Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/897#discussion_r13360735
--- 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>.
+ * @param sp The precision value for the sparse set, between 0 and 32.
+ * If <code>sp</code> equals 0, the sparse representation is
skipped.
*/
@Experimental
+ def countApproxDistinct(p: Int, sp: Int): Long = {
+ require(p >= 4, s"p ($p) must be greater than 0")
+ require(sp <= 32, s"sp ($sp) cannot be greater than 32")
+ require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)")
+ val zeroCounter = new HyperLogLogPlus(p, sp)
+ aggregate(zeroCounter)(
+ (hll: HyperLogLogPlus, v: T) => {
+ hll.offer(v)
+ hll
+ },
+ (h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
+ h1.addAll(h2)
+ h2
+ }).cardinality()
+ }
+
+ /**
+ * Return approximate number of distinct elements in the 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://research.google.com/pubs/pub40671.html";>here</a>.
+ */
+ @deprecated("Use countApproxDistinct with parameter p and sp", "1.0.1")
def countApproxDistinct(relativeSD: Double = 0.05): Long = {
- val zeroCounter = new SerializableHyperLogLog(new
HyperLogLog(relativeSD))
- aggregate(zeroCounter)(_.add(_), _.merge(_)).value.cardinality()
+ // See stream-lib's HyperLogLog implementation on the conversion from
relativeSD to p.
+ val p = (math.log((1.106 / relativeSD) * (1.106 / relativeSD)) /
math.log(2)).toInt
--- End diff --
See the formula I mentioned above.
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