Fix code style and a nondeterministic RDD issue in ALS

mllib/src/main/scala/spark/mllib/recommendation/ALS.scala

@@ -123,18 +123,27 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
     val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock)
     val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock)
 
-    // Initialize user and product factors randomly
-    var users = userOutLinks.mapPartitions {itr =>
-      val rand = new Random()
-      itr.map({case (x, y) =>
-	       (x, y.elementIds.map(u => randomFactor(rank, rand)))
-	     })
+    // Initialize user and product factors randomly, but use a deterministic seed for each partition
+    // so that fault recovery works
+    val seedGen = new Random()
+    val seed1 = seedGen.nextInt()
+    val seed2 = seedGen.nextInt()
+    // Hash an integer to propagate random bits at all positions, similar to java.util.HashTable
+    def hash(x: Int): Int = {
+      val r = x ^ (x >>> 20) ^ (x >>> 12)
+      r ^ (r >>> 7) ^ (r >>> 4)
     }
-    var products = productOutLinks.mapPartitions {itr =>
-      val rand = new Random()
-      itr.map({case (x, y) =>
-	(x, y.elementIds.map(u => randomFactor(rank, rand)))
-	     })
+    var users = userOutLinks.mapPartitionsWithIndex { (index, itr) =>
+      val rand = new Random(hash(seed1 ^ index))
+      itr.map { case (x, y) =>
+        (x, y.elementIds.map(_ => randomFactor(rank, rand)))
+      }
+    }
+    var products = productOutLinks.mapPartitionsWithIndex { (index, itr) =>
+      val rand = new Random(hash(seed2 ^ index))
+      itr.map { case (x, y) =>
+        (x, y.elementIds.map(_ => randomFactor(rank, rand)))
+      }
     }
 
     for (iter <- 0 until iterations) {