Is the issue that you have a different topology depending on the key? On Mon, May 24, 2021 at 2:49 PM Stephan Hoyer <[email protected]> wrote:
> Exactly, my use-case has another nested GroupByKey to apply per key. But > even if it could be done in a streaming fashion, it's way too much data (1 > TB) to process on a single worker in a reasonable amount of time. > > On Mon, May 24, 2021 at 2:46 PM Kenneth Knowles <[email protected]> wrote: > >> I was thinking there was some non-trivial topology (such as further GBKs) >> within the logic to be applied to each key group. >> >> Kenn >> >> On Mon, May 24, 2021 at 2:38 PM Brian Hulette <[email protected]> >> wrote: >> >>> Isn't it possible to read the grouped values produced by a GBK from an >>> Iterable and yield results as you go, without needing to collect all of >>> each input into memory? Perhaps I'm misunderstanding your use-case. >>> >>> Brian >>> >>> On Mon, May 24, 2021 at 10:41 AM Kenneth Knowles <[email protected]> >>> wrote: >>> >>>> I'm just pinging this thread because I think it is an interesting >>>> problem and don't want it to slip by. >>>> >>>> I bet a lot of users have gone through the tedious conversion you >>>> describe. Of course, it may often not be possible if you are using a >>>> library transform. There are a number of aspects of the Beam model that are >>>> designed a specific way explicitly *because* we need to assume that a large >>>> number of composites in your pipeline are not modifiable by you. Most >>>> closely related: this is why windowing is something carried along >>>> implicitly rather than just a parameter to GBK - that would require all >>>> transforms to expose how they use GBK under the hood and they would all >>>> have to plumb this extra key/WindowFn through every API. Instead, we have >>>> this way to implicitly add a second key to any transform :-) >>>> >>>> So in addition to being tedious for you, it would be good to have a >>>> better solution. >>>> >>>> Kenn >>>> >>>> On Fri, May 21, 2021 at 7:18 PM Stephan Hoyer <[email protected]> >>>> wrote: >>>> >>>>> I'd like to write a Beam PTransform that applies an *existing* Beam >>>>> transform to each set of grouped values, separately, and combines the >>>>> result. Is anything like this possible with Beam using the Python SDK? >>>>> >>>>> Here are the closest things I've come up with: >>>>> 1. If each set of *inputs* to my transform fit into memory, I could >>>>> use GroupByKey followed by FlatMap. >>>>> 2. If each set of *outputs* from my transform fit into memory, I >>>>> could use CombinePerKey. >>>>> 3. If I knew the static number of groups ahead of time, I could use >>>>> Partition, followed by applying my transform multiple times, followed by >>>>> Flatten. >>>>> >>>>> In my scenario, none of these holds true. For example, currently I >>>>> have ~20 groups of values, with each group holding ~1 TB of data. My >>>>> custom >>>>> transform simply shuffles this TB of data around, so each set of outputs >>>>> is >>>>> also 1TB in size. >>>>> >>>>> In my particular case, it seems my options are to either relax these >>>>> constraints, or to manually convert each step of my existing transform to >>>>> apply per key. This conversion process is tedious, but very >>>>> straightforward, e.g., the GroupByKey and ParDo that my transform is built >>>>> out of just need to deal with an expanded key. >>>>> >>>>> I wonder, could this be something built into Beam itself, e.g,. as >>>>> TransformPerKey? The ptranforms that result from combining other Beam >>>>> transforms (e.g., _ChainPTransform in Python) are private, so this seems >>>>> like something that would need to exist in Beam itself, if it could exist >>>>> at all. >>>>> >>>>> Cheers, >>>>> Stephan >>>>> >>>>
