org.apache.spark.graphx.impl

GraphImpl

class GraphImpl[VD, ED] extends Graph[VD, ED] with Serializable

An implementation of org.apache.spark.graphx.Graph to support computation on graphs.

Graphs are represented using two RDDs: vertices, which contains vertex attributes and the routing information for shipping vertex attributes to edge partitions, and replicatedVertexView, which contains edges and the vertex attributes mentioned by each edge.

Linear Supertypes
Graph[VD, ED], Serializable, Serializable, AnyRef, Any
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  2. Graph
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Instance Constructors

  1. new GraphImpl()(implicit arg0: ClassTag[VD], arg1: ClassTag[ED])

    Default constructor is provided to support serialization

    Default constructor is provided to support serialization

    Attributes
    protected
  2. new GraphImpl(vertices: VertexRDD[VD], replicatedVertexView: ReplicatedVertexView[VD, ED])(implicit arg0: ClassTag[VD], arg1: ClassTag[ED])

    Attributes
    protected

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def cache(): Graph[VD, ED]

    Caches the vertices and edges associated with this graph at the previously-specified target storage levels, which default to MEMORY_ONLY.

    Caches the vertices and edges associated with this graph at the previously-specified target storage levels, which default to MEMORY_ONLY. This is used to pin a graph in memory enabling multiple queries to reuse the same construction process.

    Definition Classes
    GraphImplGraph
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. val edges: EdgeRDD[ED, VD]

    An RDD containing the edges and their associated attributes.

    An RDD containing the edges and their associated attributes. The entries in the RDD contain just the source id and target id along with the edge data.

    returns

    an RDD containing the edges in this graph

    Definition Classes
    GraphImplGraph
    See also

    triplets to get an RDD which contains all the edges along with their vertex data.

    Edge for the edge type.

  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def groupEdges(merge: (ED, ED) ⇒ ED): Graph[VD, ED]

    Merges multiple edges between two vertices into a single edge.

    Merges multiple edges between two vertices into a single edge. For correct results, the graph must have been partitioned using partitionBy.

    merge

    the user-supplied commutative associative function to merge edge attributes for duplicate edges.

    returns

    The resulting graph with a single edge for each (source, dest) vertex pair.

    Definition Classes
    GraphImplGraph
  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  17. def mapEdges[ED2](f: (PartitionID, Iterator[Edge[ED]]) ⇒ Iterator[ED2])(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]

    Transforms each edge attribute using the map function, passing it a whole partition at a time.

    Transforms each edge attribute using the map function, passing it a whole partition at a time. The map function is given an iterator over edges within a logical partition as well as the partition's ID, and it should return a new iterator over the new values of each edge. The new iterator's elements must correspond one-to-one with the old iterator's elements. If adjacent vertex values are desired, use mapTriplets.

    ED2

    the new edge data type

    Definition Classes
    GraphImplGraph
    Note

    This does not change the structure of the graph or modify the values of this graph. As a consequence the underlying index structures can be reused.

  18. def mapEdges[ED2](map: (Edge[ED]) ⇒ ED2)(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]

    Transforms each edge attribute in the graph using the map function.

    Transforms each edge attribute in the graph using the map function. The map function is not passed the vertex value for the vertices adjacent to the edge. If vertex values are desired, use mapTriplets.

    ED2

    the new edge data type

    map

    the function from an edge object to a new edge value.

    Definition Classes
    Graph
    Example:
    1. This function might be used to initialize edge attributes.

    Note

    This graph is not changed and that the new graph has the same structure. As a consequence the underlying index structures can be reused.

  19. def mapReduceTriplets[A](mapFunc: (EdgeTriplet[VD, ED]) ⇒ Iterator[(VertexId, A)], reduceFunc: (A, A) ⇒ A, activeSetOpt: Option[(VertexRDD[_], EdgeDirection)] = None)(implicit arg0: ClassTag[A]): VertexRDD[A]

    Aggregates values from the neighboring edges and vertices of each vertex.

    Aggregates values from the neighboring edges and vertices of each vertex. The user supplied mapFunc function is invoked on each edge of the graph, generating 0 or more "messages" to be "sent" to either vertex in the edge. The reduceFunc is then used to combine the output of the map phase destined to each vertex.

    A

    the type of "message" to be sent to each vertex

    mapFunc

    the user defined map function which returns 0 or more messages to neighboring vertices

    reduceFunc

    the user defined reduce function which should be commutative and associative and is used to combine the output of the map phase

    activeSetOpt

    optionally, a set of "active" vertices and a direction of edges to consider when running mapFunc. If the direction is In, mapFunc will only be run on edges with destination in the active set. If the direction is Out, mapFunc will only be run on edges originating from vertices in the active set. If the direction is Either, mapFunc will be run on edges with *either* vertex in the active set . If the direction is Both, mapFunc will be run on edges with *both* vertices in the active set. The active set must have the same index as the graph's vertices.

    Definition Classes
    GraphImplGraph
    Example:
    1. We can use this function to compute the in-degree of each vertex

      val rawGraph: Graph[(),()] = Graph.textFile("twittergraph")
      val inDeg: RDD[(VertexId, Int)] =
        mapReduceTriplets[Int](et => Iterator((et.dst.id, 1)), _ + _)
    Note

    By expressing computation at the edge level we achieve maximum parallelism. This is one of the core functions in the Graph API in that enables neighborhood level computation. For example this function can be used to count neighbors satisfying a predicate or implement PageRank.

  20. def mapTriplets[ED2](f: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) ⇒ Iterator[ED2])(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]

    Transforms each edge attribute a partition at a time using the map function, passing it the adjacent vertex attributes as well.

    Transforms each edge attribute a partition at a time using the map function, passing it the adjacent vertex attributes as well. The map function is given an iterator over edge triplets within a logical partition and should yield a new iterator over the new values of each edge in the order in which they are provided. If adjacent vertex values are not required, consider using mapEdges instead.

    ED2

    the new edge data type

    Definition Classes
    GraphImplGraph
    Note

    This does not change the structure of the graph or modify the values of this graph. As a consequence the underlying index structures can be reused.

  21. def mapTriplets[ED2](map: (EdgeTriplet[VD, ED]) ⇒ ED2)(implicit arg0: ClassTag[ED2]): Graph[VD, ED2]

    Transforms each edge attribute using the map function, passing it the adjacent vertex attributes as well.

    Transforms each edge attribute using the map function, passing it the adjacent vertex attributes as well. If adjacent vertex values are not required, consider using mapEdges instead.

    ED2

    the new edge data type

    map

    the function from an edge object to a new edge value.

    Definition Classes
    Graph
    Example:
    1. This function might be used to initialize edge attributes based on the attributes associated with each vertex.

      val rawGraph: Graph[Int, Int] = someLoadFunction()
      val graph = rawGraph.mapTriplets[Int]( edge =>
        edge.src.data - edge.dst.data)
    Note

    This does not change the structure of the graph or modify the values of this graph. As a consequence the underlying index structures can be reused.

  22. def mapVertices[VD2](f: (VertexId, VD) ⇒ VD2)(implicit arg0: ClassTag[VD2], eq: =:=[VD, VD2] = null): Graph[VD2, ED]

    Transforms each vertex attribute in the graph using the map function.

    Transforms each vertex attribute in the graph using the map function.

    VD2

    the new vertex data type

    Definition Classes
    GraphImplGraph
    Example:
    1. We might use this operation to change the vertex values from one type to another to initialize an algorithm.

      val rawGraph: Graph[(), ()] = Graph.textFile("hdfs://file")
      val root = 42
      var bfsGraph = rawGraph.mapVertices[Int]((vid, data) => if (vid == root) 0 else Math.MaxValue)
    Note

    The new graph has the same structure. As a consequence the underlying index structures can be reused.

  23. def mask[VD2, ED2](other: Graph[VD2, ED2])(implicit arg0: ClassTag[VD2], arg1: ClassTag[ED2]): Graph[VD, ED]

    Restricts the graph to only the vertices and edges that are also in other, but keeps the attributes from this graph.

    Restricts the graph to only the vertices and edges that are also in other, but keeps the attributes from this graph.

    other

    the graph to project this graph onto

    returns

    a graph with vertices and edges that exist in both the current graph and other, with vertex and edge data from the current graph

    Definition Classes
    GraphImplGraph
  24. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  25. final def notify(): Unit

    Definition Classes
    AnyRef
  26. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  27. val ops: GraphOps[VD, ED]

    The associated GraphOps object.

    The associated GraphOps object.

    Definition Classes
    Graph
  28. def outerJoinVertices[U, VD2](other: RDD[(VertexId, U)])(updateF: (VertexId, VD, Option[U]) ⇒ VD2)(implicit arg0: ClassTag[U], arg1: ClassTag[VD2], eq: =:=[VD, VD2] = null): Graph[VD2, ED]

    Joins the vertices with entries in the table RDD and merges the results using mapFunc.

    Joins the vertices with entries in the table RDD and merges the results using mapFunc. The input table should contain at most one entry for each vertex. If no entry in other is provided for a particular vertex in the graph, the map function receives None.

    U

    the type of entry in the table of updates

    VD2

    the new vertex value type

    other

    the table to join with the vertices in the graph. The table should contain at most one entry for each vertex.

    Definition Classes
    GraphImplGraph
    Example:
    1. This function is used to update the vertices with new values based on external data. For example we could add the out-degree to each vertex record:

      val rawGraph: Graph[_, _] = Graph.textFile("webgraph")
      val outDeg: RDD[(VertexId, Int)] = rawGraph.outDegrees
      val graph = rawGraph.outerJoinVertices(outDeg) {
        (vid, data, optDeg) => optDeg.getOrElse(0)
      }
  29. def partitionBy(partitionStrategy: PartitionStrategy, numPartitions: Int): Graph[VD, ED]

    Repartitions the edges in the graph according to partitionStrategy.

    Repartitions the edges in the graph according to partitionStrategy.

    partitionStrategy

    the partitioning strategy to use when partitioning the edges in the graph.

    numPartitions

    the number of edge partitions in the new graph.

    Definition Classes
    GraphImplGraph
  30. def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED]

    Repartitions the edges in the graph according to partitionStrategy.

    Repartitions the edges in the graph according to partitionStrategy.

    partitionStrategy

    the partitioning strategy to use when partitioning the edges in the graph.

    Definition Classes
    GraphImplGraph
  31. def persist(newLevel: StorageLevel): Graph[VD, ED]

    Caches the vertices and edges associated with this graph at the specified storage level, ignoring any target storage levels previously set.

    Caches the vertices and edges associated with this graph at the specified storage level, ignoring any target storage levels previously set.

    newLevel

    the level at which to cache the graph.

    returns

    A reference to this graph for convenience.

    Definition Classes
    GraphImplGraph
  32. val replicatedVertexView: ReplicatedVertexView[VD, ED]

  33. def reverse: Graph[VD, ED]

    Reverses all edges in the graph.

    Reverses all edges in the graph. If this graph contains an edge from a to b then the returned graph contains an edge from b to a.

    Definition Classes
    GraphImplGraph
  34. def subgraph(epred: (EdgeTriplet[VD, ED]) ⇒ Boolean = x => true, vpred: (VertexId, VD) ⇒ Boolean = (a, b) => true): Graph[VD, ED]

    Restricts the graph to only the vertices and edges satisfying the predicates.

    Restricts the graph to only the vertices and edges satisfying the predicates. The resulting subgraph satisifies

    V' = {v : for all v in V where vpred(v)}
    E' = {(u,v): for all (u,v) in E where epred((u,v)) && vpred(u) && vpred(v)}
    epred

    the edge predicate, which takes a triplet and evaluates to true if the edge is to remain in the subgraph. Note that only edges where both vertices satisfy the vertex predicate are considered.

    vpred

    the vertex predicate, which takes a vertex object and evaluates to true if the vertex is to be included in the subgraph

    returns

    the subgraph containing only the vertices and edges that satisfy the predicates

    Definition Classes
    GraphImplGraph
  35. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  36. def toString(): String

    Definition Classes
    AnyRef → Any
  37. lazy val triplets: RDD[EdgeTriplet[VD, ED]]

    Return a RDD that brings edges together with their source and destination vertices.

    Return a RDD that brings edges together with their source and destination vertices.

    Definition Classes
    GraphImplGraph
  38. def unpersistVertices(blocking: Boolean = true): Graph[VD, ED]

    Uncaches only the vertices of this graph, leaving the edges alone.

    Uncaches only the vertices of this graph, leaving the edges alone. This is useful in iterative algorithms that modify the vertex attributes but reuse the edges. This method can be used to uncache the vertex attributes of previous iterations once they are no longer needed, improving GC performance.

    Definition Classes
    GraphImplGraph
  39. val vertices: VertexRDD[VD]

    An RDD containing the vertices and their associated attributes.

    An RDD containing the vertices and their associated attributes.

    returns

    an RDD containing the vertices in this graph

    Definition Classes
    GraphImplGraph
    Note

    vertex ids are unique.

  40. final def wait(): Unit

    Definition Classes
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    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit

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  42. final def wait(arg0: Long): Unit

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Inherited from Graph[VD, ED]

Inherited from Serializable

Inherited from Serializable

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