Packages

abstract class Graph[VD, ED] extends Serializable

The Graph abstractly represents a graph with arbitrary objects associated with vertices and edges. The graph provides basic operations to access and manipulate the data associated with vertices and edges as well as the underlying structure. Like Spark RDDs, the graph is a functional data-structure in which mutating operations return new graphs.

VD

the vertex attribute type

ED

the edge attribute type

Source
Graph.scala
Note

GraphOps contains additional convenience operations and graph algorithms.

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GraphImpl
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Instance Constructors

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

    Attributes
    protected

Abstract Value Members

  1. abstract 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.

  2. abstract def checkpoint(): Unit

    Mark this Graph for checkpointing.

    Mark this Graph for checkpointing. It will be saved to a file inside the checkpoint directory set with SparkContext.setCheckpointDir() and all references to its parent RDDs will be removed. It is strongly recommended that this Graph is persisted in memory, otherwise saving it on a file will require recomputation.

  3. abstract val edges: EdgeRDD[ED]

    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

    See also

    Edge for the edge type.

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

  4. abstract def getCheckpointFiles: Seq[String]

    Gets the name of the files to which this Graph was checkpointed.

    Gets the name of the files to which this Graph was checkpointed. (The vertices RDD and edges RDD are checkpointed separately.)

  5. abstract 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.

  6. abstract def isCheckpointed: Boolean

    Return whether this Graph has been checkpointed or not.

    Return whether this Graph has been checkpointed or not. This returns true iff both the vertices RDD and edges RDD have been checkpointed.

  7. abstract def mapEdges[ED2](map: (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

    map

    a function that takes a partition id and an iterator over all the edges in the partition, and must return an iterator over the new values for each edge in the order of the input iterator

    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.

  8. abstract def mapTriplets[ED2](map: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) ⇒ Iterator[ED2], tripletFields: TripletFields)(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

    map

    the iterator transform

    tripletFields

    which fields should be included in the edge triplet passed to the map function. If not all fields are needed, specifying this can improve performance.

    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.

  9. abstract def mapVertices[VD2](map: (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

    map

    the function from a vertex object to a new vertex value

    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.

  10. abstract 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

  11. abstract def outerJoinVertices[U, VD2](other: RDD[(VertexId, U)])(mapFunc: (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.

    mapFunc

    the function used to compute the new vertex values. The map function is invoked for all vertices, even those that do not have a corresponding entry in the table.

    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)
}
  12. abstract 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.

  13. abstract 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.

  14. abstract def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): 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.

  15. abstract 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.

  16. abstract def subgraph(epred: (EdgeTriplet[VD, ED]) ⇒ Boolean = x => true, vpred: (VertexId, VD) ⇒ Boolean = (v, d) => 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 satisfies

    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

  17. abstract val triplets: RDD[EdgeTriplet[VD, ED]]

    An RDD containing the edge triplets, which are edges along with the vertex data associated with the adjacent vertices.

    An RDD containing the edge triplets, which are edges along with the vertex data associated with the adjacent vertices. The caller should use edges if the vertex data are not needed, i.e. if only the edge data and adjacent vertex ids are needed.

    returns

    an RDD containing edge triplets

    Example:

    1. This operation might be used to evaluate a graph coloring where we would like to check that both vertices are a different color.

      type Color = Int
val graph: Graph[Color, Int] = GraphLoader.edgeListFile("hdfs://file.tsv")
val numInvalid = graph.triplets.map(e => if (e.src.data == e.dst.data) 1 else 0).sum
  18. abstract def unpersist(blocking: Boolean = false): Graph[VD, ED]

    Uncaches both vertices and edges of this graph.

    Uncaches both vertices and edges of this graph. This is useful in iterative algorithms that build a new graph in each iteration.

    blocking

    Whether to block until all data is unpersisted (default: false)

  19. abstract def unpersistVertices(blocking: Boolean = false): 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.

    blocking

    Whether to block until all data is unpersisted (default: false)

  20. abstract 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

    Note

    vertex ids are unique.

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. def aggregateMessages[A](sendMsg: (EdgeContext[VD, ED, A]) ⇒ Unit, mergeMsg: (A, A) ⇒ A, tripletFields: TripletFields = TripletFields.All)(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 sendMsg function is invoked on each edge of the graph, generating 0 or more messages to be sent to either vertex in the edge. The mergeMsg function is then used to combine all messages destined to the same vertex.

    A

    the type of message to be sent to each vertex

    sendMsg

    runs on each edge, sending messages to neighboring vertices using the EdgeContext.

    mergeMsg

    used to combine messages from sendMsg destined to the same vertex. This combiner should be commutative and associative.

    tripletFields

    which fields should be included in the EdgeContext passed to the sendMsg function. If not all fields are needed, specifying this can improve performance.

    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)] =
  rawGraph.aggregateMessages[Int](ctx => ctx.sendToDst(1), _ + _)
    Note

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

  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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    @throws( ... ) @native()
  7. final def eq(arg0: AnyRef): Boolean
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  9. def finalize(): Unit
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  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. 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.

    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.

  14. def mapTriplets[ED2](map: (EdgeTriplet[VD, ED]) ⇒ ED2, tripletFields: TripletFields)(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.

    tripletFields

    which fields should be included in the edge triplet passed to the map function. If not all fields are needed, specifying this can improve performance.

    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.

  15. 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.

    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.

  16. final def ne(arg0: AnyRef): Boolean
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  18. final def notifyAll(): Unit
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  19. val ops: GraphOps[VD, ED]

    The associated GraphOps object.

  20. final def synchronized[T0](arg0: ⇒ T0): T0
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  23. final def wait(arg0: Long, arg1: Int): Unit
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  24. final def wait(arg0: Long): Unit
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