perhaps renaming to Spark ML would actually clear up code and documentation confusion?
+1 for rename > On Apr 5, 2016, at 7:00 PM, Reynold Xin <r...@databricks.com> wrote: > > +1 > > This is a no brainer IMO. > > >> On Tue, Apr 5, 2016 at 7:32 PM, Joseph Bradley <jos...@databricks.com> wrote: >> +1 By the way, the JIRA for tracking (Scala) API parity is: >> https://issues.apache.org/jira/browse/SPARK-4591 >> >>> On Tue, Apr 5, 2016 at 4:58 PM, Matei Zaharia <matei.zaha...@gmail.com> >>> wrote: >>> This sounds good to me as well. The one thing we should pay attention to is >>> how we update the docs so that people know to start with the spark.ml >>> classes. Right now the docs list spark.mllib first and also seem more >>> comprehensive in that area than in spark.ml, so maybe people naturally move >>> towards that. >>> >>> Matei >>> >>>> On Apr 5, 2016, at 4:44 PM, Xiangrui Meng <m...@databricks.com> wrote: >>>> >>>> Yes, DB (cc'ed) is working on porting the local linear algebra library >>>> over (SPARK-13944). There are also frequent pattern mining algorithms we >>>> need to port over in order to reach feature parity. -Xiangrui >>>> >>>>> On Tue, Apr 5, 2016 at 12:08 PM Shivaram Venkataraman >>>>> <shiva...@eecs.berkeley.edu> wrote: >>>>> Overall this sounds good to me. One question I have is that in >>>>> addition to the ML algorithms we have a number of linear algebra >>>>> (various distributed matrices) and statistical methods in the >>>>> spark.mllib package. Is the plan to port or move these to the spark.ml >>>>> namespace in the 2.x series ? >>>>> >>>>> Thanks >>>>> Shivaram >>>>> >>>>> On Tue, Apr 5, 2016 at 11:48 AM, Sean Owen <so...@cloudera.com> wrote: >>>>> > FWIW, all of that sounds like a good plan to me. Developing one API is >>>>> > certainly better than two. >>>>> > >>>>> > On Tue, Apr 5, 2016 at 7:01 PM, Xiangrui Meng <men...@gmail.com> wrote: >>>>> >> Hi all, >>>>> >> >>>>> >> More than a year ago, in Spark 1.2 we introduced the ML pipeline API >>>>> >> built >>>>> >> on top of Spark SQL’s DataFrames. Since then the new DataFrame-based >>>>> >> API has >>>>> >> been developed under the spark.ml package, while the old RDD-based API >>>>> >> has >>>>> >> been developed in parallel under the spark.mllib package. While it was >>>>> >> easier to implement and experiment with new APIs under a new package, >>>>> >> it >>>>> >> became harder and harder to maintain as both packages grew bigger and >>>>> >> bigger. And new users are often confused by having two sets of APIs >>>>> >> with >>>>> >> overlapped functions. >>>>> >> >>>>> >> We started to recommend the DataFrame-based API over the RDD-based API >>>>> >> in >>>>> >> Spark 1.5 for its versatility and flexibility, and we saw the >>>>> >> development >>>>> >> and the usage gradually shifting to the DataFrame-based API. Just >>>>> >> counting >>>>> >> the lines of Scala code, from 1.5 to the current master we added ~10000 >>>>> >> lines to the DataFrame-based API while ~700 to the RDD-based API. So, >>>>> >> to >>>>> >> gather more resources on the development of the DataFrame-based API >>>>> >> and to >>>>> >> help users migrate over sooner, I want to propose switching RDD-based >>>>> >> MLlib >>>>> >> APIs to maintenance mode in Spark 2.0. What does it mean exactly? >>>>> >> >>>>> >> * We do not accept new features in the RDD-based spark.mllib package, >>>>> >> unless >>>>> >> they block implementing new features in the DataFrame-based spark.ml >>>>> >> package. >>>>> >> * We still accept bug fixes in the RDD-based API. >>>>> >> * We will add more features to the DataFrame-based API in the 2.x >>>>> >> series to >>>>> >> reach feature parity with the RDD-based API. >>>>> >> * Once we reach feature parity (possibly in Spark 2.2), we will >>>>> >> deprecate >>>>> >> the RDD-based API. >>>>> >> * We will remove the RDD-based API from the main Spark repo in Spark >>>>> >> 3.0. >>>>> >> >>>>> >> Though the RDD-based API is already in de facto maintenance mode, this >>>>> >> announcement will make it clear and hence important to both MLlib >>>>> >> developers >>>>> >> and users. So we’d greatly appreciate your feedback! >>>>> >> >>>>> >> (As a side note, people sometimes use “Spark ML” to refer to the >>>>> >> DataFrame-based API or even the entire MLlib component. This also >>>>> >> causes >>>>> >> confusion. To be clear, “Spark ML” is not an official name and there >>>>> >> are no >>>>> >> plans to rename MLlib to “Spark ML” at this time.) >>>>> >> >>>>> >> Best, >>>>> >> Xiangrui >>>>> > >>>>> > --------------------------------------------------------------------- >>>>> > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>>>> > For additional commands, e-mail: user-h...@spark.apache.org >>>>> > >
