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 > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
