Yes. At this rate I think it's better to do 2.4 next, followed by 3.0.
On Fri, Jun 15, 2018 at 10:52 AM Mridul Muralidharan <mri...@gmail.com> wrote: > I agree, I dont see pressing need for major version bump as well. > > > Regards, > Mridul > On Fri, Jun 15, 2018 at 10:25 AM Mark Hamstra <m...@clearstorydata.com> > wrote: > > > > Changing major version numbers is not about new features or a vague > notion that it is time to do something that will be seen to be a > significant release. It is about breaking stable public APIs. > > > > I still remain unconvinced that the next version can't be 2.4.0. > > > > On Fri, Jun 15, 2018 at 1:34 AM Andy <andyye...@gmail.com> wrote: > >> > >> Dear all: > >> > >> It have been 2 months since this topic being proposed. Any progress > now? 2018 has been passed about 1/2. > >> > >> I agree with that the new version should be some exciting new feature. > How about this one: > >> > >> 6. ML/DL framework to be integrated as core component and feature. > (Such as Angel / BigDL / ……) > >> > >> 3.0 is a very important version for an good open source project. It > should be better to drift away the historical burden and focus in new area. > Spark has been widely used all over the world as a successful big data > framework. And it can be better than that. > >> > >> Andy > >> > >> > >> On Thu, Apr 5, 2018 at 7:20 AM Reynold Xin <r...@databricks.com> wrote: > >>> > >>> There was a discussion thread on scala-contributors about Apache Spark > not yet supporting Scala 2.12, and that got me to think perhaps it is about > time for Spark to work towards the 3.0 release. By the time it comes out, > it will be more than 2 years since Spark 2.0. > >>> > >>> For contributors less familiar with Spark’s history, I want to give > more context on Spark releases: > >>> > >>> 1. Timeline: Spark 1.0 was released May 2014. Spark 2.0 was July 2016. > If we were to maintain the ~ 2 year cadence, it is time to work on Spark > 3.0 in 2018. > >>> > >>> 2. Spark’s versioning policy promises that Spark does not break stable > APIs in feature releases (e.g. 2.1, 2.2). API breaking changes are > sometimes a necessary evil, and can be done in major releases (e.g. 1.6 to > 2.0, 2.x to 3.0). > >>> > >>> 3. That said, a major version isn’t necessarily the playground for > disruptive API changes to make it painful for users to update. The main > purpose of a major release is an opportunity to fix things that are broken > in the current API and remove certain deprecated APIs. > >>> > >>> 4. Spark as a project has a culture of evolving architecture and > developing major new features incrementally, so major releases are not the > only time for exciting new features. For example, the bulk of the work in > the move towards the DataFrame API was done in Spark 1.3, and Continuous > Processing was introduced in Spark 2.3. Both were feature releases rather > than major releases. > >>> > >>> > >>> You can find more background in the thread discussing Spark 2.0: > http://apache-spark-developers-list.1001551.n3.nabble.com/A-proposal-for-Spark-2-0-td15122.html > >>> > >>> > >>> The primary motivating factor IMO for a major version bump is to > support Scala 2.12, which requires minor API breaking changes to Spark’s > APIs. Similar to Spark 2.0, I think there are also opportunities for other > changes that we know have been biting us for a long time but can’t be > changed in feature releases (to be clear, I’m actually not sure they are > all good ideas, but I’m writing them down as candidates for consideration): > >>> > >>> 1. Support Scala 2.12. > >>> > >>> 2. Remove interfaces, configs, and modules (e.g. Bagel) deprecated in > Spark 2.x. > >>> > >>> 3. Shade all dependencies. > >>> > >>> 4. Change the reserved keywords in Spark SQL to be more ANSI-SQL > compliant, to prevent users from shooting themselves in the foot, e.g. > “SELECT 2 SECOND” -- is “SECOND” an interval unit or an alias? To make it > less painful for users to upgrade here, I’d suggest creating a flag for > backward compatibility mode. > >>> > >>> 5. Similar to 4, make our type coercion rule in DataFrame/SQL more > standard compliant, and have a flag for backward compatibility. > >>> > >>> 6. Miscellaneous other small changes documented in JIRA already (e.g. > “JavaPairRDD flatMapValues requires function returning Iterable, not > Iterator”, “Prevent column name duplication in temporary view”). > >>> > >>> > >>> Now the reality of a major version bump is that the world often thinks > in terms of what exciting features are coming. I do think there are a > number of major changes happening already that can be part of the 3.0 > release, if they make it in: > >>> > >>> 1. Scala 2.12 support (listing it twice) > >>> 2. Continuous Processing non-experimental > >>> 3. Kubernetes support non-experimental > >>> 4. A more flushed out version of data source API v2 (I don’t think it > is realistic to stabilize that in one release) > >>> 5. Hadoop 3.0 support > >>> 6. ... > >>> > >>> > >>> > >>> Similar to the 2.0 discussion, this thread should focus on the > framework and whether it’d make sense to create Spark 3.0 as the next > release, rather than the individual feature requests. Those are important > but are best done in their own separate threads. > >>> > >>> > >>> > >>> >
