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 >> <https://contributors.scala-lang.org/t/spark-as-a-scala-gateway-drug-and-the-2-12-failure/1747> >> 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. >> >> >> >> >>
