Github user ash211 commented on a diff in the pull request:
https://github.com/apache/spark/pull/897#discussion_r13114928
--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
---
@@ -218,14 +217,29 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
* 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.
+ * [[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 at
+ * [[http://research.google.com/pubs/pub40671.html]].
*/
def countApproxDistinctByKey(relativeSD: Double, partitioner:
Partitioner): RDD[(K, Long)] = {
- val createHLL = (v: V) => new SerializableHyperLogLog(new
HyperLogLog(relativeSD)).add(v)
- val mergeValueHLL = (hll: SerializableHyperLogLog, v: V) => hll.add(v)
- val mergeHLL = (h1: SerializableHyperLogLog, h2:
SerializableHyperLogLog) => h1.merge(h2)
+ val precision = (math.log((1.106 / relativeSD) * (1.106 / relativeSD))
/ math.log(2)).toInt
--- End diff --
Where does this magic value of 1.106 come from?
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