Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/897#discussion_r13365446
--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
---
@@ -214,39 +213,88 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
}
/**
+ * :: Experimental ::
+ *
* Return approximate number of distinct values for each key in this 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 vice
versa. Uses the provided
- * Partitioner to partition the output RDD.
+ *
+ * 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://dx.doi.org/10.1145/2452376.2452456";>here</a>.
+ *
+ * The relative accuracy 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.
+ *
+ *@param p The precision value for the normal set.
+ * `p` must be a value between 4 and `sp` (32 max).
--- End diff --
+ "if `sp` is not zero"
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