On Fri, Oct 20, 2023 at 11:35 AM Kenneth Knowles <k...@apache.org> wrote:
>
> A couple other bits on having an expression language:
>
> - You already have Python lambdas at places, right? so that's quite a lot
> more complex than SQL project/aggregate expressions
> - It really does save a lot of pain for users (at the cost of implementation
> complexity) when you need to "SUM(col1*col2)" where otherwise you have to Map
> first. This could be viewed as desirable as well, of course.
>
> Anyhow I'm pretty much in agreement with all your reasoning as to why *not*
> to use SQL-like expressions in strings. But it does seem odd when juxtaposed
> with Python snippets.
Well, we say "here's a Python expression" when we're using a Python
string. But "SUM(col1*col2)" isn't as transparent. (Agree about the
niceties of being able to provide an expression rather than a column.)
> On Thu, Oct 19, 2023 at 4:00 PM Robert Bradshaw via dev <dev@beam.apache.org>
> wrote:
>>
>> On Thu, Oct 19, 2023 at 12:53 PM Reuven Lax <re...@google.com> wrote:
>> >
>> > Is the schema Group transform (in Java) something along these lines?
>>
>> Yes, for sure it is. It (and Python's and Typescript's equivalent) are
>> linked in the original post. The open question is how to best express
>> this in YAML.
>>
>> > On Wed, Oct 18, 2023 at 1:11 PM Robert Bradshaw via dev
>> > <dev@beam.apache.org> wrote:
>> >>
>> >> Beam Yaml has good support for IOs and mappings, but one key missing
>> >> feature for even writing a WordCount is the ability to do Aggregations
>> >> [1]. While the traditional Beam primitive is GroupByKey (and
>> >> CombineValues), we're eschewing KVs in the notion of more schema'd
>> >> data (which has some precedence in our other languages, see the links
>> >> below). The key components the user needs to specify are (1) the key
>> >> fields on which the grouping will take place, (2) the fields
>> >> (expressions?) involved in the aggregation, and (3) what aggregating
>> >> fn to use.
>> >>
>> >> A straw-man example could be something like
>> >>
>> >> type: Aggregating
>> >> config:
>> >> key: [field1, field2]
>> >> aggregating:
>> >> total_cost:
>> >> fn: sum
>> >> value: cost
>> >> max_cost:
>> >> fn: max
>> >> value: cost
>> >>
>> >> This would basically correspond to the SQL expression
>> >>
>> >> "SELECT field1, field2, sum(cost) as total_cost, max(cost) as max_cost
>> >> from table GROUP BY field1, field2"
>> >>
>> >> (though I'm not requiring that we use this as an implementation
>> >> strategy). I do not think we need a separate (non aggregating)
>> >> Grouping operation, this can be accomplished by having a concat-style
>> >> combiner.
>> >>
>> >> There are still some open questions here, notably around how to
>> >> specify the aggregation fns themselves. We could of course provide a
>> >> number of built-ins (like SQL does). This gets into the question of
>> >> how and where to document this complete set, but some basics should
>> >> take us pretty far. Many aggregators, however, are parameterized (e.g.
>> >> quantiles); where do we put the parameters? We could go with something
>> >> like
>> >>
>> >> fn:
>> >> type: ApproximateQuantiles
>> >> config:
>> >> n: 10
>> >>
>> >> but others are even configured by functions themselves (e.g. LargestN
>> >> that wants a comparator Fn). Maybe we decide not to support these
>> >> (yet?)
>> >>
>> >> One thing I think we should support, however, is referencing custom
>> >> CombineFns. We have some precedent for this with our Fns from
>> >> MapToFields, where we accept things like inline lambdas and external
>> >> references. Again the topic of how to configure them comes up, as
>> >> these custom Fns are more likely to be parameterized than Map Fns
>> >> (though, to be clear, perhaps it'd be good to allow parameterizatin of
>> >> MapFns as well). Maybe we allow
>> >>
>> >> language: python. # like MapToFields (and here it'd be harder to mix
>> >> and match per Fn)
>> >> fn:
>> >> type: ???
>> >> # should these be nested as config?
>> >> name: fully.qualiied.name
>> >> path: /path/to/defining/file
>> >> args: [...]
>> >> kwargs: {...}
>> >>
>> >> which would invoke the constructor.
>> >>
>> >> I'm also open to other ways of naming/structuring these essential
>> >> parameters if it makes things more clear.
>> >>
>> >> - Robert
>> >>
>> >>
>> >> Java:
>> >> https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/schemas/transforms/Group.html
>> >> Python:
>> >> https://beam.apache.org/documentation/transforms/python/aggregation/groupby
>> >> Typescript:
>> >> https://beam.apache.org/releases/typedoc/current/classes/transforms_group_and_combine.GroupBy.html
>> >>
>> >> [1] One can of course use SqlTransform for this, but I'm leaning
>> >> towards offering something more native.
