I took a crack at trying to replace GBK+CombineValues with CBK so then it doesn't matter what the user chooses to do.
https://github.com/apache/beam/pull/32592 On Fri, Sep 27, 2024 at 4:32 PM Joey Tran <joey.t...@schrodinger.com> wrote: > Hmm, it makes sense from a runner optimization perspective, but I think > it's a lot less ergonomic to publish combinefns instead of ptransforms. > Another drawback to the stacked combinefn is the label will have to be a > mash of possibly very different combiners squashed into one > > On Fri, Sep 27, 2024 at 3:52 PM Robert Bradshaw via dev < > dev@beam.apache.org> wrote: > >> 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/> >>>>>>>>> >>>>>>>>
