Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/2142#discussion_r16816578
--- Diff: python/pyspark/rdd.py ---
@@ -1993,11 +1993,38 @@ def meanApprox(self, timeout, confidence=0.95):
>>> (rdd.meanApprox(1000) - r) / r < 0.05
True
"""
- jrdd = self.map(float)._to_jrdd()
+ jrdd = self.map(float)._to_java_object_rdd()
jdrdd = self.ctx._jvm.JavaDoubleRDD.fromRDD(jrdd.rdd())
r = jdrdd.meanApprox(timeout, confidence).getFinalValue()
return BoundedFloat(r.mean(), r.confidence(), r.low(), r.high())
+ def countApproxDistinct(self, relativeSD=0.05):
+ """
+ :: Experimental ::
+ 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://dx.doi.org/10.1145/2452376.2452456";>here</a>.
+
+ This support all the types of objects, which is supported by
+ Pyrolite, nearly all builtin types.
+
+ @param relativeSD Relative accuracy. Smaller values create
+ counters that require more space.
+ It must be greater than 0.000017.
+
+ >>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct()
--- End diff --
can u add a test to make sure that if u have 1000 non-distinct elements
(i.e. the same element appearing 1000 times), this doesn't return ~ 1000?
Asking because I'm not sure how pyspark interacts with Java - if it is
through byte array, then the hashcode could be wrong for byte arrays.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
