我在一台机器上运行 JanusGraph (0.1.0) 和 Spark (1.6.1)。我按照此处所述进行了配置。使用 SparkGraphComputer 访问 gremlin-console 上的图形时，它始终为空。我在日志文件中找不到任何错误，它只是一个空图。
是否有人将 JanusGraph 与 Spark 一起使用并且可以共享他的配置和属性？
使用 JanusGraph，我得到了预期的输出：
gremlin> graph=JanusGraphFactory.open('conf/test.properties')
==>standardjanusgraph[cassandrathrift:[127.0.0.1]]
gremlin> g=graph.traversal()
==>graphtraversalsource[standardjanusgraph[cassandrathrift:[127.0.0.1]], standard]
gremlin> g.V().count()
14:26:10 WARN org.janusgraph.graphdb.transaction.StandardJanusGraphTx - Query requires iterating over all vertices [()]. For better performance, use indexes
==>1000001
gremlin>
使用带有 Spark 作为 GraphComputer 的 HadoopGraph，该图是空的：
gremlin> graph=GraphFactory.open('conf/test.properties')
==>hadoopgraph[cassandrainputformat->gryooutputformat]
gremlin> g=graph.traversal().withComputer(SparkGraphComputer)
==>graphtraversalsource[hadoopgraph[cassandrainputformat->gryooutputformat], sparkgraphcomputer]
gremlin> g.V().count()
==>0==============================================> (14 + 1) / 15]
我的 conf/test.properties：
#
# Hadoop Graph Configuration
#
gremlin.graph=org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph
gremlin.hadoop.graphInputFormat=org.janusgraph.hadoop.formats.cassandra.CassandraInputFormat
gremlin.hadoop.graphOutputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat
gremlin.hadoop.memoryOutputFormat=org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat
gremlin.hadoop.memoryOutputFormat=org.apache.tinkerpop.gremlin.hadoop.structure.io.gryo.GryoOutputFormat
gremlin.hadoop.deriveMemory=false
gremlin.hadoop.jarsInDistributedCache=true
gremlin.hadoop.inputLocation=none
gremlin.hadoop.outputLocation=output
#
# Titan Cassandra InputFormat configuration
#
janusgraphmr.ioformat.conf.storage.backend=cassandrathrift
janusgraphmr.ioformat.conf.storage.hostname=127.0.0.1
janusgraphmr.ioformat.conf.storage.keyspace=janusgraph
storage.backend=cassandrathrift
storage.hostname=127.0.0.1
storage.keyspace=janusgraph
#
# Apache Cassandra InputFormat configuration
#
cassandra.input.partitioner.class=org.apache.cassandra.dht.Murmur3Partitioner
cassandra.input.keyspace=janusgraph
cassandra.input.predicate=0c00020b0001000000000b000200000000020003000800047fffffff0000
cassandra.input.columnfamily=edgestore
cassandra.range.batch.size=2147483647
#
# SparkGraphComputer Configuration
#
spark.master=spark://127.0.0.1:7077
spark.serializer=org.apache.spark.serializer.KryoSerializer
spark.executor.memory=100g
gremlin.spark.persistContext=true
gremlin.hadoop.defaultGraphComputer=org.apache.tinkerpop.gremlin.spark.process.computer.SparkGraphComputer
HDFS 似乎配置正确，如此处所述：
gremlin> hdfs
==>storage[DFS[DFSClient[clientName=DFSClient_NONMAPREDUCE_178390072_1, ugi=cassandra (auth:SIMPLE)]]]