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JanusGraph 0.2.0 gremlin-hadoop数

JanusGraph 0.2.0 gremlin-hadoop数

作者: 清歌笑染红尘 | 来源:发表于2017-11-15 11:41 被阅读1138次

    janusgraph 0.2.0 相关问题与解决方案

    • 由于janusgraph 0.2.0的lib文件夹下面缺少hadoop-hdfs-2.7.2.jar,需要手动添加相关文件到lib文件夹下面。
    • No FileSystem for scheme: hdfs这个问题需要在hadoop的配置文件core-site.xml中添加如下配置
     <property>
        <name>fs.hdfs.impl</name>
        <value>org.apache.hadoop.hdfs.DistributedFileSystem</value>
    </property>
    

    环境变量配置

    # gremlin console的地址。这个配置是可选项目,用于解决janusgraph缺少相关jar的问题。
    export GREMLIN_HOME=/opt/apache-tinkerpop-gremlin-console-3.2.6
    # hadoop的配置文件地址
    export HADOOP_CONF_DIR=/etc/hadoop/conf
    # gremlin console下载的插件的lib文件地址。这个配置是可选项目,用于解决janusgraph缺少相关jar的问题。
    export HADOOP_GREMLIN_LIBS=$GREMLIN_HOME/ext/hadoop-gremlin/plugin:$GREMLIN_HOME/ext/spark-gremlin/plugin
    export HBASE_CONF_DIR=/etc/hbase/conf
    export CLASSPATH=$HADOOP_CONF_DIR:$HADOOP_GREMLIN_LIBS:$HBASE_CONF_DIR
    

    如果手动添加了相关jar,则不需要配置gremlin console的相关配置项。安装gremlin-console插件的步骤

    • hadoop插件
    • :install org.apache.tinkerpop hadoop-gremlin 3.2.6
    • :plugin use tinkerpop.hadoop
    • giraph-gremlin插件
    • :install org.apache.tinkerpop giraph-gremlin 3.2.6
    • :plugin use tinkerpop.giraph
    • spark-gremlin插件
    • :install org.apache.tinkerpop spark-gremlin 3.2.6
    • :plugin use tinkerpop.spark

    导入数据并查询

    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
    
    

    相关配置文件

    janusgraph-hbase.properties

    gremlin.graph=org.janusgraph.core.JanusGraphFactory
    storage.backend=hbase
    storage.hostname= kg-server-96.kg.com,kg-agent-95.kg.com,kg-agent-97.kg.com
    cache.db-cache=true
    cache.db-cache-clean-wait=20
    cache.db-cache-time=180000
    cache.db-cache-size=0.5
    index.search.backend=elasticsearch
    index.search.hostname=10.110.18.52
    storage.hbase.ext.zookeeper.znode.parent=/hbase-unsecure
    storage.hbase.table=Medical-POC
    index.search.index-name=Medical-POC
    

    grateful-dead-janusgraph-schema.groovy

    def defineGratefulDeadSchema(janusGraph) {
        m = janusGraph.openManagement()
        // vertex labels
        artist = m.makeVertexLabel("artist").make()
        song   = m.makeVertexLabel("song").make()
        // edge labels
        sungBy     = m.makeEdgeLabel("sungBy").make()
        writtenBy  = m.makeEdgeLabel("writtenBy").make()
        followedBy = m.makeEdgeLabel("followedBy").make()
        // vertex and edge properties
        blid         = m.makePropertyKey("bulkLoader.vertex.id").dataType(Long.class).make()
        name         = m.makePropertyKey("name").dataType(String.class).make()
        songType     = m.makePropertyKey("songType").dataType(String.class).make()
        performances = m.makePropertyKey("performances").dataType(Integer.class).make()
        weight       = m.makePropertyKey("weight").dataType(Integer.class).make()
        // global indices
        m.buildIndex("byBulkLoaderVertexId", Vertex.class).addKey(blid).buildCompositeIndex()
        m.buildIndex("artistsByName", Vertex.class).addKey(name).indexOnly(artist).buildCompositeIndex()
        m.buildIndex("songsByName", Vertex.class).addKey(name).indexOnly(song).buildCompositeIndex()
        // vertex centric indices
        m.buildEdgeIndex(followedBy, "followedByWeight", Direction.BOTH, Order.decr, weight)
        m.commit()
    }
    

    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=local[*]
    spark.executor.memory=1g
    spark.serializer=org.apache.spark.serializer.KryoSerializer
    

    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
    #只需要配置一个hbase节点的ip就可以
    janusgraphmr.ioformat.conf.storage.hostname=127.0.0.1
    janusgraphmr.ioformat.conf.storage.hbase.table=Medical-POC
    #如果不配置会报org.apache.hadoop.hbase.client.RetriesExhaustedException: Can't get the locations
    zookeeper.znode.parent=/hbase-unsecure
    
    #
    # SparkGraphComputer Configuration
    #
    spark.master=local[4]
    spark.serializer=org.apache.spark.serializer.KryoSerializer
    

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