diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
index 529709c2f9..a119980992 100644
--- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
@@ -32,13 +32,13 @@ object BroadcastTest {
     System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast." + bcName + "BroadcastFactory")
     System.setProperty("spark.broadcast.blockSize", blockSize)
 
-    val sc = new SparkContext(args(0), "Broadcast Test 2",
+    val sc = new SparkContext(args(0), "Broadcast Test",
       System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     
     val slices = if (args.length > 1) args(1).toInt else 2
     val num = if (args.length > 2) args(2).toInt else 1000000
 
-    var arr1 = new Array[Int](num)
+    val arr1 = new Array[Int](num)
     for (i <- 0 until arr1.length) {
       arr1(i) = i
     }
@@ -48,9 +48,9 @@ object BroadcastTest {
       println("===========")
       val startTime = System.nanoTime
       val barr1 = sc.broadcast(arr1)
-      sc.parallelize(1 to 10, slices).foreach {
-        i => println(barr1.value.size)
-      }
+      val observedSizes = sc.parallelize(1 to 10, slices).map(_ => barr1.value.size)
+      // Collect the small RDD so we can print the observed sizes locally.
+      observedSizes.collect().foreach(i => println(i))
       println("Iteration %d took %.0f milliseconds".format(i, (System.nanoTime - startTime) / 1E6))
     }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
index f79f0142b8..e1afc29f9a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
@@ -18,35 +18,38 @@
 package org.apache.spark.examples
 
 import org.apache.spark.SparkContext
+import org.apache.spark.rdd.RDD
 
 object MultiBroadcastTest {
   def main(args: Array[String]) {
     if (args.length == 0) {
-      System.err.println("Usage: BroadcastTest <master> [<slices>] [numElem]")
+      System.err.println("Usage: MultiBroadcastTest <master> [<slices>] [numElem]")
       System.exit(1)
     }
 
-    val sc = new SparkContext(args(0), "Broadcast Test",
+    val sc = new SparkContext(args(0), "Multi-Broadcast Test",
       System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     val slices = if (args.length > 1) args(1).toInt else 2
     val num = if (args.length > 2) args(2).toInt else 1000000
 
-    var arr1 = new Array[Int](num)
+    val arr1 = new Array[Int](num)
     for (i <- 0 until arr1.length) {
       arr1(i) = i
     }
 
-    var arr2 = new Array[Int](num)
+    val arr2 = new Array[Int](num)
     for (i <- 0 until arr2.length) {
       arr2(i) = i
     }
 
     val barr1 = sc.broadcast(arr1)
     val barr2 = sc.broadcast(arr2)
-    sc.parallelize(1 to 10, slices).foreach {
-      i => println(barr1.value.size + barr2.value.size)
+    val observedSizes: RDD[(Int, Int)] = sc.parallelize(1 to 10, slices).map { _ =>
+      (barr1.value.size, barr2.value.size)
     }
+    // Collect the small RDD so we can print the observed sizes locally.
+    observedSizes.collect().foreach(i => println(i))
 
     System.exit(0)
   }