diff options
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/graphx-programming-guide.md | 254 |
1 files changed, 166 insertions, 88 deletions
diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md index 5641f9f137..3dfed7bea9 100644 --- a/docs/graphx-programming-guide.md +++ b/docs/graphx-programming-guide.md @@ -108,7 +108,7 @@ with user defined objects attached to each vertex and edge. A directed multigra graph with potentially multiple parallel edges sharing the same source and destination vertex. The ability to support parallel edges simplifies modeling scenarios where there can be multiple relationships (e.g., co-worker and friend) between the same vertices. Each vertex is keyed by a -*unique* 64-bit long identifier (`VertexId`). GraphX does not impose any ordering constraints on +*unique* 64-bit long identifier (`VertexID`). GraphX does not impose any ordering constraints on the vertex identifiers. Similarly, edges have corresponding source and destination vertex identifiers. @@ -149,12 +149,12 @@ class Graph[VD, ED] { } {% endhighlight %} -The classes `VertexRDD[VD]` and `EdgeRDD[ED]` extend and are optimized versions of `RDD[(VertexId, +The classes `VertexRDD[VD]` and `EdgeRDD[ED]` extend and are optimized versions of `RDD[(VertexID, VD)]` and `RDD[Edge[ED]]` respectively. Both `VertexRDD[VD]` and `EdgeRDD[ED]` provide additional functionality built around graph computation and leverage internal optimizations. We discuss the `VertexRDD` and `EdgeRDD` API in greater detail in the section on [vertex and edge RDDs](#vertex_and_edge_rdds) but for now they can be thought of as simply RDDs of the form: -`RDD[(VertexId, VD)]` and `RDD[Edge[ED]]`. +`RDD[(VertexID, VD)]` and `RDD[Edge[ED]]`. ### Example Property Graph @@ -186,7 +186,7 @@ code constructs a graph from a collection of RDDs: // Assume the SparkContext has already been constructed val sc: SparkContext // Create an RDD for the vertices -val users: RDD[(VertexID, (String, String))] = +val users: RDD[(VertexId, (String, String))] = sc.parallelize(Array((3L, ("rxin", "student")), (7L, ("jgonzal", "postdoc")), (5L, ("franklin", "prof")), (2L, ("istoica", "prof")))) // Create an RDD for edges @@ -201,7 +201,7 @@ val graph = Graph(users, relationships, defaultUser) In the above example we make use of the [`Edge`][Edge] case class. Edges have a `srcId` and a `dstId` corresponding to the source and destination vertex identifiers. In addition, the `Edge` -class contains the `attr` member which contains the edge property. +class has an `attr` member which stores the edge property. [Edge]: api/graphx/index.html#org.apache.spark.graphx.Edge @@ -217,7 +217,7 @@ graph.edges.filter(e => e.srcId > e.dstId).count {% endhighlight %} > Note that `graph.vertices` returns an `VertexRDD[(String, String)]` which extends -> `RDD[(VertexId, (String, String))]` and so we use the scala `case` expression to deconstruct the +> `RDD[(VertexID, (String, String))]` and so we use the scala `case` expression to deconstruct the > tuple. On the other hand, `graph.edges` returns an `EdgeRDD` containing `Edge[String]` objects. > We could have also used the case class type constructor as in the following: > {% highlight scala %} @@ -284,6 +284,75 @@ able to support different graph representations in the future. Each graph repre provide implementations of the core operations and reuse many of the useful operations defined in [`GraphOps`][GraphOps]. +### Summary List of Operators +The following is a quick summary of the functionality defined in both [`Graph`][Graph] and +[`GraphOps`][GraphOps] but presented as members of Graph for simplicity. Note that some function +signatures have been simplified (e.g., default arguments and type constraints removed) and some more +advanced functionality has been removed so please consult the API docs for the official list of +operations. + +{% highlight scala %} +/** Summary of the functionality in the property graph */ +class Graph[VD, ED] { + // Information about the Graph =================================================================== + val numEdges: Long + val numVertices: Long + val inDegrees: VertexRDD[Int] + val outDegrees: VertexRDD[Int] + val degrees: VertexRDD[Int] + // Views of the graph as collections ============================================================= + val vertices: VertexRDD[VD] + val edges: EdgeRDD[ED] + val triplets: RDD[EdgeTriplet[VD, ED]] + // Functions for caching graphs ================================================================== + def persist(newLevel: StorageLevel = StorageLevel.MEMORY_ONLY): Graph[VD, ED] + def cache(): Graph[VD, ED] + def unpersistVertices(blocking: Boolean = true): Graph[VD, ED] + // Change the partitioning heuristic ============================================================ + def partitionBy(partitionStrategy: PartitionStrategy): Graph[VD, ED] + // Transform vertex and edge attributes ========================================================== + def mapVertices[VD2](map: (VertexID, VD) => VD2): Graph[VD2, ED] + def mapEdges[ED2](map: Edge[ED] => ED2): Graph[VD, ED2] + def mapEdges[ED2](map: (PartitionID, Iterator[Edge[ED]]) => Iterator[ED2]): Graph[VD, ED2] + def mapTriplets[ED2](map: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2] + def mapTriplets[ED2](map: (PartitionID, Iterator[EdgeTriplet[VD, ED]]) => Iterator[ED2]) + : Graph[VD, ED2] + // Modify the graph structure ==================================================================== + def reverse: Graph[VD, ED] + def subgraph( + epred: EdgeTriplet[VD,ED] => Boolean = (x => true), + vpred: (VertexID, VD) => Boolean = ((v, d) => true)) + : Graph[VD, ED] + def mask[VD2, ED2](other: Graph[VD2, ED2]): Graph[VD, ED] + def groupEdges(merge: (ED, ED) => ED): Graph[VD, ED] + // Join RDDs with the graph ====================================================================== + def joinVertices[U](table: RDD[(VertexID, U)])(mapFunc: (VertexID, VD, U) => VD): Graph[VD, ED] + def outerJoinVertices[U, VD2](other: RDD[(VertexID, U)]) + (mapFunc: (VertexID, VD, Option[U]) => VD2) + : Graph[VD2, ED] + // Aggregate information about adjacent triplets ================================================= + def collectNeighborIds(edgeDirection: EdgeDirection): VertexRDD[Array[VertexID]] + def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[Array[(VertexID, VD)]] + def mapReduceTriplets[A: ClassTag]( + mapFunc: EdgeTriplet[VD, ED] => Iterator[(VertexID, A)], + reduceFunc: (A, A) => A, + activeSetOpt: Option[(VertexRDD[_], EdgeDirection)] = None) + : VertexRDD[A] + // Iterative graph-parallel computation ========================================================== + def pregel[A](initialMsg: A, maxIterations: Int, activeDirection: EdgeDirection)( + vprog: (VertexID, VD, A) => VD, + sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexID,A)], + mergeMsg: (A, A) => A) + : Graph[VD, ED] + // Basic graph algorithms ======================================================================== + def pageRank(tol: Double, resetProb: Double = 0.15): Graph[Double, Double] + def connectedComponents(): Graph[VertexID, ED] + def triangleCount(): Graph[Int, ED] + def stronglyConnectedComponents(numIter: Int): Graph[VertexID, ED] +} +{% endhighlight %} + + ## Property Operators In direct analogy to the RDD `map` operator, the property @@ -291,7 +360,7 @@ graph contains the following: {% highlight scala %} class Graph[VD, ED] { - def mapVertices[VD2](map: (VertexID, VD) => VD2): Graph[VD2, ED] + def mapVertices[VD2](map: (VertexId, VD) => VD2): Graph[VD2, ED] def mapEdges[ED2](map: Edge[ED] => ED2): Graph[VD, ED2] def mapTriplets[ED2](map: EdgeTriplet[VD, ED] => ED2): Graph[VD, ED2] } @@ -313,7 +382,7 @@ val newGraph = Graph(newVertices, graph.edges) val newGraph = graph.mapVertices((id, attr) => mapUdf(id, attr)) {% endhighlight %} -[Graph.mapVertices]: api/graphx/index.html#org.apache.spark.graphx.Graph@mapVertices[VD2]((VertexID,VD)⇒VD2)(ClassTag[VD2]):Graph[VD2,ED] +[Graph.mapVertices]: api/graphx/index.html#org.apache.spark.graphx.Graph@mapVertices[VD2]((VertexId,VD)⇒VD2)(ClassTag[VD2]):Graph[VD2,ED] These operators are often used to initialize the graph for a particular computation or project away unnecessary properties. For example, given a graph with the out-degrees as the vertex properties @@ -339,7 +408,7 @@ add more in the future. The following is a list of the basic structural operato class Graph[VD, ED] { def reverse: Graph[VD, ED] def subgraph(epred: EdgeTriplet[VD,ED] => Boolean, - vpred: (VertexID, VD) => Boolean): Graph[VD, ED] + vpred: (VertexId, VD) => Boolean): Graph[VD, ED] def mask[VD2, ED2](other: Graph[VD2, ED2]): Graph[VD, ED] def groupEdges(merge: (ED, ED) => ED): Graph[VD,ED] } @@ -358,11 +427,11 @@ satisfy the edge predicate *and connect vertices that satisfy the vertex predica operator can be used in number of situations to restrict the graph to the vertices and edges of interest or eliminate broken links. For example in the following code we remove broken links: -[Graph.subgraph]: api/graphx/index.html#org.apache.spark.graphx.Graph@subgraph((EdgeTriplet[VD,ED])⇒Boolean,(VertexID,VD)⇒Boolean):Graph[VD,ED] +[Graph.subgraph]: api/graphx/index.html#org.apache.spark.graphx.Graph@subgraph((EdgeTriplet[VD,ED])⇒Boolean,(VertexId,VD)⇒Boolean):Graph[VD,ED] {% highlight scala %} // Create an RDD for the vertices -val users: RDD[(VertexID, (String, String))] = +val users: RDD[(VertexId, (String, String))] = sc.parallelize(Array((3L, ("rxin", "student")), (7L, ("jgonzal", "postdoc")), (5L, ("franklin", "prof")), (2L, ("istoica", "prof")), (4L, ("peter", "student")))) @@ -425,9 +494,9 @@ using the *join* operators. Below we list the key join operators: {% highlight scala %} class Graph[VD, ED] { - def joinVertices[U](table: RDD[(VertexID, U)])(map: (VertexID, VD, U) => VD) + def joinVertices[U](table: RDD[(VertexId, U)])(map: (VertexId, VD, U) => VD) : Graph[VD, ED] - def outerJoinVertices[U, VD2](table: RDD[(VertexID, U)])(map: (VertexID, VD, Option[U]) => VD2) + def outerJoinVertices[U, VD2](table: RDD[(VertexId, U)])(map: (VertexId, VD, Option[U]) => VD2) : Graph[VD2, ED] } {% endhighlight %} @@ -437,13 +506,13 @@ returns a new graph with the vertex properties obtained by applying the user def to the result of the joined vertices. Vertices without a matching value in the RDD retain their original value. -[GraphOps.joinVertices]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@joinVertices[U](RDD[(VertexID,U)])((VertexID,VD,U)⇒VD)(ClassTag[U]):Graph[VD,ED] +[GraphOps.joinVertices]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@joinVertices[U](RDD[(VertexId,U)])((VertexId,VD,U)⇒VD)(ClassTag[U]):Graph[VD,ED] > Note that if the RDD contains more than one value for a given vertex only one will be used. It > is therefore recommended that the input RDD be first made unique using the following which will > also *pre-index* the resulting values to substantially accelerate the subsequent join. > {% highlight scala %} -val nonUniqueCosts: RDD[(VertexId, Double)] +val nonUniqueCosts: RDD[(VertexID, Double)] val uniqueCosts: VertexRDD[Double] = graph.vertices.aggregateUsingIndex(nonUnique, (a,b) => a + b) val joinedGraph = graph.joinVertices(uniqueCosts)( @@ -456,7 +525,7 @@ property type. Because not all vertices may have a matching value in the input function takes an `Option` type. For example, we can setup a graph for PageRank by initializing vertex properties with their `outDegree`. -[Graph.outerJoinVertices]: api/graphx/index.html#org.apache.spark.graphx.Graph@outerJoinVertices[U,VD2](RDD[(VertexID,U)])((VertexID,VD,Option[U])⇒VD2)(ClassTag[U],ClassTag[VD2]):Graph[VD2,ED] +[Graph.outerJoinVertices]: api/graphx/index.html#org.apache.spark.graphx.Graph@outerJoinVertices[U,VD2](RDD[(VertexId,U)])((VertexId,VD,Option[U])⇒VD2)(ClassTag[U],ClassTag[VD2]):Graph[VD2,ED] {% highlight scala %} @@ -475,7 +544,7 @@ val degreeGraph = graph.outerJoinVertices(outDegrees) { (id, oldAttr, outDegOpt) > provide type annotation for the user defined function: > {% highlight scala %} val joinedGraph = graph.joinVertices(uniqueCosts, - (id: VertexId, oldCost: Double, extraCost: Double) => oldCost + extraCost) + (id: VertexID, oldCost: Double, extraCost: Double) => oldCost + extraCost) {% endhighlight %} @@ -490,7 +559,7 @@ PageRank Value, shortest path to the source, and smallest reachable vertex id). ### Map Reduce Triplets (mapReduceTriplets) <a name="mrTriplets"></a> -[Graph.mapReduceTriplets]: api/graphx/index.html#org.apache.spark.graphx.Graph@mapReduceTriplets[A](mapFunc:org.apache.spark.graphx.EdgeTriplet[VD,ED]=>Iterator[(org.apache.spark.graphx.VertexID,A)],reduceFunc:(A,A)=>A,activeSetOpt:Option[(org.apache.spark.graphx.VertexRDD[_],org.apache.spark.graphx.EdgeDirection)])(implicitevidence$10:scala.reflect.ClassTag[A]):org.apache.spark.graphx.VertexRDD[A] +[Graph.mapReduceTriplets]: api/graphx/index.html#org.apache.spark.graphx.Graph@mapReduceTriplets[A](mapFunc:org.apache.spark.graphx.EdgeTriplet[VD,ED]=>Iterator[(org.apache.spark.graphx.VertexId,A)],reduceFunc:(A,A)=>A,activeSetOpt:Option[(org.apache.spark.graphx.VertexRDD[_],org.apache.spark.graphx.EdgeDirection)])(implicitevidence$10:scala.reflect.ClassTag[A]):org.apache.spark.graphx.VertexRDD[A] The core (heavily optimized) aggregation primitive in GraphX is the [`mapReduceTriplets`][Graph.mapReduceTriplets] operator: @@ -498,7 +567,7 @@ The core (heavily optimized) aggregation primitive in GraphX is the {% highlight scala %} class Graph[VD, ED] { def mapReduceTriplets[A]( - map: EdgeTriplet[VD, ED] => Iterator[(VertexID, A)], + map: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)], reduce: (A, A) => A) : VertexRDD[A] } @@ -513,26 +582,26 @@ containing the aggregate message (of type `A`) destined to each vertex. Vertice receive a message are not included in the returned `VertexRDD`. <blockquote> -<p> -Note that <code>mapReduceTriplets</code> takes an additional optional <code>activeSet</code> -(see API docs) which restricts the map phase to edges adjacent to the vertices in the provided -<code>VertexRDD</code>: -</p> + +<p>Note that <code>mapReduceTriplets</code> takes an additional optional <code>activeSet</code> +(not shown above see API docs for details) which restricts the map phase to edges adjacent to the +vertices in the provided <code>VertexRDD</code>: </p> + {% highlight scala %} activeSetOpt: Option[(VertexRDD[_], EdgeDirection)] = None {% endhighlight %} -<p> -The EdgeDirection specifies which edges adjacent to the vertex set are included in the map phase. If -the direction is <code>In</code>, <code>mapFunc</code> will only be run only on edges with -destination in the active set. If the direction is <code>Out</code>, <code>mapFunc</code> will only -be run only on edges originating from vertices in the active set. If the direction is -<code>Either</code>, <code>mapFunc</code> will be run only on edges with <i>either</i> vertex in the -active set. If the direction is <code>Both</code>, <code>mapFunc</code> will be run only on edges -with both vertices in the active set. The active set must be derived from the set of vertices in -the graph. Restricting computation to triplets adjacent to a subset of the vertices is often -necessary in incremental iterative computation and is a key part of the GraphX implementation of -Pregel. -</p> + +<p>The EdgeDirection specifies which edges adjacent to the vertex set are included in the map +phase. If the direction is <code>In</code>, then the user defined <code>map</code> function will +only be run only on edges with the destination vertex in the active set. If the direction is +<code>Out</code>, then the <code>map</code> function will only be run only on edges originating from +vertices in the active set. If the direction is <code>Either</code>, then the <code>map</code> +function will be run only on edges with <i>either</i> vertex in the active set. If the direction is +<code>Both</code>, then the <code>map</code> function will be run only on edges with both vertices +in the active set. The active set must be derived from the set of vertices in the graph. +Restricting computation to triplets adjacent to a subset of the vertices is often necessary in +incremental iterative computation and is a key part of the GraphX implementation of Pregel. </p> + </blockquote> In the following example we use the `mapReduceTriplets` operator to compute the average age of the @@ -565,8 +634,8 @@ val avgAgeOfOlderFollowers: VertexRDD[Double] = avgAgeOfOlderFollowers.collect.foreach(println(_)) {% endhighlight %} -> Note that the `mapReduceTriplets` operation performs optimally when the messages (and their sums) -> are constant sized (e.g., floats and addition instead of lists and concatenation). More +> Note that the `mapReduceTriplets` operation performs optimally when the messages (and the sums of +> messages) are constant sized (e.g., floats and addition instead of lists and concatenation). More > precisely, the result of `mapReduceTriplets` should ideally be sub-linear in the degree of each > vertex. @@ -580,13 +649,13 @@ compute the max in, out, and total degrees: {% highlight scala %} // Define a reduce operation to compute the highest degree vertex -def max(a: (VertexID, Int), b: (VertexID, Int)): (VertexID, Int) = { +def max(a: (VertexId, Int), b: (VertexId, Int)): (VertexId, Int) = { if (a._2 > b._2) a else b } // Compute the max degrees -val maxInDegree: (VertexID, Int) = graph.inDegrees.reduce(max) -val maxOutDegree: (VertexID, Int) = graph.outDegrees.reduce(max) -val maxDegrees: (VertexID, Int) = graph.degrees.reduce(max) +val maxInDegree: (VertexId, Int) = graph.inDegrees.reduce(max) +val maxOutDegree: (VertexId, Int) = graph.outDegrees.reduce(max) +val maxDegrees: (VertexId, Int) = graph.degrees.reduce(max) {% endhighlight %} ### Collecting Neighbors @@ -596,14 +665,14 @@ attributes at each vertex. This can be easily accomplished using the [`collectNeighborIds`][GraphOps.collectNeighborIds] and the [`collectNeighbors`][GraphOps.collectNeighbors] operators. -[GraphOps.collectNeighborIds]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@collectNeighborIds(EdgeDirection):VertexRDD[Array[VertexID]] -[GraphOps.collectNeighbors]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@collectNeighbors(EdgeDirection):VertexRDD[Array[(VertexID,VD)]] +[GraphOps.collectNeighborIds]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@collectNeighborIds(EdgeDirection):VertexRDD[Array[VertexId]] +[GraphOps.collectNeighbors]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@collectNeighbors(EdgeDirection):VertexRDD[Array[(VertexId,VD)]] {% highlight scala %} class GraphOps[VD, ED] { - def collectNeighborIds(edgeDirection: EdgeDirection): VertexRDD[Array[VertexID]] - def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[ Array[(VertexID, VD)] ] + def collectNeighborIds(edgeDirection: EdgeDirection): VertexRDD[Array[VertexId]] + def collectNeighbors(edgeDirection: EdgeDirection): VertexRDD[ Array[(VertexId, VD)] ] } {% endhighlight %} @@ -611,11 +680,20 @@ class GraphOps[VD, ED] { > substantial communication. If possible try expressing the same computation using the > `mapReduceTriplets` operator directly. +## Caching and Uncaching + +In Spark, RDDs are not persisted in memory by default. To avoid recomputation, they must be explicitly cached when using them multiple times (see the [Spark Programming Guide][RDD Persistence]). Graphs in GraphX behave the same way. **When using a graph multiple times, make sure to call [`Graph.cache()`][Graph.cache] on it first.** + +[RDD Persistence]: scala-programming-guide.html#rdd-persistence +[Graph.cache]: api/graphx/index.html#org.apache.spark.graphx.Graph@cache():Graph[VD,ED] + +In iterative computations, *uncaching* may also be necessary for best performance. By default, cached RDDs and graphs will remain in memory until memory pressure forces them to be evicted in LRU order. For iterative computation, intermediate results from previous iterations will fill up the cache. Though they will eventually be evicted, the unnecessary data stored in memory will slow down garbage collection. It would be more efficient to uncache intermediate results as soon as they are no longer necessary. This involves materializing (caching and forcing) a graph or RDD every iteration, uncaching all other datasets, and only using the materialized dataset in future iterations. However, because graphs are composed of multiple RDDs, it can be difficult to unpersist them correctly. **For iterative computation we recommend using the Pregel API, which correctly unpersists intermediate results.** + # Pregel API <a name="pregel"></a> Graphs are inherently recursive data-structures as properties of vertices depend on properties of -their neighbors which intern depend on properties of *their* neighbors. As a +their neighbors which in turn depend on properties of *their* neighbors. As a consequence many important graph algorithms iteratively recompute the properties of each vertex until a fixed-point condition is reached. A range of graph-parallel abstractions have been proposed to express these iterative algorithms. GraphX exposes a Pregel-like operator which is a fusion of @@ -638,7 +716,7 @@ messages remaining. The following is the type signature of the [Pregel operator][GraphOps.pregel] as well as a *sketch* of its implementation (note calls to graph.cache have been removed): -[GraphOps.pregel]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@pregel[A](A,Int,EdgeDirection)((VertexID,VD,A)⇒VD,(EdgeTriplet[VD,ED])⇒Iterator[(VertexID,A)],(A,A)⇒A)(ClassTag[A]):Graph[VD,ED] +[GraphOps.pregel]: api/graphx/index.html#org.apache.spark.graphx.GraphOps@pregel[A](A,Int,EdgeDirection)((VertexId,VD,A)⇒VD,(EdgeTriplet[VD,ED])⇒Iterator[(VertexId,A)],(A,A)⇒A)(ClassTag[A]):Graph[VD,ED] {% highlight scala %} class GraphOps[VD, ED] { @@ -646,8 +724,8 @@ class GraphOps[VD, ED] { (initialMsg: A, maxIter: Int = Int.MaxValue, activeDir: EdgeDirection = EdgeDirection.Out) - (vprog: (VertexID, VD, A) => VD, - sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexID, A)], + (vprog: (VertexId, VD, A) => VD, + sendMsg: EdgeTriplet[VD, ED] => Iterator[(VertexId, A)], mergeMsg: (A, A) => A) : Graph[VD, ED] = { // Receive the initial message at each vertex @@ -692,7 +770,7 @@ import org.apache.spark.graphx.util.GraphGenerators // A graph with edge attributes containing distances val graph: Graph[Int, Double] = GraphGenerators.logNormalGraph(sc, numVertices = 100).mapEdges(e => e.attr.toDouble) -val sourceId: VertexID = 42 // The ultimate source +val sourceId: VertexId = 42 // The ultimate source // Initialize the graph such that all vertices except the root have distance infinity. val initialGraph = graph.mapVertices((id, _) => if (id == sourceId) 0.0 else Double.PositiveInfinity) val sssp = initialGraph.pregel(Double.PositiveInfinity)( @@ -739,7 +817,7 @@ It creates a `Graph` from the specified edges, automatically creating any vertic {% highlight scala %} object Graph { def apply[VD, ED]( - vertices: RDD[(VertexID, VD)], + vertices: RDD[(VertexId, VD)], edges: RDD[Edge[ED]], defaultVertexAttr: VD = null) : Graph[VD, ED] @@ -749,7 +827,7 @@ object Graph { defaultValue: VD): Graph[VD, ED] def fromEdgeTuples[VD]( - rawEdges: RDD[(VertexID, VertexID)], + rawEdges: RDD[(VertexId, VertexId)], defaultValue: VD, uniqueEdges: Option[PartitionStrategy] = None): Graph[VD, Int] @@ -765,8 +843,8 @@ object Graph { [PartitionStrategy]: api/graphx/index.html#org.apache.spark.graphx.PartitionStrategy$ [GraphLoader.edgeListFile]: api/graphx/index.html#org.apache.spark.graphx.GraphLoader$@edgeListFile(SparkContext,String,Boolean,Int):Graph[Int,Int] -[Graph.apply]: api/graphx/index.html#org.apache.spark.graphx.Graph$@apply[VD,ED](RDD[(VertexID,VD)],RDD[Edge[ED]],VD)(ClassTag[VD],ClassTag[ED]):Graph[VD,ED] -[Graph.fromEdgeTuples]: api/graphx/index.html#org.apache.spark.graphx.Graph$@fromEdgeTuples[VD](RDD[(VertexID,VertexID)],VD,Option[PartitionStrategy])(ClassTag[VD]):Graph[VD,Int] +[Graph.apply]: api/graphx/index.html#org.apache.spark.graphx.Graph$@apply[VD,ED](RDD[(VertexId,VD)],RDD[Edge[ED]],VD)(ClassTag[VD],ClassTag[ED]):Graph[VD,ED] +[Graph.fromEdgeTuples]: api/graphx/index.html#org.apache.spark.graphx.Graph$@fromEdgeTuples[VD](RDD[(VertexId,VertexId)],VD,Option[PartitionStrategy])(ClassTag[VD]):Graph[VD,Int] [Graph.fromEdges]: api/graphx/index.html#org.apache.spark.graphx.Graph$@fromEdges[VD,ED](RDD[Edge[ED]],VD)(ClassTag[VD],ClassTag[ED]):Graph[VD,ED] # Vertex and Edge RDDs @@ -779,47 +857,46 @@ respectively. In this section we review some of the additional useful functiona ## VertexRDDs -The `VertexRDD[A]` extends the more traditional `RDD[(VertexId, A)]` but adds the additional -constraint that each `VertexId` occurs only *once*. Moreover, `VertexRDD[A]` represents a *set* of -vertices each with an attribute of type `A`. Internally, this is achieved by storing the vertex -attributes in a reusable hash-map data-structure. As a consequence if two `VertexRDD`s are derived -from the same base `VertexRDD` (e.g., by `filter` or `mapValues`) they can be joined in constant -time without hash evaluations. To leverage this indexed data-structure, the `VertexRDD` exposes the -following additional functionality: +The `VertexRDD[A]` extends `RDD[(VertexID, A)]` and adds the additional constraint that each +`VertexID` occurs only *once*. Moreover, `VertexRDD[A]` represents a *set* of vertices each with an +attribute of type `A`. Internally, this is achieved by storing the vertex attributes in a reusable +hash-map data-structure. As a consequence if two `VertexRDD`s are derived from the same base +`VertexRDD` (e.g., by `filter` or `mapValues`) they can be joined in constant time without hash +evaluations. To leverage this indexed data-structure, the `VertexRDD` exposes the following +additional functionality: {% highlight scala %} -class VertexRDD[VD] { +class VertexRDD[VD] extends RDD[(VertexID, VD)] { // Filter the vertex set but preserves the internal index - def filter(pred: Tuple2[VertexID, VD] => Boolean): VertexRDD[VD] + def filter(pred: Tuple2[VertexId, VD] => Boolean): VertexRDD[VD] // Transform the values without changing the ids (preserves the internal index) def mapValues[VD2](map: VD => VD2): VertexRDD[VD2] - def mapValues[VD2](map: (VertexID, VD) => VD2): VertexRDD[VD2] + def mapValues[VD2](map: (VertexId, VD) => VD2): VertexRDD[VD2] // Remove vertices from this set that appear in the other set def diff(other: VertexRDD[VD]): VertexRDD[VD] // Join operators that take advantage of the internal indexing to accelerate joins (substantially) - def leftJoin[VD2, VD3](other: RDD[(VertexID, VD2)])(f: (VertexID, VD, Option[VD2]) => VD3): VertexRDD[VD3] - def innerJoin[U, VD2](other: RDD[(VertexID, U)])(f: (VertexID, VD, U) => VD2): VertexRDD[VD2] + def leftJoin[VD2, VD3](other: RDD[(VertexId, VD2)])(f: (VertexId, VD, Option[VD2]) => VD3): VertexRDD[VD3] + def innerJoin[U, VD2](other: RDD[(VertexId, U)])(f: (VertexId, VD, U) => VD2): VertexRDD[VD2] // Use the index on this RDD to accelerate a `reduceByKey` operation on the input RDD. - def aggregateUsingIndex[VD2](other: RDD[(VertexID, VD2)], reduceFunc: (VD2, VD2) => VD2): VertexRDD[VD2] + def aggregateUsingIndex[VD2](other: RDD[(VertexId, VD2)], reduceFunc: (VD2, VD2) => VD2): VertexRDD[VD2] } {% endhighlight %} Notice, for example, how the `filter` operator returns an `VertexRDD`. Filter is actually implemented using a `BitSet` thereby reusing the index and preserving the ability to do fast joins with other `VertexRDD`s. Likewise, the `mapValues` operators do not allow the `map` function to -change the `VertexId` thereby enabling the same `HashMap` data-structures to be reused. Both the +change the `VertexID` thereby enabling the same `HashMap` data-structures to be reused. Both the `leftJoin` and `innerJoin` are able to identify when joining two `VertexRDD`s derived from the same `HashMap` and implement the join by linear scan rather than costly point lookups. -The `aggregateUsingIndex` operator can be slightly confusing but is also useful for efficient -construction of a new `VertexRDD` from an `RDD[(VertexId, A)]`. Conceptually, if I have constructed -a `VertexRDD[B]` over a set of vertices, *which is a super-set* of the vertices in some -`RDD[(VertexId, A)]` then I can reuse the index to both aggregate and then subsequently index the -RDD. For example: +The `aggregateUsingIndex` operator is useful for efficient construction of a new `VertexRDD` from an +`RDD[(VertexID, A)]`. Conceptually, if I have constructed a `VertexRDD[B]` over a set of vertices, +*which is a super-set* of the vertices in some `RDD[(VertexID, A)]` then I can reuse the index to +both aggregate and then subsequently index the `RDD[(VertexID, A)]`. For example: {% highlight scala %} val setA: VertexRDD[Int] = VertexRDD(sc.parallelize(0L until 100L).map(id => (id, 1))) -val rddB: RDD[(VertexID, Double)] = sc.parallelize(0L until 100L).flatMap(id => List((id, 1.0), (id, 2.0))) +val rddB: RDD[(VertexId, Double)] = sc.parallelize(0L until 100L).flatMap(id => List((id, 1.0), (id, 2.0))) // There should be 200 entries in rddB rddB.count val setB: VertexRDD[Double] = setA.aggregateUsingIndex(rddB, _ + _) @@ -831,10 +908,10 @@ val setC: VertexRDD[Double] = setA.innerJoin(setB)((id, a, b) => a + b) ## EdgeRDDs -The `EdgeRDD[ED]`, which extends `RDD[Edge[ED]]` is considerably simpler than the `VertexRDD`. -GraphX organizes the edges in blocks partitioned using one of the various partitioning strategies -defined in [`PartitionStrategy`][PartitionStrategy]. Within each partition, edge attributes and -adjacency structure, are stored separately enabling maximum reuse when changing attribute values. +The `EdgeRDD[ED]`, which extends `RDD[Edge[ED]]` organizes the edges in blocks partitioned using one +of the various partitioning strategies defined in [`PartitionStrategy`][PartitionStrategy]. Within +each partition, edge attributes and adjacency structure, are stored separately enabling maximum +reuse when changing attribute values. [PartitionStrategy]: api/graphx/index.html#org.apache.spark.graphx.PartitionStrategy @@ -845,11 +922,11 @@ def mapValues[ED2](f: Edge[ED] => ED2): EdgeRDD[ED2] // Revere the edges reusing both attributes and structure def reverse: EdgeRDD[ED] // Join two `EdgeRDD`s partitioned using the same partitioning strategy. -def innerJoin[ED2, ED3](other: EdgeRDD[ED2])(f: (VertexID, VertexID, ED, ED2) => ED3): EdgeRDD[ED3] +def innerJoin[ED2, ED3](other: EdgeRDD[ED2])(f: (VertexId, VertexId, ED, ED2) => ED3): EdgeRDD[ED3] {% endhighlight %} In most applications we have found that operations on the `EdgeRDD` are accomplished through the -graph or rely on operations defined in the base `RDD` class. +graph operators or rely on operations defined in the base `RDD` class. # Optimized Representation @@ -871,7 +948,9 @@ reduce both the communication and storage overhead. Logically, this corresponds to machines and allowing vertices to span multiple machines. The exact method of assigning edges depends on the [`PartitionStrategy`][PartitionStrategy] and there are several tradeoffs to the various heuristics. Users can choose between different strategies by repartitioning the graph with -the [`Graph.partitionBy`][Graph.partitionBy] operator. +the [`Graph.partitionBy`][Graph.partitionBy] operator. The default partitioning strategy is to use +the initial partitioning of the edges as provided on graph construction. However, users can easily +switch to 2D-partitioning or other heuristics included in GraphX. [Graph.partitionBy]: api/graphx/index.html#org.apache.spark.graphx.Graph$@partitionBy(partitionStrategy:org.apache.spark.graphx.PartitionStrategy):org.apache.spark.graphx.Graph[VD,ED] @@ -885,16 +964,15 @@ the [`Graph.partitionBy`][Graph.partitionBy] operator. Once the edges have be partitioned the key challenge to efficient graph-parallel computation is efficiently joining vertex attributes with the edges. Because real-world graphs typically have more -edges than vertices, we move vertex attributes to the edges. - - - - +edges than vertices, we move vertex attributes to the edges. Because not all partitions will +contain edges adjacent to all vertices we internally maintain a routing table which identifies where +to broadcast vertices when implementing the join required for operations like `triplets` and +`mapReduceTriplets`. # Graph Algorithms <a name="graph_algorithms"></a> -GraphX includes a set of graph algorithms in to simplify analytics. The algorithms are contained in the `org.apache.spark.graphx.lib` package and can be accessed directly as methods on `Graph` via [`GraphOps`][GraphOps]. This section describes the algorithms and how they are used. +GraphX includes a set of graph algorithms to simplify analytics tasks. The algorithms are contained in the `org.apache.spark.graphx.lib` package and can be accessed directly as methods on `Graph` via [`GraphOps`][GraphOps]. This section describes the algorithms and how they are used. ## PageRank <a name="pagerank"></a> |