[tl;dr stable API's are important - sorry, this is slightly meandering] Hey - just wanted to chime in on this as I was travelling. Sean, you bring up great points here about the velocity and stability of Spark. Many projects have fairly customized semantics around what versions actually mean (HBase is a good, if somewhat hard-to-comprehend, example).
What the 1.X label means to Spark is that we are willing to guarantee stability for Spark's core API. This is something that actually, Spark has been doing for a while already (we've made few or no breaking changes to the Spark core API for several years) and we want to codify this for application developers. In this regard Spark has made a bunch of changes to enforce the integrity of our API's: - We went through and clearly annotated internal, or experimental API's. This was a huge project-wide effort and included Scaladoc and several other components to make it clear to users. - We implemented automated byte-code verification of all proposed pull requests that they don't break public API's. Pull requests after 1.0 will fail if they break API's that are not explicitly declared private or experimental. I can't possibly emphasize enough the importance of API stability. What we want to avoid is the Hadoop approach. Candidly, Hadoop does a poor job on this. There really isn't a well defined stable API for any of the Hadoop components, for a few reasons: 1. Hadoop projects don't do any rigorous checking that new patches don't break API's. Of course, the results in regular API breaks and a poor understanding of what is a public API. 2. In several cases it's not possible to do basic things in Hadoop without using deprecated or private API's. 3. There is significant vendor fragmentation of API's. The main focus of the Hadoop vendors is making consistent cuts of the core projects work together (HDFS/Pig/Hive/etc) - so API breaks are sometimes considered "fixed" as long as the other projects work around them (see [1]). We also regularly need to do archaeology (see [2]) and directly interact with Hadoop committers to understand what API's are stable and in which versions. One goal of Spark is to deal with the pain of inter-operating with Hadoop so that application writers don't to. We'd like to retain the property that if you build an application against the (well defined, stable) Spark API's right now, you'll be able to run it across many Hadoop vendors and versions for the entire Spark 1.X release cycle. Writing apps against Hadoop can be very difficult... consider how much more engineering effort we spent maintaining YARN support than Mesos support. There are many factors, but one is that Mesos has a single, narrow, stable API. We've never had to make a change in Mesos due to an API change, for several years. YARN on the other hand, there are at least 3 YARN API's that currently exist, which are all binary incompatible. We'd like to offer apps the ability to build against Spark's API and just let us deal with it. As more vendors packaging Spark, I'd like to see us put tools in the upstream Spark repo that do validation for vendor packages of Spark, so that we don't end up with fragmentation. Of course, vendors can enhance the API and are encouraged to, but we need a kernel of API's that vendors must maintain (think POSIX) to be considered compliant with Apache Spark. I believe some other projects like OpenStack have done this to avoid fragmentation. - Patrick [1] https://issues.apache.org/jira/browse/MAPREDUCE-5830 [2] http://2.bp.blogspot.com/-GO6HF0OAFHw/UOfNEH-4sEI/AAAAAAAAAD0/dEWFFYTRgYw/s1600/output-file.png On Sun, May 18, 2014 at 2:13 AM, Mridul Muralidharan <mri...@gmail.com> wrote: > So I think I need to clarify a few things here - particularly since > this mail went to the wrong mailing list and a much wider audience > than I intended it for :-) > > > Most of the issues I mentioned are internal implementation detail of > spark core : which means, we can enhance them in future without > disruption to our userbase (ability to support large number of > input/output partitions. Note: this is of order of 100k input and > output partitions with uniform spread of keys - very rarely seen > outside of some crazy jobs). > > Some of the issues I mentioned would reqiure DeveloperApi changes - > which are not user exposed : they would impact developer use of these > api's - which are mostly internally provided by spark. (Like fixing > blocks > 2G would require change to Serializer api) > > A smaller faction might require interface changes - note, I am > referring specifically to configuration changes (removing/deprecating > some) and possibly newer options to submit/env, etc - I dont envision > any programming api change itself. > The only api change we did was from Seq -> Iterable - which is > actually to address some of the issues I mentioned (join/cogroup). > > Remaining are bugs which need to be addressed or the feature > removed/enhanced like shuffle consolidation. > > There might be semantic extension of some things like OFF_HEAP storage > level to address other computation models - but that would not have an > impact on end user - since other options would be pluggable with > default set to Tachyon so that there is no user expectation change. > > > So will the interface possibly change ? Sure though we will try to > keep it backwardly compatible (as we did with 1.0). > Will the api change - other than backward compatible enhancements, probably > not. > > > Regards, > Mridul > > > On Sun, May 18, 2014 at 12:11 PM, Mridul Muralidharan <mri...@gmail.com> > wrote: >> >> On 18-May-2014 5:05 am, "Mark Hamstra" <m...@clearstorydata.com> wrote: >>> >>> I don't understand. We never said that interfaces wouldn't change from >>> 0.9 >> >> Agreed. >> >>> to 1.0. What we are committing to is stability going forward from the >>> 1.0.0 baseline. Nobody is disputing that backward-incompatible behavior >>> or >>> interface changes would be an issue post-1.0.0. The question is whether >> >> The point is, how confident are we that these are the right set of interface >> definitions. >> We think it is, but we could also have gone through a 0.10 to vet the >> proposed 1.0 changes to stabilize them. >> >> To give examples for which we don't have solutions currently (which we are >> facing internally here btw, so not academic exercise) : >> >> - Current spark shuffle model breaks very badly as number of partitions >> increases (input and output). >> >> - As number of nodes increase, the overhead per node keeps going up. Spark >> currently is more geared towards large memory machines; when the RAM per >> node is modest (8 to 16 gig) but large number of them are available, it does >> not do too well. >> >> - Current block abstraction breaks as data per block goes beyond 2 gig. >> >> - Cogroup/join when value per key or number of keys (or both) is high breaks >> currently. >> >> - Shuffle consolidation is so badly broken it is not funny. >> >> - Currently there is no way of effectively leveraging accelerator >> cards/coprocessors/gpus from spark - to do so, I suspect we will need to >> redefine OFF_HEAP. >> >> - Effectively leveraging ssd is still an open question IMO when you have mix >> of both available. >> >> We have resolved some of these and looking at the rest. These are not unique >> to our internal usage profile, I have seen most of these asked elsewhere >> too. >> >> Thankfully some of the 1.0 changes actually are geared towards helping to >> alleviate some of the above (Iterable change for ex), most of the rest are >> internal impl detail of spark core which helps a lot - but there are cases >> where this is not so. >> >> Unfortunately I don't know yet if the unresolved/uninvestigated issues will >> require more changes or not. >> >> Given this I am very skeptical of expecting current spark interfaces to be >> sufficient for next 1 year (forget 3) >> >> I understand this is an argument which can be made to never release 1.0 :-) >> Which is why I was ok with a 1.0 instead of 0.10 release in spite of my >> preference. >> >> This is a good problem to have IMO ... People are using spark extensively >> and in circumstances that we did not envision : necessitating changes even >> to spark core. >> >> But the claim that 1.0 interfaces are stable is not something I buy - they >> are not, we will need to break them soon and cost of maintaining backward >> compatibility will be high. >> >> We just need to make an informed decision to live with that cost, not hand >> wave it away. >> >> Regards >> Mridul >> >>> there is anything apparent now that is expected to require such disruptive >>> changes if we were to commit to the current release candidate as our >>> guaranteed 1.0.0 baseline. >>> >>> >>> On Sat, May 17, 2014 at 2:05 PM, Mridul Muralidharan >>> <mri...@gmail.com>wrote: >>> >>> > I would make the case for interface stability not just api stability. >>> > Particularly given that we have significantly changed some of our >>> > interfaces, I want to ensure developers/users are not seeing red flags. >>> > >>> > Bugs and code stability can be addressed in minor releases if found, but >>> > behavioral change and/or interface changes would be a much more invasive >>> > issue for our users. >>> > >>> > Regards >>> > Mridul >>> > On 18-May-2014 2:19 am, "Matei Zaharia" <matei.zaha...@gmail.com> wrote: >>> > >>> > > As others have said, the 1.0 milestone is about API stability, not >>> > > about >>> > > saying "we've eliminated all bugs". The sooner you declare 1.0, the >>> > sooner >>> > > users can confidently build on Spark, knowing that the application >>> > > they >>> > > build today will still run on Spark 1.9.9 three years from now. This >>> > > is >>> > > something that I've seen done badly (and experienced the effects >>> > > thereof) >>> > > in other big data projects, such as MapReduce and even YARN. The >>> > > result >>> > is >>> > > that you annoy users, you end up with a fragmented userbase where >>> > everyone >>> > > is building against a different version, and you drastically slow down >>> > > development. >>> > > >>> > > With a project as fast-growing as fast-growing as Spark in particular, >>> > > there will be new bugs discovered and reported continuously, >>> > > especially >>> > in >>> > > the non-core components. Look at the graph of # of contributors in >>> > > time >>> > to >>> > > Spark: https://www.ohloh.net/p/apache-spark (bottom-most graph; >>> > "commits" >>> > > changed when we started merging each patch as a single commit). This >>> > > is >>> > not >>> > > slowing down, and we need to have the culture now that we treat API >>> > > stability and release numbers at the level expected for a 1.0 project >>> > > instead of having people come in and randomly change the API. >>> > > >>> > > I'll also note that the issues marked "blocker" were marked so by >>> > > their >>> > > reporters, since the reporter can set the priority. I don't consider >>> > stuff >>> > > like parallelize() not partitioning ranges in the same way as other >>> > > collections a blocker -- it's a bug, it would be good to fix it, but it >>> > only >>> > > affects a small number of use cases. Of course if we find a real >>> > > blocker >>> > > (in particular a regression from a previous version, or a feature >>> > > that's >>> > > just completely broken), we will delay the release for that, but at >>> > > some >>> > > point you have to say "okay, this fix will go into the next >>> > > maintenance >>> > > release". Maybe we need to write a clear policy for what the issue >>> > > priorities mean. >>> > > >>> > > Finally, I believe it's much better to have a culture where you can >>> > > make >>> > > releases on a regular schedule, and have the option to make a >>> > > maintenance >>> > > release in 3-4 days if you find new bugs, than one where you pile up >>> > stuff >>> > > into each release. This is what much large project than us, like >>> > > Linux, >>> > do, >>> > > and it's the only way to avoid indefinite stalling with a large >>> > contributor >>> > > base. In the worst case, if you find a new bug that warrants immediate >>> > > release, it goes into 1.0.1 a week after 1.0.0 (we can vote on 1.0.1 >>> > > in >>> > > three days with just your bug fix in it). And if you find an API that >>> > you'd >>> > > like to improve, just add a new one and maybe deprecate the old one -- >>> > > at >>> > > some point we have to respect our users and let them know that code >>> > > they >>> > > write today will still run tomorrow. >>> > > >>> > > Matei >>> > > >>> > > On May 17, 2014, at 10:32 AM, Kan Zhang <kzh...@apache.org> wrote: >>> > > >>> > > > +1 on the running commentary here, non-binding of course :-) >>> > > > >>> > > > >>> > > > On Sat, May 17, 2014 at 8:44 AM, Andrew Ash <and...@andrewash.com> >>> > > wrote: >>> > > > >>> > > >> +1 on the next release feeling more like a 0.10 than a 1.0 >>> > > >> On May 17, 2014 4:38 AM, "Mridul Muralidharan" <mri...@gmail.com> >>> > > wrote: >>> > > >> >>> > > >>> I had echoed similar sentiments a while back when there was a >>> > > discussion >>> > > >>> around 0.10 vs 1.0 ... I would have preferred 0.10 to stabilize >>> > > >>> the >>> > api >>> > > >>> changes, add missing functionality, go through a hardening release >>> > > before >>> > > >>> 1.0 >>> > > >>> >>> > > >>> But the community preferred a 1.0 :-) >>> > > >>> >>> > > >>> Regards, >>> > > >>> Mridul >>> > > >>> >>> > > >>> On 17-May-2014 3:19 pm, "Sean Owen" <so...@cloudera.com> wrote: >>> > > >>>> >>> > > >>>> On this note, non-binding commentary: >>> > > >>>> >>> > > >>>> Releases happen in local minima of change, usually created by >>> > > >>>> internally enforced code freeze. Spark is incredibly busy now due >>> > > >>>> to >>> > > >>>> external factors -- recently a TLP, recently discovered by a >>> > > >>>> large >>> > new >>> > > >>>> audience, ease of contribution enabled by Github. It's getting >>> > > >>>> like >>> > > >>>> the first year of mainstream battle-testing in a month. It's been >>> > very >>> > > >>>> hard to freeze anything! I see a number of non-trivial issues >>> > > >>>> being >>> > > >>>> reported, and I don't think it has been possible to triage all of >>> > > >>>> them, even. >>> > > >>>> >>> > > >>>> Given the high rate of change, my instinct would have been to >>> > release >>> > > >>>> 0.10.0 now. But won't it always be very busy? I do think the rate >>> > > >>>> of >>> > > >>>> significant issues will slow down. >>> > > >>>> >>> > > >>>> Version ain't nothing but a number, but if it has any meaning >>> > > >>>> it's >>> > the >>> > > >>>> semantic versioning meaning. 1.0 imposes extra handicaps around >>> > > >>>> striving to maintain backwards-compatibility. That may end up >>> > > >>>> being >>> > > >>>> bent to fit in important changes that are going to be required in >>> > this >>> > > >>>> continuing period of change. Hadoop does this all the time >>> > > >>>> unfortunately and gets away with it, I suppose -- minor version >>> > > >>>> releases are really major. (On the other extreme, HBase is at >>> > > >>>> 0.98 >>> > and >>> > > >>>> quite production-ready.) >>> > > >>>> >>> > > >>>> Just consider this a second vote for focus on fixes and 1.0.x >>> > > >>>> rather >>> > > >>>> than new features and 1.x. I think there are a few steps that >>> > > >>>> could >>> > > >>>> streamline triage of this flood of contributions, and make all of >>> > this >>> > > >>>> easier, but that's for another thread. >>> > > >>>> >>> > > >>>> >>> > > >>>> On Fri, May 16, 2014 at 8:50 PM, Mark Hamstra < >>> > > m...@clearstorydata.com >>> > > >>> >>> > > >>> wrote: >>> > > >>>>> +1, but just barely. We've got quite a number of outstanding >>> > > >>>>> bugs >>> > > >>>>> identified, and many of them have fixes in progress. I'd hate >>> > > >>>>> to >>> > see >>> > > >>> those >>> > > >>>>> efforts get lost in a post-1.0.0 flood of new features targeted >>> > > >>>>> at >>> > > >>> 1.1.0 -- >>> > > >>>>> in other words, I'd like to see 1.0.1 retain a high priority >>> > relative >>> > > >>> to >>> > > >>>>> 1.1.0. >>> > > >>>>> >>> > > >>>>> Looking through the unresolved JIRAs, it doesn't look like any >>> > > >>>>> of >>> > the >>> > > >>>>> identified bugs are show-stoppers or strictly regressions >>> > (although I >>> > > >>> will >>> > > >>>>> note that one that I have in progress, SPARK-1749, is a bug that >>> > > >>>>> we >>> > > >>>>> introduced with recent work -- it's not strictly a regression >>> > because >>> > > >>> we >>> > > >>>>> had equally bad but different behavior when the DAGScheduler >>> > > >> exceptions >>> > > >>>>> weren't previously being handled at all vs. being slightly >>> > > >> mis-handled >>> > > >>>>> now), so I'm not currently seeing a reason not to release. >>> > > >>> >>> > > >> >>> > > >>> > > >>> >
