tasks cannot access value of accumulator

core/src/main/scala/spark/Accumulators.scala

@@ -11,7 +11,7 @@ class Accumulable[T,R] (
   
   val id = Accumulators.newId
   @transient
-  var value_ = initialValue // Current value on master
+  private var value_ = initialValue // Current value on master
   val zero = param.zero(initialValue)  // Zero value to be passed to workers
   var deserialized = false
 
@@ -30,7 +30,13 @@ class Accumulable[T,R] (
    * @param term the other Accumulable that will get merged with this
    */
   def ++= (term: T) { value_ = param.addInPlace(value_, term)}
-  def value = this.value_
+  def value = {
+    if (!deserialized) value_
+    else throw new UnsupportedOperationException("Can't use read value in task")
+  }
+
+  private[spark] def localValue = value_
+
   def value_= (t: T) {
     if (!deserialized) value_ = t
     else throw new UnsupportedOperationException("Can't use value_= in task")
@@ -126,7 +132,7 @@ private object Accumulators {
   def values: Map[Long, Any] = synchronized {
     val ret = Map[Long, Any]()
     for ((id, accum) <- localAccums.getOrElse(Thread.currentThread, Map())) {
-      ret(id) = accum.value
+      ret(id) = accum.localValue
     }
     return ret
   }

core/src/test/scala/spark/AccumulatorSuite.scala

@@ -63,60 +63,19 @@ class AccumulatorSuite extends FunSuite with ShouldMatchers {
   }
 
 
-  test ("value readable in tasks") {
-    import spark.util.Vector
-    //stochastic gradient descent with weights stored in accumulator -- should be able to read value as we go
-
-    //really easy data
-    val N = 10000  // Number of data points
-    val D = 10   // Numer of dimensions
-    val R = 0.7  // Scaling factor
-    val ITERATIONS = 5
-    val rand = new Random(42)
-
-    case class DataPoint(x: Vector, y: Double)
-
-    def generateData = {
-      def generatePoint(i: Int) = {
-        val y = if(i % 2 == 0) -1 else 1
-        val goodX = Vector(D, _ => 0.0001 * rand.nextGaussian() + y)
-        val noiseX = Vector(D, _ => rand.nextGaussian())
-        val x = Vector((goodX.elements.toSeq ++ noiseX.elements.toSeq): _*)
-        DataPoint(x, y)
-      }
-      Array.tabulate(N)(generatePoint)
-    }
-
-    val data = generateData
-    for (nThreads <- List(1, 10)) {
-      //test single & multi-threaded
-      val sc = new SparkContext("local[" + nThreads + "]", "test")
-      val weights = Vector.zeros(2*D)
-      val weightDelta = sc.accumulator(Vector.zeros(2 * D))
-      for (itr <- 1 to ITERATIONS) {
-        val eta = 0.1 / itr
-        val badErrs = sc.accumulator(0)
-        sc.parallelize(data).foreach {
-          p => {
-            //XXX Note the call to .value here.  That is required for this to be an online gradient descent
-            // instead of a batch version.  Should it change to .localValue, and should .value throw an error
-            // if you try to do this??
-            val prod = weightDelta.value.plusDot(weights, p.x)
-            val trueClassProb = (1 / (1 + exp(-p.y * prod)))  // works b/c p(-z) = 1 - p(z) (where p is the logistic function)
-            val update = p.x * trueClassProb * p.y * eta
-            //we could also include a momentum term here if our weightDelta accumulator saved a momentum
-            weightDelta.value += update
-            if (trueClassProb <= 0.95)
-              badErrs += 1
-          }
+  test ("value not readable in tasks") {
+    import SetAccum._
+    val maxI = 1000
+    for (nThreads <- List(1, 10)) { //test single & multi-threaded
+    val sc = new SparkContext("local[" + nThreads + "]", "test")
+      val acc: Accumulable[mutable.Set[Any], Any] = sc.accumulable(new mutable.HashSet[Any]())
+      val d = sc.parallelize(1 to maxI)
+      val thrown = evaluating {
+        d.foreach {
+          x => acc.value += x
         }
-        println("Iteration " + itr + " had badErrs = " + badErrs.value)
-        weights += weightDelta.value
-        println(weights)
-        //TODO I should check the number of bad errors here, but for some reason spark tries to serialize the assertion ...
-//        val assertVal = badErrs.value
-//        assert (assertVal < 100)
-      }
+      } should produce [SparkException]
+      println(thrown)
     }
   }