Ah! That is exactly the kind of primitive I was looking for but thought didn't exist. Thanks for pointing it out. Yeah that works well for me, I'll use that in my combiners (with an API of `PerGroupedValues`). Thanks!
If we did want to add `PerGroupedValues` to our current combiners I'd also be happy to put up a PR doing that On Fri, Sep 27, 2024 at 2:01 PM Valentyn Tymofieiev <[email protected]> wrote: > The closest primitve to that intent seems to be CombineValues: > https://github.com/apache/beam/blob/c2c640f8c33071d5bb3e854e82c554c03a0bc851/sdks/python/apache_beam/transforms/core.py#L3010 > , and you should be able to write: > > max_sample_size = 100_000 > ( keyed_nums > | GroupByKey() > | Map(lambda k_nums: (k, nums[:max_sample_size])) > | CombineValues(MeanCombineFn()) > ``` > Would that work for other scenarios you have in mind? > > Haven't thought too much about this but from looking at > https://github.com/apache/beam/blob/c2c640f8c33071d5bb3e854e82c554c03a0bc851/sdks/python/apache_beam/transforms/combiners.py#L90, > I could see us adding Mean.GroupedValues or Mean.PerGroupedValues there. > > > On Fri, Sep 27, 2024 at 10:41 AM Joey Tran <[email protected]> > wrote: > >> It feels more natural because it's only using GroupByKey once instead of >> once per combiner. Which I think is still more efficient even accounting >> for combiner lifting (unless there's some kind of pipeline optimization >> that merges multiple groupbykey's on the same pcollection into a single >> GBK). >> >> You can imagine a different use case where this pattern might arise that >> isn't just trying to reduce GBKs though. For example: >> >> ``` >> max_sample_size = 100_000 >> ( keyed_nums >> | GroupByKey() >> | Map(lambda k_nums: (k, nums[:max_sample_size])) >> | #?? Mean.PerGrouped()? >> ``` >> >> To take the mean of every grouped_values using current combiners, I think >> you'd have to use an inverted groupbykey and then call `Mean.PerKey()` >> unless I'm missing something. >> >> (I recognize that writing a Map that takes a mean is simple enough, but >> in a real use case we might have a more complicated combiner) >> >> On Fri, Sep 27, 2024 at 1:31 PM Valentyn Tymofieiev via user < >> [email protected]> wrote: >> >>> >>> >>> On Fri, Sep 27, 2024 at 8:35 AM Joey Tran <[email protected]> >>> wrote: >>> >>>> Hey all, >>>> >>>> Just curious if this pattern comes up for others and if people have >>>> worked out a good convention. >>>> >>>> There are many combiners and a lot of them have two forms: a global >>>> form (e.g. Count.Globally) and a per key form (e.g. Count.PerKey). These >>>> are convenient but it feels like often we're running into the case where we >>>> GroupBy a set of data once and then wish to perform a series of combines on >>>> them, in which case neither of these forms work, and it begs another form >>>> which operates on pre-grouped KVs. >>>> >>>> Contrived example: maybe you have a pcollection of keyed numbers and >>>> you want to calculate some summary statistics on them. You could do: >>>> ``` >>>> keyed_means = (keyed_nums >>>> | Mean.PerKey()) >>>> keyed_counts = (keyed_num >>>> | Count.PerKey()) >>>> ... # other combines >>>> ``` >>>> But it'd feel more natural to pre-group the pcollection. >>>> >>> >>> Does it feel more natural because it feels as though it would be more >>> performant? Because it seems like it adds an extra grouping step to the >>> pipeline code, which otherwise might be not necessary. Note that Dataflow >>> has the "combiner lifting" optimization, and combiner-specified-reduction >>> happens before the data is written into shuffle as much as possible: >>> https://cloud.google.com/dataflow/docs/pipeline-lifecycle#combine_optimization >>> . >>> >>> >>>> ``` >>>> grouped_nums = keyed_nums | GBK() >>>> keyed_means = (grouped_nums | Mean.PerGrouped()) >>>> keyed_counts (grouped_nums | Count.PerGrouped()) >>>> ``` >>>> But these "PerGrouped" variants don't actually currently exist. Does >>>> anyone else run into this pattern often? I might be missing an obvious >>>> pattern here. >>>> >>>> -- >>>> >>>> Joey Tran | Staff Developer | AutoDesigner TL >>>> >>>> *he/him* >>>> >>>> [image: Schrödinger, Inc.] <https://schrodinger.com/> >>>> >>>
