Graphx处理janusGraph数据实现

声明：
此方案是在spark直接执行gremlinSQL方案实现受阻的情况下的备选方案，不涉及工作机密，不存在泄密可能，纯属个人思考，希望抛砖引玉

方案：
将gremlinSql的查询结果转化为startGraph，然后转写到HDFS，spark读取hdfs的starGraphJSon构建graphx可用的图，然后就可以调用graphx丰富的图计算算法；从而将实现graphX操作janusgraph的目的

1. gremlinSql的查询结果转换成starGraphJson

由于org.apache.tinkerpop.gremlin.structure.io.graphson.GraphSONWriter保存的graphSon格式无法满足需求，所以将查询出的带path的点边数据自己转换成单点图的json结构，转化方法如下，然后存到hdfs，存储方法不再赘述。

public StringBuilder generatorStarGraphJson(Vertex vertex, Edge edge, StringBuilder starGraphJson){ String inVId; String outVId; String VId; starGraphJson.append("{").append("\"id\":"+vertex.id()+","+"\"label\":\""+vertex.label()+"\","); //这种情况有outE和inE，outE中会有inV信息,inE会有一个inV inVId = edge.inVertex().id().toString(); outVId = edge.outVertex().id().toString(); VId = vertex.id().toString(); if(inVId.equalsIgnoreCase(VId)){ starGraphJson.append("\"outE\":{").append("\""+edge.label()+"\":[{").append("\"id\":\""+edge.id()+"\",") .append("\"inV\":"+edge.inVertex().id()+",").append("\"properties\":{"+concatEdgeProperties(edge)+"}}]},"); }else if(outVId.equalsIgnoreCase(VId)){ starGraphJson.append("\"inE\":{").append("\""+edge.label()+"\":[{").append("\"id\":\""+edge.id()+"\",") .append("\"outV\":"+edge.inVertex().id()+",").append("\"properties\":{"+concatEdgeProperties(edge)+"}}]},"); }else{ throw new Exception("点边不对应数据错误！！！"); } //拼接点的properties starGraphJson.append("\"properties\":{").append(concatVertexProperties(vertex)).append("}}"); return starGraphJson; } 

2. spark读取指定路径的starGraph转成graph

class GraphSon2GraphXRDD() extends Serializable { def getGraphConf(HDFSFilePath : String): BaseConfiguration ={ val inputGraphConf = new BaseConfiguration inputGraphConf.setProperty("gremlin.graph", classOf\[HadoopGraph\].getName) inputGraphConf.setProperty(Constants.GREMLIN\_HADOOP\_GRAPH\_READER, classOf\[GraphSONInputFormat\].getName) inputGraphConf.setProperty(Constants.GREMLIN\_HADOOP\_INPUT\_LOCATION, HDFSFilePath) inputGraphConf.setProperty(Constants.MAPREDUCE\_INPUT\_FILEINPUTFORMAT_INPUTDIR, HDFSFilePath) inputGraphConf } def getSc(sparkHost:String ,isRemote:Boolean): SparkContext ={ var sparkConf = new SparkConf() if(isRemote){ //待完善 }else{ sparkConf.setMaster("local\[*\]").setAppName("GraphSon2GraphX") } val sc = new SparkContext(sparkConf) sc } def getJavaRDD(conf : BaseConfiguration, sc : SparkContext): JavaPairRDD\[AnyRef, VertexWritable\] ={ val jsc = JavaSparkContext.fromSparkContext(sc) val graphRDDInput = new InputFormatRDD val vertexWritableJavaPairRDD = graphRDDInput.readGraphRDD(conf, jsc) vertexWritableJavaPairRDD } def getVertexRDD(vertexWritableJavaPairRDD : JavaPairRDD\[AnyRef, VertexWritable\]): RDD\[(Long,util.HashMap\[String,java.io.Serializable\])\] ={ vertexWritableJavaPairRDD.rdd.map((tuple2: Tuple2\[AnyRef, VertexWritable\]) => { // Get the center vertex val v = tuple2._2.get val g = StarGraph.of(v) // In case the vertex id in TinkerGraph is not long type // val vid = convertStringIDToLongID([v.id](http://v.id)().toString) val vid = [v.id](http://v.id)().toString.toLong // Pass the vertex properties to GraphX vertex value map and remain the original vertex id var graphxValueMap : util.HashMap\[String,java.io.Serializable\] = new util.HashMapString,java.io.Serializable graphxValueMap.put("originalID",[v.id](http://v.id)().toString) graphxValueMap.putAll(g.traversal.V([v.id](http://v.id)).valueMap().next(1).get(0)) (vid,graphxValueMap) }) } def getEdgeRDD(vertexWritableJavaPairRDD : JavaPairRDD\[AnyRef, VertexWritable\]): RDD\[graphx.Edge\[util.HashMap\[String, java.io.Serializable\]\]\] ={ val edge = vertexWritableJavaPairRDD.rdd.flatMap((tuple2: Tuple2\[AnyRef, VertexWritable\]) => { val v = tuple2._2.get val g = StarGraph.of(v) val edgelist:util.List\[Edge\] = g.traversal.V([v.id](http://v.id)).outE().toList // Put all edges of the center vertex into the list val list = new collection.mutable.ArrayBuffer[graphx.Edge[util.HashMap[String,java.io.Serializable]]]() var x = 0 for(x &lt;- 0 until edgelist.size()){ var srcId = edgelist.get(x).inVertex.id().toString var dstId = edgelist.get(x).outVertex.id().toString // val md1 = convertStringIDToLongID(srcId) // val md2 = convertStringIDToLongID(dstId) val md1 = srcId.toLong val md2 = dstId.toLong // Get the properties of the edge var edgeAttr = new util.HashMap[String,java.io.Serializable]() var perporties : util.Iterator[Property[Nothing]] = edgelist.get(x).properties() while(perporties.hasNext){ val property = perporties.next() edgeAttr.put(property.key(),property.value().toString) } list.append(graphx.Edge(md1,md2,edgeAttr)) } list }) val edgeRDD = edge.distinct() edgeRDD } def doLAP(vertexWritableJavaPairRDD : JavaPairRDD\[AnyRef, VertexWritable\], iterationNum : Int): Array\[Array\[String\]\] = { val vertexRDD = getVertexRDD(vertexWritableJavaPairRDD) val edgeRDD = getEdgeRDD(vertexWritableJavaPairRDD) val graph = graphx.Graph[util.HashMap[String,java.io.Serializable], util.HashMap[String,java.io.Serializable]](vertexRDD,edgeRDD,new util.HashMap[String,java.io.Serializable]()) val LVMRsult = lib.LabelPropagation.run(graph , iterationNum).vertices.collect.sortWith (_._1 &lt; _._1).map(f =&gt; { println(f.toString()) f}) getFinalCommunit(LVMRsult) } def getFinalCommunit(LVMRsult:Array\[(Long,Long)\]): Array\[Array\[String\]\] ={ var result = new Array[Array\[String\]](LVMRsult.length) var tmp = new ArrayBufferString for(i <- 0 until LVMRsult.length){ var k = 0 val array = new ArrayBufferString //社区中包含多个值 for(j &lt;- (i+1) until LVMRsult.length) { if(LVMRsult(i)._2.equals(LVMRsult(j)._2)){ if(!tmp.contains(LVMRsult(i)._1.toString)){ array += LVMRsult(i)._1.toString tmp += LVMRsult(i)._1.toString } if(!tmp.contains(LVMRsult(j)._1.toString)){ array += LVMRsult(j)._1.toString tmp += LVMRsult(j)._1.toString } k = k+1 } } //自己为一个社区 if(k.equals(0)){ if(!tmp.contains(LVMRsult(i)._1.toString)){ array += LVMRsult(i)._1.toString tmp += LVMRsult(i)._1.toString } } if(array.length &gt; 0){ result.update(i,array.toArray.distinct) } } result.filter(f =&gt; { println(if (f.length &gt;0) f.mkString("(",",",")")) f != null }) } def doPageRank(vertexWritableJavaPairRDD : JavaPairRDD\[AnyRef, VertexWritable\], stopThreshold : Double): Array\[Array\[Any\]\] = { val vertexRDD:RDD\[(Long,util.HashMap\[String,java.io.Serializable\])\] = getVertexRDD(vertexWritableJavaPairRDD) val edgeRDD = getEdgeRDD(vertexWritableJavaPairRDD) val graph = graphx.Graph[util.HashMap[String,java.io.Serializable], util.HashMap[String,java.io.Serializable]](vertexRDD,edgeRDD,new util.HashMap[String,java.io.Serializable]()) val gpgraph = graph.pageRank(stopThreshold).cache() val titleAndPrGraph = graph.outerJoinVertices(gpgraph.vertices) { (v, title, rank) =&gt; (rank.getOrElse(0.0), title) } //倒序 false 正序 true // titleAndPrGraph.vertices.sortBy((entry: (VertexId, (Double, Object))) => entry.\_2.\_1, false).foreach(f => println(f.\_1+":"+f.\_2._1)) val pageRank = titleAndPrGraph.vertices.sortBy((entry: (VertexId, (Double, Object))) =&gt; entry._2._1, false).map(f =&gt; { println(f._1+":"+f._2._1) Array(f._1.toString,f._2._1) }) pageRank.collect() } }

这样就贯通了janusgraph和graphx，调用graphx的丰富的图计算功能就畅通无阻，就是实现有点挫，希望抛砖引玉