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
