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 >>>> >>>
