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From Xiangrui Meng <men...@gmail.com>
Subject Re: [VOTE][SPARK-27396] SPIP: Public APIs for extended Columnar Processing Support
Date Mon, 22 Apr 2019 15:49:24 GMT
Per Robert's comment on the JIRA, ETL is the main use case for the SPIP. I
think the SPIP should list a concrete ETL use case (from POC?) that can
benefit from this *public Java/Scala API, *does *vectorization*, and
significantly *boosts the performance *even with data conversion overhead.

The current mid-term success (Pandas UDF) doesn't match the purpose of SPIP
and it can be done without exposing any public APIs.

Depending how much benefit it brings, we might agree that a public
Java/Scala API is needed. Then we might want to step slightly into how. I
saw three options mentioned in the JIRA and discussion threads:

1. Expose `Array[Byte]` in Arrow format. Let user decode it using an Arrow
library.
2. Expose `ArrowRecordBatch`. It makes Spark expose third-party APIs.
3. Expose `ColumnarBatch` and make it Arrow-compatible, which is also used
by Spark internals. It makes us hard to change Spark internals in the
future.
4. Expose something like `SparkRecordBatch` that is Arrow-compatible and
maintain conversion between internal `ColumnarBatch` and
`SparkRecordBatch`. It might cause conversion overhead in the future if our
internal becomes different from Arrow.

Note that both 3 and 4 will make many APIs public to be Arrow compatible.
So we should really give concrete ETL cases to prove that it is important
for us to do so.

On Mon, Apr 22, 2019 at 8:27 AM Tom Graves <tgraves_cs@yahoo.com> wrote:

>
> Based on there is still discussion and Spark Summit is this week, I'm
> going to extend the vote til Friday the 26th.
>
> Tom
> On Monday, April 22, 2019, 8:44:00 AM CDT, Bobby Evans <revans2@gmail.com>
> wrote:
>
>
> Yes, it is technically possible for the layout to change.  No, it is not
> going to happen.  It is already baked into several different official
> libraries which are widely used, not just for holding and processing the
> data, but also for transfer of the data between the various
> implementations.  There would have to be a really serious reason to force
> an incompatible change at this point.  So in the worst case, we can version
> the layout and bake that into the API that exposes the internal layout of
> the data.  That way code that wants to program against a JAVA API can do so
> using the API that Spark provides, those who want to interface with
> something that expects the data in arrow format will already have to know
> what version of the format it was programmed against and in the worst case
> if the layout does change we can support the new layout if needed.
>
> On Sun, Apr 21, 2019 at 12:45 AM Bryan Cutler <cutlerb@gmail.com> wrote:
>
> The Arrow data format is not yet stable, meaning there are no guarantees
> on backwards/forwards compatibility. Once version 1.0 is released, it will
> have those guarantees but it's hard to say when that will be. The remaining
> work to get there can be seen at
> https://cwiki.apache.org/confluence/display/ARROW/Columnar+Format+1.0+Milestone.
> So yes, it is a risk that exposing Spark data as Arrow could cause an issue
> if handled by a different version that is not compatible. That being said,
> changes to format are not taken lightly and are backwards compatible when
> possible. I think it would be fair to mark the APIs exposing Arrow data as
> experimental for the time being, and clearly state the version that must be
> used to be compatible in the docs. Also, adding features like this and
> SPARK-24579 will probably help adoption of Arrow and accelerate a 1.0
> release. Adding the Arrow dev list to CC.
>
> Bryan
>
> On Sat, Apr 20, 2019 at 5:25 PM Matei Zaharia <matei.zaharia@gmail.com>
> wrote:
>
> Okay, that makes sense, but is the Arrow data format stable? If not, we
> risk breakage when Arrow changes in the future and some libraries using
> this feature are begin to use the new Arrow code.
>
> Matei
>
> > On Apr 20, 2019, at 1:39 PM, Bobby Evans <revans2@gmail.com> wrote:
> >
> > I want to be clear that this SPIP is not proposing exposing Arrow
> APIs/Classes through any Spark APIs.  SPARK-24579 is doing that, and
> because of the overlap between the two SPIPs I scaled this one back to
> concentrate just on the columnar processing aspects. Sorry for the
> confusion as I didn't update the JIRA description clearly enough when we
> adjusted it during the discussion on the JIRA.  As part of the columnar
> processing, we plan on providing arrow formatted data, but that will be
> exposed through a Spark owned API.
> >
> > On Sat, Apr 20, 2019 at 1:03 PM Matei Zaharia <matei.zaharia@gmail.com>
> wrote:
> > FYI, I’d also be concerned about exposing the Arrow API or format as a
> public API if it’s not yet stable. Is stabilization of the API and format
> coming soon on the roadmap there? Maybe someone can work with the Arrow
> community to make that happen.
> >
> > We’ve been bitten lots of times by API changes forced by external
> libraries even when those were widely popular. For example, we used Guava’s
> Optional for a while, which changed at some point, and we also had issues
> with Protobuf and Scala itself (especially how Scala’s APIs appear in
> Java). API breakage might not be as serious in dynamic languages like
> Python, where you can often keep compatibility with old behaviors, but it
> really hurts in Java and Scala.
> >
> > The problem is especially bad for us because of two aspects of how Spark
> is used:
> >
> > 1) Spark is used for production data transformation jobs that people
> need to keep running for a long time. Nobody wants to make changes to a job
> that’s been working fine and computing something correctly for years just
> to get a bug fix from the latest Spark release or whatever. It’s much
> better if they can upgrade Spark without editing every job.
> >
> > 2) Spark is often used as “glue” to combine data processing code in
> other libraries, and these might start to require different versions of our
> dependencies. For example, the Guava class exposed in Spark became a
> problem when third-party libraries started requiring a new version of
> Guava: those new libraries just couldn’t work with Spark. Protobuf was
> especially bad because some users wanted to read data stored as Protobufs
> (or in a format that uses Protobuf inside), so they needed a different
> version of the library in their main data processing code.
> >
> > If there was some guarantee that this stuff would remain
> backward-compatible, we’d be in a much better stuff. It’s not that hard to
> keep a storage format backward-compatible: just document the format and
> extend it only in ways that don’t break the meaning of old data (for
> example, add new version numbers or field types that are read in a
> different way). It’s a bit harder for a Java API, but maybe Spark could
> just expose byte arrays directly and work on those if the API is not
> guaranteed to stay stable (that is, we’d still use our own classes to
> manipulate the data internally, and end users could use the Arrow library
> if they want it).
> >
> > Matei
> >
> > > On Apr 20, 2019, at 8:38 AM, Bobby Evans <revans2@gmail.com> wrote:
> > >
> > > I think you misunderstood the point of this SPIP. I responded to your
> comments in the SPIP JIRA.
> > >
> > > On Sat, Apr 20, 2019 at 12:52 AM Xiangrui Meng <mengxr@gmail.com>
> wrote:
> > > I posted my comment in the JIRA. Main concerns here:
> > >
> > > 1. Exposing third-party Java APIs in Spark is risky. Arrow might have
> 1.0 release someday.
> > > 2. ML/DL systems that can benefits from columnar format are mostly in
> Python.
> > > 3. Simple operations, though benefits vectorization, might not be
> worth the data exchange overhead.
> > >
> > > So would an improved Pandas UDF API would be good enough? For example,
> SPARK-26412 (UDF that takes an iterator of of Arrow batches).
> > >
> > > Sorry that I should join the discussion earlier! Hope it is not too
> late:)
> > >
> > > On Fri, Apr 19, 2019 at 1:20 PM <tcondie@gmail.com> wrote:
> > > +1 (non-binding) for better columnar data processing support.
> > >
> > >
> > >
> > > From: Jules Damji <dmatrix@comcast.net>
> > > Sent: Friday, April 19, 2019 12:21 PM
> > > To: Bryan Cutler <cutlerb@gmail.com>
> > > Cc: Dev <dev@spark.apache.org>
> > > Subject: Re: [VOTE][SPARK-27396] SPIP: Public APIs for extended
> Columnar Processing Support
> > >
> > >
> > >
> > > + (non-binding)
> > >
> > > Sent from my iPhone
> > >
> > > Pardon the dumb thumb typos :)
> > >
> > >
> > > On Apr 19, 2019, at 10:30 AM, Bryan Cutler <cutlerb@gmail.com> wrote:
> > >
> > > +1 (non-binding)
> > >
> > >
> > >
> > > On Thu, Apr 18, 2019 at 11:41 AM Jason Lowe <jlowe@apache.org> wrote:
> > >
> > > +1 (non-binding).  Looking forward to seeing better support for
> processing columnar data.
> > >
> > >
> > >
> > > Jason
> > >
> > >
> > >
> > > On Tue, Apr 16, 2019 at 10:38 AM Tom Graves
> <tgraves_cs@yahoo.com.invalid> wrote:
> > >
> > > Hi everyone,
> > >
> > >
> > >
> > > I'd like to call for a vote on SPARK-27396 - SPIP: Public APIs for
> extended Columnar Processing Support.  The proposal is to extend the
> support to allow for more columnar processing.
> > >
> > >
> > >
> > > You can find the full proposal in the jira at:
> https://issues.apache.org/jira/browse/SPARK-27396. There was also a
> DISCUSS thread in the dev mailing list.
> > >
> > >
> > >
> > > Please vote as early as you can, I will leave the vote open until next
> Monday (the 22nd), 2pm CST to give people plenty of time.
> > >
> > >
> > >
> > > [ ] +1: Accept the proposal as an official SPIP
> > >
> > > [ ] +0
> > >
> > > [ ] -1: I don't think this is a good idea because ...
> > >
> > >
> > >
> > >
> > >
> > > Thanks!
> > >
> > > Tom Graves
> > >
> >
>
>
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