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From Sean Owen <>
Subject Re: Switch RDD-based MLlib APIs to maintenance mode in Spark 2.0
Date Tue, 05 Apr 2016 18:48:22 GMT
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 <> 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 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
> 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

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