I think these are good questions to bring up, Imran. Here are my thoughts on them (I’ve thought about some of these in the past):
On Feb 6, 2014, at 12:39 PM, Imran Rashid <[email protected]> wrote: > I don't really agree with this logic. I think we haven't broken API so far > because we just keep adding stuff on to it, and we haven't bothered to > clean the api up, specifically to *avoid* breaking things. Here's a > handful of api breaking things that we might want to consider: > > * should we look at all the various configuration properties, and maybe > some of them should get renamed for consistency / clarity? I know that some names are suboptimal, but I absolutely detest breaking APIs, config names, etc. I’ve seen it happen way too often in other projects (even things we depend on that are officially post-1.0, like Akka or Protobuf or Hadoop), and it’s very painful. I think that we as fairly cutting-edge users are okay with libraries occasionally changing, but many others will consider it a show-stopper. Given this, I think that any cosmetic change now, even though it might improve clarity slightly, is not worth the tradeoff in terms of creating an update barrier for existing users. > * do all of the functions on RDD need to be in core? or do some of them > that are simple additions built on top of the primitives really belong in a > "utils" package or something? Eg., maybe we should get rid of all the > variants of the mapPartitions / mapWith / etc. just have map, and > mapPartitionsWithIndex (too many choices in the api can also be confusing > to the user) Again, for the reason above, I’d keep them where they are and consider adding other stuff later. Also personally I want to optimize the API for usability, not for Spark developers. If it’s easier for a user to call RDD.mapPartitions instead of AdvancedUtils.mapPartitions(rdd, func), and the only cost is a longer RDD.scala class, I’d go for the former. If you think there are some API methods that should just go away, that would be good to discuss — we can deprecate them for example. > * are the right things getting tracked in SparkListener? Do we need to add > or remove anything? This is an API that will probably be experimental or semi-private at first. Anyway, as I said, these are good questions — I’d be happy to see suggestions on any of these fronts. I just wanted to point out the importance of compatibility. I think it’s been awesome that most of our users have been able to keep up with the latest version of Spark, getting all the new fixes and simultaneously increasing the amount of contributions we get on master and decreasing the backporting burden on old branches. We might take it for granted, but I’ve seen similar projects that didn't manage to do this. In particular, compatibility in Hadoop has been a mess, with some major users diverging from Apache early (e.g. Facebook) and never being able to contribute back, and with big API cleanups (e.g. mapred -> mapreduce) being proposed after the project already had a lot of momentum and never making it through. The experience of seeing those has made me very conservative. The longer we can keep a unified community, the better it will be for all users of the project. Matei > > This is probably not the right list of questions, that's just an idea of > the kind of thing we should be thinking about. > > Its also fine with me if 1.0 is next, I just think that we ought to be > asking these kinds of questions up and down the entire api before we > release 1.0. And given that we haven't even started that discussion, it > seems possible that there could be new features we'd like to release in > 0.10 before that discussion is finished. > > > > On Thu, Feb 6, 2014 at 12:56 PM, Matei Zaharia <[email protected]>wrote: > >> I think it's important to do 1.0 next. The project has been around for 4 >> years, and I'd be comfortable maintaining the current codebase for a long >> time in an API and binary compatible way through 1.x releases. Over the >> past 4 years we haven't actually had major changes to the user-facing API -- >> the only ones were changing the package to org.apache.spark, and upgrading >> the Scala version. I'd be okay leaving 1.x to always use Scala 2.10 for >> example, or later cross-building it for Scala 2.11. Updating to 1.0 says >> two things: it tells users that they can be confident that version will be >> maintained for a long time, which we absolutely want to do, and it lets >> outsiders see that the project is now fairly mature (for many people, >> pre-1.0 might still cause them not to try it). I think both are good for >> the community. >> >> Regarding binary compatibility, I agree that it's what we should strive >> for, but it just seems premature to codify now. Let's see how it works >> between, say, 1.0 and 1.1, and then we can codify it. >> >> Matei >> >> On Feb 6, 2014, at 10:43 AM, Henry Saputra <[email protected]> >> wrote: >> >>> Thanks Patick to initiate the discussion about next road map for Apache >> Spark. >>> >>> I am +1 for 0.10.0 for next version. >>> >>> It will give us as community some time to digest the process and the >>> vision and make adjustment accordingly. >>> >>> Release a 1.0.0 is a huge milestone and if we do need to break API >>> somehow or modify internal behavior dramatically we could take >>> advantage to release 1.0.0 as good step to go to. >>> >>> >>> - Henry >>> >>> >>> >>> On Wed, Feb 5, 2014 at 9:52 PM, Andrew Ash <[email protected]> wrote: >>>> Agree on timeboxed releases as well. >>>> >>>> Is there a vision for where we want to be as a project before declaring >> the >>>> first 1.0 release? While we're in the 0.x days per semver we can break >>>> backcompat at will (though we try to avoid it where possible), and that >>>> luxury goes away with 1.x I just don't want to release a 1.0 simply >>>> because it seems to follow after 0.9 rather than making an intentional >>>> decision that we're at the point where we can stand by the current APIs >> and >>>> binary compatibility for the next year or so of the major release. >>>> >>>> Until that decision is made as a group I'd rather we do an immediate >>>> version bump to 0.10.0-SNAPSHOT and then if discussion warrants it >> later, >>>> replace that with 1.0.0-SNAPSHOT. It's very easy to go from 0.10 to 1.0 >>>> but not the other way around. >>>> >>>> https://github.com/apache/incubator-spark/pull/542 >>>> >>>> Cheers! >>>> Andrew >>>> >>>> >>>> On Wed, Feb 5, 2014 at 9:49 PM, Heiko Braun <[email protected] >>> wrote: >>>> >>>>> +1 on time boxed releases and compatibility guidelines >>>>> >>>>> >>>>>> Am 06.02.2014 um 01:20 schrieb Patrick Wendell <[email protected]>: >>>>>> >>>>>> Hi Everyone, >>>>>> >>>>>> In an effort to coordinate development amongst the growing list of >>>>>> Spark contributors, I've taken some time to write up a proposal to >>>>>> formalize various pieces of the development process. The next release >>>>>> of Spark will likely be Spark 1.0.0, so this message is intended in >>>>>> part to coordinate the release plan for 1.0.0 and future releases. >>>>>> I'll post this on the wiki after discussing it on this thread as >>>>>> tentative project guidelines. >>>>>> >>>>>> == Spark Release Structure == >>>>>> Starting with Spark 1.0.0, the Spark project will follow the semantic >>>>>> versioning guidelines (http://semver.org/) with a few deviations. >>>>>> These small differences account for Spark's nature as a multi-module >>>>>> project. >>>>>> >>>>>> Each Spark release will be versioned: >>>>>> [MAJOR].[MINOR].[MAINTENANCE] >>>>>> >>>>>> All releases with the same major version number will have API >>>>>> compatibility, defined as [1]. Major version numbers will remain >>>>>> stable over long periods of time. For instance, 1.X.Y may last 1 year >>>>>> or more. >>>>>> >>>>>> Minor releases will typically contain new features and improvements. >>>>>> The target frequency for minor releases is every 3-4 months. One >>>>>> change we'd like to make is to announce fixed release dates and merge >>>>>> windows for each release, to facilitate coordination. Each minor >>>>>> release will have a merge window where new patches can be merged, a QA >>>>>> window when only fixes can be merged, then a final period where voting >>>>>> occurs on release candidates. These windows will be announced >>>>>> immediately after the previous minor release to give people plenty of >>>>>> time, and over time, we might make the whole release process more >>>>>> regular (similar to Ubuntu). At the bottom of this document is an >>>>>> example window for the 1.0.0 release. >>>>>> >>>>>> Maintenance releases will occur more frequently and depend on specific >>>>>> patches introduced (e.g. bug fixes) and their urgency. In general >>>>>> these releases are designed to patch bugs. However, higher level >>>>>> libraries may introduce small features, such as a new algorithm, >>>>>> provided they are entirely additive and isolated from existing code >>>>>> paths. Spark core may not introduce any features. >>>>>> >>>>>> When new components are added to Spark, they may initially be marked >>>>>> as "alpha". Alpha components do not have to abide by the above >>>>>> guidelines, however, to the maximum extent possible, they should try >>>>>> to. Once they are marked "stable" they have to follow these >>>>>> guidelines. At present, GraphX is the only alpha component of Spark. >>>>>> >>>>>> [1] API compatibility: >>>>>> >>>>>> An API is any public class or interface exposed in Spark that is not >>>>>> marked as semi-private or experimental. Release A is API compatible >>>>>> with release B if code compiled against release A *compiles cleanly* >>>>>> against B. This does not guarantee that a compiled application that is >>>>>> linked against version A will link cleanly against version B without >>>>>> re-compiling. Link-level compatibility is something we'll try to >>>>>> guarantee that as well, and we might make it a requirement in the >>>>>> future, but challenges with things like Scala versions have made this >>>>>> difficult to guarantee in the past. >>>>>> >>>>>> == Merging Pull Requests == >>>>>> To merge pull requests, committers are encouraged to use this tool [2] >>>>>> to collapse the request into one commit rather than manually >>>>>> performing git merges. It will also format the commit message nicely >>>>>> in a way that can be easily parsed later when writing credits. >>>>>> Currently it is maintained in a public utility repository, but we'll >>>>>> merge it into mainline Spark soon. >>>>>> >>>>>> [2] >>>>> https://github.com/pwendell/spark-utils/blob/master/apache_pr_merge.py >>>>>> >>>>>> == Tentative Release Window for 1.0.0 == >>>>>> Feb 1st - April 1st: General development >>>>>> April 1st: Code freeze for new features >>>>>> April 15th: RC1 >>>>>> >>>>>> == Deviations == >>>>>> For now, the proposal is to consider these tentative guidelines. We >>>>>> can vote to formalize these as project rules at a later time after >>>>>> some experience working with them. Once formalized, any deviation to >>>>>> these guidelines will be subject to a lazy majority vote. >>>>>> >>>>>> - Patrick >>>>> >> >>
