Hi guys

@Kenn,

I just wanted to mention that I did answered your question on dependencies here: https://lists.apache.org/thread.html/5a85caac41e796c2aa351d835b3483808ebbbd4512b480940d494439@%3Cdev.beam.apache.org%3E

regarding jars:

I don't like 3 jars either.

I'm not in favor of having the 2 runners in one jar, the point about having 2 jars was to:

- avoid making promises to users on a work in progress runner (make it explicit with a different jar)

- avoid confusion for them (why are there 2 pipeline options? etc....)

If the community believes that there is no confusion or wrong promises with the one jar solution, we could leave the 2 runners in one jar.

Maybe we could start a vote on that?

Etienne

On 31/10/2019 02:06, Kenneth Knowles wrote:
Very good points. We definitely ship a lot of code/features in very early stages, and there seems to be no problem.

I intend mostly to leave this judgment to people like you who know better about Spark users.

But I do think 1 or 2 jars is better than 3. I really don't like "3 jars" and I did give two reasons:

1. diamond deps where things overlap
2. figuring out which thing to depend on

Both are annoying for users. I am not certain if it could lead to a real unsolvable situation. This is just a Java ecosystem problem so I feel qualified to comment.

I did also ask if there were major dependency differences between the two that could cause problems for users. This question was dropped and no one cares to comment so I assume it is not an issue. So then I favor having just 1 jar with both runners.

Kenn

On Wed, Oct 30, 2019 at 2:46 PM Ismaël Mejía <ieme...@gmail.com <mailto:ieme...@gmail.com>> wrote:

    I am still a bit lost about why we are discussing options without
    giving any
    arguments or reasons for the options? Why is 2 modules better than
    3 or 3 better
    than 2, or even better, what forces us to have something different
    than a single
    module?

    What are the reasons for wanting to have separate jars? If the
    issue is that the
    code is unfinished or not passing the tests, the impact for end
    users is minimal
    because they cannot accidentally end up running the new runner,
    and if they
    decide to do so we can warn them it is at their own risk and not
    ready for
    production in the documentation + runner.

    If the fear is that new code may end up being intertwined with the
    classic and
    portable runners and have some side effects. We have the
    ValidatesRunner +
    Nexmark in the CI to cover this so again I do not see what is the
    problem that
    requires modules to be separate.

    If the issue is being uncomfortable about having in-progress code
    in released
    artifacts we have been doing this in Beam forever, for example
    most of the work
    on portability and Schema/SQL, and all of those were still part of
    artifacts
    long time before they were ready for prime use, so I still don't
    see why this
    case is different to require different artifacts.

    I have the impression we are trying to solve a non-issue by adding
    a lot of
    artificial complexity (in particular to the users), or am I
    missing something
    else?

    On Wed, Oct 30, 2019 at 7:40 PM Kenneth Knowles <k...@apache.org
    <mailto:k...@apache.org>> wrote:
    >
    > Oh, I mean that we ship just 2 jars.
    >
    > And since Spark users always build an uber jar, they can still
    depend on both of ours and be able to switch runners with a flag.
    >
    > I really dislike projects shipping overlapping jars. It is
    confusing and causes major diamond dependency problems.
    >
    > Kenn
    >
    > On Wed, Oct 30, 2019 at 11:12 AM Alexey Romanenko
    <aromanenko....@gmail.com <mailto:aromanenko....@gmail.com>> wrote:
    >>
    >> Yes, agree, two jars included in uber jar will work in the
    similar way. Though having 3 jars looks still quite confusing for me.
    >>
    >> On 29 Oct 2019, at 23:54, Kenneth Knowles <k...@apache.org
    <mailto:k...@apache.org>> wrote:
    >>
    >> Is it just as easy to have two jars and build an uber jar with
    both included? Then the runner can still be toggled with a flag.
    >>
    >> Kenn
    >>
    >> On Tue, Oct 29, 2019 at 9:38 AM Alexey Romanenko
    <aromanenko....@gmail.com <mailto:aromanenko....@gmail.com>> wrote:
    >>>
    >>> Hmm, I don’t think that jar size should play a big role
    comparing to the whole size of shaded jar of users job. Even more,
    I think it will be quite confusing for users to choose which jar
    to use if we will have 3 different ones for similar purposes.
    Though, let’s see what others think.
    >>>
    >>> On 29 Oct 2019, at 15:32, Etienne Chauchot
    <echauc...@apache.org <mailto:echauc...@apache.org>> wrote:
    >>>
    >>> Hi Alexey,
    >>>
    >>> Thanks for your opinion !
    >>>
    >>> Comments inline
    >>>
    >>> Etienne
    >>>
    >>> On 28/10/2019 17:34, Alexey Romanenko wrote:
    >>>
    >>> Let me share some of my thoughts on this.
    >>>
    >>>     - shall we filter out the package name from the release?
    >>>
    >>> Until new runner is not ready to be used in production (or, at
    least, be used for beta testing but users should be clearly warned
    about that in this case), I believe we need to filter out its
    classes from published jar to avoid a confusion.
    >>>
    >>> Yes that is what I think also
    >>>
    >>>     - should we release 2 jars: one for the old and one for
    the new ?
    >>>
    >>>     - should we release 3 jars: one for the new, one for the
    new and one for both ?
    >>>
    >>> Once new runner will be released, then I think we need to
    provide only one single jar and allow user to switch between
    different Spark runners with CLI option.
    >>>
    >>> I would vote for 3 jars: one for new, one for old, and one for
    both. Indeed, in some cases, users are looking very closely at the
    size of jars. This solution meets all use cases
    >>>
    >>>     - should we create a special entry to the capability matrix ?
    >>>
    >>> Sure, since it has its own uniq characteristics and
    implementation, but again, only once new runner will be
    "officially released".
    >>>
    >>> +1
    >>>
    >>>
    >>>
    >>> On 28 Oct 2019, at 10:27, Etienne Chauchot
    <echauc...@apache.org <mailto:echauc...@apache.org>> wrote:
    >>>
    >>> Hi guys,
    >>>
    >>> Any opinions on the point2 communication to users ?
    >>>
    >>> Etienne
    >>>
    >>> On 24/10/2019 15:44, Etienne Chauchot wrote:
    >>>
    >>> Hi guys,
    >>>
    >>> I'm glad to announce that the PR for the merge to master of
    the new runner based on Spark Structured Streaming framework is
    submitted:
    >>>
    >>> https://github.com/apache/beam/pull/9866
    >>>
    >>>
    >>> 1. Regarding the status of the runner:
    >>>
    >>> -the runner passes 93% of the validates runner tests in batch
    mode.
    >>>
    >>> -Streaming mode is barely started (waiting for the
    multi-aggregations support in spark Structured Streaming framework
    from the Spark community)
    >>>
    >>> -Runner can execute Nexmark
    >>>
    >>> -Some things are not wired up yet
    >>>
    >>>   -Beam Schemas not wired with Spark Schemas
    >>>
    >>>   -Optional features of the model not implemented: state api,
    timer api, splittable doFn api, …
    >>>
    >>>
    >>> 2. Regarding the communication to users:
    >>>
    >>> - for reasons explained by Ismael: the runner is in the same
    module as the "older" one. But it is in a different sub-package
    and both runners share the same build.
    >>>
    >>> - How should we communicate to users:
    >>>
    >>>     - shall we filter out the package name from the release?
    >>>
    >>>     - should we release 2 jars: one for the old and one for
    the new ?
    >>>
    >>>     - should we release 3 jars: one for the new, one for the
    new and one for both ?
    >>>
    >>>     - should we create a special entry to the capability matrix ?
    >>>
    >>> WDYT ?
    >>>
    >>> Best
    >>>
    >>> Etienne
    >>>
    >>>
    >>> On 23/10/2019 19:11, Mikhail Gryzykhin wrote:
    >>>
    >>> +1 to merge.
    >>>
    >>> It is worth keeping things in master with explicitly marked
    status. It will make effort more visible to users and easier to
    get feedback upon.
    >>>
    >>> --Mikhail
    >>>
    >>> On Wed, Oct 23, 2019 at 8:36 AM Etienne Chauchot
    <echauc...@apache.org <mailto:echauc...@apache.org>> wrote:
    >>>>
    >>>> Hi guys,
    >>>>
    >>>> The new spark runner now supports beam coders and passes 93%
    of the batch validates runner tests (+4%). I think it is time to
    merge it to master. I will submit a PR in the coming days.
    >>>>
    >>>> next steps: support schemas and thus better leverage catalyst
    optimizer (among other things optims based on data), port perfs
    optims that were done in the current runner.
    >>>>
    >>>> Best
    >>>>
    >>>> Etienne
    >>>>
    >>>> On 11/10/2019 22:48, Pablo Estrada wrote:
    >>>>
    >>>> +1 for merging : )
    >>>>
    >>>> On Fri, Oct 11, 2019 at 12:43 PM Robert Bradshaw
    <rober...@google.com <mailto:rober...@google.com>> wrote:
    >>>>>
    >>>>> Sounds like a good plan to me.
    >>>>>
    >>>>> On Fri, Oct 11, 2019 at 6:20 AM Etienne Chauchot
    <echauc...@apache.org <mailto:echauc...@apache.org>> wrote:
    >>>>>>
    >>>>>> Comments inline
    >>>>>>
    >>>>>> On 10/10/2019 23:44, Ismaël Mejía wrote:
    >>>>>>
    >>>>>> +1
    >>>>>>
    >>>>>> The earlier we get to master the better to encourage not
    only code
    >>>>>> contributions but as important to have early user feedback.
    >>>>>>
    >>>>>> Question is: do we keep the "old" spark runner for a while
    or not (or just keep on previous version/tag on git) ?
    >>>>>>
    >>>>>> It is still too early to even start discussing when to
    remove the
    >>>>>> classical runner given that the new runner is still a WIP.
    However the
    >>>>>> overall goal is that this runner becomes the de-facto one
    once the VR
    >>>>>> tests and the performance become at least equal to the
    classical
    >>>>>> runner, in the meantime the best for users is that they
    co-exist,
    >>>>>> let’s not forget that the other runner has been already
    battle tested
    >>>>>> for more than 3 years and has had lots of improvements in
    the last
    >>>>>> year.
    >>>>>>
    >>>>>> +1 on what Ismael says: no soon removal,
    >>>>>>
    >>>>>> The plan I had in mind at first (that I showed at the
    apacheCon) was this but I'm proposing moving the first gray label
    to before the red box.
    >>>>>>
    >>>>>> <beogijnhpieapoll.png>
    >>>>>>
    >>>>>>
    >>>>>> I don't think the number of commits should be an issue--we
    shouldn't
    >>>>>> just squash years worth of history away. (OTOH, if this is
    a case of
    >>>>>> this branch containing lots of little, irrelevant commits
    that would
    >>>>>> have normally been squashed away in the normal review
    process we do
    >>>>>> for the main branch, then, yes, some cleanup could be nice.)
    >>>>>>
    >>>>>> About the commits we should encourage a clear history but
    we have also
    >>>>>> to remove useless commits that are still present in the branch,
    >>>>>> commits of the “Fix errorprone” / “Cleaning” kind and even
    commits
    >>>>>> that make a better narrative sense together should be probably
    >>>>>> squashed, because they do not bring much to the history. It
    is not
    >>>>>> about more or less commits it is about its relevance as Robert
    >>>>>> mentions.
    >>>>>>
    >>>>>> I think our experiences with things that go to master early
    have been very good. So I am in favor ASAP. We can exclude it from
    releases easily until it is ready for end users.
    >>>>>> I have the same question as Robert - how much is
    modifications and how much is new? I notice it is in a
    subdirectory of the beam-runners-spark module.
    >>>>>>
    >>>>>> In its current form we cannot exclude it but this relates
    to the other
    >>>>>> question, so better to explain a bit of history: The new
    runner used
    >>>>>> to live in its own module and subdirectory because it is a
    full blank
    >>>>>> page rewrite and the decision was not to use any of the
    classical
    >>>>>> runner classes to not be constrained by its evolution.
    >>>>>>
    >>>>>> However the reason to put it back in the same module as a
    subdirectory
    >>>>>> was to encourage early use, in more detail: The way you
    deploy spark
    >>>>>> jobs today is usually by packaging and staging an uber jar
    (~200MB of
    >>>>>> pure dependency joy) that contains the user pipeline
    classes, the
    >>>>>> spark runner module and its dependencies. If we have two
    spark runners
    >>>>>> in separate modules the user would need to repackage and
    redeploy
    >>>>>> their pipelines every time they want to switch from the
    classical
    >>>>>> Spark runner to the structured streaming runner which is
    painful and
    >>>>>> time and space consuming compared with the one module
    approach where
    >>>>>> they just change the name of the runner class and that’s
    it. The idea
    >>>>>> here is to make easy for users to test the new runner, but
    at the same
    >>>>>> time to make easy to come back to the classical runner in
    case of any
    >>>>>> issue.
    >>>>>>
    >>>>>> Ismaël
    >>>>>>
    >>>>>> On Thu, Oct 10, 2019 at 9:02 PM Kenneth Knowles
    <k...@apache.org <mailto:k...@apache.org>> wrote:
    >>>>>>
    >>>>>> +1
    >>>>>>
    >>>>>> I think our experiences with things that go to master early
    have been very good. So I am in favor ASAP. We can exclude it from
    releases easily until it is ready for end users.
    >>>>>>
    >>>>>> I have the same question as Robert - how much is
    modifications and how much is new? I notice it is in a
    subdirectory of the beam-runners-spark module.
    >>>>>>
    >>>>>> I did not see any major changes to dependencies but I will
    also ask if it has major version differences so that you might
    want a separate artifact?
    >>>>>>
    >>>>>> Kenn
    >>>>>>
    >>>>>> On Thu, Oct 10, 2019 at 11:50 AM Robert Bradshaw
    <rober...@google.com <mailto:rober...@google.com>> wrote:
    >>>>>>
    >>>>>> On Thu, Oct 10, 2019 at 12:39 AM Etienne Chauchot
    <echauc...@apache.org <mailto:echauc...@apache.org>> wrote:
    >>>>>>
    >>>>>> Hi guys,
    >>>>>>
    >>>>>> You probably know that there has been for several months an
    work
    >>>>>> developing a new Spark runner based on Spark Structured
    Streaming
    >>>>>> framework. This work is located in a feature branch here:
    >>>>>>
    https://github.com/apache/beam/tree/spark-runner_structured-streaming
    >>>>>>
    >>>>>> To attract more contributors and get some user feedback, we
    think it is
    >>>>>> time to merge it to master. Before doing so, some steps
    need to be achieved:
    >>>>>>
    >>>>>> - finish the work on spark Encoders (that allow to call
    Beam coders)
    >>>>>> because, right now, the runner is in an unstable state
    (some transforms
    >>>>>> use the new way of doing ser/de and some use the old one,
    making a
    >>>>>> pipeline incoherent toward serialization)
    >>>>>>
    >>>>>> - clean history: The history contains commits from November
    2018, so
    >>>>>> there is a good amount of work, thus a consequent number of
    commits.
    >>>>>> They were already squashed but not from September 2019
    >>>>>>
    >>>>>> I don't think the number of commits should be an issue--we
    shouldn't
    >>>>>> just squash years worth of history away. (OTOH, if this is
    a case of
    >>>>>> this branch containing lots of little, irrelevant commits
    that would
    >>>>>> have normally been squashed away in the normal review
    process we do
    >>>>>> for the main branch, then, yes, some cleanup could be nice.)
    >>>>>>
    >>>>>> Regarding status:
    >>>>>>
    >>>>>> - the runner passes 89% of the validates runner tests in
    batch mode. We
    >>>>>> hope to pass more with the new Encoders
    >>>>>>
    >>>>>> - Streaming mode is barely started (waiting for the
    multi-aggregations
    >>>>>> support in spark SS framework from the Spark community)
    >>>>>>
    >>>>>> - Runner can execute Nexmark
    >>>>>>
    >>>>>> - Some things are not wired up yet
    >>>>>>
    >>>>>>      - Beam Schemas not wired with Spark Schemas
    >>>>>>
    >>>>>>      - Optional features of the model not implemented: 
    state api, timer
    >>>>>> api, splittable doFn api, …
    >>>>>>
    >>>>>> WDYT, can we merge it to master once the 2 steps are done ?
    >>>>>>
    >>>>>> I think that as long as it sits parallel to the existing
    runner, and
    >>>>>> is clearly marked with its status, it makes sense to me.
    How many
    >>>>>> changes does it make to the existing codebase (as opposed
    to add new
    >>>>>> code)?
    >>>
    >>>
    >>>
    >>

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