires that the
-provided script maintains a method with the following signature:
-
-[source,groovy]
-def stringify(Vertex vertex) { ... }
-
-An appropriate `stringify()` to produce output in the same format that was shown in the `ScriptInputFormat` sample is:
-
-[source,groovy]
-def stringify(vertex) {
-    def v = vertex.values('name', 'age', 'lang').inject(vertex.id(), vertex.label()).join(':')
-    def outE = vertex.outE().map {
-        def e = it.get()
-        e.values('weight').inject(e.label(), e.inV().next().id()).join(':')
-    }.join(',')
-    return [v, outE].join('\t')
-}
-
-
-
-Storage Systems
-~~~~~~~~~~~~~~~
-
-Hadoop-Gremlin provides two implementations of the `Storage` API:
-
-* `FileSystemStorage`: Access HDFS and local file system data.
-* `SparkContextStorage`: Access Spark persisted RDD data.
-
-[[interacting-with-hdfs]]
-Interacting with HDFS
-^^^^^^^^^^^^^^^^^^^^^
-
-The distributed file system of Hadoop is called link:http://en.wikipedia.org/wiki/Apache_Hadoop#Hadoop_distributed_file_system[HDFS].
-The results of any OLAP operation are stored in HDFS accessible via `hdfs`. For local file system access, there is `local`.
-
-[gremlin-groovy]
-----
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-graph.compute(SparkGraphComputer).program(PeerPressureVertexProgram.build().create(graph)).mapReduce(ClusterCountMapReduce.build().memoryKey('clusterCount').create()).submit().get();
-hdfs.ls()
-hdfs.ls('output')
-hdfs.head('output', GryoInputFormat)
-hdfs.head('output', 'clusterCount', SequenceFileInputFormat)
-hdfs.rm('output')
-hdfs.ls()
-----
-
-[[interacting-with-spark]]
-Interacting with Spark
-^^^^^^^^^^^^^^^^^^^^^^
-
-If a Spark context is persisted, then Spark RDDs will remain the Spark cache and accessible over subsequent jobs.
-RDDs are retrieved and saved to the `SparkContext` via `PersistedInputRDD` and `PersistedOutputRDD` respectivly.
-Persisted RDDs can be accessed using `spark`.
-
-[gremlin-groovy]
-----
-Spark.create('local[4]')
-graph = GraphFactory.open('conf/hadoop/hadoop-gryo.properties')
-graph.configuration().setProperty('gremlin.spark.graphOutputRDD', PersistedOutputRDD.class.getCanonicalName())
-graph.configuration().clearProperty('gremlin.hadoop.graphOutputFormat')
-graph.configuration().setProperty('gremlin.spark.persistContext',true)
-graph.compute(SparkGraphComputer).program(PeerPressureVertexProgram.build().create(graph)).mapReduce(ClusterCountMapReduce.build().memoryKey('clusterCount').create()).submit().get();
-spark.ls()
-spark.ls('output')
-spark.head('output', PersistedInputRDD)
-spark.head('output', 'clusterCount', PersistedInputRDD)
-spark.rm('output')
-spark.ls()
-----
-
-A Command Line Example
-~~~~~~~~~~~~~~~~~~~~~~
-
-image::pagerank-logo.png[width=300]
-
-The classic link:http://en.wikipedia.org/wiki/PageRank[PageRank] centrality algorithm can be executed over the
-TinkerPop graph from the command line using `GiraphGraphComputer`.
-
-WARNING: Be sure that the `HADOOP_GREMLIN_LIBS` references the location `lib` directory of the respective
-`GraphComputer` engine being used or else the requisite dependencies will not be uploaded to the Hadoop cluster.
-
-[source,text]
-----
-$ hdfs dfs -copyFromLocal data/tinkerpop-modern.json tinkerpop-modern.json
-$ hdfs dfs -ls
-Found 2 items
--rw-r--r--   1 marko supergroup       2356 2014-07-28 13:00 /user/marko/tinkerpop-modern.json
-$ hadoop jar target/giraph-gremlin-x.y.z-job.jar org.apache.tinkerpop.gremlin.giraph.process.computer.GiraphGraphComputer ../hadoop-gremlin/conf/hadoop-graphson.properties
-15/09/11 08:02:08 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
-15/09/11 08:02:11 INFO computer.GiraphGraphComputer: HadoopGremlin(Giraph): PageRankVertexProgram[alpha=0.85,iterations=30]
-15/09/11 08:02:12 INFO mapreduce.JobSubmitter: number of splits:3
-15/09/11 08:02:12 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1441915907347_0028
-15/09/11 08:02:12 INFO impl.YarnClientImpl: Submitted application application_1441915907347_0028
-15/09/11 08:02:12 INFO job.GiraphJob: Tracking URL: http://markos-macbook:8088/proxy/application_1441915907347_0028/
-15/09/11 08:02:12 INFO job.GiraphJob: Waiting for resources... Job will start only when it gets all 3 mappers
-15/09/11 08:03:54 INFO mapreduce.Job: Running job: job_1441915907347_0028
-15/09/11 08:03:55 INFO mapreduce.Job: Job job_1441915907347_0028 running in uber mode : false
-15/09/11 08:03:55 INFO mapreduce.Job:  map 33% reduce 0%
-15/09/11 08:03:57 INFO mapreduce.Job:  map 67% reduce 0%
-15/09/11 08:04:01 INFO mapreduce.Job:  map 100% reduce 0%
-15/09/11 08:06:17 INFO mapreduce.Job: Job job_1441915907347_0028 completed successfully
-15/09/11 08:06:17 INFO mapreduce.Job: Counters: 80
-    File System Counters
-        FILE: Number of bytes read=0
-        FILE: Number of bytes written=483918
-        FILE: Number of read operations=0
-        FILE: Number of large read operations=0
-        FILE: Number of write operations=0
-        HDFS: Number of bytes read=1465
-        HDFS: Number of bytes written=1760
-        HDFS: Number of read operations=39
-        HDFS: Number of large read operations=0
-        HDFS: Number of write operations=20
-    Job Counters
-        Launched map tasks=3
-        Other local map tasks=3
-        Total time spent by all maps in occupied slots (ms)=458105
-        Total time spent by all reduces in occupied slots (ms)=0
-        Total time spent by all map tasks (ms)=458105
-        Total vcore-seconds taken by all map tasks=458105
-        Total megabyte-seconds taken by all map tasks=469099520
-    Map-Reduce Framework
-        Map input records=3
-        Map output records=0
-        Input split bytes=132
-        Spilled Records=0
-        Failed Shuffles=0
-        Merged Map outputs=0
-        GC time elapsed (ms)=1594
-        CPU time spent (ms)=0
-        Physical memory (bytes) snapshot=0
-        Virtual memory (bytes) snapshot=0
-        Total committed heap usage (bytes)=527958016
-    Giraph Stats
-        Aggregate edges=0
-        Aggregate finished vertices=0
-        Aggregate sent message message bytes=13535
-        Aggregate sent messages=186
-        Aggregate vertices=6
-        Current master task partition=0
-        Current workers=2
-        Last checkpointed superstep=0
-        Sent message bytes=438
-        Sent messages=6
-        Superstep=31
-    Giraph Timers
-        Initialize (ms)=2996
-        Input superstep (ms)=5209
-        Setup (ms)=59
-        Shutdown (ms)=9324
-        Superstep 0 GiraphComputation (ms)=3861
-        Superstep 1 GiraphComputation (ms)=4027
-        Superstep 10 GiraphComputation (ms)=4000
-        Superstep 11 GiraphComputation (ms)=4004
-        Superstep 12 GiraphComputation (ms)=3999
-        Superstep 13 GiraphComputation (ms)=4000
-        Superstep 14 GiraphComputation (ms)=4005
-        Superstep 15 GiraphComputation (ms)=4003
-        Superstep 16 GiraphComputation (ms)=4001
-        Superstep 17 GiraphComputation (ms)=4007
-        Superstep 18 GiraphComputation (ms)=3998
-        Superstep 19 GiraphComputation (ms)=4006
-        Superstep 2 GiraphComputation (ms)=4007
-        Superstep 20 GiraphComputation (ms)=3996
-        Superstep 21 GiraphComputation (ms)=4006
-        Superstep 22 GiraphComputation (ms)=4002
-        Superstep 23 GiraphComputation (ms)=3998
-        Superstep 24 GiraphComputation (ms)=4003
-        Superstep 25 GiraphComputation (ms)=4001
-        Superstep 26 GiraphComputation (ms)=4003
-        Superstep 27 GiraphComputation (ms)=4005
-        Superstep 28 GiraphComputation (ms)=4002
-        Superstep 29 GiraphComputation (ms)=4001
-        Superstep 3 GiraphComputation (ms)=3988
-        Superstep 30 GiraphComputation (ms)=4248
-        Superstep 4 GiraphComputation (ms)=4010
-        Superstep 5 GiraphComputation (ms)=3998
-        Superstep 6 GiraphComputation (ms)=3996
-        Superstep 7 GiraphComputation

<TRUNCATED>


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