In this design space, what we have done in the past is: 1) ensure that runners all reject pipelines they cannot run correctly 2) if there is a default/workaround/slower implementation, provide it as an override
This is largely ignoring portability but I think/hope it will still work. At one time I put some effort into ensuring Java Pipeline objects and proto representations could roundtrip with all the necessary information for pre-portability runners to still work, which is the same prereqs as pre-portable "Override" implementations to still work. TBH I'm 50/50 on the idea. If something is going to be implemented more slowly or less scalably as a fallback, I think it may be best to simply be upfront about being unable to really run it. It would depend on the situation. For requiring time sorted input, the manual implementation is probably similar to what a streaming runner might do, so it might make sense. Kenn On Fri, Jan 19, 2024 at 11:05 AM Robert Burke <rob...@frantil.com> wrote: > I certainly don't have the deeper java insight here. So one more portable > based reply and then I'll step back on the Java specifics. > > Portable runners only really have the "unknown Composite" fallback option, > where if the Composite's URN isn't known to the runner, it should use the > subgraph that is being wrapped. > > I suppose the protocol could be expanded : If a composite transform with a > ParDo payload, and urn has features the runner can't handle, then it could > use the fallback graph as well. > > The SDK would have then still needed to have construct the fallback graph > into the Pipeline proto. This doesn't sound incompatible with what you've > suggested the Java SDK could do, but it avoids the runner needing to be > aware of a specific implementation requirement around a feature it doesn't > support. If it has to do something specific to support an SDK specific > mechanism, that's still supporting the feature, but I fear it's not a great > road to tread on for runners to add SDK specific implementation details. > > If a (portable) runner is going to spend work on doing something to handle > RequiresTimeSortedInput, it's probably easier to handle it generally than > to try to enable a Java specific work around. I'm not even sure how that > could work since the SDK would then need a special interpretation of what a > runner sent back for it to do any SDK side special backup handling, vs the > simple execution of the given transform. > > It's entirely possible I've over simplified the "fallback" protocol > described above, so this thread is still useful for my Prism work, > especially if I see any similar situations once I start on the Java > Validates Runner suite. > > Robert Burke > Beam Go Busybody > > On Fri, Jan 19, 2024, 6:41 AM Jan Lukavský <je...@seznam.cz> wrote: > >> I was primarily focused on Java SDK (and core-contruction-java), but >> generally speaking, any SDK can provide default expansion that runners can >> use so that it is not (should not be) required to implement this manually. >> Currently, in Java SDK, the annotation is wired up into >> StatefulDoFnRunner, which (as name suggests) can be used for running >> stateful DoFns. The problem is that not every runner is using this >> facility. Java SDK generally supports providing default expansions of >> transforms, but _only for transforms that do not have to work with dynamic >> state_. This is not the case for this annotation - a default implementation >> for @RequiresTimeSortedInput has to take another DoFn as input, and wire >> its lifecycle in a way that elements are buffered in (dynamically created) >> buffer and fed into the downstream DoFn only when timer fires. >> >> If I narrow down my line of thinking, it would be possible to: >> a) create something like "dynamic pipeline expansion", which would make >> it possible work with PTransforms in this way (probably would require some >> ByteBuddy magic) >> b) wire this up to DoFnInvoker, which takes DoFn and creates class that >> is used by runners for feeding data >> >> Option b) would ensure that actually all runners support such expansion, >> but seems to be somewhat hacky and too specific to this case. Moreover, it >> would require knowledge if the expansion is actually required by the runner >> (e.g. if the annotation is supported explicitly - most likely for batch >> execution). Therefore I'd be in favor of option a), this might be reusable >> by a broader range of default expansions. >> >> In other SDKs than Java this might have different implications, the >> reason why it is somewhat more complicated to do dynamic (or generic?) >> expansions of PTransforms in Java is mostly due to how DoFns are >> implemented in terms of annotations and the DoFnInvokers involved for >> efficiency. >> >> Jan >> >> On 1/18/24 18:35, Robert Burke wrote: >> >> I agree that variable support across Runners does limit the adoption of a >> feature. But it's also then limited if the SDKs and their local / direct >> runners don't yet support the feature. The Go SDK doesn't currently have a >> way of specifying that annotation, preventing use. (The lack of mention of >> the Python direct runner your list implies it's not yet supported by the >> Python SDK, and a quick search shows that's likely [0]) >> >> While not yet widely available to the other SDKs, Prism, the new Go SDK >> Local Runner, maintains data in event time sorted heaps [1]. The intent was >> to implement the annotation (among other features) once I start running the >> Java and Python Validates Runner suites against it. >> >> I think stateful transforms are getting the event ordering on values for >> "free" as a result [2], but there's no special/behavior at present if the >> DoFn is consuming the result of a Group By Key. >> >> Part of the issue is that by definition, a GBK "loses" the timestamps of the >> values, and doesn't emit them, outside of using them to determine the >> resulting timestamp of the Key... [3]. To make use of the timestamp in the >> aggregation stage a runner would need to do something different in the GBK, >> namely sorting by the timestamp as the data is ingested, and keeping that >> timestamp around to continue the sort. This prevents a more efficient >> implementation of directly arranging the received element bytes into the >> Iterator format, requiring a post process filtering. Not hard, but a little >> dissatisfying. >> >> Skimming through the discussion, I agree with the general utility goal of >> the annotation, but as with many Beam features, there may be a >> discoverability problem. The feature isn't mentioned in the Programming >> Guide (AFAICT), and trying to find anything on the beam site, the top result >> is the Javadoc for the annotation (which is good, but you still need to know >> to look for it), and then the next time related bit is OrderedListState >> which doesn't yet have a meaningful portable representation last I checked >> [4], once again limiting adoption. >> >> Probably the most critical bit is, while we have broad "handling" of the >> annotation, I'm hard pressed to say we even use the annotation outside of >> tests. A search [5] doesn't show any "Transforms" or "IOs" making use of it >> with the only markdown/documentation about it being the Beam 2.20.0 release >> notes saying it's now supported in Flink and Spark [6]. >> >> I will say, this isn't grounds for removing the feature, as I can only check >> what's in the repo, and not what end users have, but it does indicate we >> didn't drive the feature to completion and enable user adoption beyond "This >> Exists, and we can tell you about it if you ask.". >> >> AFAICT this is just one of those features we built, but then proceeded not >> to use within Beam, and evangelize. This is a point we could certainly do >> better on in Beam as a whole. >> >> Robert Burke >> Beam Go Busybody >> >> [0] >> https://github.com/search?q=repo%3Aapache%2Fbeam+TIME_SORTED_INPUT+language%3APython&type=code >> >> [1] >> https://github.com/apache/beam/blob/master/sdks/go/pkg/beam/runners/prism/internal/engine/elementmanager.go#L93 >> >> [2] >> https://github.com/apache/beam/blob/master/sdks/go/pkg/beam/runners/prism/internal/engine/elementmanager.go#L1094 >> >> [3] >> https://github.com/apache/beam/blob/master/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto#L1132 >> >> [4] >> https://github.com/apache/beam/issues?q=is%3Aissue+is%3Aopen+OrderedListState >> >> [5] >> https://github.com/search?q=repo%3Aapache%2Fbeam+RequiresTimeSortedInput&type=code&p=2 >> >> [6] >> https://github.com/apache/beam/blob/b4c23b32f2b80ce052c8a235e5064c69f37df992/website/www/site/content/en/blog/beam-2.20.0.md?plain=1#L46 >> >> On 2024/01/18 16:14:56 Jan Lukavský wrote: >> >> Hi, >> >> recently I came across the fact that most runners do not support >> @RequiresTimeSortedInput annotation for sorting per-key data by event >> timestamp [1]. Actually, runners supporting it seem to be Direct java, >> Flink and Dataflow batch (as it is a noop there). The annotation has >> use-cases in time-series data processing, in transaction processing and >> more. Though it is absolutely possible to implement the time-sorting >> manually (e.g. [2]), this is actually efficient only in streaming mode, >> in batch mode the runner typically wants to leverage the internal >> sort-grouping it already does. >> >> The original idea was to implement this annotation inside >> StatefulDoFnRunner, which would be used by majority of runners. It turns >> out that this is not the case. The question now is, should we use an >> alternative place to implement the annotation (e.g. Pipeline expansion, >> or DoFnInvoker) so that more runners can benefit from it automatically >> (at least for streaming case, batch case needs to be implemented >> manually)? Do the community find the annotation useful? I'm linking a >> rather old (and long :)) thread that preceded introduction of the >> annotation [3] for more context. >> >> I sense the current adoption of the annotation by runners makes it >> somewhat use-less. >> >> Looking forward to any comments on this. >> >> Best, >> >> Jan >> >> [1] >> https://beam.apache.org/releases/javadoc/2.53.0/org/apache/beam/sdk/transforms/DoFn.RequiresTimeSortedInput.html >> >> [2] >> https://cloud.google.com/spanner/docs/change-streams/use-dataflow#order-by-key >> >> [3] https://lists.apache.org/thread/bkl9kk8l44xw2sw08s7m54k1wsc3n4tn >> >>
