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第二部分 批量导入配置和测试
Janusgraph是一个分布式图数据库，继承自titan。Janusgraph的批量导入（bulkload）默认使用spark的local模式运行，不支持yarn-cluster模式。虽然支持yarn-client模式，但官方没有说明如何配置，配置起来有许多坑。本文将介绍如何配置yarn-client模式的批量导入。
首先介绍基本配置，然后介绍导入批量导入的配置，最后介绍批量导入的优化。
本文所用软件版本：
janusgraph: 0.1.1
hbase: 1.1.2
hadoop: 2.7.1
基本配置
- 首先从官网下载并解压janusgraph到本地/data/janusgraph/目录。
- 然后配置图数据库前后端。由于我们用的是es + hbase， 所以直接修改/data/janusgraph/conf/janusgraph-hbase-es.properties ：
#重要
gremlin.graph=org.janusgraph.core.JanusGraphFactory
#hbase配置
storage.batch-loading=true
storage.backend=hbase
storage.hostname=c1-nn1.bdp.idc,c1-nn2.bdp.idc,c1-nn3.bdp.idc
storage.hbase.ext.hbase.zookeeper.property.clientPort=2181
storage.hbase.table = yisou:test_graph
#es配置
index.search.backend=elasticsearch
index.search.hostname=10.120.64.69 #es是只安装在本地，此为本机ip。
index.search.elasticsearch.client-only=true
index.search.index-name=yisou_test_graph
#默认cache配置
cache.db-cache = true
cache.db-cache-clean-wait = 20
cache.db-cache-time = 180000
cache.db-cache-size = 0.5
3.修改/data/janusgraph/lib下的jar包。由于在跑yarn-client批量导入时有guava等jar包冲突，我根据冲突情况对lib下面的jar包作了调整。主要调整了3个jar包：
- hbase-client-1.2.4.jar ==> yisou-hbase-1.0-SNAPSHOT.jar
由于lib下的hbase-client-1.2.4.jar用的guava与我们yarn集群的guava版本有冲突，所以我们用了公司内部的去除了guava的hbase-client，即yisou-hbase-1.0-SNAPSHOT.jar 。
如果不替换，报错 "Caused by: java.lang.IllegalAccessError: tried to access method com.google.common.base.Stopwatch.<init>()V from class org.apache.hadoop.hbase.zookeeper.MetaTableLocator"- spark-assembly-1.6.1-hadoop2.6.0.jar ==> spark-assembly-1.6.2-hadoop2.6.0.jar
lib自带的spark-assembly-1.6.1-hadoop2.6.0.jar也会引起guava冲突，我将其替换成spark-assembly-1.6.2-hadoop2.6.0.jar。
如果不替换，将会报错"java.lang.NoSuchMethodError: groovy.lang.MetaClassImpl.hasCustomStaticInvokeMethod()Z"- 删除 hbase-protocol-1.2.4.jar.
如果不删除，将会报错 "com.google.protobuf.ServiceException: java.lang.NoSuchMethodError: org.apache.hadoop.hbase.protobuf.generated.RPCProtos$ConnectionHeader$Builder.setVersionInfo(Lorg/apache/hadoop/hbase/protobuf/generated/RPCProtos$VersionInfo;)Lorg/apache/hadoop/hbase/protobuf/generated/RPCProtos$ConnectionHeader$Builder;"
4.配置图中边和节点属性，具体参考官网，本文不展开。
批量导入配置
由于需要与yarn配合，将导入程序放在yarn上执行，所以需要hadoop相关环境配置。需要修改两个配置文件，一个是Janusgraph的启动脚本/data/janusgraph/lib/gremlin.sh, 另一个是hadoop和spark相关的配置/data/janusgraph/conf/hadoop-graph/hadoop-script.properties。
1.复制/data/janusgraph/lib/gremlin.sh, 假定命名为yarn-gremlin.sh。 然后增加hadoop的配置到JAVA_OPTIONS和CLASSPATH中。这样能保证hadoop相关配置能被程序读取到，便于正常启动spark在yarn上的任务。
#!/bin/bash
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export HADOOP_HOME=/usr/local/hadoop-2.7.1
export JAVA_OPTIONS="$JAVA_OPTIONS -Djava.library.path=$HADOOP_HOME/lib/native"
export CLASSPATH=$HADOOP_CONF_DIR
#JANUSGRAPH_HOME为用户安装janusgraph的目录/data/janusgraph/
cd $JANUSGRAPH_HOME
./bin/gremlin.sh
2.修改/data/janusgraph/conf/hadoop-graph/hadoop-script.properties
主要根据要导入文件的格式修改inputFormat、指定要导入的hdfs文件路径、parse函数路径以及spark master指定为yarn-client等。
#
# Hadoop Graph Configuration
#
gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
gremlin.hadoop.graphInputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.script.ScriptInputFormat
gremlin.hadoop.graphOutputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.graphson.GraphSONOutputFormat
gremlin.hadoop.jarsInDistributedCache=true
#导入文件的hdfs路径。也可以在加载该配置文件后指定
gremlin.hadoop.inputLocation=/user/yisou/taotian1/janus/data/fewData.test.dup
#解析hdfs文件的parse函数路径。也可以在加载该配置文件后指定
gremlin.hadoop.scriptInputFormat.script=/user/yisou/taotian1/janus/data/conf/vertex_parse.groovy
#gremlin.hadoop.outputLocation=output
#
# SparkGraphComputer with Yarn Configuration
#
spark.master=yarn-client
spark.executor.memory=6g
spark.executor.instances=10
spark.executor.cores=2
spark.serializer=org.apache.spark.serializer.KryoSerializer
# spark.kryo.registrationRequired=true
# spark.storage.memoryFraction=0.2
# spark.eventLog.enabled=true
# spark.eventLog.dir=/tmp/spark-event-logs
# spark.ui.killEnabled=true
#cache config
gremlin.spark.persistContext=true
gremlin.spark.graphStorageLevel=MEMORY_AND_DISK
#gremlin.spark.persistStorageLevel=DISK_ONLY
#####################################
# GiraphGraphComputer Configuration #
#####################################
giraph.minWorkers=2
giraph.maxWorkers=3
giraph.useOutOfCoreGraph=true
giraph.useOutOfCoreMessages=true
mapred.map.child.java.opts=-Xmx1024m
mapred.reduce.child.java.opts=-Xmx1024m
giraph.numInputThreads=4
giraph.numComputeThreads=4
# giraph.maxPartitionsInMemory=1
# giraph.userPartitionCount=2
执行批量导入
启动命令：
sh /data/janusgraph/lib/yarn-gremlin.sh
批量导入命令：
local_root="/data/janusgraph"
hdfs_root="/user/yisou/taotian1/janus"
social_graph="${local_root}/conf/janusgraph-hbase-es.properties"
graph = GraphFactory.open("${local_root}/conf/hadoop-script.properties")
graph.configuration().setProperty("gremlin.hadoop.inputLocation","/user/yisou/taotian1/janus/data/fewData.test.dup")
graph.configuration().setProperty("gremlin.hadoop.scriptInputFormat.script", "${hdfs_root}/conf/vertex_parse.groovy")
blvp = BulkLoaderVertexProgram.build().writeGraph(social_graph).create(graph)
graph.compute(SparkGraphComputer).program(blvp).submit().get()
运行结果：
sh /data/janusgraph/lib/yarn-gremlin.sh
\,,,/
(o o)
-----oOOo-(3)-oOOo-----
plugin activated: janusgraph.imports
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/data2/janusgraph-0.1.1-hadoop2/lib/slf4j-log4j12-1.7.12.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/data2/janusgraph-0.1.1-hadoop2/lib/logback-classic-1.1.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/data2/janusgraph-0.1.1-hadoop2/lib/spark-assembly-1.6.2-hadoop2.6.0.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/data2/janusgraph-0.1.1-hadoop2/lib/yisou-hbase-1.0-SNAPSHOT.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
21:22:00,392 INFO HadoopGraph:87 - HADOOP_GREMLIN_LIBS is set to: /data2/janusgraph-0.1.1-hadoop2/lib
plugin activated: tinkerpop.hadoop
plugin activated: tinkerpop.spark
plugin activated: tinkerpop.tinkergraph
gremlin>
gremlin> local_root="/data2/janusgraph-0.1.1-hadoop2/social"
==>/data2/janusgraph-0.1.1-hadoop2/social
gremlin> hdfs_root="/user/yisou/taotian1/janus"
==>/user/yisou/taotian1/janus
gremlin> social_graph="${local_root}/conf/janusgraph-hbase-es-social.properties"
==>/data2/janusgraph-0.1.1-hadoop2/social/conf/janusgraph-hbase-es-social.properties
gremlin> graph = GraphFactory.open("${local_root}/conf/hadoop-yarn.properties")
==>hadoopgraph[scriptinputformat->graphsonoutputformat]
gremlin> graph.configuration().setProperty("gremlin.hadoop.inputLocation","/user/yisou/taotian1/janus/tmp1person/")
==>null
gremlin> graph.configuration().setProperty("gremlin.hadoop.scriptInputFormat.script", "${hdfs_root}/person_parse.groovy")
==>null
gremlin> blvp = BulkLoaderVertexProgram.build().writeGraph(social_graph).create(graph)
==>BulkLoaderVertexProgram[bulkLoader=IncrementalBulkLoader, vertexIdProperty=bulkLoader.vertex.id, userSuppliedIds=false, keepOriginalIds=true, batchSize=0]
gremlin> graph.compute(SparkGraphComputer).program(blvp).submit().get()
21:25:04,666 INFO deprecation:1173 - mapred.reduce.child.java.opts is deprecated. Instead, use mapreduce.reduce.java.opts
21:25:04,667 INFO deprecation:1173 - mapred.map.child.java.opts is deprecated. Instead, use mapreduce.map.java.opts
21:25:04,680 INFO KryoShimServiceLoader:117 - Set KryoShimService provider to org.apache.tinkerpop.gremlin.hadoop.structure.io.HadoopPoolShimService@4cb2918c (class org.apache.tinkerpop.gremlin.hadoop.structure.io.HadoopPoolShimService) because its priority value (0) is the highest available
21:25:04,680 INFO KryoShimServiceLoader:123 - Configuring KryoShimService provider org.apache.tinkerpop.gremlin.hadoop.structure.io.HadoopPoolShimService@4cb2918c with user-provided configuration
21:25:10,479 WARN SparkConf:70 - The configuration key 'spark.yarn.user.classpath.first' has been deprecated as of Spark 1.3 and may be removed in the future. Please use spark.{driver,executor}.userClassPathFirst instead.
21:25:10,505 INFO SparkContext:58 - Running Spark version 1.6.2
21:25:10,524 WARN SparkConf:70 - The configuration key 'spark.yarn.user.classpath.first' has been deprecated as of Spark 1.3 and may be removed in the future. Please use spark.{driver,executor}.userClassPathFirst instead.
21:25:10,564 INFO SecurityManager:58 - Changing view acls to: yisou
21:25:10,565 INFO SecurityManager:58 - Changing modify acls to: yisou
21:25:10,566 INFO SecurityManager:58 - SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(yisou); users with modify permissions: Set(yisou)
21:25:10,833 WARN SparkConf:70 - The configuration key 'spark.yarn.user.classpath.first' has been deprecated as of Spark 1.3 and may be removed in the future. Please use spark.{driver,executor}.userClassPathFirst instead.
21:25:10,835 WARN SparkConf:70 - The configuration key 'spark.yarn.user.classpath.first' has been deprecated as of Spark 1.3 and may be removed in the future. Please use spark.{driver,executor}.userClassPathFirst instead.
21:25:11,035 INFO Utils:58 - Successfully started service 'sparkDriver' on port 36502.
21:25:11,576 INFO Slf4jLogger:80 - Slf4jLogger started
21:25:11,646 INFO Remoting:74 - Starting remoting
............
21:25:20,736 INFO Client:58 - Submitting application 2727164 to ResourceManager
21:25:20,771 INFO YarnClientImpl:273 - Submitted application application_1466564207556_2727164
21:25:21,780 INFO Client:58 - Application report for application_1466564207556_2727164 (state: ACCEPTED)
21:25:21,785 INFO Client:58 -
client token: N/A
diagnostics: N/A
ApplicationMaster host: N/A
ApplicationMaster RPC port: -1
queue: root.yisou
start time: 1500297920750
final status: UNDEFINED
tracking URL: http://c1-nn3.bdp.idc:8981/proxy/application_1466564207556_2727164/
21:25:22,787 INFO Client:58 - Application report for application_1466564207556_2727164 (state: ACCEPTED)
21:25:23,789 INFO Client:58 - Application report for application_1466564207556_2727164 (state: ACCEPTED)
21:25:24,791 INFO Client:58 - Application report for application_1466564207556_2727164 (state: ACCEPTED)
21:25:25,793 INFO Client:58 - Application report for application_1466564207556_2727164 (state: ACCEPTED)
21:25:39,585 INFO JettyUtils:58 - Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
21:25:39,823 INFO Client:58 - Application report for application_1466564207556_2727164 (state: RUNNING)
21:25:39,824 INFO Client:58 -
client token: N/A
diagnostics: N/A
ApplicationMaster host: 10.130.1.50
ApplicationMaster RPC port: 0
queue: root.yisou
start time: 1500297920750
final status: UNDEFINED
tracking URL: http://c1-nn3.bdp.idc:8981/proxy/application_1466564207556_2727164/
..........
21:25:42,864 INFO SparkContext:58 - Added JAR /data2/janusgraph-0.1.1-hadoop2/lib/commons-codec-1.7.jar at http://10.130.64.69:38209/jars/commons-codec-1.7.jar with timestamp 1500297942864
21:25:42,866 INFO SparkContext:58 - Added JAR /data2/janusgraph-0.1.1-hadoop2/lib/commons-lang-2.5.jar at http://10.130.64.69:38209/jars/commons-lang-2.5.jar with timestamp 1500297942866
21:25:42,869 INFO SparkContext:58 - Added JAR /data2/janusgraph-0.1.1-hadoop2/lib/commons-collections-3.2.2.jar at http://10.130.64.69:38209/jars/commons-collections-3.2.2.jar with timestamp 1500297942869
21:25:42,872 INFO SparkContext:58 - Added JAR /data2/janusgraph-0.1.1-hadoop2/lib/commons-io-2.3.jar at http://10.130.64.69:38209/jars/commons-io-2.3.jar with timestamp 1500297942872
21:25:42,874 INFO SparkContext:58 - Added JAR /data2/janusgraph-0.1.1-hadoop2/lib/jetty-util-6.1.26.jar at http://10.130.64.69:38209/jars/jetty-util-6.1.26.jar with timestamp 1500297942874
21:25:42,879 INFO SparkContext:58 - Added JAR /data2/janusgraph-0.1.1-hadoop2/lib/htrace-core-3.1.0-incubating.jar at http://10.130.64.69:38209/jars/htrace-core-3.1.0-incubating.jar with timestamp 1
............
21:26:14,751 INFO MapOutputTrackerMaster:58 - Size of output statuses for shuffle 2 is 146 bytes
21:26:14,767 INFO TaskSetManager:58 - Finished task 0.0 in stage 6.0 (TID 4) in 40 ms on c1-dn31.bdp.idc (1/1)
21:26:14,767 INFO YarnScheduler:58 - Removed TaskSet 6.0, whose tasks have all completed, from pool
21:26:14,767 INFO DAGScheduler:58 - ResultStage 6 (foreachPartition at SparkExecutor.java:173) finished in 0.042 s
21:26:14,768 INFO DAGScheduler:58 - Job 1 finished: foreachPartition at SparkExecutor.java:173, took 1.776125 s
21:26:14,775 INFO ShuffledRDD:58 - Removing RDD 2 from persistence list
21:26:14,785 INFO BlockManager:58 - Removing RDD 2
==>result[hadoopgraph[scriptinputformat->graphsonoutputformat],memory[size:0]]
gremlin> 21:26:22,515 INFO YarnClientSchedulerBackend:58 - Registered executor NettyRpcEndpointRef(null) (c1-dn9.bdp.idc:60762) with ID 8
批量导入性能优化
如果不做优化，janusgraph批量导入的速度非常慢，导入4千万条数据大约需要3.5小时。优化后可降低到1小时.
1.加大ids.block-size和storage.buffer-size参数的大小（在janusgraph-hbase-es.properties中配置）。
ids.block-size=100000000
storage.buffer-size=102400
2.指定hbase初始的region数目（在janusgraph-hbase-es.properties中配置）。
storage.hbase.region-count = 50
3.边和顶点同时导入，而不是顶点和边分成不同的文件，分开导入。格式可参考/data/janusgraph/data/grateful-dead.txt。
总结
本文主要讲解了janusgraph中如何配置yarn-client的方式批量导入节点和边。
分为基本配置和批量导入的配置两部分，基本配置中需要注意janusgraph自带jar包与用户yarn环境中jar包的冲突问题，可替换或者删除相关jar包。
批量导入配置中重点是在gremlin.sh中添加hadoop的相关配置，将hadoop环境配置到JAVA_OPTIONS和CLASSPATH中。
