diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
index fa533a512d53b..fcfb0cb81b679 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/Analytics.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.graphx.lib
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark._
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.PartitionStrategy._
@@ -146,6 +148,45 @@ object Analytics extends Logging {
         }.reduce(_ + _) / 3)
         sc.stop()
 
+      case "ShortestPaths" =>
+        var numEPart = 4
+        var partitionStrategy: Option[PartitionStrategy] = None
+        val landmarks: ArrayBuffer[VertexId] = ArrayBuffer.empty
+        var outFname = ""
+        options.foreach{
+          case ("numEPart", v) => numEPart = v.toInt
+          case ("partStrategy", v) => partitionStrategy = Some(pickPartitioner(v))
+          case ("landmark", v) => landmarks += v.toLong
+          case ("output", v) => outFname = v
+          case (opt, _) => throw new IllegalArgumentException("Invalid option: " + opt)
+        }
+        println("======================================")
+        println("|           Shortest Paths           |")
+        println("======================================")
+
+        if (landmarks.isEmpty) {
+          println("Specify landmarks by passing --landmark=<VERTEX_ID> one or more times.")
+          sys.exit(1)
+        }
+
+        val sc = new SparkContext(host, "ShortestPaths(" + fname + ")", conf)
+        val unpartitionedGraph = GraphLoader.edgeListFile(sc, fname,
+          minEdgePartitions = numEPart).cache()
+        val graph = partitionStrategy.foldLeft(unpartitionedGraph)(_.partitionBy(_))
+        val distances = ShortestPaths.run(graph, landmarks)
+
+        if (outFname.nonEmpty) {
+          println("Writing output in 'vid<TAB>landmark<TAB>distance' format to " + outFname)
+          distances.vertices.flatMap {
+            case (vid: VertexId, d: ShortestPaths.SPMap) => d.iterator.map {
+              case (landmark: VertexId, distance: Int) =>
+                "%d\t%d\t%d".format(vid, landmark, distance)
+            }
+          }.saveAsTextFile(outFname)
+        }
+
+        sc.stop()
+
       case _ =>
         println("Invalid task type.")
     }