I'd be worried about encouraging the anti-pattern of GroupByKey() + CombinePerGroup() which would make the important (often essential) combiner lifting optimization harder (pattern detection in the runner vs. composite detection).
You might also be interested in the TupleCombineFns https://github.com/apache/beam/blob/release-2.13.0/sdks/python/apache_beam/transforms/combiners.py#L717 keyed_nums = ... combined_nums = keyed_nums | CombinePerKey( combiners.SingleInputTupleCombineFn( sum, combiners.MeanCombineFn(), ...)) FWIW, I never liked the .Globall() and .PerKey() transforms as they're not very compressible. I think they were needed to make java typing work out well in java 7 and then copied to Python, but I would suggest just using CombineGlobally(...) and CombinePerKey(...) over these. You might also be interested to know that in Python we already have combiner consolidation https://github.com/apache/beam/blob/release-2.48.0/sdks/python/apache_beam/runners/portability/fn_api_runner/translations.py#L953 https://github.com/apache/beam/blob/release-2.48.0/sdks/python/apache_beam/runners/portability/fn_api_runner/translations.py#L1161 I don't remember what the status is of enabling this by default (IIRC, they're conditionally enabled by decorating a transform). On Fri, Sep 27, 2024 at 12:01 PM Joey Tran <joey.t...@schrodinger.com> wrote: > Thoughts @dev <dev@beam.apache.org> for a `GroupedValues` version of > combiners? Named `PerGroup` or `PerGroupedValues`? > > On Fri, Sep 27, 2024 at 2:57 PM Valentyn Tymofieiev <valen...@google.com> > wrote: > >> >> On Fri, Sep 27, 2024 at 11:13 AM Joey Tran <joey.t...@schrodinger.com> >> wrote: >> >>> 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 >>> >> >> I don't see why not. I'd run by dev@ for naming ideas. PerGroup is >> another possibility. >> >> >> >> >>> >>> On Fri, Sep 27, 2024 at 2:01 PM Valentyn Tymofieiev <valen...@google.com> >>> 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 <joey.t...@schrodinger.com> >>>> 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 < >>>>> u...@beam.apache.org> wrote: >>>>> >>>>>> >>>>>> >>>>>> On Fri, Sep 27, 2024 at 8:35 AM Joey Tran <joey.t...@schrodinger.com> >>>>>> 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/> >>>>>>> >>>>>>
