I'm not concerned with key-dependent topologies, which I didn't even think was possible to express in Beam.
It's more that I already wrote a PTransform for processing a *single* 1 TB dataset. Now I want to write a single PTransform that effectively runs the original PTransform in groups over ~20 such datasets (ideally without needing to know that number 20 ahead of time). On Mon, May 24, 2021 at 3:30 PM Reuven Lax <re...@google.com> wrote: > Is the issue that you have a different topology depending on the key? > > On Mon, May 24, 2021 at 2:49 PM Stephan Hoyer <sho...@google.com> 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 <k...@apache.org> 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 <bhule...@google.com> >>> 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 <k...@apache.org> >>>> 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 <sho...@google.com> >>>>> 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 >>>>>> >>>>>
