Merge pull request #424 from jegonzal/GraphXProgrammingGuide

Additional edits for clarity in the graphx programming guide.

Added an overview of the Graph and GraphOps functions and fixed numerous typos.

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
 
@@ -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
@@ -443,7 +512,7 @@ original value.
 > 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)(
@@ -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 %}
 
 
@@ -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.
 
@@ -788,16 +857,16 @@ 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]
   // Transform the values without changing the ids (preserves the internal index)
@@ -816,15 +885,14 @@ class VertexRDD[VD] {
 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)))
@@ -840,10 +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
 
@@ -858,7 +926,7 @@ def innerJoin[ED2, ED3](other: EdgeRDD[ED2])(f: (VertexID, VertexID, ED, ED2) =>
 {% 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
 
@@ -880,7 +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]
 
@@ -894,16 +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>