On Tue, Jan 8, 2019 at 9:15 PM Reuven Lax <re...@google.com> wrote:
>
> I wonder if we could do this _only_ over the FnApi. The FnApi already does
> batching I believe. What if we made schemas a fundamental part of our protos,
> and had no SchemaCoder.
One advantage of SchemaCoders is that they allow nesting inside other coders.
Schemas are not (I don't think) a replacement for coders at the
implementation level, but in the user API they obviate the need for
most users to interact with coders (as well as providing a richer,
language-independent way of describing elements in most cases).
> The FnApi could then batch up a bunch of rows an encode using Arrow before
> sending over the wire to the harness.
The encoding of records across the FnAPI can be more expressive than
Coders, regardless of schemas.
> Of course this still turns back into individual records before it goes back
> to user code. However well-known combiners can be executed directly in the
> harness, which means aggregations like "sum a field" can be run inside the
> harness over the columnar data. Moving these combiners into the harness might
> itself be a huge perf benefit for Python, as we could then run them in a
> more-performant language.
You don't even have to move them out of Python to take advantage of
this if you're using the right libraries and have the right
representation. If you do move them it doesn't have to be into the
harness, it could be an adjacent SDK. I envision a large suite of
known URNs that can be placed wherever it's best.
On Tue, Jan 8, 2019 at 7:12 PM Kenneth Knowles <k...@apache.org> wrote:
>
> And even more for SQL, or about the same since you are referring to
> DataFrames which have roughly relational semantics. Having the columnar model
> all the way to the data source would be big. Having near-zero-parsing cost
> for transmitted data would be big. These changes would make Beam a rather
> different project.
I think beam would be qualitatively the same project, but it would
open up a lot of areas for optimization (both in the "computer
resource" sense and "easy for people to get stuff done" sense).
> Reuven
>
> On Tue, Jan 8, 2019 at 7:44 AM Robert Bradshaw <rober...@google.com> wrote:
>>
>> On Tue, Jan 8, 2019 at 4:32 PM Reuven Lax <re...@google.com> wrote:
>> >
>> > I agree with this, but I think it's a significant rethinking of Beam that
>> > I didn't want to couple to schemas. In addition to rethinking the API, it
>> > might also require rethinking all of our runners.
>>
>> We're already marshaling (including batching) data over the FnApi, so
>> it might not be that big of a change. Also, the choice of encoding
>> over the data channel is already parametrizable via a coder, so it's
>> easy to make this an optional feature that runners and SDKs can opt
>> into. I agree that we don't want to couple it to schemas (though
>> that's where it becomes even more useful).
>>
>> > Also while columnar can be a large perf win, I suspect that we currently
>> > have lower-hanging fruit to optimize when it comes to performance.
>>
>> It's probably a bigger win for Python than for Java.
>>
>> >
>> > Reuven
>> >
>> > On Tue, Jan 8, 2019 at 5:25 AM Robert Bradshaw <rober...@google.com> wrote:
>> >>
>> >> On Fri, Jan 4, 2019 at 12:54 AM Reuven Lax <re...@google.com> wrote:
>> >> >
>> >> > I looked at Apache Arrow as a potential serialization format for Row
>> >> > coders. At the time it didn't seem a perfect fit - Beam's programming
>> >> > model is record-at-a-time, and Arrow is optimized for large batches of
>> >> > records (while Beam has a concept of "bundles" they are completely non
>> >> > deterministic, and records might bundle different on retry). You could
>> >> > use Arrow with single-record batches, but I suspect that would end up
>> >> > adding a lot of extra overhead. That being said, I think it's still
>> >> > something worth investigating further.
>> >>
>> >> Though Beam's model is row-oriented, I think it'd make a lot of sense
>> >> to support column-oriented transfer of data across the data plane
>> >> (we're already concatenating serialized records lengthwise), with
>> >> Arrow as a first candidate, and (either as part of the public API or
>> >> as an implementation detail) columnar processing as well (e.g.
>> >> projections, maps, filters, and aggregations can often be done more
>> >> efficiently in a columnar fashion). While this is often a significant
>> >> win in C++ (and presumably Java), it's essential for doing
>> >> high-performance computing in Python (e.g. Numpy, SciPy, Pandas,
>> >> Tensorflow, ... all have batch-oriented APIs and avoid representing
>> >> records as individual objects, something we'll need to tackle for
>> >> BeamPython at least).
>> >>
>> >> >
>> >> > Reuven
>> >> >
>> >> >
>> >> >
>> >> > On Fri, Jan 4, 2019 at 12:34 AM Gleb Kanterov <g...@spotify.com> wrote:
>> >> >>
>> >> >> Reuven, it sounds great. I see there is a similar thing to Row coders
>> >> >> happening in Apache Arrow, and there is a similarity between Apache
>> >> >> Arrow Flight and data exchange service in portability. How do you see
>> >> >> these two things relate to each other in the long term?
>> >> >>
>> >> >> On Fri, Jan 4, 2019 at 12:13 AM Reuven Lax <re...@google.com> wrote:
>> >> >>>
>> >> >>> The biggest advantage is actually readability and usability. A
>> >> >>> secondary advantage is that it means that Go will be able to interact
>> >> >>> seamlessly with BeamSQL, which would be a big win for Go.
>> >> >>>
>> >> >>> A schema is basically a way of saying that a record has a specific
>> >> >>> set of (possibly nested, possibly repeated) fields. So for instance
>> >> >>> let's say that the user's type is a struct with fields named user,
>> >> >>> country, purchaseCost. This allows us to provide transforms that
>> >> >>> operate on field names. Some example (using the Java API):
>> >> >>>
>> >> >>> PCollection users = events.apply(Select.fields("user")); // Select
>> >> >>> out only the user field.
>> >> >>>
>> >> >>> PCollection joinedEvents =
>> >> >>> queries.apply(Join.innerJoin(clicks).byFields("user")); // Join two
>> >> >>> PCollections by user.
>> >> >>>
>> >> >>> // For each country, calculate the total purchase cost as well as the
>> >> >>> top 10 purchases.
>> >> >>> // A new schema is created containing fields total_cost and
>> >> >>> top_purchases, and rows are created with the aggregation results.
>> >> >>> PCollection purchaseStatistics = events.apply(
>> >> >>> Group.byFieldNames("country")
>> >> >>> .aggregateField("purchaseCost", Sum.ofLongs(),
>> >> >>> "total_cost"))
>> >> >>> .aggregateField("purchaseCost", Top.largestLongs(10),
>> >> >>> "top_purchases"))
>> >> >>>
>> >> >>>
>> >> >>> This is far more readable than what we have today, and what unlocks
>> >> >>> this is that Beam actually knows the structure of the record instead
>> >> >>> of assuming records are uncrackable blobs.
>> >> >>>
>> >> >>> Note that a coder is basically a special case of a schema that has a
>> >> >>> single field.
>> >> >>>
>> >> >>> In BeamJava we have a SchemaRegistry which knows how to turn user
>> >> >>> types into schemas. We use reflection to analyze many user types
>> >> >>> (e.g. simple POJO structs, JavaBean classes, Avro records, protocol
>> >> >>> buffers, etc.) to determine the schema, however this is done only
>> >> >>> when the graph is initially generated. We do use code generation (in
>> >> >>> Java we do bytecode generation) to make this somewhat more efficient.
>> >> >>> I'm willing to bet that the code generator you've written for structs
>> >> >>> could be very easily modified for schemas instead, so it would not be
>> >> >>> wasted work if we went with schemas.
>> >> >>>
>> >> >>> One of the things I'm working on now is documenting Beam schemas.
>> >> >>> They are already very powerful and useful, but since there is still
>> >> >>> nothing in our documentation about them, they are not yet widely
>> >> >>> used. I expect to finish draft documentation by the end of January.
>> >> >>>
>> >> >>> Reuven
>> >> >>>
>> >> >>> On Thu, Jan 3, 2019 at 11:32 PM Robert Burke <r...@google.com> wrote:
>> >> >>>>
>> >> >>>> That's an interesting idea. I must confess I don't rightly know the
>> >> >>>> difference between a schema and coder, but here's what I've got with
>> >> >>>> a bit of searching through memory and the mailing list. Please let
>> >> >>>> me know if I'm off track.
>> >> >>>>
>> >> >>>> As near as I can tell, a schema, as far as Beam takes it is a
>> >> >>>> mechanism to define what data is extracted from a given row of data.
>> >> >>>> So in principle, there's an opportunity to be more efficient with
>> >> >>>> data with many columns that aren't being used, and only extract the
>> >> >>>> data that's meaningful to the pipeline.
>> >> >>>> The trick then is how to apply the schema to a given serialization
>> >> >>>> format, which is something I'm missing in my mental model (and then
>> >> >>>> how to do it efficiently in Go).
>> >> >>>>
>> >> >>>> I do know that the Go client package for BigQuery does something
>> >> >>>> like that, using field tags. Similarly, the "encoding/json" package
>> >> >>>> in the Go Standard Library permits annotating fields and it will
>> >> >>>> read out and deserialize the JSON fields and that's it.
>> >> >>>>
>> >> >>>> A concern I have is that Go (at present) would require pre-compile
>> >> >>>> time code generation for schemas to be efficient, and they would
>> >> >>>> still mostly boil down to turning []bytes into real structs. Go
>> >> >>>> reflection doesn't keep up.
>> >> >>>> Go has no mechanism I'm aware of to Just In Time compile more
>> >> >>>> efficient processing of values.
>> >> >>>> It's also not 100% clear how Schema's would play with protocol
>> >> >>>> buffers or similar.
>> >> >>>> BigQuery has a mechanism of generating a JSON schema from a proto
>> >> >>>> file, but that's only the specification half, not the using half.
>> >> >>>>
>> >> >>>> As it stands, the code generator I've been building these last
>> >> >>>> months could (in principle) statically analyze a user's struct, and
>> >> >>>> then generate an efficient dedicated coder for it. It just has no
>> >> >>>> where to put them such that the Go SDK would use it.
>> >> >>>>
>> >> >>>>
>> >> >>>> On Thu, Jan 3, 2019 at 1:39 PM Reuven Lax <re...@google.com> wrote:
>> >> >>>>>
>> >> >>>>> I'll make a different suggestion. There's been some chatter that
>> >> >>>>> schemas are a better tool than coders, and that in Beam 3.0 we
>> >> >>>>> should make schemas the basic semantics instead of coders. Schemas
>> >> >>>>> provide everything a coder provides, but also allows for far more
>> >> >>>>> readable code. We can't make such a change in Beam Java 2.X for
>> >> >>>>> compatibility reasons, but maybe in Go we're better off starting
>> >> >>>>> with schemas instead of coders?
>> >> >>>>>
>> >> >>>>> Reuven
>> >> >>>>>
>> >> >>>>> On Thu, Jan 3, 2019 at 8:45 PM Robert Burke <rob...@frantil.com>
>> >> >>>>> wrote:
>> >> >>>>>>
>> >> >>>>>> One area that the Go SDK currently lacks: is the ability for users
>> >> >>>>>> to specify their own coders for types.
>> >> >>>>>>
>> >> >>>>>> I've written a proposal document, and while I'm confident about
>> >> >>>>>> the core, there are certainly some edge cases that require
>> >> >>>>>> discussion before getting on with the implementation.
>> >> >>>>>>
>> >> >>>>>> At presently, the SDK only permits primitive value types (all
>> >> >>>>>> numeric types but complex, strings, and []bytes) which are coded
>> >> >>>>>> with beam coders, and structs whose exported fields are of those
>> >> >>>>>> type, which is then encoded as JSON. Protocol buffer support is
>> >> >>>>>> hacked in to avoid the type anaiyzer, and presents the current
>> >> >>>>>> work around this issue.
>> >> >>>>>>
>> >> >>>>>> The high level proposal is to catch up with Python and Java, and
>> >> >>>>>> have a coder registry. In addition, arrays, and maps should be
>> >> >>>>>> permitted as well.
>> >> >>>>>>
>> >> >>>>>> If you have alternatives, or other suggestions and opinions, I'd
>> >> >>>>>> love to hear them! Otherwise my intent is to get a PR ready by the
>> >> >>>>>> end of January.
>> >> >>>>>>
>> >> >>>>>> Thanks!
>> >> >>>>>> Robert Burke
>> >> >>>>
>> >> >>>>
>> >> >>>>
>> >> >>>> --
>> >> >>>> http://go/where-is-rebo
>> >> >>
>> >> >>
>> >> >>
>> >> >> --
>> >> >> Cheers,
>> >> >> Gleb
