Resource hints were originally conceived as ways to customize the runtime environment (ram, accelerators, I could imagine things like custom containers, data dependencies, etc. being useful here too). They also apply recursively through composite transforms, and get reflected in the attached environment (which, it might be noted, a GBK doesn't have).
I would say this case, as well as think like fanout ratio or key preserving properties, would be most naturally attached to transforms as annotations, which are arbitrary key-value pairs that can be attached to any transform, and runners may be free to inspect (and act on) or ignore. For example, they are used in ManagedIO (on Dataflow) to understand the semantic meaning of otherwise opaque IOs. Right now the only way to populate annotations is to override the annotations() method when subclassing PTransform, but this is a limitation that would be very nice to remove (e.g. a with_annotations(key=value) in Python, or withAnnotation(key, value) in java. - Robert On Tue, Jan 14, 2025 at 8:45 AM Jan Lukavský <je...@seznam.cz> wrote: > > Generally, these points are aligned with my line of thinking as well. There > are two points that made me start this thread, I didn't stress these > explicitly, so I'll rephrase these: > > a) looking at the current usage of ResourceHints, there are hints that > actually affect how runner (and we can say Dataflow, because apparently no > other runner works with these hints, currently) allocate computation nodes to > tasks. There are two hints that actually cannot be (simply) ignored, as they > can result in failures (accelerator, minRam) > b) more hints can be added only by modifying ResourceHints class directly, > adding new hint to the core > > c) some hints might make sense only in the context of a runner, because they > can (at least theoretically) affect how a _specific_ runner expands a > transform > > d) the need to modify core to get some functionality for a specific runner > adds more unnecessary tight coupling between core and runners, my (long-term) > position is that this coupling should be loose, whenever it is possible > > Last note is that I like the mentioned examples of "hints" (FanoutRatio, > KeyInvariance), but these seem not to fit well into my definition of > "resource". The question is if we could create some more dynamic approach, > because if "hints" can be ignored (not affecting semantics, only execution), > this can lead to separating Pipeline and some "hint configuration", that > could be provided at pipeline submission runtime, not compile time. The > ResourceHints could then be special case of those. > > On 1/14/25 17:04, Robert Burke wrote: > > +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 >>>
