/* * 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
