+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 >> > >> > >
