Spark GraphX 入门实例完整scala代码

由于天然符合互联网中很多场景的需求，图计算正受到越来越多的青睐。Spark GraphX 是作为 Spark 技术堆栈中的一员，担负起了 Spark 在图计算领域中的重任。网络上已经有很多图计算和 Spark GraphX 的概念介绍，此处就不再赘述。 本文将一篇很好的 Spark GraphX 入门文章中代码块整合为一个完整的可执行类，并加上必要注释以及执行结果，以方便有兴趣的朋友快速从 API 角度了解 Spark GraphX。

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.graphximport org.apache.spark.graphx._import org.apache.spark.rdd.RDDimport 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 graphSolution 1:============David is 42Fran is 50Charlie is 65Ed is 55Solution 2:============David is 42Fran is 50Charlie is 65Ed is 55Solution 3:============David is 42Fran is 50Charlie is 65Ed 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 AliceBob likes DavidCharlie likes BobCharlie likes FranDavid likes AliceEd likes BobEd likes CharlieEd likes FranFor extra credit, find the lovers if has:============Bob loves AliceEd loves Charlie[D] Graph OperatorsUsing 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.DavidBob[D.1] The Map Reduce Triplets OperatorFind 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] Subgraphrestrict our graph to the users that are 30 or older:============David is in component 4Fran is in component 3Charlie is in component 3Ed is in component 3