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. >>> >>> >>> >>> --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
