SparkGraphX图操作符.pdf

Spark GraphX 图操作符 Spark GraphX图操作符 正如RDDs有基本的操作map , filter和reduceByKey⼀样，属性图也有基本的集合操作， 这些操作采⽤⽤户⾃定义的函数并产⽣ 含转换特征和结构的新图。定义在Graph 中 的核⼼操作是经过优化的实现。表⽰为核⼼操作的组合的便捷操作定义在GraphOps 中。然⽽，因为有Scala的隐式转换，定义在GraphOps中的操作可以作为Graph的成 员⾃动使⽤。例如，我们可以通过下⾯的⽅式计算每个顶点(定义在GraphOps中)的⼊ 度。 val graph: Graph[(String, String), String] // Use the implicit GraphOps.inDegrees operator val inDegrees: Vertex DD[Int] = graph.inDegrees 区分核⼼图操作和GraphOps的原因是为了在将来⽀持不同的图表⽰。每个图表⽰都 必须提供核⼼操作的实现并重⽤很多定义在GraphOps中的有⽤操作。 操作⼀览 ⼀下是定义在Graph和GraphOps中 （为了简单起见，表现为图的成员）的功能的快 速浏览。注意，某些函数签名已经简化 （如默认参数和类型的限制已删除），⼀些更 ⾼级的功能已经被删除，所以请参阅API⽂档了解官⽅的操作列表。 /** Summary of the functionality in the property graph */ class Graph[VD, ED] { // Information about the Graph ================================ val numEdges: Long val numVertices: Long val inDegrees: Vertex DD[Int] val outDegrees: Vertex DD[Int] val degrees: Vertex DD[Int] // Views of the graph as collections ========================== val vertices: Vertex DD[VD] val edges: Edge DD[ED] val triplets: DD[EdgeTriplet[VD, ED]] // Functions for caching graphs =============================== def persist(newLevel: StorageLevel = StorageLevel.MEMO Y_ONLY): def cache(): Graph[VD, ED] def unpersistVertices(blocking: Boolean = true): Graph[VD, ED] // Change the partitioning heuristic ========================= def partitionBy(partitionStrategy : PartitionStrategy): Graph[VD, // Transform vertex and edge attributes ======================= def mapVertices[VD2](map: (VertexID, VD) = VD2): Graph[VD2, ED] def mapEdges[ED2](map: Edge[ED] = ED2): Graph[VD, ED2] def mapEdges[ED2](map: (PartitionID, Iterator[Edge[ED]]) = Ite def mapTriplets[ED2](map: EdgeTriplet[VD, ED] = ED2): Graph[VD, def mapTriplets[ED2](map: (PartitionID, Iterator[EdgeTriplet[VD, : Graph[VD, ED2] // Modify the graph structure ================================= def reverse: Graph[VD, ED]