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 <- 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 < _._1).map(f => {
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 <- (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 > 0){
result.update(i,array.toArray.distinct)
}
}
result.filter(f => {
println(if (f.length >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) => (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))) => entry._2._1, false).map(f => {
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
