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Posted to commits@tinkerpop.apache.org by sp...@apache.org on 2016/02/25 15:00:55 UTC
[07/43] incubator-tinkerpop git commit: splitted
implementations.asciidoc into implementations-hadoop.asciidoc and
implementations-neo4j.asciidoc
splitted implementations.asciidoc into implementations-hadoop.asciidoc and implementations-neo4j.asciidoc
Project: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/commit/bbf5b3f4
Tree: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/tree/bbf5b3f4
Diff: http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/diff/bbf5b3f4
Branch: refs/heads/TINKERPOP-1107
Commit: bbf5b3f4d61c0aa2266b7430a1e083e8f4c01920
Parents: 70a3065
Author: Daniel Kuppitz <da...@hotmail.com>
Authored: Thu Feb 18 23:52:55 2016 +0100
Committer: Daniel Kuppitz <da...@hotmail.com>
Committed: Thu Feb 18 23:52:55 2016 +0100
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.../reference/implementations-hadoop.asciidoc | 929 +++++++++
.../reference/implementations-neo4j.asciidoc | 921 +++++++++
docs/src/reference/implementations.asciidoc | 1835 ------------------
docs/src/reference/index.asciidoc | 3 +-
4 files changed, 1852 insertions(+), 1836 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-tinkerpop/blob/bbf5b3f4/docs/src/reference/implementations-hadoop.asciidoc
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diff --git a/docs/src/reference/implementations-hadoop.asciidoc b/docs/src/reference/implementations-hadoop.asciidoc
new file mode 100644
index 0000000..376f377
--- /dev/null
+++ b/docs/src/reference/implementations-hadoop.asciidoc
@@ -0,0 +1,929 @@
+////
+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.
+////
+[[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 (ms)=4005
+ Superstep 8 GiraphComputation (ms)=4009
+ Superstep 9 GiraphComputation (ms)=3994
+ Total (ms)=138788
+ File Input Format Counters
+ Bytes Read=0
+ File Output Format Counters
+ Bytes Written=0
+$ hdfs dfs -cat output/~g/*
+{"id":1,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.15000000000000002}],"name":[{"id":0,"value":"marko"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":3.0}],"age":[{"id":1,"value":29}]}}
+{"id":5,"label":"software","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":35,"value":0.23181250000000003}],"name":[{"id":8,"value":"ripple"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":6,"value":0.0}],"lang":[{"id":9,"value":"java"}]}}
+{"id":3,"label":"software","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.4018125}],"name":[{"id":4,"value":"lop"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":0.0}],"lang":[{"id":5,"value":"java"}]}}
+{"id":4,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.19250000000000003}],"name":[{"id":6,"value":"josh"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":2.0}],"age":[{"id":7,"value":32}]}}
+{"id":2,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":35,"value":0.19250000000000003}],"name":[{"id":2,"value":"vadas"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":6,"value":0.0}],"age":[{"id":3,"value":27}]}}
+{"id":6,"label":"person","properties":{"gremlin.pageRankVertexProgram.pageRank":[{"id":35,"value":0.15000000000000002}],"name":[{"id":10,"value":"peter"}],"gremlin.pageRankVertexProgram.edgeCount":[{"id":6,"value":1.0}],"age":[{"id":11,"value":35}]}}
+----
+
+Vertex 4 ("josh") is isolated below:
+
+[source,js]
+----
+{
+ "id":4,
+ "label":"person",
+ "properties": {
+ "gremlin.pageRankVertexProgram.pageRank":[{"id":39,"value":0.19250000000000003}],
+ "name":[{"id":6,"value":"josh"}],
+ "gremlin.pageRankVertexProgram.edgeCount":[{"id":10,"value":2.0}],
+ "age":[{"id":7,"value":32}]}
+ }
+}
+----
+
+Hadoop-Gremlin for Graph System Providers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+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:
+
+[source,java]
+----
+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.graphInputFormat` and
+`gremlin.hadoop.graphOutputFormat`.
+
+IMPORTANT: 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. <<gryo-io-format,`GryoOutputFormat`>>, <<graphson-io-format,`GraphSONOutputFormat`>>,
+and <<script-io-format,`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.
+