Rill is definitely SQL-oriented but I think that's going to be the most
common. Dataframes are explicitly modeled on the relational approach so
that's going to look a lot like SQL, which leaves us with S-style formulas
(which I like but are pretty niche) and I guess pivot tables coming from
the spreadsheet world. Does make me wonder what Rails' ORM looks like these
days (I last used v4), it had some aggregation support and was pretty
declarative...
On Wed, Oct 18, 2023 at 6:06 PM Robert Bradshaw <rober...@google.com> wrote:
> On Wed, Oct 18, 2023 at 5:06 PM Byron Ellis <byronel...@google.com> wrote:
> >
> > Is it worth taking a look at similar prior art in the space?
>
> +1. Pointers welcome.
>
> > The first one that comes to mind is Transform, but with the dbt labs
> acquisition that spec is a lot harder to find. Rill is pretty similar
> though.
>
> Rill seems to be very SQL-based.
>
> > On Wed, Oct 18, 2023 at 1:12 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.
>
