SparkGraphx计算指定节点的N度关系节点源码

直接上代码：

package horizon.graphx.utilimport java.security.InvalidParameterExceptionimport horizon.graphx.util.CollectionUtil.CollectionHelperimport org.apache.spark.graphx._import org.apache.spark.rdd.RDDimport org.apache.spark.storage.StorageLevelimport scala.collection.mutable.ArrayBufferimport scala.reflect.ClassTag/** * Created by yepei.ye on 2017/1/19. * Description:用于在图中为指定的节点计算这些节点的N度关系节点，输出这些节点与源节点的路径长度和节点id */object GraphNdegUtil { val maxNDegVerticesCount = 10000 val maxDegree = 1000 /** * 计算节点的N度关系 * * @param edges * @param choosedVertex * @param degree * @tparam ED * @return */ def aggNdegreedVertices[ED: ClassTag](edges: RDD[(VertexId, VertexId)], choosedVertex: RDD[VertexId], degree: Int): VertexRDD[Map[Int, Set[VertexId]]] = { val simpleGraph = Graph.fromEdgeTuples(edges, 0, Option(PartitionStrategy.EdgePartition2D), StorageLevel.MEMORY_AND_DISK_SER, StorageLevel.MEMORY_AND_DISK_SER) aggNdegreedVertices(simpleGraph, choosedVertex, degree) } def aggNdegreedVerticesWithAttr[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED], choosedVertex: RDD[VertexId], degree: Int, sendFilter: (VD, VD) => Boolean = (_: VD, _: VD) => true): VertexRDD[Map[Int, Set[VD]]] = { val ndegs: VertexRDD[Map[Int, Set[VertexId]]] = aggNdegreedVertices(graph, choosedVertex, degree, sendFilter) val flated: RDD[Ver[VD]] = ndegs.flatMap(e => e._2.flatMap(t => t._2.map(s => Ver(e._1, s, t._1, null.asInstanceOf[VD])))).persist(StorageLevel.MEMORY_AND_DISK_SER) val matched: RDD[Ver[VD]] = flated.map(e => (e.id, e)).join(graph.vertices).map(e => e._2._1.copy(attr = e._2._2)).persist(StorageLevel.MEMORY_AND_DISK_SER) flated.unpersist(blocking = false) ndegs.unpersist(blocking = false) val grouped: RDD[(VertexId, Map[Int, Set[VD]])] = matched.map(e => (e.source, ArrayBuffer(e))).reduceByKey(_ ++= _).map(e => (e._1, e._2.map(t => (t.degree, Set(t.attr))).reduceByKey(_ ++ _).toMap)) matched.unpersist(blocking = false) VertexRDD(grouped) } def aggNdegreedVertices[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED],              choosedVertex: RDD[VertexId],              degree: Int,              sendFilter: (VD, VD) => Boolean = (_: VD, _: VD) => true              ): VertexRDD[Map[Int, Set[VertexId]]] = { if (degree < 1) {  throw new InvalidParameterException("度参数错误:" + degree) } val initVertex = choosedVertex.map(e => (e, true)).persist(StorageLevel.MEMORY_AND_DISK_SER) var g: Graph[DegVertex[VD], Int] = graph.outerJoinVertices(graph.degrees)((_, old, deg) => (deg.getOrElse(0), old))  .subgraph(vpred = (_, a) => a._1 <= maxDegree)  //去掉大节点  .outerJoinVertices(initVertex)((id, old, hasReceivedMsg) => {  DegVertex(old._2, hasReceivedMsg.getOrElse(false), ArrayBuffer((id, 0))) //初始化要发消息的节点 }).mapEdges(_ => 0).cache() //简化边属性 choosedVertex.unpersist(blocking = false) var i = 0 var prevG: Graph[DegVertex[VD], Int] = null var newVertexRdd: VertexRDD[ArrayBuffer[(VertexId, Int)]] = null while (i < degree + 1) {  prevG = g  //发第i+1轮消息  newVertexRdd = prevG.aggregateMessages[ArrayBuffer[(VertexId, Int)]](sendMsg(_, sendFilter), (a, b) => reduceVertexIds(a ++ b)).persist(StorageLevel.MEMORY_AND_DISK_SER)  g = g.outerJoinVertices(newVertexRdd)((vid, old, msg) => if (msg.isDefined) updateVertexByMsg(vid, old, msg.get) else old.copy(init = false)).cache()  prevG.unpersistVertices(blocking = false)  prevG.edges.unpersist(blocking = false)  newVertexRdd.unpersist(blocking = false)  i += 1 } newVertexRdd.unpersist(blocking = false) val maped = g.vertices.join(initVertex).mapValues(e => sortResult(e._1)).persist(StorageLevel.MEMORY_AND_DISK_SER) initVertex.unpersist() g.unpersist(blocking = false) VertexRDD(maped) } private case class Ver[VD: ClassTag](source: VertexId, id: VertexId, degree: Int, attr: VD = null.asInstanceOf[VD]) private def updateVertexByMsg[VD: ClassTag](vertexId: VertexId, oldAttr: DegVertex[VD], msg: ArrayBuffer[(VertexId, Int)]): DegVertex[VD] = { val addOne = msg.map(e => (e._1, e._2 + 1)) val newMsg = reduceVertexIds(oldAttr.degVertices ++ addOne) oldAttr.copy(init = msg.nonEmpty, degVertices = newMsg) } private def sortResult[VD: ClassTag](degs: DegVertex[VD]): Map[Int, Set[VertexId]] = degs.degVertices.map(e => (e._2, Set(e._1))).reduceByKey(_ ++ _).toMap case class DegVertex[VD: ClassTag](var attr: VD, init: Boolean = false, degVertices: ArrayBuffer[(VertexId, Int)]) case class VertexDegInfo[VD: ClassTag](var attr: VD, init: Boolean = false, degVertices: ArrayBuffer[(VertexId, Int)]) private def sendMsg[VD: ClassTag](e: EdgeContext[DegVertex[VD], Int, ArrayBuffer[(VertexId, Int)]], sendFilter: (VD, VD) => Boolean): Unit = { try {  val src = e.srcAttr  val dst = e.dstAttr  //只有dst是ready状态才接收消息  if (src.degVertices.size < maxNDegVerticesCount && (src.init || dst.init) && dst.degVertices.size < maxNDegVerticesCount && !isAttrSame(src, dst)) {  if (sendFilter(src.attr, dst.attr)) {   e.sendToDst(reduceVertexIds(src.degVertices))  }  if (sendFilter(dst.attr, dst.attr)) {   e.sendToSrc(reduceVertexIds(dst.degVertices))  }  } } catch {  case ex: Exception =>  println(s"==========error found: exception:${ex.getMessage}," +   s"edgeTriplet:(srcId:${e.srcId},srcAttr:(${e.srcAttr.attr},${e.srcAttr.init},${e.srcAttr.degVertices.size}))," +   s"dstId:${e.dstId},dstAttr:(${e.dstAttr.attr},${e.dstAttr.init},${e.dstAttr.degVertices.size}),attr:${e.attr}")  ex.printStackTrace()  throw ex } } private def reduceVertexIds(ids: ArrayBuffer[(VertexId, Int)]): ArrayBuffer[(VertexId, Int)] = ArrayBuffer() ++= ids.reduceByKey(Math.min) private def isAttrSame[VD: ClassTag](a: DegVertex[VD], b: DegVertex[VD]): Boolean = a.init == b.init && allKeysAreSame(a.degVertices, b.degVertices) private def allKeysAreSame(a: ArrayBuffer[(VertexId, Int)], b: ArrayBuffer[(VertexId, Int)]): Boolean = { val aKeys = a.map(e => e._1).toSet val bKeys = b.map(e => e._1).toSet if (aKeys.size != bKeys.size || aKeys.isEmpty) return false aKeys.diff(bKeys).isEmpty && bKeys.diff(aKeys).isEmpty }}

其中sortResult方法里对Traversable[(K,V)]类型的集合使用了reduceByKey方法，这个方法是自行封装的，使用时需要导入，代码如下：

/** * Created by yepei.ye on 2016/12/21. * Description: */object CollectionUtil { /** * 对具有Traversable[(K, V)]类型的集合添加reduceByKey相关方法 * * @param collection * @param kt * @param vt * @tparam K * @tparam V */ implicit class CollectionHelper[K, V](collection: Traversable[(K, V)])(implicit kt: ClassTag[K], vt: ClassTag[V]) { def reduceByKey(f: (V, V) => V): Traversable[(K, V)] = collection.groupBy(_._1).map { case (_: K, values: Traversable[(K, V)]) => values.reduce((a, b) => (a._1, f(a._2, b._2))) } /**  * reduceByKey的同时，返回被reduce掉的元素的集合  *  * @param f  * @return  */ def reduceByKeyWithReduced(f: (V, V) => V)(implicit kt: ClassTag[K], vt: ClassTag[V]): (Traversable[(K, V)], Traversable[(K, V)]) = {  val reduced: ArrayBuffer[(K, V)] = ArrayBuffer()  val newSeq = collection.groupBy(_._1).map {  case (_: K, values: Traversable[(K, V)]) => values.reduce((a, b) => {   val newValue: V = f(a._2, b._2)   val reducedValue: V = if (newValue == a._2) b._2 else a._2   val reducedPair: (K, V) = (a._1, reducedValue)   reduced += reducedPair   (a._1, newValue)  })  }  (newSeq, reduced.toTraversable) } }}

总结

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