| //// |
| 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 |
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| //// |
| [[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 TinkerPop graph, then Hadoop-Gremlin can be used to process the graph |
| using both TinkerPop'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/r2.7.2/hadoop-project-dist/hadoop-common/SingleCluster.html[Single Node Setup] |
| tutorial. Moreover, if using `SparkGraphComputer` it is advisable that the reader also |
| familiarize their self with and Spark (link:http://spark.apache.org/docs/latest/quick-start.html[Quick Start]). |
| |
| === Installing Hadoop-Gremlin |
| |
| 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> |
| ---- |
| |
| It is important that the `CLASSPATH` environmental variable references `HADOOP_CONF_DIR` and that the configuration |
| files in `HADOOP_CONF_DIR` contain references to a live Hadoop cluster. It is easy to verify a proper configuration |
| from within the Gremlin Console. If `hdfs` references the local file system, then there is a configuration issue. |
| |
| [source,text] |
| ---- |
| gremlin> hdfs |
| ==>storage[org.apache.hadoop.fs.LocalFileSystem@65bb9029] // BAD |
| |
| gremlin> hdfs |
| ==>storage[DFS[DFSClient[clientName=DFSClient_NONMAPREDUCE_1229457199_1, ugi=user (auth:SIMPLE)]]] // GOOD |
| ---- |
| |
| The `HADOOP_GREMLIN_LIBS` references locations that contain 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 colon-separated (`:`) and all jars from all locations will |
| be loaded into the cluster. Locations can be local paths (e.g. `/path/to/libs`), but may also be prefixed with a file |
| scheme to reference files or directories in different file systems (e.g. `hdfs:///path/to/distributed/libs`). |
| Typically, only the jars of the respective `GraphComputer` are required to be loaded. |
| |
| === Properties Files |
| |
| `HadoopGraph` makes use of properties files which ultimately get turned into Apache configurations and/or |
| Hadoop configurations. |
| |
| [source,text] |
| gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph |
| gremlin.hadoop.inputLocation=tinkerpop-modern.kryo |
| gremlin.hadoop.graphReader=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoInputFormat |
| gremlin.hadoop.outputLocation=output |
| gremlin.hadoop.graphWriter=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat |
| gremlin.hadoop.jarsInDistributedCache=true |
| gremlin.hadoop.defaultGraphComputer=org.apache.tinkerpop.gremlin.spark.process.computer.SparkGraphComputer |
| #################################### |
| # Spark Configuration # |
| #################################### |
| spark.master=local[4] |
| spark.executor.memory=1g |
| spark.serializer=org.apache.tinkerpop.gremlin.spark.structure.io.gryo.GryoSerializer |
| gremlin.spark.persistContext=true |
| |
| A review of the Hadoop-Gremlin specific properties are provided in the table below. For the respective OLAP |
| engines (<<sparkgraphcomputer,`SparkGraphComputer`>> 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.graphReader |The class that the graph input file(s) are read with (e.g. an `InputFormat`). |
| |gremlin.hadoop.outputLocation |The location to write the computed HadoopGraph to. |
| |gremlin.hadoop.graphWriter |The class that the graph output file(s) are written with (e.g. an `OutputFormat`). |
| |gremlin.hadoop.jarsInDistributedCache |Whether to upload the Hadoop-Gremlin jars to a distributed cache (necessary if jars are not on the machines' classpaths). |
| |gremlin.hadoop.defaultGraphComputer |The default `GraphComputer` to use when `graph.compute()` is called. This is optional. |
| |========================================================= |
| |
| 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, 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)`. |
| |
| WARNING: 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 TinkerPop can support any number of `GraphComputer` implementations. Out of the box, Hadoop-Gremlin |
| supports the following two implementations. |
| |
| * <<sparkgraphcomputer,`SparkGraphComputer`>>: Leverages Apache Spark to execute TinkerPop 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). |
| |
| 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. |
| |
| [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 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.spark |
| ==>tinkerpop.spark activated |
| ---- |
| |
| WARNING: Hadoop and Spark 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 may be necessary to manually cleanup dependency conflicts among different plugins. |
