I also agree with this given the way we develop stuff. We don't really want to move to possibly-API-breaking major releases super often, but we do have lots of large features that come out all the time, and our current name doesn't convey that.
Matei > On Jul 28, 2016, at 4:15 PM, Reynold Xin <r...@databricks.com> wrote: > > Yea definitely. Those are consistent with what is defined here: > https://cwiki.apache.org/confluence/display/SPARK/Spark+Versioning+Policy > <https://cwiki.apache.org/confluence/display/SPARK/Spark+Versioning+Policy> > > The only change I'm proposing is replacing "minor" with "feature". > > > On Thu, Jul 28, 2016 at 4:10 PM, Sean Owen <so...@cloudera.com > <mailto:so...@cloudera.com>> wrote: > Although 'minor' is the standard term, the important thing is making > the nature of the release understood. 'feature release' seems OK to me > as an additional description. > > Is it worth agreeing on or stating a little more about the theory? > > patch release: backwards/forwards compatible within a minor release, > generally fixes only > minor/feature release: backwards compatible within a major release, > not forward; generally also includes new features > major release: not backwards compatible and may remove or change > existing features > > On Thu, Jul 28, 2016 at 3:46 PM, Reynold Xin <r...@databricks.com > <mailto:r...@databricks.com>> wrote: > > tl;dr > > > > I would like to propose renaming “minor release” to “feature release” in > > Apache Spark. > > > > > > details > > > > Apache Spark’s official versioning policy follows roughly semantic > > versioning. Each Spark release is versioned as > > [major].[minor].[maintenance]. That is to say, 1.0.0 and 2.0.0 are both > > “major releases”, whereas “1.1.0” and “1.3.0” would be minor releases. > > > > I have gotten a lot of feedback from users that the word “minor” is > > confusing and does not accurately describes those releases. When users hear > > the word “minor”, they think it is a small update that introduces couple > > minor features and some bug fixes. But if you look at the history of Spark > > 1.x, here are just a subset of large features added: > > > > Spark 1.1: sort-based shuffle, JDBC/ODBC server, new stats library, 2-5X > > perf improvement for machine learning. > > > > Spark 1.2: HA for streaming, new network module, Python API for streaming, > > ML pipelines, data source API. > > > > Spark 1.3: DataFrame API, Spark SQL graduate out of alpha, tons of new > > algorithms in machine learning. > > > > Spark 1.4: SparkR, Python 3 support, DAG viz, robust joins in SQL, math > > functions, window functions, SQL analytic functions, Python API for > > pipelines. > > > > Spark 1.5: code generation, Project Tungsten > > > > Spark 1.6: automatic memory management, Dataset API, ML pipeline persistence > > > > > > So while “minor” is an accurate depiction of the releases from an API > > compatibiility point of view, we are miscommunicating and doing Spark a > > disservice by calling these releases “minor”. I would actually call these > > releases “major”, but then it would be a larger deviation from semantic > > versioning. I think calling these “feature releases” would be a smaller > > change and a more accurate depiction of what they are. > > > > That said, I’m not attached to the name “feature” and am open to > > suggestions, as long as they don’t convey the notion of “minor”. > > > > >
