[SPARK-4917] Add a function to convert into a graph with canonical edges in GraphOps

Convert bi-directional edges into uni-directional ones instead of 'canonicalOrientation' in GraphLoader.edgeListFile.
This function is useful when a graph is loaded as it is and then is transformed into one with canonical edges.
It rewrites the vertex ids of edges so that srcIds are bigger than dstIds, and merges the duplicated edges.

Author: Takeshi Yamamuro <linguin.m.s@gmail.com>

Closes #3760 from maropu/ConvertToCanonicalEdgesSpike and squashes the following commits:

7f8b580 [Takeshi Yamamuro] Add a function to convert into a graph with canonical edges in GraphOps

2 files changed, 41 insertions, 0 deletions

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 116d1ea700..dc8b4789c4 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala
@@ -279,6 +279,32 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali
   }
 
   /**
+   * Convert bi-directional edges into uni-directional ones.
+   * Some graph algorithms (e.g., TriangleCount) assume that an input graph
+   * has its edges in canonical direction.
+   * This function rewrites the vertex ids of edges so that srcIds are bigger
+   * than dstIds, and merges the duplicated edges.
+   *
+   * @param mergeFunc the user defined reduce function which should
+   * be commutative and associative and is used to combine the output
+   * of the map phase
+   *
+   * @return the resulting graph with canonical edges
+   */
+  def convertToCanonicalEdges(
+      mergeFunc: (ED, ED) => ED = (e1, e2) => e1): Graph[VD, ED] = {
+    val newEdges =
+      graph.edges
+        .map {
+          case e if e.srcId < e.dstId => ((e.srcId, e.dstId), e.attr)
+          case e => ((e.dstId, e.srcId), e.attr)
+        }
+        .reduceByKey(mergeFunc)
+        .map(e => new Edge(e._1._1, e._1._2, e._2))
+    Graph(graph.vertices, newEdges)
+  }
+
+  /**
    * Execute a Pregel-like iterative vertex-parallel abstraction.  The
    * user-defined vertex-program `vprog` is executed in parallel on
    * each vertex receiving any inbound messages and computing a new
diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala
index ea94d4accb..9bc8007ce4 100644
--- a/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala
+++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphOpsSuite.scala
@@ -79,6 +79,21 @@ class GraphOpsSuite extends FunSuite with LocalSparkContext {
     }
   }
 
+  test ("convertToCanonicalEdges") {
+    withSpark { sc =>
+      val vertices =
+        sc.parallelize(Seq[(VertexId, String)]((1, "one"), (2, "two"), (3, "three")), 2)
+      val edges =
+        sc.parallelize(Seq(Edge(1, 2, 1), Edge(2, 1, 1), Edge(3, 2, 2)))
+      val g: Graph[String, Int] = Graph(vertices, edges)
+
+      val g1 = g.convertToCanonicalEdges()
+
+      val e = g1.edges.collect().toSet
+      assert(e === Set(Edge(1, 2, 1), Edge(2, 3, 2)))
+    }
+  }
+
   test("collectEdgesCycleDirectionOut") {
     withSpark { sc =>
       val graph = getCycleGraph(sc, 100)