diff --git a/scalding-core/src/main/scala/com/twitter/scalding/CumulativeSum.scala b/scalding-core/src/main/scala/com/twitter/scalding/CumulativeSum.scala
new file mode 100644
index 0000000000..f0555b650e
--- /dev/null
+++ b/scalding-core/src/main/scala/com/twitter/scalding/CumulativeSum.scala
@@ -0,0 +1,113 @@
+package com.twitter.scalding.typed
+
+import com.twitter.algebird._
+
+/**
+ * Extension for TypedPipe to add a cumulativeSum method.
+ * Given a TypedPipe with T = (GroupField, (SortField, SummableField))
+ * cumulaitiveSum will return a SortedGrouped with the SummableField accumulated
+ * according to the sort field.
+ * eg:
+ *  ('San Francisco', (100, 100)),
+ *  ('San Francisco', (101, 50)),
+ *  ('San Francisco', (200, 200)),
+ *  ('Vancouver', (100, 50)),
+ *  ('Vancouver', (101, 300)),
+ *  ('Vancouver', (200, 100))
+ * becomes
+ *  ('San Francisco', (100, 100)),
+ *  ('San Francisco', (101, 150)),
+ *  ('San Francisco', (200, 300)),
+ *  ('Vancouver', (100, 50)),
+ *  ('Vancouver', (101, 350)),
+ *  ('Vancouver', (200, 450))
+ *
+ * If you provide cumulativeSum a partition function you get the same result
+ * but you allow for more than one reducer per group. This is useful for
+ * when you have a single group that has a very large number of entries.
+ * For example in the previous example if you gave a partition function of the
+ * form { _ / 100 } then you would never have any one reducer deal with more
+ * than 2 entries.
+ */
+object CumulativeSum {
+  implicit def toCumulativeSum[K, U, V](pipe: TypedPipe[(K, (U, V))]) =
+    new CumulativeSumExtension(pipe)
+
+  class CumulativeSumExtension[K, U, V](
+    val pipe: TypedPipe[(K, (U, V))]) {
+    /** Takes a sortable field and a monoid and returns the cumulative sum of that monoid **/
+    def cumulativeSum(
+      implicit sg: Semigroup[V], ordU: Ordering[U], ordK: Ordering[K]): SortedGrouped[K, (U, V)] = {
+      pipe.group
+        .sortBy { case (u: U, _) => u }
+        .scanLeft(Nil: List[(U, V)]) {
+          case (acc, (u: U, v: V)) =>
+            acc match {
+              case List((previousU, previousSum)) => List((u, sg.plus(previousSum, v)))
+              case _ => List((u, v))
+            }
+        }
+        .flattenValues
+    }
+    /**
+     * An optimization of cumulativeSum for cases when a particular key has many
+     * entries. Requires a sortable partitioning of U.
+     * Accomplishes the optimization by not requiring all the entries for a
+     * single key to go through a single scan. Instead requires the sums of the
+     * partitions for a single key to go through a single scan.
+     */
+    def cumulativeSum[S](partition: U => S)(
+      implicit ordS: Ordering[S],
+      sg: Semigroup[V],
+      ordU: Ordering[U],
+      ordK: Ordering[K]): TypedPipe[(K, (U, V))] = {
+
+      val sumPerS = pipe
+        .map { case (k, (u: U, v: V)) => (k, partition(u)) -> v }
+        .sumByKey
+        .map { case ((k, s), v) => (k, (s, v)) }
+        .group
+        .sortBy { case (s, v) => s }
+        .scanLeft(None: Option[(Option[V], V, S)]) {
+          case (acc, (s, v)) =>
+            acc match {
+              case Some((previousPreviousSum, previousSum, previousS)) => {
+                Some((Some(previousSum), sg.plus(v, previousSum), s))
+              }
+              case _ => Some((None, v, s))
+            }
+        }
+        .flatMap{
+          case (k, maybeAcc) =>
+            for (
+              acc <- maybeAcc;
+              previousSum <- acc._1
+            ) yield { (k, acc._3) -> (None, previousSum) }
+        }
+
+      val summands = pipe
+        .map {
+          case (k, (u: U, v: V)) =>
+            (k, partition(u)) -> (Some(u), v)
+        } ++ sumPerS
+
+      summands
+        .group
+        .sortBy { case (u, _) => u }
+        .scanLeft(None: Option[(Option[U], V)]) {
+          case (acc, (maybeU, v)) =>
+            acc match {
+              case Some((_, previousSum)) => Some((maybeU, sg.plus(v, previousSum)))
+              case _ => Some((maybeU, v))
+            }
+        }
+        .flatMap {
+          case ((k, s), acc) =>
+            for (uv <- acc; u <- uv._1) yield {
+              (k, (u, uv._2))
+            }
+        }
+    }
+  }
+}
+
diff --git a/scalding-core/src/test/scala/com/twitter/scalding/CumulitiveSumTest.scala b/scalding-core/src/test/scala/com/twitter/scalding/CumulitiveSumTest.scala
new file mode 100644
index 0000000000..df76ab3fe9
--- /dev/null
+++ b/scalding-core/src/test/scala/com/twitter/scalding/CumulitiveSumTest.scala
@@ -0,0 +1,93 @@
+package com.twitter.scalding
+
+import org.specs._
+import com.twitter.scalding._
+
+import com.twitter.scalding.typed.CumulativeSum._
+
+class AddRankingWithCumulativeSum(args: Args) extends Job(args) {
+  TypedPipe.from(TypedTsv[(String, Double)]("input1"))
+    .map {
+      case (gender, height) =>
+        (gender, (height, 1L))
+    }
+    .cumulativeSum
+    .map {
+      case (gender, (height, rank)) =>
+        (gender, height, rank)
+    }
+    .write(TypedTsv("result1"))
+}
+
+class AddRankingWithPartitionedCumulativeSum(args: Args) extends Job(args) {
+  TypedPipe.from(TypedTsv[(String, Double)]("input1"))
+    .map {
+      case (gender, height) =>
+        (gender, (height, 1L))
+    }
+    .cumulativeSum { h => (h / 100).floor.toLong }
+    .map {
+      case (gender, (height, rank)) =>
+        (gender, height, rank)
+    }
+    .write(TypedTsv("result1"))
+}
+
+class CumulativeSumTest1 extends Specification {
+
+  // --- A simple ranking job
+  val sampleInput1 = List(
+    ("male", "165.2"),
+    ("female", "172.2"),
+    ("male", "184.1"),
+    ("male", "125.4"),
+    ("female", "128.6"),
+    ("male", "265.2"),
+    ("female", "272.2"),
+    ("male", "284.1"),
+    ("male", "225.4"),
+    ("female", "228.6"))
+
+  // Each group sorted and ranking added highest person to shortest
+  val expectedOutput1 = Set(
+    ("male", 184.1, 3),
+    ("male", 165.2, 2),
+    ("male", 125.4, 1),
+    ("female", 172.2, 2),
+    ("female", 128.6, 1),
+    ("male", 284.1, 6),
+    ("male", 265.2, 5),
+    ("male", 225.4, 4),
+    ("female", 272.2, 4),
+    ("female", 228.6, 3))
+
+  "A simple ranking cumulative sum job" should {
+    JobTest("com.twitter.scalding.AddRankingWithCumulativeSum")
+      .source(TypedTsv[(String, Double)]("input1"), sampleInput1)
+      .sink[(String, Double, Long)](TypedTsv[(String, Double, Long)]("result1")) { outBuf1 =>
+        "produce correct number of records when filtering out null values" in {
+          outBuf1.size must_== 10
+        }
+        "create correct ranking per group, 1st being the heighest person of that group" in {
+          outBuf1.toSet must_== expectedOutput1
+        }
+      }
+      .run
+      .finish
+  }
+
+  "A partitioned ranking cumulative sum job" should {
+    JobTest("com.twitter.scalding.AddRankingWithPartitionedCumulativeSum")
+      .source(TypedTsv[(String, Double)]("input1"), sampleInput1)
+      .sink[(String, Double, Long)](TypedTsv[(String, Double, Long)]("result1")) { outBuf1 =>
+        "produce correct number of records when filtering out null values" in {
+          outBuf1.size must_== 10
+        }
+        "create correct ranking per group, 1st being the heighest person of that group" in {
+          outBuf1.toSet must_== expectedOutput1
+        }
+      }
+      .run
+      .finish
+  }
+}