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 datastructure 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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new
Graph()(implicit arg0: ClassTag[VD], arg1: ClassTag[ED])
 Attributes
 protected
Abstract Value Members

abstract
def
cache(): Graph[VD, ED]
Caches the vertices and edges associated with this graph at the previouslyspecified target storage levels, which default to
MEMORY_ONLY
.Caches the vertices and edges associated with this graph at the previouslyspecified 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. 
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.

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.

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.)

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 usersupplied commutative associative function to merge edge attributes for duplicate edges.
 returns
The resulting graph with a single edge for each (source, dest) vertex pair.

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.

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 onetoone 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.

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.

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
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.
Example: 
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

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 usingmapFunc
.Joins the vertices with entries in the
table
RDD and merges the results usingmapFunc
. The input table should contain at most one entry for each vertex. If no entry inother
is provided for a particular vertex in the graph, the map function receivesNone
. 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.
This function is used to update the vertices with new values based on external data. For example we could add the outdegree 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) }
Example: 
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.

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.

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.

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.

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

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
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
Example: 
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)

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)

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

final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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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 usersupplied
sendMsg
function is invoked on each edge of the graph, generating 0 or more messages to be sent to either vertex in the edge. ThemergeMsg
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.
We can use this function to compute the indegree 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.
Example: 
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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.
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.
Example: 
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.
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.
Example: 
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.
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.
Example: 
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val
ops: GraphOps[VD, ED]
The associated GraphOps object.

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