Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/897#discussion_r13360624
--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -921,15 +920,44 @@ abstract class RDD[T: ClassTag](
* :: Experimental ::
* Return approximate number of distinct elements in the RDD.
*
- * The accuracy of approximation can be controlled through the relative
standard deviation
- * (relativeSD) parameter, which also controls the amount of memory
used. Lower values result in
- * more accurate counts but increase the memory footprint and vise
versa. The default value of
- * relativeSD is 0.05.
+ * The algorithm used is based on streamlib's implementation of
"HyperLogLog in Practice:
+ * Algorithmic Engineering of a State of The Art Cardinality Estimation
Algorithm", available
+ * <a href="http://research.google.com/pubs/pub40671.html";>here</a>.
+ *
+ * @param p The precision value for the normal set.
+ * <code>p</code> must be a value between 4 and <code>sp</code>.
--- End diff --
The relative standard error is approximately `1.054 / sqrt(2^p)`. Setting a
nonzero `sp > p` would trigger sparse representation of registers, which may
reduce the memory consumption and increase accuracy WHEN the cardinality is
small.
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