+1 to Danny's comments. Technically the place to document these on a broader runner perspective should be the Runner Capability matrix.
A similar hint would be "FanoutRatio" which can mark transforms that have a high fanout per element and lead the runner to make different optimization decisions. Another is KeyInvariance which can also affect optimization (eg. If the key is known to not change The only requirement is that the hint doesn't affect correctness, just performance, should the hint be ignored. (I'm aware that in principle there are likely some areas they may overlap. To that I say, that the runner must always prefer correctness when it's ambiguous.) On Tue, Jan 14, 2025, 7:36 AM Danny McCormick via dev <dev@beam.apache.org> wrote: > In my opinion, what you are describing fits the intention/current behavior > of resource hints. Resource hints are just hints which allow the runner to > optimize the execution environment where possible, so it should be legal > for any runner to ignore any hints; as long as we're maintaining that > behavior, I think it is ok. > > > Should we introduce some runner-specific way of creating hints > applicable only to a specific runner? > > IMO this just makes the pipeline less portable and doesn't really do > anything to make switching runners easier. Ideally I could have a pipeline > with a set of hints, some of which apply to only Spark, some of which apply > to only Flink, and some of which apply only to Dataflow, and the pipeline > should be fully portable across those environments without making > modifications. Your use case fits this paradigm well since running > input.apply(GroupByKey.create().setResourceHints(ResourceHints.huge())) on > any non-Spark runner should still work fine (assuming the runner has an > out-of-memory GBK implementation by default. > > It would, however, be nice to at least have a matrix where we document > which resource hints impact which runners. > > Thanks, > Danny > > On Tue, Jan 14, 2025 at 6:02 AM Jan Lukavský <je...@seznam.cz> wrote: > >> Hi, >> >> as part of reviewing [1], I came across a question, which might be >> solved using resource hints. Seems the usage of these hints is currently >> limited, though. I'll explain the case in a few points: >> >> a) a generic implementation of GBK on Spark assumes that all values >> fit into memory >> >> b) this can be changed to implementation which uses Spark's internal >> sorting mechanism to group by key and window without the need for the >> data to fit into memory >> >> c) this optimization can be more expensive for cases where a) is >> sufficient >> >> There is currently no simple way of knowing if a GBK fits to memory or >> not. This could be solved using ResourceHints, e.g.: >> >> input.apply(GroupByKey.create().setResourceHints(ResourceHints.huge())) >> >> The expansion could then pick only the appropriate transforms, but it >> requires changing the generic ResourceHints class. Is this intentional >> and the expected approach? We can create pipeline-level hints, but this >> seems not correct in this situation. Should we introduce some >> runner-specific way of creating hints applicable only to a specific >> runner? >> >> Alternative option seems to be somewhat similar concept of >> "annotations", which seems to be introduced and currently used only for >> error handlers. >> >> Thanks for any opinions! >> Jan >> >> [1] https://github.com/apache/beam/pull/33521 >> >>
