import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD
val vertexArray = Array(
(1L, ("Alice", 28)),
(2L, ("Bob", 27)),
(3L, ("Charlie", 65)),
(4L, ("David", 42)),
(5L, ("Ed", 55)),
(6L, ("Fran", 50)),
(7L, ("Tian", 55))
)
//边的数据类型ED:Int
val edgeArray = Array(
Edge(2L, 1L, 7),
Edge(2L, 4L, 2),
Edge(3L, 2L, 4),
Edge(3L, 6L, 3),
Edge(4L, 1L, 1),
Edge(5L, 2L, 2),
Edge(5L, 3L, 8),
Edge(5L, 6L, 3),
Edge(7L, 3L, 8),
Edge(7L, 6L, 3),
Edge(7L, 6L, 6)
)
val vertexRDD: RDD[(Long, (String, Int))] = sc.parallelize(vertexArray)
val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray)
val graph: Graph[(String, Int), Int] = Graph(vertexRDD, edgeRDD)
scala> import scala.collection.immutable.HashSet
import scala.collection.immutable.HashSet
获取顶点3的首度邻居
scala> val firstNeighbor=graph.aggregateMessages[Int](triplet=>{if(triplet.srcId==3) triplet.sendToDst(1)},(a,b)=>a )
firstNeighbor: org.apache.spark.graphx.VertexRDD[Int] = VertexRDDImpl[21] at RDD at VertexRDD.scala:57
scala> var fistids=new HashSet[Long]()
fistids: scala.collection.immutable.HashSet[Long] = Set()
scala> firstNeighbor.collect.foreach(a=>fistids+=a._1)
顶点3的首度邻居列表如下
scala> fistids
res1: scala.collection.immutable.HashSet[Long] = Set(6, 2)
边信息如下：
Edge(3L, 2L, 4),
Edge(3L, 6L, 3),
可以确定首度邻居为2,6
对顶点3的首度邻居进行循环获取二度邻居的顶点ID列表
scala> var secondids=new HashSet[Long]()
secondids: scala.collection.immutable.HashSet[Long] = Set()
scala> fistids.foreach(a=>{val secondneibors=graph.aggregateMessages[Int](trp=>{if(trp.srcId==a) trp.sendToDst(1)},(a,b)=>a);secondneibors.collect.foreach(a=>{secondids+=a._1})})
scala> secondids
res7: scala.collection.immutable.HashSet[Long] = Set(1, 4)
根据边可以看出，6没有出度
2的出度为1和4
获取二度邻居的信息
scala> graph.vertices.filter(a=>secondids.contains(a._1)).collect
res8: Array[(org.apache.spark.graphx.VertexId, (String, Int))] = Array((1,(Alice,28)), (4,(David,42)))
获取一度二度关系
scala> graph.vertices.filter(a=>secondids.contains(a._1) || fistids.contains(a._1)).collect
res10: Array[(org.apache.spark.graphx.VertexId, (String, Int))] = Array((1,(Alice,28)), (2,(Bob,27)), (4,(David,42)), (6,(Fran,50)))
----------------------------------------
顶点2的入度出度邻居
scala> graph.collectNeighbors(EdgeDirection.Either).filter(_._1==2).map(_._2).collect
res15: Array[Array[(org.apache.spark.graphx.VertexId, (String, Int))]] = Array(Array((1,(Alice,28)), (4,(David,42)), (3,(Charlie,65)), (5,(Ed,55))))
顶点2的入度邻居
scala> graph.collectNeighbors(EdgeDirection.In).filter(_._1==2).map(_._2).collect
res16: Array[Array[(org.apache.spark.graphx.VertexId, (String, Int))]] = Array(Array((3,(Charlie,65)), (5,(Ed,55))))
顶点2的出度邻居
scala> graph.collectNeighbors(EdgeDirection.Out).filter(_._1==2).map(_._2).collect
res17: Array[Array[(org.apache.spark.graphx.VertexId, (String, Int))]] = Array(Array((1,(Alice,28)), (4,(David,42))))
------------------------------------------------------
其他方式 获取二度出度邻居
scala> val firstNids=graph.collectNeighborIds(EdgeDirection.Out).filter(_._1==3).map(a=>a._2).first
firstNids: Array[org.apache.spark.graphx.VertexId] = Array(2, 6)
scala> graph.collectNeighbors(EdgeDirection.Out).filter(a=>firstNids.contains(a._1)).map(_._2).filter(_.length>0).first
res116: Array[(org.apache.spark.graphx.VertexId, (String, Int))] = Array((1,(Alice,28)), (4,(David,42)))
--------------------------------------------------
获取双向二度邻居
获取顶点3的邻居ID列表
scala> val firstNids=graph.collectNeighborIds(EdgeDirection.Either).filter(_._1==3).map(a=>a._2).first
firstNids: Array[org.apache.spark.graphx.VertexId] = Array(2, 6, 5, 7)
获取首度邻居顶点的邻居，生成一个多维数组，使用reduce 合并多维数组为一维数组并去重，然后将顶点3以及顶点3的邻居过滤掉获取二度邻居
scala> graph.collectNeighbors(EdgeDirection.Either).filter(a=>firstNids.contains(a._1)).map(_._2).reduce((a,b)=>a++b).distinct.filter(a=>{!firstNids.contains(a._1) && a._1!=3 })
res155: Array[(org.apache.spark.graphx.VertexId, (String, Int))] = Array((1,(Alice,28)), (4,(David,42)))
