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From Dongjoon Hyun <dongjoon.h...@gmail.com>
Subject Re: [VOTE] Amend Spark's Semantic Versioning Policy
Date Sat, 07 Mar 2020 19:28:02 GMT
This new policy has a good indention, but can we narrow down on the
migration from Apache Spark 2.4.5 to Apache Spark 3.0+?

I saw that there already exists a reverting PR to bring back Spark 1.4 and
1.5 APIs based on this AS-IS suggestion.

The AS-IS policy is clearly mentioning that JVM/Scala-level difficulty, and
it's nice.

However, for the other cases, it sounds like `recommending older APIs as
much as possible` due to the following.

     > How long has the API been in Spark?

We had better be more careful when we add a new policy and should aim not
to mislead the users and 3rd party library developers to say "older is
better".

Technically, I'm wondering who will use new APIs in their examples (of
books and StackOverflow) if they need to write an additional warning like
`this only works at 2.4.0+` always .

Bests,
Dongjoon.

On Fri, Mar 6, 2020 at 7:10 PM Mridul Muralidharan <mridul@gmail.com> wrote:

> I am in broad agreement with the prposal, as any developer, I prefer
> stable well designed API's :-)
>
> Can we tie the proposal to stability guarantees given by spark and
> reasonable expectation from users ?
> In my opinion, an unstable or evolving could change - while an
> experimental api which has been around for ages should be more
> conservatively handled.
> Which brings in question what are the stability guarantees as
> specified by annotations interacting with the proposal.
>
> Also, can we expand on 'when' an API change can occur ?  Since we are
> proposing to diverge from semver.
> Patch release ? Minor release ? Only major release ? Based on 'impact'
> of API ? Stability guarantees ?
>
> Regards,
> Mridul
>
>
>
> On Fri, Mar 6, 2020 at 7:01 PM Michael Armbrust <michael@databricks.com>
> wrote:
> >
> > I'll start off the vote with a strong +1 (binding).
> >
> > On Fri, Mar 6, 2020 at 1:01 PM Michael Armbrust <michael@databricks.com>
> wrote:
> >>
> >> I propose to add the following text to Spark's Semantic Versioning
> policy and adopt it as the rubric that should be used when deciding to
> break APIs (even at major versions such as 3.0).
> >>
> >>
> >> I'll leave the vote open until Tuesday, March 10th at 2pm. As this is a
> procedural vote, the measure will pass if there are more favourable votes
> than unfavourable ones. PMC votes are binding, but the community is
> encouraged to add their voice to the discussion.
> >>
> >>
> >> [ ] +1 - Spark should adopt this policy.
> >>
> >> [ ] -1  - Spark should not adopt this policy.
> >>
> >>
> >> <new policy>
> >>
> >>
> >> Considerations When Breaking APIs
> >>
> >> The Spark project strives to avoid breaking APIs or silently changing
> behavior, even at major versions. While this is not always possible, the
> balance of the following factors should be considered before choosing to
> break an API.
> >>
> >>
> >> Cost of Breaking an API
> >>
> >> Breaking an API almost always has a non-trivial cost to the users of
> Spark. A broken API means that Spark programs need to be rewritten before
> they can be upgraded. However, there are a few considerations when thinking
> about what the cost will be:
> >>
> >> Usage - an API that is actively used in many different places, is
> always very costly to break. While it is hard to know usage for sure, there
> are a bunch of ways that we can estimate:
> >>
> >> How long has the API been in Spark?
> >>
> >> Is the API common even for basic programs?
> >>
> >> How often do we see recent questions in JIRA or mailing lists?
> >>
> >> How often does it appear in StackOverflow or blogs?
> >>
> >> Behavior after the break - How will a program that works today, work
> after the break? The following are listed roughly in order of increasing
> severity:
> >>
> >> Will there be a compiler or linker error?
> >>
> >> Will there be a runtime exception?
> >>
> >> Will that exception happen after significant processing has been done?
> >>
> >> Will we silently return different answers? (very hard to debug, might
> not even notice!)
> >>
> >>
> >> Cost of Maintaining an API
> >>
> >> Of course, the above does not mean that we will never break any APIs.
> We must also consider the cost both to the project and to our users of
> keeping the API in question.
> >>
> >> Project Costs - Every API we have needs to be tested and needs to keep
> working as other parts of the project changes. These costs are
> significantly exacerbated when external dependencies change (the JVM,
> Scala, etc). In some cases, while not completely technically infeasible,
> the cost of maintaining a particular API can become too high.
> >>
> >> User Costs - APIs also have a cognitive cost to users learning Spark or
> trying to understand Spark programs. This cost becomes even higher when the
> API in question has confusing or undefined semantics.
> >>
> >>
> >> Alternatives to Breaking an API
> >>
> >> In cases where there is a "Bad API", but where the cost of removal is
> also high, there are alternatives that should be considered that do not
> hurt existing users but do address some of the maintenance costs.
> >>
> >>
> >> Avoid Bad APIs - While this is a bit obvious, it is an important point.
> Anytime we are adding a new interface to Spark we should consider that we
> might be stuck with this API forever. Think deeply about how new APIs
> relate to existing ones, as well as how you expect them to evolve over time.
> >>
> >> Deprecation Warnings - All deprecation warnings should point to a clear
> alternative and should never just say that an API is deprecated.
> >>
> >> Updated Docs - Documentation should point to the "best" recommended way
> of performing a given task. In the cases where we maintain legacy
> documentation, we should clearly point to newer APIs and suggest to users
> the "right" way.
> >>
> >> Community Work - Many people learn Spark by reading blogs and other
> sites such as StackOverflow. However, many of these resources are out of
> date. Update them, to reduce the cost of eventually removing deprecated
> APIs.
> >>
> >>
> >> </new policy>
>
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