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diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.partial
+
+import java.util.{HashMap => JHashMap}
+import java.util.{Map => JMap}
+
+import scala.collection.Map
+import scala.collection.mutable.HashMap
+import scala.collection.JavaConversions.mapAsScalaMap
+
+import cern.jet.stat.Probability
+
+import it.unimi.dsi.fastutil.objects.{Object2LongOpenHashMap => OLMap}
+
+/**
+ * An ApproximateEvaluator for counts by key. Returns a map of key to confidence interval.
+ */
+private[spark] class GroupedCountEvaluator[T](totalOutputs: Int, confidence: Double)
+  extends ApproximateEvaluator[OLMap[T], Map[T, BoundedDouble]] {
+
+  var outputsMerged = 0
+  var sums = new OLMap[T]   // Sum of counts for each key
+
+  override def merge(outputId: Int, taskResult: OLMap[T]) {
+    outputsMerged += 1
+    val iter = taskResult.object2LongEntrySet.fastIterator()
+    while (iter.hasNext) {
+      val entry = iter.next()
+      sums.put(entry.getKey, sums.getLong(entry.getKey) + entry.getLongValue)
+    }
+  }
+
+  override def currentResult(): Map[T, BoundedDouble] = {
+    if (outputsMerged == totalOutputs) {
+      val result = new JHashMap[T, BoundedDouble](sums.size)
+      val iter = sums.object2LongEntrySet.fastIterator()
+      while (iter.hasNext) {
+        val entry = iter.next()
+        val sum = entry.getLongValue()
+        result(entry.getKey) = new BoundedDouble(sum, 1.0, sum, sum)
+      }
+      result
+    } else if (outputsMerged == 0) {
+      new HashMap[T, BoundedDouble]
+    } else {
+      val p = outputsMerged.toDouble / totalOutputs
+      val confFactor = Probability.normalInverse(1 - (1 - confidence) / 2)
+      val result = new JHashMap[T, BoundedDouble](sums.size)
+      val iter = sums.object2LongEntrySet.fastIterator()
+      while (iter.hasNext) {
+        val entry = iter.next()
+        val sum = entry.getLongValue
+        val mean = (sum + 1 - p) / p
+        val variance = (sum + 1) * (1 - p) / (p * p)
+        val stdev = math.sqrt(variance)
+        val low = mean - confFactor * stdev
+        val high = mean + confFactor * stdev
+        result(entry.getKey) = new BoundedDouble(mean, confidence, low, high)
+      }
+      result
+    }
+  }
+}