diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 0e228d8f28706823532053a52317f83df301f50e..572afc101b8d5a5ad13ebdf62583e70268295715 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -667,9 +667,7 @@ things to worry about.)
# 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 an implicit conversion to [`Algorithms`][Algorithms]. This section describes the algorithms and how they are used.
-
-[Algorithms]: api/graphx/index.html#org.apache.spark.graphx.lib.Algorithms
+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.
## PageRank
<a name="pagerank"></a>
@@ -681,8 +679,6 @@ GraphX comes with static and dynamic implementations of PageRank as methods on t
[PageRank]: api/graphx/index.html#org.apache.spark.graphx.lib.PageRank$
{% highlight scala %}
-// Load the implicit conversion to Algorithms
-import org.apache.spark.graphx.lib._
// Load the datasets into a graph
val users = sc.textFile("graphx/data/users.txt").map { line =>
val fields = line.split("\\s+")
@@ -710,8 +706,7 @@ The connected components algorithm labels each connected component of the graph
[ConnectedComponents]: api/graphx/index.html#org.apache.spark.graphx.lib.ConnectedComponents$
{% highlight scala %}
-// Load the implicit conversion and graph as in the PageRank example
-import org.apache.spark.graphx.lib._
+// Load the graph as in the PageRank example
val users = ...
val followers = ...
val graph = Graph(users, followers)
@@ -733,8 +728,7 @@ A vertex is part of a triangle when it has two adjacent vertices with an edge be
[Graph.partitionBy]: api/graphx/index.html#org.apache.spark.graphx.Graph@partitionBy(PartitionStrategy):Graph[VD,ED]
{% highlight scala %}
-// Load the implicit conversion and graph as in the PageRank example
-import org.apache.spark.graphx.lib._
+// Load the graph as in the PageRank example
val users = ...
// Load the edges in canonical order and partition the graph for triangle count
val graph = GraphLoader.edgeListFile(sc, "graphx/data/followers.txt", true).partitionBy(RandomVertexCut)
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index 56513cac201507d51de4fad38741a4f85ad5eb4f..7d4f0de3d6f07faabd96d8e1fd3483a72391b0b1 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -15,9 +15,7 @@ import org.apache.spark.storage.StorageLevel
* RDDs, the graph is a functional data-structure in which mutating
* operations return new graphs.
*
- * @note [[GraphOps]] contains additional convenience operations.
- * [[lib.Algorithms]] contains graph algorithms; to access these,
- * import `org.apache.spark.graphx.lib._`.
+ * @note [[GraphOps]] contains additional convenience operations and graph algorithms.
*
* @tparam VD the vertex attribute type
* @tparam ED the edge attribute type
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
index 4fdff29f5a05ff2ffef1cfb3a9cd4059ce244be0..2b3b95e2ca70ecf6799f80a434adc68c07b343c4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
@@ -2,9 +2,10 @@ package org.apache.spark.graphx
import scala.reflect.ClassTag
-import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.SparkException
+import org.apache.spark.graphx.lib._
+import org.apache.spark.rdd.RDD
/**
* Contains additional functionality for [[Graph]]. All operations are expressed in terms of the
@@ -298,4 +299,52 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) {
Pregel(graph, initialMsg, maxIterations, activeDirection)(vprog, sendMsg, mergeMsg)
}
+ /**
+ * Run a dynamic version of PageRank returning a graph with vertex attributes containing the
+ * PageRank and edge attributes containing the normalized edge weight.
+ *
+ * @see [[org.apache.spark.graphx.lib.PageRank]], method `runUntilConvergence`.
+ */
+ def pageRank(tol: Double, resetProb: Double = 0.15): Graph[Double, Double] = {
+ PageRank.runUntilConvergence(graph, tol, resetProb)
+ }
+
+ /**
+ * Run PageRank for a fixed number of iterations returning a graph with vertex attributes
+ * containing the PageRank and edge attributes the normalized edge weight.
+ *
+ * @see [[org.apache.spark.graphx.lib.PageRank]], method `run`.
+ */
+ def staticPageRank(numIter: Int, resetProb: Double = 0.15): Graph[Double, Double] = {
+ PageRank.run(graph, numIter, resetProb)
+ }
+
+ /**
+ * Compute the connected component membership of each vertex and return a graph with the vertex
+ * value containing the lowest vertex id in the connected component containing that vertex.
+ *
+ * @see [[org.apache.spark.graphx.lib.ConnectedComponents]]
+ */
+ def connectedComponents(): Graph[VertexID, ED] = {
+ ConnectedComponents.run(graph)
+ }
+
+ /**
+ * Compute the number of triangles passing through each vertex.
+ *
+ * @see [[org.apache.spark.graphx.lib.TriangleCount]]
+ */
+ def triangleCount(): Graph[Int, ED] = {
+ TriangleCount.run(graph)
+ }
+
+ /**
+ * Compute the strongly connected component (SCC) of each vertex and return a graph with the
+ * vertex value containing the lowest vertex id in the SCC containing that vertex.
+ *
+ * @see [[org.apache.spark.graphx.lib.StronglyConnectedComponents]]
+ */
+ def stronglyConnectedComponents(numIter: Int): Graph[VertexID, ED] = {
+ StronglyConnectedComponents.run(graph, numIter)
+ }
} // end of GraphOps
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/Algorithms.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/Algorithms.scala
deleted file mode 100644
index cbcd9c24a0126b04d20cc4ebabb23a95094c7cfa..0000000000000000000000000000000000000000
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/Algorithms.scala
+++ /dev/null
@@ -1,66 +0,0 @@
-package org.apache.spark.graphx.lib
-
-import scala.reflect.ClassTag
-
-import org.apache.spark.graphx._
-
-/**
- * Provides graph algorithms directly on [[org.apache.spark.graphx.Graph]] via an implicit
- * conversion.
- * @example
- * {{{
- * import org.apache.spark.graph.lib._
- * val graph: Graph[_, _] = loadGraph()
- * graph.connectedComponents()
- * }}}
- */
-class Algorithms[VD: ClassTag, ED: ClassTag](self: Graph[VD, ED]) {
- /**
- * Run a dynamic version of PageRank returning a graph with vertex attributes containing the
- * PageRank and edge attributes containing the normalized edge weight.
- *
- * @see [[org.apache.spark.graphx.lib.PageRank]], method `runUntilConvergence`.
- */
- def pageRank(tol: Double, resetProb: Double = 0.15): Graph[Double, Double] = {
- PageRank.runUntilConvergence(self, tol, resetProb)
- }
-
- /**
- * Run PageRank for a fixed number of iterations returning a graph with vertex attributes
- * containing the PageRank and edge attributes the normalized edge weight.
- *
- * @see [[org.apache.spark.graphx.lib.PageRank]], method `run`.
- */
- def staticPageRank(numIter: Int, resetProb: Double = 0.15): Graph[Double, Double] = {
- PageRank.run(self, numIter, resetProb)
- }
-
- /**
- * Compute the connected component membership of each vertex and return a graph with the vertex
- * value containing the lowest vertex id in the connected component containing that vertex.
- *
- * @see [[org.apache.spark.graphx.lib.ConnectedComponents]]
- */
- def connectedComponents(): Graph[VertexID, ED] = {
- ConnectedComponents.run(self)
- }
-
- /**
- * Compute the number of triangles passing through each vertex.
- *
- * @see [[org.apache.spark.graphx.lib.TriangleCount]]
- */
- def triangleCount(): Graph[Int, ED] = {
- TriangleCount.run(self)
- }
-
- /**
- * Compute the strongly connected component (SCC) of each vertex and return a graph with the
- * vertex value containing the lowest vertex id in the SCC containing that vertex.
- *
- * @see [[org.apache.spark.graphx.lib.StronglyConnectedComponents]]
- */
- def stronglyConnectedComponents(numIter: Int): Graph[VertexID, ED] = {
- StronglyConnectedComponents.run(self, numIter)
- }
-}
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala
deleted file mode 100644
index f6f2626c9dc2d027db5b259cf358528a4ad913e4..0000000000000000000000000000000000000000
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala
+++ /dev/null
@@ -1,8 +0,0 @@
-package org.apache.spark.graphx
-
-import scala.reflect.ClassTag
-
-package object lib {
- implicit def graphToAlgorithms[VD: ClassTag, ED: ClassTag](
- graph: Graph[VD, ED]): Algorithms[VD, ED] = new Algorithms(graph)
-}