I think coming up with a formal comprehensive guide for determining if we
can merge these sort of huge impacting features is a great idea.

I'm also on board with applying the same standard to the experimental
features.

On Wed, Jul 1, 2020 at 1:45 PM Joshua McKenzie <jmcken...@apache.org> wrote:

> 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
> >     >>>
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