Yes - it's very interesting. However, ideally we should have a version of hyperloglog that can work directly against some raw bytes in memory (rather than java objects), in order for this to fit the Tungsten execution model where everything is operating directly against some memory address.
On Wed, Jul 1, 2015 at 11:00 PM, Nick Pentreath <nick.pentre...@gmail.com> wrote: > Sure I can copy the code but my aim was more to understand: > > (A) if this is broadly interesting enough to folks to think about updating > / extending the existing UDAF within Spark > (b) how to register ones own custom UDAF - in which case it could be a > Spark package for example > > All examples deal with registering a UDF but nothing about UDAFs > > — > Sent from Mailbox <https://www.dropbox.com/mailbox> > > > On Wed, Jul 1, 2015 at 6:32 PM, Daniel Darabos < > daniel.dara...@lynxanalytics.com> wrote: > >> It's already possible to just copy the code from countApproxDistinct >> <https://github.com/apache/spark/blob/v1.4.0/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L1153> >> and >> access the HLL directly, or do anything you like. >> >> On Wed, Jul 1, 2015 at 5:26 PM, Nick Pentreath <nick.pentre...@gmail.com> >> wrote: >> >>> Any thoughts? >>> >>> — >>> Sent from Mailbox <https://www.dropbox.com/mailbox> >>> >>> >>> On Tue, Jun 23, 2015 at 11:19 AM, Nick Pentreath < >>> nick.pentre...@gmail.com> wrote: >>> >>>> Hey Spark devs >>>> >>>> I've been looking at DF UDFs and UDAFs. The approx distinct is using >>>> hyperloglog, >>>> but there is only an option to return the count as a Long. >>>> >>>> It can be useful to be able to return and store the actual data >>>> structure (ie serialized HLL). This effectively allows one to do >>>> aggregation / rollups over columns while still preserving the ability to >>>> get distinct counts. >>>> >>>> For example, one can store daily aggregates of events, grouped by >>>> various columns, while storing for each grouping the HLL of say unique >>>> users. So you can get the uniques per day directly but could also very >>>> easily do arbitrary aggregates (say monthly, annually) and still be able to >>>> get a unique count for that period by merging the daily HLLS. >>>> >>>> I did this a while back as a Hive UDAF ( >>>> https://github.com/MLnick/hive-udf) which returns a Struct field >>>> containing a "cardinality" field and a "binary" field containing the >>>> serialized HLL. >>>> >>>> I was wondering if there would be interest in something like this? I am >>>> not so clear on how UDTs work with regards to SerDe - so could one adapt >>>> the HyperLogLogUDT to be a Struct with the serialized HLL as a field as >>>> well as count as a field? Then I assume this would automatically play >>>> nicely with DataFrame I/O etc. The gotcha is one needs to then call >>>> "approx_count_field.count" (or is there a concept of a "default field" for >>>> a Struct?). >>>> >>>> Also, being able to provide the bitsize parameter may be useful... >>>> >>>> The same thinking would apply potentially to other approximate (and >>>> mergeable) data structures like T-Digest and maybe CMS. >>>> >>>> Nick >>>> >>> >>> >> >
