@Eugene: "workaround" as specific to the IO each time and therefore still highlight a lack in the core.
Other comments inline 2017-11-19 7:40 GMT+01:00 Robert Bradshaw <rober...@google.com.invalid>: > There is a possible fourth issue that we don't handle well: efficiency. For > very large bundles, it may be advantageous to avoid replaying a bunch of > idempotent operations if there were a way to record what ones we're sure > went through. Not sure if that's the issue here (though one could possibly > do this with SDFs, one can preemptively returning periodically before an > element (or portion thereof) is done). +1, also lead to the IO handling its own chunking/bundles and therefore solves all issues at once. > > On Sat, Nov 18, 2017 at 6:58 PM, Eugene Kirpichov < > kirpic...@google.com.invalid> wrote: > >> I disagree that the usage of document id in ES is a "workaround" - it does >> not address any *accidental *complexity >> <https://en.wikipedia.org/wiki/No_Silver_Bullet> coming from shortcomings >> of Beam, it addresses the *essential* complexity that a distributed system >> forces one to take it as a fact of nature that the same write >> (mutation) will happen multiple times, so if you want a mutation to happen >> "as-if" it happened exactly once, the mutation itself must be idempotent >> <https://en.wikipedia.org/wiki/Idempotence>. Insert-with-id (upsert >> <https://en.wikipedia.org/wiki/Merge_(SQL)>) is a classic example of an >> idempotent mutation, and it's very good that Elasticsearch provides it - if >> it didn't, no matter how good of an API Beam had, achieving exactly-once >> writes would be theoretically impossible. Are we in agreement on this so >> far? >> >> Next: you seem to be discussing 3 issues together, all of which are valid >> issues, but they seem unrelated to me: >> 1. Exactly-once mutation >> 2. Batching multiple mutations into one RPC. >> 3. Backpressure >> >> #1: was considered above. The system the IO is talking to has to support >> idempotent mutations, in an IO-specific way, and the IO has to take >> advantage of them, in the IO-specific way - end of story. Agree but don't forget the original point was about "chunks" and not individual records. >> >> #2: a batch of idempotent operations is also idempotent, so this doesn't >> add anything new semantically. Syntactically - Beam already allows you to >> write your own batching by notifying you of permitted batch boundaries >> (Start/FinishBundle). Sure, it could do more, but from my experience the >> batching in IOs I've seen is one of the easiest and least error-prone >> parts, so I don't see something worth an extended discussion here. "Beam already allows you to write your own batching by notifying you of permitted batch boundaries (Start/FinishBundle)" Is wrong since the bundle is potentially the whole PCollection (spark) so this is not even an option until you use the SDF (back to the same point). Once again the API looks fine but no implementation makes it true. It would be easy to change it in spark, flink can be ok since it targets more the streaming case, not sure of others, any idea? >> >> #3: handling backpressure is a complex problem with multiple facets: 1) how >> do you know you're being throttled, and by how much are you exceeding the >> external system's capacity? This is the whole point of backpressure, the system sends it back to you (header like or status technic in general) >> 2) how do you communicate this signal to the >> runner? You are a client so you get the meta in the response - whatever techno. >> 3) what does the runner do in response? Runner nothing but the IO adapts its handling as mentionned before (wait and retry, skip, ... depending the config) >> 4) how do you wait until >> it's ok to try again? This is one point to probably enhance in beam but waiting in the processing is an option if the source has some buffering otherwise it requires to have a buffer fallback and max size if the wait mode is activated. >> You seem to be advocating for solving one facet of this problem, which is: >> you want it to be possible to signal to the runner "I'm being throttled, >> please end the bundle", right? If so - I think this (ending the bundle) is >> unnecessary: the DoFn can simply do an exponential back-off sleep loop. Agree, never said the runner should know but GBK+output doesnt work cause you dont own the GBK. >> This is e.g. what DatastoreIO does >> <https://github.com/apache/beam/blob/master/sdks/java/io/ >> google-cloud-platform/src/main/java/org/apache/beam/sdk/ >> io/gcp/datastore/DatastoreV1.java#L1318> >> and >> this is in general how most systems I've seen handle backpressure. Is there >> something I'm missing? In particular, is there any compelling reason why >> you think it'd be beneficial e.g. for DatastoreIO to commit the results of >> the bundle so far before processing other elements? It was more about ensuring you validate early a subset of the whole bundle and avoid to reprocess it if it fails later. So to summarize I see 2 outcomes: 1. impl SDF in all runners 2. make the bundle size upper bounded - through a pipeline option - in all runners, not sure this one is doable everywhere since I mainly checked spark case >> >> Again, it might be that I'm still misunderstanding what you're trying to >> say. One of the things it would help to clarify would be - exactly what do >> you mean by "how batch frameworks solved that for years": can you point at >> an existing API in some other framework that achieves what you want? >> >> On Sat, Nov 18, 2017 at 1:02 PM Romain Manni-Bucau <rmannibu...@gmail.com> >> wrote: >> >> > Eugene, point - and issue with a single sample - is you can always find >> > *workarounds* on a case by case basis as the id one with ES but beam >> doesnt >> > solve the problem as a framework. >> > >> > From my past, I clearly dont see how batch frameworks solved that for >> years >> > and beam is not able to do it - keep in mind it is the same kind of >> techno, >> > it just uses different sources and bigger clusters so no real reason to >> not >> > have the same feature quality. The only potential reason i see is there >> is >> > no tracking of the state into the cluster - e2e. But i dont see why there >> > wouldnt be. Do I miss something here? >> > >> > An example could be: take a github crawler computing stats on the whole >> > girhub repos which is based on a rest client as example. You will need to >> > handle the rate limit and likely want to "commit" each time you reach a >> > rate limit with likely some buffering strategy with a max size before >> > really waiting. How do you do it with a GBK independent of your dofn? You >> > are not able to compose correctly the fn between them :(. >> > >> > >> > Le 18 nov. 2017 20:48, "Eugene Kirpichov" <kirpic...@google.com.invalid> >> a >> > écrit : >> > >> > After giving this thread my best attempt at understanding exactly what is >> > the problem and the proposed solution, I'm afraid I still fail to >> > understand both. To reiterate, I think the only way to make progress here >> > is to be more concrete: (quote) take some IO that you think could be >> easier >> > to write with your proposed API, give the contents of a hypothetical >> > PCollection being written to this IO, give the code of a hypothetical >> DoFn >> > implementing the write using your API, and explain what you'd expect to >> > happen at runtime. I'm going to re-engage in this thread after such an >> > example is given. >> > >> > On Sat, Nov 18, 2017, 5:00 AM Romain Manni-Bucau <rmannibu...@gmail.com> >> > wrote: >> > >> > > First bundle retry is unusable with dome runners like spark where the >> > > bundle size is the collection size / number of work. This means a user >> > cant >> > > use bundle API or feature reliably and portably - which is beam >> promise. >> > > Aligning chunking and bundles would guarantee that bit can be not >> > desired, >> > > that is why i thought it can be another feature. >> > > >> > > GBK works until the IO knows about that and both concepts are not >> always >> > > orthogonal - backpressure like systems is a trivial common example. >> This >> > > means the IO (dofn) must be able to do it itself at some point. >> > > >> > > Also note the GBK works only if the IO can take a list which is never >> the >> > > case today. >> > > >> > > Big questions for me are: is SDF the way to go since it provides the >> > needed >> > > API bit is not yet supported? What about existing IO? Should beam >> provide >> > > an auto wrapping of dofn for that pre-aggregated support and simulate >> > > bundles to the actual IO impl to keep the existing API? >> > > >> > > >> > > Le 17 nov. 2017 19:20, "Raghu Angadi" <rang...@google.com.invalid> a >> > > écrit : >> > > >> > > On Fri, Nov 17, 2017 at 1:02 AM, Romain Manni-Bucau < >> > rmannibu...@gmail.com >> > > > >> > > wrote: >> > > >> > > > Yep, just take ES IO, if a part of a bundle fails you are in an >> > > > unmanaged state. This is the case for all O (of IO ;)). Issue is not >> > > > much about "1" (the code it takes) but more the fact it doesn't >> > > > integrate with runner features and retries potentially: what happens >> > > > if a bundle has a failure? => undefined today. 2. I'm fine with it >> > > > while we know exactly what happens when we restart after a bundle >> > > > failure. With ES the timestamp can be used for instance. >> > > > >> > > >> > > This deterministic batching can be achieved even now with an extra >> > > GroupByKey (and if you want ordering on top of that, will need another >> > > GBK). Don't know if that is too costly in your case. I would need bit >> > more >> > > details on handling ES IO write retries to see it could be simplified. >> > Note >> > > that retries occur with or without any failures in your DoFn. >> > > >> > > The biggest negative with GBK approach is that it doesn't provide same >> > > guarantees on Flink. >> > > >> > > I don't see how GroubIntoBatches in Beam provides specific guarantees >> on >> > > deterministic batches. >> > > >> > > Thinking about it the SDF is really a way to do it since the SDF will >> > > > manage the bulking and associated with the runner "retry" it seems it >> > > > covers the needs. >> > > > >> > > > Romain Manni-Bucau >> > > > @rmannibucau | Blog | Old Blog | Github | LinkedIn >> > > > >> > > > >> > > > 2017-11-17 9:23 GMT+01:00 Eugene Kirpichov >> > <kirpic...@google.com.invalid >> > > >: >> > > > > I must admit I'm still failing to understand the problem, so let's >> > step >> > > > > back even further. >> > > > > >> > > > > Could you give an example of an IO that is currently difficult to >> > > > implement >> > > > > specifically because of lack of the feature you're talking about? >> > > > > >> > > > > I'm asking because I've reviewed almost all Beam IOs and don't >> recall >> > > > > seeing a similar problem. Sure, a lot of IOs do batching within a >> > > bundle, >> > > > > but 1) it doesn't take up much code (granted, it would be even >> easier >> > > if >> > > > > Beam did it for us) and 2) I don't remember any of them requiring >> the >> > > > > batches to be deterministic, and I'm having a hard time imagining >> > what >> > > > kind >> > > > > of storage system would be able to deduplicate if batches were >> > > > > deterministic but wouldn't be able to deduplicate if they weren't. >> > > > > >> > > > > On Thu, Nov 16, 2017 at 11:50 PM Romain Manni-Bucau < >> > > > rmannibu...@gmail.com> >> > > > > wrote: >> > > > > >> > > > >> Ok, let me try to step back and summarize what we have today and >> > what >> > > I >> > > > >> miss: >> > > > >> >> > > > >> 1. we can handle chunking in beam through group in batch (or >> > > equivalent) >> > > > >> but: >> > > > >> > it is not built-in into the transforms (IO) and it is >> > controlled >> > > > >> from outside the transforms so no way for a transform to do it >> > > > >> properly without handling itself a composition and links between >> > > > >> multiple dofns to have notifications and potentially react >> properly >> > or >> > > > >> handle backpressure from its backend >> > > > >> 2. there is no restart feature because there is no real state >> > handling >> > > > >> at the moment. this sounds fully delegated to the runner but I was >> > > > >> hoping to have more guarantees from the used API to be able to >> > restart >> > > > >> a pipeline (mainly batch since it can be irrelevant or delegates >> to >> > > > >> the backend for streams) and handle only not commited records so >> it >> > > > >> requires some persistence outside the main IO storages to do it >> > > > >> properly >> > > > >> > note this is somehow similar to the monitoring topic which >> miss >> > > > >> persistence ATM so it can end up to beam to have a pluggable >> storage >> > > > >> for a few concerns >> > > > >> >> > > > >> >> > > > >> Short term I would be happy with 1 solved properly, long term I >> hope >> > 2 >> > > > >> will be tackled without workarounds requiring custom wrapping of >> IO >> > to >> > > > >> use a custom state persistence. >> > > > >> >> > > > >> >> > > > >> >> > > > >> Romain Manni-Bucau >> > > > >> @rmannibucau | Blog | Old Blog | Github | LinkedIn >> > > > >> >> > > > >> >> > > > >> 2017-11-17 7:44 GMT+01:00 Jean-Baptiste Onofré <j...@nanthrax.net>: >> > > > >> > Thanks for the explanation. Agree, we might talk about different >> > > > things >> > > > >> > using the same wording. >> > > > >> > >> > > > >> > I'm also struggling to understand the use case (for a generic >> > DoFn). >> > > > >> > >> > > > >> > Regards >> > > > >> > JB >> > > > >> > >> > > > >> > >> > > > >> > On 11/17/2017 07:40 AM, Eugene Kirpichov wrote: >> > > > >> >> >> > > > >> >> To avoid spending a lot of time pursuing a false path, I'd like >> > to >> > > > say >> > > > >> >> straight up that SDF is definitely not going to help here, >> > despite >> > > > the >> > > > >> >> fact >> > > > >> >> that its API includes the term "checkpoint". In SDF, the >> > > "checkpoint" >> > > > >> >> captures the state of processing within a single element. If >> > you're >> > > > >> >> applying an SDF to 1000 elements, it will, like any other DoFn, >> > be >> > > > >> applied >> > > > >> >> to each of them independently and in parallel, and you'll have >> > 1000 >> > > > >> >> checkpoints capturing the state of processing each of these >> > > elements, >> > > > >> >> which >> > > > >> >> is probably not what you want. >> > > > >> >> >> > > > >> >> I'm afraid I still don't understand what kind of checkpoint you >> > > > need, if >> > > > >> >> it >> > > > >> >> is not just deterministic grouping into batches. "Checkpoint" >> is >> > a >> > > > very >> > > > >> >> broad term and it's very possible that everybody in this thread >> > is >> > > > >> talking >> > > > >> >> about different things when saying it. So it would help if you >> > > could >> > > > >> give >> > > > >> >> a >> > > > >> >> more concrete example: for example, take some IO that you think >> > > > could be >> > > > >> >> easier to write with your proposed API, give the contents of a >> > > > >> >> hypothetical >> > > > >> >> PCollection being written to this IO, give the code of a >> > > hypothetical >> > > > >> DoFn >> > > > >> >> implementing the write using your API, and explain what you'd >> > > expect >> > > > to >> > > > >> >> happen at runtime. >> > > > >> >> >> > > > >> >> On Thu, Nov 16, 2017 at 10:33 PM Romain Manni-Bucau >> > > > >> >> <rmannibu...@gmail.com> >> > > > >> >> wrote: >> > > > >> >> >> > > > >> >>> @Eugene: yes and the other alternative of Reuven too but it is >> > > still >> > > > >> >>> 1. relying on timers, 2. not really checkpointed >> > > > >> >>> >> > > > >> >>> In other words it seems all solutions are to create a chunk of >> > > size >> > > > 1 >> > > > >> >>> and replayable to fake the lack of chunking in the framework. >> > This >> > > > >> >>> always implies a chunk handling outside the component >> (typically >> > > > >> >>> before for an output). My point is I think IO need it in their >> > own >> > > > >> >>> "internal" or at least control it themselves since the chunk >> > size >> > > is >> > > > >> >>> part of the IO handling most of the time. >> > > > >> >>> >> > > > >> >>> I think JB spoke of the same "group before" trick using >> > > restrictions >> > > > >> >>> which can work I have to admit if SDF are implemented by >> > runners. >> > > Is >> > > > >> >>> there a roadmap/status on that? Last time I checked SDF was a >> > > great >> > > > >> >>> API without support :(. >> > > > >> >>> >> > > > >> >>> >> > > > >> >>> >> > > > >> >>> Romain Manni-Bucau >> > > > >> >>> @rmannibucau | Blog | Old Blog | Github | LinkedIn >> > > > >> >>> >> > > > >> >>> >> > > > >> >>> 2017-11-17 7:25 GMT+01:00 Eugene Kirpichov >> > > > >> >>> <kirpic...@google.com.invalid>: >> > > > >> >>>> >> > > > >> >>>> JB, not sure what you mean? SDFs and triggers are unrelated, >> > and >> > > > the >> > > > >> >>>> post >> > > > >> >>>> doesn't mention the word. Did you mean something else, e.g. >> > > > >> restriction >> > > > >> >>>> perhaps? Either way I don't think SDFs are the solution here; >> > > SDFs >> > > > >> have >> > > > >> >>> >> > > > >> >>> to >> > > > >> >>>> >> > > > >> >>>> do with the ability to split the processing of *a single >> > element* >> > > > over >> > > > >> >>>> multiple calls, whereas Romain I think is asking for >> repeatable >> > > > >> grouping >> > > > >> >>> >> > > > >> >>> of >> > > > >> >>>> >> > > > >> >>>> *multiple* elements. >> > > > >> >>>> >> > > > >> >>>> Romain - does >> > > > >> >>>> >> > > > >> >>> >> > > > >> >>> >> > > > >> https://github.com/apache/beam/blob/master/sdks/java/ >> > > > core/src/main/java/org/apache/beam/sdk/transforms/ >> GroupIntoBatches.java >> > > > >> >>>> >> > > > >> >>>> do what >> > > > >> >>>> you want? >> > > > >> >>>> >> > > > >> >>>> On Thu, Nov 16, 2017 at 10:19 PM Jean-Baptiste Onofré < >> > > > >> j...@nanthrax.net> >> > > > >> >>>> wrote: >> > > > >> >>>> >> > > > >> >>>>> It sounds like the "Trigger" in the Splittable DoFn, no ? >> > > > >> >>>>> >> > > > >> >>>>> https://beam.apache.org/blog/2017/08/16/splittable-do-fn. >> html >> > > > >> >>>>> >> > > > >> >>>>> Regards >> > > > >> >>>>> JB >> > > > >> >>>>> >> > > > >> >>>>> >> > > > >> >>>>> On 11/17/2017 06:56 AM, Romain Manni-Bucau wrote: >> > > > >> >>>>>> >> > > > >> >>>>>> it gives the fn/transform the ability to save a state - it >> > can >> > > > get >> > > > >> >>>>>> back on "restart" / whatever unit we can use, probably >> runner >> > > > >> >>>>>> dependent? Without that you need to rewrite all IO usage >> with >> > > > >> >>>>>> something like the previous pattern which makes the IO not >> > self >> > > > >> >>>>>> sufficient and kind of makes the entry cost and usage of >> beam >> > > way >> > > > >> >>>>>> further. >> > > > >> >>>>>> >> > > > >> >>>>>> In my mind it is exactly what jbatch/spring-batch uses but >> > > > adapted >> > > > >> to >> > > > >> >>>>>> beam (stream in particular) case. >> > > > >> >>>>>> >> > > > >> >>>>>> Romain Manni-Bucau >> > > > >> >>>>>> @rmannibucau | Blog | Old Blog | Github | LinkedIn >> > > > >> >>>>>> >> > > > >> >>>>>> >> > > > >> >>>>>> 2017-11-17 6:49 GMT+01:00 Reuven Lax >> > <re...@google.com.invalid >> > > >: >> > > > >> >>>>>>> >> > > > >> >>>>>>> Romain, >> > > > >> >>>>>>> >> > > > >> >>>>>>> Can you define what you mean by checkpoint? What are the >> > > > semantics, >> > > > >> >>> >> > > > >> >>> what >> > > > >> >>>>>>> >> > > > >> >>>>>>> does it accomplish? >> > > > >> >>>>>>> >> > > > >> >>>>>>> Reuven >> > > > >> >>>>>>> >> > > > >> >>>>>>> On Fri, Nov 17, 2017 at 1:40 PM, Romain Manni-Bucau < >> > > > >> >>>>> >> > > > >> >>>>> rmannibu...@gmail.com> >> > > > >> >>>>>>> >> > > > >> >>>>>>> wrote: >> > > > >> >>>>>>> >> > > > >> >>>>>>>> Yes, what I propose earlier was: >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> I. checkpoint marker: >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> @AnyBeamAnnotation >> > > > >> >>>>>>>> @CheckpointAfter >> > > > >> >>>>>>>> public void someHook(SomeContext ctx); >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> II. pipeline.apply(ParDo.of(new >> > > > >> MyFn()).withCheckpointAlgorithm(new >> > > > >> >>>>>>>> CountingAlgo())) >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> III. (I like this one less) >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> // in the dofn >> > > > >> >>>>>>>> @CheckpointTester >> > > > >> >>>>>>>> public boolean shouldCheckpoint(); >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> IV. @Checkpointer Serializable getCheckpoint(); in the >> dofn >> > > per >> > > > >> >>> >> > > > >> >>> element >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> Romain Manni-Bucau >> > > > >> >>>>>>>> @rmannibucau | Blog | Old Blog | Github | LinkedIn >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> 2017-11-17 6:06 GMT+01:00 Raghu Angadi >> > > > <rang...@google.com.invalid >> > > > >> >>>> >> > > > >> >>>> : >> > > > >> >>>>>>>>> >> > > > >> >>>>>>>>> How would you define it (rough API is fine)?. Without >> more >> > > > >> details, >> > > > >> >>>>> >> > > > >> >>>>> it is >> > > > >> >>>>>>>>> >> > > > >> >>>>>>>>> not easy to see wider applicability and feasibility in >> > > > runners. >> > > > >> >>>>>>>>> >> > > > >> >>>>>>>>> On Thu, Nov 16, 2017 at 1:13 PM, Romain Manni-Bucau < >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> rmannibu...@gmail.com> >> > > > >> >>>>>>>>> >> > > > >> >>>>>>>>> wrote: >> > > > >> >>>>>>>>> >> > > > >> >>>>>>>>>> This is a fair summary of the current state but also >> > where >> > > > beam >> > > > >> >>> >> > > > >> >>> can >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> have a >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> very strong added value and make big data great and >> > smooth. >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> Instead of this replay feature isnt checkpointing >> > willable? >> > > > In >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> particular >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> with SDF no? >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> Le 16 nov. 2017 19:50, "Raghu Angadi" >> > > > >> <rang...@google.com.invalid> >> > > > >> >>> >> > > > >> >>> a >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> écrit : >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>>> Core issue here is that there is no explicit concept >> of >> > > > >> >>> >> > > > >> >>> 'checkpoint' >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> in >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> Beam (UnboundedSource has a method 'getCheckpointMark' >> > but >> > > > that >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> refers to >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> the checkoint on external source). Runners do >> checkpoint >> > > > >> >>> >> > > > >> >>> internally >> > > > >> >>>>> >> > > > >> >>>>> as >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> implementation detail. Flink's checkpoint model is >> > > entirely >> > > > >> >>>>> >> > > > >> >>>>> different >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> from >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> Dataflow's and Spark's. >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> @StableReplay helps, but it does not explicitly talk >> > about >> > > a >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> checkpoint >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> by >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> design. >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> If you are looking to achieve some guarantees with a >> > > > >> sink/DoFn, I >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> think >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> it >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> is better to start with the requirements. I worked on >> > > > >> >>> >> > > > >> >>> exactly-once >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> sink >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>>>> for >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> Kafka (see KafkaIO.write().withEOS()), where we >> > > essentially >> > > > >> >>> >> > > > >> >>> reshard >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> the >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> elements and assign sequence numbers to elements with >> in >> > > > each >> > > > >> >>> >> > > > >> >>> shard. >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> Duplicates in replays are avoided based on these >> > sequence >> > > > >> >>> >> > > > >> >>> numbers. >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> DoFn >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> state API is used to buffer out-of order replays. The >> > > > >> >>> >> > > > >> >>> implementation >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> strategy works in Dataflow but not in Flink which has >> a >> > > > >> >>> >> > > > >> >>> horizontal >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> checkpoint. KafkaIO checks for compatibility. >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>> On Wed, Nov 15, 2017 at 12:38 AM, Romain Manni-Bucau < >> > > > >> >>>>>>>>>>> rmannibu...@gmail.com> >> > > > >> >>>>>>>>>>> wrote: >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>>>> Hi guys, >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> The subject is a bit provocative but the topic is >> real >> > > and >> > > > >> >>> >> > > > >> >>> coming >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> again and again with the beam usage: how a dofn can >> > > handle >> > > > >> some >> > > > >> >>>>>>>>>>>> "chunking". >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> The need is to be able to commit each N records but >> > with >> > > N >> > > > not >> > > > >> >>> >> > > > >> >>> too >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> big. >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> The natural API for that in beam is the bundle one >> but >> > > > bundles >> > > > >> >>> >> > > > >> >>> are >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> not >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> reliable since they can be very small (flink) - we >> can >> > > say >> > > > it >> > > > >> is >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> "ok" >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> even if it has some perf impacts - or too big (spark >> > does >> > > > full >> > > > >> >>> >> > > > >> >>> size >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> / >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> #workers). >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> The workaround is what we see in the ES I/O: a >> maxSize >> > > > which >> > > > >> >>> >> > > > >> >>> does >> > > > >> >>>>> >> > > > >> >>>>> an >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> eager flush. The issue is that then the checkpoint is >> > not >> > > > >> >>> >> > > > >> >>> respected >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> and you can process multiple times the same records. >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> Any plan to make this API reliable and controllable >> > from >> > > a >> > > > >> beam >> > > > >> >>>>>>>> >> > > > >> >>>>>>>> point >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> of view (at least in a max manner)? >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>>> Thanks, >> > > > >> >>>>>>>>>>>> Romain Manni-Bucau >> > > > >> >>>>>>>>>>>> @rmannibucau | Blog | Old Blog | Github | LinkedIn >> > > > >> >>>>>>>>>>>> >> > > > >> >>>>>>>>>>> >> > > > >> >>>>>>>>>> >> > > > >> >>>>>>>> >> > > > >> >>>>> >> > > > >> >>>>> -- >> > > > >> >>>>> Jean-Baptiste Onofré >> > > > >> >>>>> jbono...@apache.org >> > > > >> >>>>> http://blog.nanthrax.net >> > > > >> >>>>> Talend - http://www.talend.com >> > > > >> >>>>> >> > > > >> >>> >> > > > >> >> >> > > > >> > >> > > > >> > -- >> > > > >> > Jean-Baptiste Onofré >> > > > >> > jbono...@apache.org >> > > > >> > http://blog.nanthrax.net >> > > > >> > Talend - http://www.talend.com >> > > > >> >> > > > >> > > >> > >>
