I think this standard design could still be made to work. Specifically, the graph would contain a DoFn that has the RequiresTimeSortedInput bit set, and as a single "subtransform" that has a different DoFn in its spec that does not require this bit to be set and whose implementation enforces this ordering (say, via state) before invoking the user's DoFn. This would work fine in Streaming for any runner, and would work OK for batch as long as the value set for any key fit into memory (or the batch state implementation spilled to disk, though that could get really slow). Runners that wanted to do better (e.g. provide timestamp sorting as part of their batch grouping, or even internally sort timestamps more efficiently than could be done via the SDK over the state API) could do so.
For Java, such a wrapper might be a bit messy, but could probably be hard coded above the ByteBuddy wrappers layer. TBD how much of our infrastructure assumes ParDo transforms do not contain subtransforms. (We could also provide a different URN for RequresTimeSortedInput DoFns whose payload would be the DoFn payload, rather than setting a bit on the payload itself.) Rather than introducing nesting, we could implement the AnyOf PTransform that would present the two implementations as siblings (which could be useful elsewhere). This can be made backward compatible by providing one of the alternatives as the composite structure. The primary hesitation I have here is that it prevents much introspection/manipulation of the pipeline before the runner capabilities are know. What we really want is a way to annotate a DoFn as RequestsTimeSortedInput, together with a way for the runner to communicate to the SDK whether or not it was able to honor this request. That may be a more invasive change to the protocol (e.g. annotating PCollections with ordering properties, which is where it belongs[1]). I suppose we could let a runner that supports this capability strip the RequestsTimeSortedInput bit (or set a new bit), and SDKs that get unmutated transforms would know they have to do the sorting themselves. - Robert [1] Ordering is an under-defined concept in Beam, but if we're going to add it my take would be that to do it properly one would want (1) Annotations on PCollections indicating whether they're unordered or ordered (by a certain ordering criteria, in this case timestamp-within-key), which could be largely inferred by (2) Annotations on PTransforms indicating whether they're order-creating, order-preserving, or order-requiring (with the default being unspeciified=order-destroying), again parameterized by an ordering criteria of some kind, which criteria could for a hierarchy. On Fri, Jan 19, 2024 at 10:40 AM Kenneth Knowles <k...@apache.org> wrote: > > 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 >>> >>>
