/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.graphx
import scala.language.implicitConversions
import scala.reflect.ClassTag
import org.apache.spark.graphx.impl._
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
/**
* 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.
*
* @note [[GraphOps]] contains additional convenience operations and graph algorithms.
*
* @tparam VD the vertex attribute type
* @tparam ED the edge attribute type
*/
abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializable {
/**
* An RDD containing the vertices and their associated attributes.
*
* @note vertex ids are unique.
* @return an RDD containing the vertices in this graph
*/
@transient val vertices: VertexRDD[VD]
/**
* 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.
*
* @return an RDD containing the edges in this graph
*
* @see [[Edge]] for the edge type.
* @see [[triplets]] to get an RDD which contains all the edges
* along with their vertex data.
*
*/
@transient val edges: EdgeRDD[ED, VD]
/**
* 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.
*
* @return an RDD containing edge triplets
*
* @example 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
* }}}
*/
@transient val triplets: RDD[EdgeTriplet[VD, ED]]
/**
* Caches the vertices and edges associated with this graph at the specified storage level,
* ignoring any target storage levels previously set.
*
* @param newLevel the level at which to cache the graph.
*
* @return A reference to this graph for convenience.
*/
def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED]
/**
* 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.
*/
def cache(): Graph[VD, ED]
/**
* 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.
*/
def unpersistVertices(blocking: Boolean = true): Graph[VD, ED]
/**
* Repartitions the edges in the graph according to `partitionStrategy`.
*
* @param partitionStrategy the partitioning strategy to use when partitioning the edges
* in the graph.
*/
def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED]
/**
* Repartitions the edges in the graph according to `partitionStrategy`.
*
* @param partitionStrategy the partitioning strategy to use when partitioning the edges
* in the graph.
* @param numPartitions the number of edge partitions in the new graph.
*/
def partitionBy(partitionStrategy: PartitionStrategy, numPartitions: Int): Graph[VD, ED]
/**
* Transforms each vertex attribute in the graph using the map function.
*
* @note The new graph has the same structure. As a consequence the underlying index structures
* can be reused.
*
* @param map the function from a vertex object to a new vertex value
*
* @tparam VD2 the new vertex data type
*
* @example 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)
* }}}
*
*/
def mapVertices[VD2: ClassTag](map: (VertexId, VD) => VD2)
(implicit eq: VD =:= VD2 = null): Graph[VD2, ED]
/**
* 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`.
*
* @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.
*
* @param map the function from an edge object to a new edge value.
*
* @tparam ED2 the new edge data type
*
* @example This function might be used to initialize edge
* attributes.
*
*/
def mapEdges[ED2: ClassTag](map: Edge[ED] => ED2): Graph[VD, ED2] = {
mapEdges((pid, iter) => iter.map(map))
}
/**
* 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`.
*
* @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.
*
* @param 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
*
* @tparam ED2 the new edge data type
*
*/
def mapEdges[ED2: ClassTag](map: (PartitionID, Iterator[Edge[ED]]) => Iterator[ED2])
: Graph[VD, ED2]
/**
* 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.
*
* @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.
*
* @param map the function from an edge object to a new edge value.
*
* @tparam ED2 the new edge data type
*
* @example 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)
* }}}
*
*/
def mapTriplets[ED2: ClassTag](map: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2] = {
mapTriplets((pid, iter) => iter.map(map))
}
/**
* 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.
*
* @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.
*
* @param map the iterator transform
*
* @tparam ED2 the new edge data type
*
*/
def mapTriplets[ED2: ClassTag](map: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) => Iterator[ED2])
: Graph[VD, ED2]
/**
* 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.
*/
def reverse: Graph[VD, ED]
/**
* 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)}
* }}}
*
* @param 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.
*
* @param vpred the vertex predicate, which takes a vertex object and
* evaluates to true if the vertex is to be included in the subgraph
*
* @return the subgraph containing only the vertices and edges that
* satisfy the predicates
*/
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 that are also in `other`, but keeps the
* attributes from this graph.
* @param other the graph to project this graph onto
* @return a graph with vertices and edges that exist in both the current graph and `other`,
* with vertex and edge data from the current graph
*/
def mask[VD2: ClassTag, ED2: ClassTag](other: Graph[VD2, ED2]): Graph[VD, ED]
/**
* Merges multiple edges between two vertices into a single edge. For correct results, the graph
* must have been partitioned using [[partitionBy]].
*
* @param merge the user-supplied commutative associative function to merge edge attributes
* for duplicate edges.
*
* @return The resulting graph with a single edge for each (source, dest) vertex pair.
*/
def groupEdges(merge: (ED, ED) => ED): Graph[VD, ED]
/**
* 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.
*
* @tparam A the type of "message" to be sent to each vertex
*
* @param mapFunc the user defined map function which returns 0 or
* more messages to neighboring vertices
*
* @param reduceFunc the user defined reduce function which should
* be commutative and associative and is used to combine the output
* of the map phase
*
* @param 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.
*
* @example 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.
*
*/
def mapReduceTriplets[A: ClassTag](
mapFunc: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)],
reduceFunc: (A, A) => A,
activeSetOpt: Option[(VertexRDD[_], EdgeDirection)] = None)
: VertexRDD[A]
/**
* 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`.
*
* @tparam U the type of entry in the table of updates
* @tparam VD2 the new vertex value type
*
* @param other the table to join with the vertices in the graph.
* The table should contain at most one entry for each vertex.
* @param 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 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)
* }
* }}}
*/
def outerJoinVertices[U: ClassTag, VD2: ClassTag](other: RDD[(VertexId, U)])
(mapFunc: (VertexId, VD, Option[U]) => VD2)(implicit eq: VD =:= VD2 = null)
: Graph[VD2, ED]
/**
* The associated [[GraphOps]] object.
*/
// Save a copy of the GraphOps object so there is always one unique GraphOps object
// for a given Graph object, and thus the lazy vals in GraphOps would work as intended.
val ops = new GraphOps(this)
} // end of Graph
/**
* The Graph object contains a collection of routines used to construct graphs from RDDs.
*/
object Graph {
/**
* Construct a graph from a collection of edges encoded as vertex id pairs.
*
* @param rawEdges a collection of edges in (src, dst) form
* @param defaultValue the vertex attributes with which to create vertices referenced by the edges
* @param uniqueEdges if multiple identical edges are found they are combined and the edge
* attribute is set to the sum. Otherwise duplicate edges are treated as separate. To enable
* `uniqueEdges`, a [[PartitionStrategy]] must be provided.
* @param edgeStorageLevel the desired storage level at which to cache the edges if necessary
* @param vertexStorageLevel the desired storage level at which to cache the vertices if necessary
*
* @return a graph with edge attributes containing either the count of duplicate edges or 1
* (if `uniqueEdges` is `None`) and vertex attributes containing the total degree of each vertex.
*/
def fromEdgeTuples[VD: ClassTag](
rawEdges: RDD[(VertexId, VertexId)],
defaultValue: VD,
uniqueEdges: Option[PartitionStrategy] = None,
edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, Int] =
{
val edges = rawEdges.map(p => Edge(p._1, p._2, 1))
val graph = GraphImpl(edges, defaultValue, edgeStorageLevel, vertexStorageLevel)
uniqueEdges match {
case Some(p) => graph.partitionBy(p).groupEdges((a, b) => a + b)
case None => graph
}
}
/**
* Construct a graph from a collection of edges.
*
* @param edges the RDD containing the set of edges in the graph
* @param defaultValue the default vertex attribute to use for each vertex
* @param edgeStorageLevel the desired storage level at which to cache the edges if necessary
* @param vertexStorageLevel the desired storage level at which to cache the vertices if necessary
*
* @return a graph with edge attributes described by `edges` and vertices
* given by all vertices in `edges` with value `defaultValue`
*/
def fromEdges[VD: ClassTag, ED: ClassTag](
edges: RDD[Edge[ED]],
defaultValue: VD,
edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED] = {
GraphImpl(edges, defaultValue, edgeStorageLevel, vertexStorageLevel)
}
/**
* Construct a graph from a collection of vertices and
* edges with attributes. Duplicate vertices are picked arbitrarily and
* vertices found in the edge collection but not in the input
* vertices are assigned the default attribute.
*
* @tparam VD the vertex attribute type
* @tparam ED the edge attribute type
* @param vertices the "set" of vertices and their attributes
* @param edges the collection of edges in the graph
* @param defaultVertexAttr the default vertex attribute to use for vertices that are
* mentioned in edges but not in vertices
* @param edgeStorageLevel the desired storage level at which to cache the edges if necessary
* @param vertexStorageLevel the desired storage level at which to cache the vertices if necessary
*/
def apply[VD: ClassTag, ED: ClassTag](
vertices: RDD[(VertexId, VD)],
edges: RDD[Edge[ED]],
defaultVertexAttr: VD = null.asInstanceOf[VD],
edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED] = {
GraphImpl(vertices, edges, defaultVertexAttr, edgeStorageLevel, vertexStorageLevel)
}
/**
* Implicitly extracts the [[GraphOps]] member from a graph.
*
* To improve modularity the Graph type only contains a small set of basic operations.
* All the convenience operations are defined in the [[GraphOps]] class which may be
* shared across multiple graph implementations.
*/
implicit def graphToGraphOps[VD: ClassTag, ED: ClassTag]
(g: Graph[VD, ED]): GraphOps[VD, ED] = g.ops
} // end of Graph object
时间： 2024-10-06 20:45:33