Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/897#discussion_r13357467
--- Diff: core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
---
@@ -672,40 +672,102 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
/**
* 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 vise
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): JavaRDD[(K, Long)] = {
- rdd.countApproxDistinctByKey(relativeSD, partitioner)
+ def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner):
JavaPairRDD[K, Long] = {
+ fromRDD(rdd.countApproxDistinctByKey(p, sp, partitioner))
}
/**
- * Return approximate number of distinct values for each key 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 vise
versa. The default value of
- * relativeSD is 0.05. Hash-partitions the output RDD using the existing
partitioner/parallelism
- * level.
+ * 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 The number of partitions in the resulting RDD.
*/
- def countApproxDistinctByKey(relativeSD: Double = 0.05): JavaRDD[(K,
Long)] = {
- rdd.countApproxDistinctByKey(relativeSD)
+ def countApproxDistinctByKey(p: Int, sp: Int, numPartitions: Int):
JavaPairRDD[K, Long] = {
+ fromRDD(rdd.countApproxDistinctByKey(p, sp, numPartitions))
}
-
/**
* 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 vise
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>.
+ *
+ * @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.
+ */
+ def countApproxDistinctByKey(p: Int, sp: Int): JavaPairRDD[K, Long] = {
+ fromRDD(rdd.countApproxDistinctByKey(p, sp))
+ }
+
+ /**
+ * Return approximate number of distinct values for each key in this
RDD. This is deprecated.
+ * Use the variant with `p` and `sp` parameters instead.
+ *
+ * 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 relativeSD The relative standard deviation for the counter.
+ * Smaller values create counters that require more
space.
*/
+ @Deprecated
--- End diff --
We need to provide some migration tips. Is there a mapping from
`relativeSD` to precision numbers? Actually, `relativeSD` is much easier for
users to understand.
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