+1 On Fri, Mar 6, 2020 at 8:59 PM Michael Armbrust <mich...@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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