Spark GraphX 入门实例完整scala代码

本文引用的代码块和多数文字描述均摘引自网文 graph-analytics-with-graphx， 在此特向作者表以感谢！

[1] 完整可执行scala 代码：

package scala.spark.graphx  import org.apache.spark.graphx._ import org.apache.spark.rdd.RDD import org.apache.spark._ import org.apache.spark.SparkContext._  object GraphXExample {   def main(args: Array[String]) {      val conf = new SparkConf().setAppName("GraphXExample")     val sc = new SparkContext(conf)      // [A] creating the Property Graph from arrays of vertices and edges     println("[A] creating the Property Graph from arrays of vertices and edges");     // Each vertex is keyed by a unique 64-bit long identifier (VertexID), like '1L'     val vertexArray = Array(       (1L, ("Alice", 28)),       (2L, ("Bob", 27)),       (3L, ("Charlie", 65)),       (4L, ("David", 42)),       (5L, ("Ed", 55)),       (6L, ("Fran", 50)))     // the Edge class stores a srcId, a dstId and the edge property     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))      // construct the following RDDs from the vertexArray and edgeArray variables.     val vertexRDD: RDD[(Long, (String, Int))] = sc.parallelize(vertexArray)     val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray)      // build a Property Graph     val graph: Graph[(String, Int), Int] = Graph(vertexRDD, edgeRDD)      // [B] Extract the vertex and edge RDD views of a graph     println("[B] Extract the vertex and edge RDD views of a graph");     // Solution 1     println("Solution 1:============")     graph.vertices.filter { case (id, (name, age)) => age > 30 }.collect.foreach {       case (id, (name, age)) => println(s"$name is $age")     }      // Solution 2     println("Solution 2:============")     graph.vertices.filter(v => v._2._2 > 30).collect.foreach(v => println(s"${v._2._1} is ${v._2._2}"))      // Solution 3     println("Solution 3:============")     for ((id, (name, age)) <- graph.vertices.filter { case (id, (name, age)) => age > 30 }.collect) {       println(s"$name is $age")     }      // [C] Exposes a triplet view which logically joins the vertex and edge properties yielding an RDD[EdgeTriplet[VD, ED]]     println("[C] Exposes a triplet view which logically joins the vertex and edge properties yielding an RDD[EdgeTriplet[VD, ED]]");     println("Use the graph.triplets view to display who likes who: ")     for (triplet <- graph.triplets.collect) {       println(s"${triplet.srcAttr._1} likes ${triplet.dstAttr._1}")     }      // For extra credit, find the lovers.     // If someone likes someone else more than 5 times than that relationship is getting pretty serious.     println("For extra credit, find the lovers if has:============")     for (triplet <- graph.triplets.filter(t => t.attr > 5).collect) {       println(s"${triplet.srcAttr._1} loves ${triplet.dstAttr._1}")     }      // [D] Graph Operators     // Property Graphs also have a collection of basic operations     println("[D] Graph Operators")      // compute the in-degree of each vertex     val inDegrees: VertexRDD[Int] = graph.inDegrees      // Define a class to more clearly model the user property     case class User(name: String, age: Int, inDeg: Int, outDeg: Int)     // Create a user Graph     val initialUserGraph: Graph[User, Int] = graph.mapVertices { case (id, (name, age)) => User(name, age, 0, 0) }      // Fill in the degree information     val userGraph = initialUserGraph.outerJoinVertices(initialUserGraph.inDegrees) {       case (id, u, inDegOpt) => User(u.name, u.age, inDegOpt.getOrElse(0), u.outDeg)     }.outerJoinVertices(initialUserGraph.outDegrees) {       case (id, u, outDegOpt) => User(u.name, u.age, u.inDeg, outDegOpt.getOrElse(0))     }      // Here we use the outerJoinVertices method of Graph which has the following (confusing) type signature:     // def outerJoinVertices[U, VD2](other: RDD[(VertexID, U)])(mapFunc: (VertexID, VD, Option[U]) => VD2): Graph[VD2, ED]      // Using the degreeGraph print the number of people who like each user:     println("Using the degreeGraph print the number of people who like each user:============")     for ((id, property) <- userGraph.vertices.collect) {       println(s"User $id is called ${property.name} and is liked by ${property.inDeg} people.")     }      // Print the names of the users who are liked by the same number of people they like.     userGraph.vertices.filter {       case (id, u) => u.inDeg == u.outDeg     }.collect.foreach {       case (id, property) => println(property.name)     }      // [D.1] The Map Reduce Triplets Operator     // The mapReduceTriplets operator enables neighborhood aggregation and find the oldest follower of each user     println("[D.1] The Map Reduce Triplets Operator")     // Find the oldest follower for each user     println("Find the oldest follower for each user:============")     val oldestFollower: VertexRDD[(String, Int)] = userGraph.mapReduceTriplets[(String, Int)](       // For each edge send a message to the destination vertex with the attribute of the source vertex       edge => Iterator((edge.dstId, (edge.srcAttr.name, edge.srcAttr.age))),       // To combine messages take the message for the older follower       (a, b) => if (a._2 > b._2) a else b)     userGraph.vertices.leftJoin(oldestFollower) { (id, user, optOldestFollower) =>       optOldestFollower match {         case None => s"${user.name} does not have any followers."         case Some((name, age)) => s"${name} is the oldest follower of ${user.name}."       }     }.collect.foreach { case (id, str) => println(str) }      // Try finding the average follower age of the followers of each user     println("Try finding the average follower age of the followers of each user:============")     val averageAge: VertexRDD[Double] = userGraph.mapReduceTriplets[(Int, Double)](       // map function returns a tuple of (1, Age)       edge => Iterator((edge.dstId, (1, edge.srcAttr.age.toDouble))),       // reduce function combines (sumOfFollowers, sumOfAge)       (a, b) => ((a._1 + b._1), (a._2 + b._2))).mapValues((id, p) => p._2 / p._1)      // Display the results     userGraph.vertices.leftJoin(averageAge) { (id, user, optAverageAge) =>       optAverageAge match {         case None => s"${user.name} does not have any followers."         case Some(avgAge) => s"The average age of ${user.name}/'s followers is $avgAge."       }     }.collect.foreach { case (id, str) => println(str) }      // [D.2] Subgraph     // The subgraph operator that takes vertex and edge predicates and returns the graph      // containing only the vertices that satisfy the vertex predicate (evaluate to true)      // and edges that satisfy the edge predicate and connect vertices that satisfy the      // vertex predicate.     println("[D.2] Subgraph")     // restrict our graph to the users that are 30 or older     println("restrict our graph to the users that are 30 or older:============")     val olderGraph = userGraph.subgraph(vpred = (id, user) => user.age >= 30)     // compute the connected components     val cc = olderGraph.connectedComponents     // display the component id of each user:     olderGraph.vertices.leftJoin(cc.vertices) {       case (id, user, comp) => s"${user.name} is in component ${comp.get}"     }.collect.foreach { case (id, str) => println(str) }    }  }

[2] 执行结果：

[A] creating the Property Graph from arrays of vertices and edges [B] Extract the vertex and edge RDD views of a graph Solution 1:============ David is 42 Fran is 50 Charlie is 65 Ed is 55 Solution 2:============ David is 42 Fran is 50 Charlie is 65 Ed is 55 Solution 3:============ David is 42 Fran is 50 Charlie is 65 Ed is 55 [C] Exposes a triplet view which logically joins the vertex and edge properties yielding an RDD[EdgeTriplet[VD, ED]] Use the graph.triplets view to display who likes who: Bob likes Alice Bob likes David Charlie likes Bob Charlie likes Fran David likes Alice Ed likes Bob Ed likes Charlie Ed likes Fran For extra credit, find the lovers if has:============ Bob loves Alice Ed loves Charlie [D] Graph Operators Using the degreeGraph print the number of people who like each user:============ User 4 is called David and is liked by 1 people. User 6 is called Fran and is liked by 2 people. User 2 is called Bob and is liked by 2 people. User 1 is called Alice and is liked by 2 people. User 3 is called Charlie and is liked by 1 people. User 5 is called Ed and is liked by 0 people. David Bob [D.1] The Map Reduce Triplets Operator Find the oldest follower for each user:============ Bob is the oldest follower of David. Charlie is the oldest follower of Fran. Charlie is the oldest follower of Bob. David is the oldest follower of Alice. Ed is the oldest follower of Charlie. Ed does not have any followers. Try finding the average follower age of the followers of each user:============ The average age of David's followers is 27.0. The average age of Fran's followers is 60.0. The average age of Bob's followers is 60.0. The average age of Alice's followers is 34.5. The average age of Charlie's followers is 55.0. Ed does not have any followers. [D.2] Subgraph restrict our graph to the users that are 30 or older:============ David is in component 4 Fran is in component 3 Charlie is in component 3 Ed is in component 3