基于apache hadoop的配置安装

安装相关的大数据组件，包括：

• hadoop 2.6.2
• spark 1.6.1
• hbase 1.0.0
• zookeeper 3.4.10
• janusgraph 0.2.0

环境变量的配置

每台机器上都需要配置如下环境变量

export JAVA_HOME=/usr/local/lib/jdk1.8.0_60
export HBASE_CONF_DIR=/opt/hbase-1.0.0/conf
export HADOOP_CONF_DIR=/opt/hadoop-2.6.5/etc/hadoop
export HADOOP_HOME=/opt/hadoop-2.6.5
export PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
export CLASSPATH=$HADOOP_CONF_DIR:$SPARK_CONF_DIR:$HBASE_CONF_DIR
export SPARK_CONF_DIR=/opt/spark-1.6.1-bin-hadoop2.6/conf

添加相应的jar到$JANUSGRAPH_HOME/lib

• 添加spark的spark-assembly-1.6.1-hadoop2.6.0.jar。由于其中包含了相应的hadoop的jar所以不需要单独的添加hadoop的jar。
• 添加hbase的相关jar。这些jar需要和hbase的发行版本相匹配，要不然会出java.net.ConnectException: Connection refused的问题。当出现这个问题的时候需要删除和版本不匹配的jar，并重启hbase的相关服务解决。

NOTE:

• 在添加相关jar之前，需要删除之前jansgraph自带的相应的jar。
• 由于hbase-client-1.0.0.jar依赖的guava版本为16，所以需要删除掉自带的guava-18.jar，更换为16版本。要不然会出现
  org.apache.hadoop.hbase.DoNotRetryIOException: java.lang.IllegalAccessError: tried to access method com.google.common.base.Stopwatch.<init>()V from class org.apache.hadoop.hbase.zookeeper.MetaTableLocator

把$JANUSGRAPH_HOME/lib分发到集群的每台机器上。

配置$JANUSGRAPH_HOME/conf/hadoop-graph/hadoop-load.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=./data/grateful-dead.kryo
gremlin.hadoop.outputLocation=output
gremlin.hadoop.jarsInDistributedCache=true

#
# GiraphGraphComputer Configuration
#
giraph.minWorkers=2
giraph.maxWorkers=2
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

#
# SparkGraphComputer Configuration
#
spark.master=yarn-client
spark.executor.memory=512m
spark.executor.instances=2
spark.executor.cores=4
spark.serializer=org.apache.spark.serializer.KryoSerializer
spark.ui.port=14040
spark.app.name=janusgraph-data-load
spark.app.id=janusgraph-data-load
#以下两个配置只对spark的jar有效，用来提高spark相关jar的加载速度
#spark.yarn.jar=hdfs://wangmaoshuai.novalocal:8020/user/root/share/lib/spark/spark-assembly-1.6.1-hadoop2.6.0.jar
#spark.yarn.archive=hdfs://wangmaoshuai.novalocal:8020/user/root/share/lib/spark/janusgraph-0.2.0.zip
spark.yarn.am.extraJavaOptions=-Djava.library.path=/opt/hadoop-2.6.5/lib/native
#配置成分发到集群的janusgraph-lib的文件地址
spark.executor.extraClassPath=/opt/janusgraph-lib/*:/opt/hadoop-2.6.5/etc/hadoop:/opt/hbase-1.0.0/conf:/opt/spark-1.6.1-bin-hadoop2.6/conf

spark.executor.extraJavaOptions=-Djava.library.path=/opt/hadoop-2.6.5/lib/native

#cache config
gremlin.spark.persistContext=true
gremlin.spark.graphStorageLevel=MEMORY_AND_DISK
#saprk history
spark.history.provider=org.apache.spark.deploy.yarn.history.YarnHistoryProvider
spark.history.ui.port=18080
spark.history.kerberos.keytab=none
spark.history.kerberos.principal=none
spark.yarn.services=org.apache.spark.deploy.yarn.history.YarnHistoryService
spark.yarn.historyServer.address=http://wangmaoshuai.novalocal:18080

配置$JANUSGRAPH_HOME/conf/hadoop-graph/read-hbase.properties

#
# Hadoop Graph Configuration
#
gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
gremlin.hadoop.graphInputFormat=org.janusgraph.hadoop.formats.hbase.HBaseInputFormat
gremlin.hadoop.graphOutputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat

gremlin.hadoop.jarsInDistributedCache=true
gremlin.hadoop.inputLocation=none
gremlin.hadoop.outputLocation=output

#
# JanusGraph HBase InputFormat configuration
#
janusgraphmr.ioformat.conf.storage.backend=hbase
janusgraphmr.ioformat.conf.storage.hostname=10.110.13.210
#zookeeper.znode.parent=/hbase-unsecure
janusgraphmr.ioformat.conf.storage.hbase.table=SparkYarnImportTest

#
# SparkGraphComputer Configuration
#
spark.master=yarn-client
spark.serializer=org.apache.spark.serializer.KryoSerializer

spark.executor.extraClassPath=/opt/janusgraph-lib/*:/opt/hadoop-2.6.5/etc/hadoop:/opt/hbase-1.0.0/conf:/opt/spark-1.6.1-bin-hadoop2.6/conf
spark.yarn.am.extraJavaOptions=-Djava.library.path=/opt/hadoop-2.6.5/lib/native
spark.executor.extraJavaOptions=-Djava.library.path=/opt/hadoop-2.6.5/lib/native

测试

bin/gremlin.sh

         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
plugin activated: janusgraph.imports
gremlin> :plugin use tinkerpop.hadoop
==>tinkerpop.hadoop activated
gremlin> :plugin use tinkerpop.spark
==>tinkerpop.spark activated
gremlin> :load data/grateful-dead-janusgraph-schema.groovy
==>true
==>true
gremlin> graph = JanusGraphFactory.open('conf/janusgraph-hbase.properties')
==>standardjanusgraph[hbase:[kg-server-96.kg.com, kg-agent-95.kg.com, kg-agent-97.kg.com]]
gremlin> defineGratefulDeadSchema(graph)
==>null
gremlin> graph.close()
==>null
gremlin> if (!hdfs.exists('data/grateful-dead.kryo')) hdfs.copyFromLocal('data/grateful-dead.kryo','data/grateful-dead.kryo')
==>null
gremlin> graph = GraphFactory.open('conf/hadoop-graph/hadoop-load.properties')
==>hadoopgraph[gryoinputformat->nulloutputformat]
gremlin> blvp = BulkLoaderVertexProgram.build().writeGraph('conf/janusgraph-hbase.properties').create(graph)
==>BulkLoaderVertexProgram[bulkLoader=IncrementalBulkLoader,vertexIdProperty=bulkLoader.vertex.id,userSuppliedIds=false,keepOriginalIds=true,batchSize=0]
gremlin> graph.compute(SparkGraphComputer).program(blvp).submit().get()
...
==>result[hadoopgraph[gryoinputformat->nulloutputformat],memory[size:0]]
gremlin> graph.close()
==>null
gremlin> graph = GraphFactory.open('conf/hadoop-graph/read-hbase.properties')
==>hadoopgraph[cassandrainputformat->gryooutputformat]
gremlin> g = graph.traversal().withComputer(SparkGraphComputer)
==>graphtraversalsource[hadoopgraph[cassandrainputformat->gryooutputformat], sparkgraphcomputer]
gremlin> g.V().count()
...
==>808