Added more connected components.

graphx/src/main/scala/org/apache/spark/graphx/lib/DataflowPagerank.scala

@@ -57,6 +57,7 @@ object DataflowPagerank extends Logging {
       case "naive" => naiveVersion(sc, fname, partitions, iters)
       case "optimized" => optimizedSpark(sc, fname, partitions, iters)
       case "cc" => connectedComponents(sc, fname, partitions)
+      case "ccOpt" => ccSlightlyOpt(sc, fname, partitions)
       case _ => throw new UnsupportedOperationException
     }
 
@@ -147,19 +148,20 @@ object DataflowPagerank extends Logging {
 
 
     // initialize ccIDs to IDs
-    var ccs: RDD[(Long, Long)] = edges.map{ case (src: Long, (dst: Long, _: Int)) => (src, src)}
-      .union(edges.map{ case (src: Long, (dst: Long, _: Int)) => (dst, dst)})
+    var ccs: RDD[(Long, Long)] = edges.map{ case (src: Long, dst: Long) => (src, src)}
+      .union(edges.map{ case (src: Long, dst: Long) => (dst, dst)})
       .distinct()
     var numUpdates = Long.MaxValue
 
     logWarning("Starting CC iterations")
+    var i = 0
     while (numUpdates > 0) {
 
       val newCCs = edges
         // get src property
         .join(ccs)
         // rekey by dst
-        .map {case (src: Long, (dst: Long, srcCC: Long)) => (dst, (src, srcCC)}
+        .map {case (src: Long, (dst: Long, srcCC: Long)) => (dst, (src, srcCC))}
         // get dst property
         .join(ccs)
         // emit min ccId to src and adst
@@ -173,14 +175,72 @@ object DataflowPagerank extends Logging {
       // check for convergence
       numUpdates = newCCs.join(ccs)
         .filter{case (vid, (newCC, oldCC)) => newCC != oldCC }.count()
-      ccs = newCCs
-
 
       logWarning(s"CC iter $i with $numUpdates updates") 
+      ccs = newCCs
+      i += 1
 
     }
     val numCCs = ccs.map{ case(_, id) => id}.distinct().count()
     logWarning(s"Num connected components: $numCCs")
 
   }
+
+  def ccSlightlyOpt(sc: SparkContext, fname: String, partitions: Int) {
+
+    val lines = sc.textFile(fname).repartition(partitions)
+    val edges: RDD[(Long, Long)] = lines.map{ s =>
+      val parts = s.split("\\s+")
+      (parts(0).toLong, parts(1).toLong)
+    }.cache()
+    logWarning("CC started")
+
+
+    // initialize ccIDs to IDs
+    var ccs: RDD[(Long, Long)] = edges.map{ case (src: Long, dst: Long) => (src, src)}
+      .union(edges.map{ case (src: Long, dst: Long) => (dst, dst)})
+      .distinct()
+    var numUpdates = Long.MaxValue
+
+    logWarning("Starting CC iterations")
+    var i = 0
+    while (numUpdates > 0) {
+
+      val newCCs = edges
+        // get src property
+        .join(ccs)
+        // rekey by dst
+        .map {case (src: Long, (dst: Long, srcCC: Long)) => (dst, (src, srcCC))}
+        // get dst property
+        .join(ccs)
+        // emit min ccId to src and adst
+        .flatMap { case (dst: Long, ((src: Long, srcCC: Long), dstCC)) =>
+            if (srcCC < dstCC)  {
+              Iterator(dst, srcCC)
+            } else if (dstCC < srcCC) {
+              Iterator(src, dstCC)
+            } else {
+              Iterator.empty
+            }
+            // Iterator((src, min(srcCC, dstCC)), (dst, min(srcCC, dstCC)))
+        }
+        .reduceByKey(min(_, _))
+
+
+      ccs = ccs.join(newCCs).map{ case (vid, (oldCC, newCC)) => (vid, min(oldCC, newCC)) }.cache()
+        // .join(ranks)
+        // .map { case (id: Long, (incomingRanks: Double, myRank: Double)) => (id, alpha*myRank + (1.0-alpha)*incomingRanks)}
+
+      // check for convergence
+      // numUpdates = newCCs.join(ccs)
+        // .filter{case (vid, (newCC, oldCC)) => newCC != oldCC }.count()
+      numUpdates = newCCs.count()
+
+      logWarning(s"CC iter $i with $numUpdates updates") 
+      // ccs = newCCs
+      i += 1
+    }
+    val numCCs = ccs.map{ case(_, id) => id}.distinct().count()
+    logWarning(s"Num connected components: $numCCs")
+  }
 }