SPARK-1117: update accumulator docs

The current doc hints spark doesn't support accumulators of type `Long`, which is wrong.

JIRA: https://spark-project.atlassian.net/browse/SPARK-1117

Author: Xiangrui Meng <[email protected]>

Closes apache#631 from mengxr/acc and squashes the following commits:

45ecd25 [Xiangrui Meng] update accumulator docs

Author: mengxr

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

@@ -189,8 +189,8 @@ class GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Ser
  * A simpler value of [[Accumulable]] where the result type being accumulated is the same
  * as the types of elements being merged, i.e. variables that are only "added" to through an
  * associative operation and can therefore be efficiently supported in parallel. They can be used
- * to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of type
- * `Int` and `Double`, and programmers can add support for new types.
+ * to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric
+ * value types, and programmers can add support for new types.
  *
  * An accumulator is created from an initial value `v` by calling [[SparkContext#accumulator]].
  * Tasks running on the cluster can then add to it using the [[Accumulable#+=]] operator.

docs/scala-programming-guide.md

@@ -344,7 +344,7 @@ After the broadcast variable is created, it should be used instead of the value
 
 ## Accumulators
 
-Accumulators are variables that are only "added" to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of type Int and Double, and programmers can add support for new types.
+Accumulators are variables that are only "added" to through an associative operation and can therefore be efficiently supported in parallel. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric value types and standard mutable collections, and programmers can add support for new types.
 
 An accumulator is created from an initial value `v` by calling `SparkContext.accumulator(v)`. Tasks running on the cluster can then add to it using the `+=` operator. However, they cannot read its value. Only the driver program can read the accumulator's value, using its `value` method.