Which questions and how we frame it aside, it's clear we have some foundational thinking to do, articulate, and agree upon as a project before we can reasonably make decisions about deprecation, promotion, or inclusion of features in the project.
Is that fair? If so, I propose we set this thread down for now in deference to us articulating the quality bar we set and how we achieve it for features in the DB and then retroactively apply them to existing experimental features. Should we determine nobody is stepping up to maintain an experimental feature in a reasonable time frame, we can cross the bridge of the implications of scale of adoption and the perceived impact on the user community of deprecation and removal at that time. On Wed, Jul 1, 2020 at 9:59 AM Benedict Elliott Smith <bened...@apache.org> wrote: > I humbly suggest these are the wrong questions to ask. Instead, two sides > of just one question matter: how did we miss these problems, and what would > we have needed to do procedurally to have not missed it. Whatever it is, > we need to do it now to have confidence other things were not missed, as > well as for all future features. > > We should start by producing a list of what we think is necessary for > deploying successful features. We can then determine what items are > missing that would have been needed to catch a problem. Obvious things > are: > > * integration tests at scale > * integration tests with a variety of extreme workloads > * integration tests with various cluster topologies > * data integrity tests as part of the above > * all of the above as reproducible tests incorporated into the source > tree > > We can then ensure Jira accurately represents all of the known issues with > MVs (and other features). This includes those that are poorly defined > (such as "doesn't scale"). > > Then we can look at all issues and ask: would this approach have caught > it, and if not what do we need to add to the guidelines to prevent a > recurrence - and also ensure this problem is unique? In future we can ask, > for bugs found in features built to these guidelines: why didn't it catch > this bug? Do the guidelines need additional items, or greater specificity > about how to meet given criteria? > > I do not think that data from deployments - even if reliably obtained - > can tell us much besides which problems we prioritise. > > > > On 01/07/2020, 01:58, "joshua.mcken...@gmail.com" < > joshua.mcken...@gmail.com> wrote: > > It would be incredibly helpful for us to have some empirical data and > agreed upon terms and benchmarks to help us navigate discussions like this: > > * How widely used is a feature in C* deployments worldwide? > * What are the primary issues users face when deploying them? > Scaling them? During failure scenarios? > * What does the engineering effort to bridge these gaps look like? > Who will do that? On what time horizon? > * What does our current test coverage for this feature look like? > * What shape of defects are arising with the feature? In a specific > subsection of the module or usage? > * Do we have an agreed upon set of standards for labeling a feature > stable? As experimental? If not, how do we get there? > * What effort will it take to bridge from where we are to where we > agree we need to be? On what timeline is this acceptable? > > I believe these are not only answerable questions, but fundamentally > the underlying themes our discussion alludes to. They’re also questions > that apply to a lot more than just MV’s and tie into what you’re speaking > to above Benedict. > > > > On Jun 30, 2020, at 8:32 PM, sankalp kohli <kohlisank...@gmail.com> > wrote: > > > > I see this discussion as several decisions which can be made in > small > > increments. > > > > 1. In release cycles, when can we propose a feature to be deprecated > or > > marked experimental. Ideally a new feature should come out > experimental if > > required but we have several who are candidates now. We can work on > > integrating this in the release lifecycle doc we already have. > > 2. What is the process of making an existing feature experimental? > How does > > it affect major releases around testing. > > 3. What is the process of deprecating/removing an experimental > feature. > > (Assuming experimental features should be deprecated/removed) > > > > Coming to MV, I think we need more data before we can say we > > should deprecate MV. Here are some of them which should be part of > > deprecation process > > 1.Talk to customers who use them and understand what is the impact. > Give > > them a forum to talk about it. > > 2. Do we have enough resources to bring this feature out of the > > experimental feature list in next 1 or 2 major releases. We cannot > have too > > many experimental features in the database. Marking a feature > experimental > > should not be a parking place for a non functioning feature but a > place > > while we stabilize it. > > > > > > > > > >> On Tue, Jun 30, 2020 at 4:52 PM <joshua.mcken...@gmail.com> wrote: > >> > >> I followed up with the clarification about unit and dtests for that > reason > >> Dinesh. We test experimental features now. > >> > >> If we’re talking about adding experimental features to the 40 > quality > >> testing effort, how does that differ from just saying “we won’t > release > >> until we’ve tested and stabilized these features and they’re no > longer > >> experimental”? > >> > >> Maybe I’m just misunderstanding something here? > >> > >>>> On Jun 30, 2020, at 7:12 PM, Dinesh Joshi <djo...@apache.org> > wrote: > >>> > >>> > >>>> > >>>> On Jun 30, 2020, at 4:05 PM, Brandon Williams <dri...@gmail.com> > wrote: > >>>> > >>>> Instead of ripping it out, we could instead disable them in the > yaml > >>>> with big fat warning comments around it. That way people already > >>>> using them can just enable them again, but it will raise the bar > for > >>>> new users who ignore/miss the warnings in the logs and just use > them. > >>> > >>> Not a bad idea. Although, the real issue is that users enable MV > on a 3 > >> node cluster with a few megs of data and conclude that MVs will > >> horizontally scale with the size of data. This is what causes > issues for > >> users who naively roll it out in production and discover that MVs > do not > >> scale with their data growth. So whatever we do, the big fat > warning should > >> educate the unsuspecting operator. > >>> > >>> Dinesh > >>> > --------------------------------------------------------------------- > >>> To unsubscribe, e-mail: dev-unsubscr...@cassandra.apache.org > >>> For additional commands, e-mail: dev-h...@cassandra.apache.org > >>> > >> > >> > --------------------------------------------------------------------- > >> To unsubscribe, e-mail: dev-unsubscr...@cassandra.apache.org > >> For additional commands, e-mail: dev-h...@cassandra.apache.org > >> > >> > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@cassandra.apache.org > For additional commands, e-mail: dev-h...@cassandra.apache.org > > > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@cassandra.apache.org > For additional commands, e-mail: dev-h...@cassandra.apache.org > >
