Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/897#discussion_r13359976
--- Diff: core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
---
@@ -214,40 +213,112 @@ 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://research.google.com/pubs/pub40671.html";>here</a>.
+ *
+ * @param p The precision value for the normal set.
+ * `p` must be a value between 4 and `sp` (32 max).
+ * @param sp The precision value for the sparse set, between 0 and 32.
+ * If `sp` equals 0, the sparse representation is skipped.
+ * @param partitioner Partitioner to use for the resulting RDD.
*/
- 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)
+ @Experimental
+ def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner):
RDD[(K, Long)] = {
+ val createHLL = (v: V) => {
+ val hll = new HyperLogLogPlus(p, sp)
+ hll.offer(v)
+ hll
+ }
+ val mergeValueHLL = (hll: HyperLogLogPlus, v: V) => {
+ hll.offer(v)
+ hll
+ }
+ val mergeHLL = (h1: HyperLogLogPlus, h2: HyperLogLogPlus) => {
+ h1.addAll(h2)
+ h1
+ }
+
+ combineByKey(createHLL, mergeValueHLL, mergeHLL,
partitioner).mapValues(_.cardinality())
+ }
+
+ /**
+ * :: Experimental ::
+ *
+ * Return approximate number of distinct values for each key in this 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>.
+ *
+ * @param p The precision value for the normal set.
+ * `p` must be a value between 4 and `sp` (32 max).
+ * @param sp The precision value for the sparse set, between 0 and 32.
+ * If `sp` equals 0, the sparse representation is skipped.
+ * @param numPartitions Number of partitions in the resulting RDD.
+ */
+ @Experimental
+ def countApproxDistinctByKey(p: Int, sp: Int, numPartitions: Int):
RDD[(K, Long)] = {
+ countApproxDistinctByKey(p, sp, new HashPartitioner(numPartitions))
+ }
- combineByKey(createHLL, mergeValueHLL, mergeHLL,
partitioner).mapValues(_.value.cardinality())
+ /**
+ * :: Experimental ::
+ *
+ * Return approximate number of distinct values for each key in this 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>.
+ *
+ * @param p The precision value for the normal set.
+ * `p` must be a value between 4 and `sp` (32 max).
+ * @param sp The precision value for the sparse set, between 0 and 32.
+ * If `sp` equals 0, the sparse representation is skipped.
+ */
+ @Experimental
+ def countApproxDistinctByKey(p: Int, sp: Int): RDD[(K, Long)] = {
+ countApproxDistinctByKey(p, sp, defaultPartitioner(self))
}
/**
* 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. HashPartitions the
- * output RDD into numPartitions.
*
+ * 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 countApproxDistinctByKey with parameter p and sp",
"1.0.1")
+ def countApproxDistinctByKey(relativeSD: Double, partitioner:
Partitioner): RDD[(K, Long)] = {
+ // 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 --
I checked the paper. I think the correct mapping should be
~~~
val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt
~~~
`HyperLogLogPlus` uses 64-bit hash. The constant `beta` is `1.054` in the
paper. To guarantee the precision, we need `ceil`. We also need to check
whether `p` is out of bound if `relativeSD` is really small.
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