[DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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[DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
 
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Hi all, @Klou Nice write up. One comment I have is I would suggest using a different configuration parameter name. The way I understand the proposal the BATCH/STREAMING/AUTOMATIC affects not only the scheduling mode but types of shuffles as well. How about `execution.mode` ? Or `execution-runtime-mode`? @Yun The way I understand it BATCH = pipelined scheduling with region failover + blocking keyBy shuffles (all pointwise shuffles pipelined) STREAM = eager scheduling with checkpointing + pipelined keyBy shuffles AUTOMATIC = choose based on source power users could still override any shuffle modes in PartitionTransformation, if we find more people interested in controlling the type of shuffles, we can think of exposing that in the DataStream as well in the future. Best, Dawid On 18/08/2020 06:18, Yun Gao wrote:
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Hey Aljoscha
A couple of thoughts for the two remaining TODOs in the doc: # Processing Time Support in BATCH/BOUNDED execution mode I think there are two somewhat orthogonal problems around this topic: 1. Firing processing timers at the end of the job 2. Having processing timers in the BATCH mode The way I see it there are three main use cases for different combinations of the aforementioned dimensions: 1. Regular streaming jobs: STREAM mode with UNBOUNDED sources - we do want to have processing timers - there is no end of the job 2. Debugging/Testing streaming jobs: STREAM mode with BOUNDED sources - we do want to have processing timers - we want to fire/wait for the timers at the end 3. batch jobs with DataStream API: - we do **NOT** want to have processing timers either during processing or at the end. We want to either fail-hard or ignore the timers. Generally speaking, in BATCH mode the processing timers do not make sense, therefore it would be better to fail-hard. It would be the safest option, as some of the user logic might depend on the processing timers. Failing hard would give the user opportunity to react to the changed behaviour. On the other hand if we want to make it possible to run exact same program both in STREAM and BATCH mode we must have an option to simply ignore processing timers. - we never want to actually trigger the timers. Neither during runtime nor at the end Having the above in mind, I am thinking if we should introduce two separate options: * processing-time.timers = ENABLE/FAIL/IGNORE * processing-time.on-end-of-input = CANCEL/WAIT/TRIGGER With the two options we can satisfy all the above cases. The default settings would be: STREAM: processing-time.timers = ENABLE processing-time.on-end-of-input = TRIGGER BATCH: processing-time.timers = IGNORE processing-time.on-end-of-input = CANCEL # Event time triggers I do say that from the implementation perspective, but I find it hard to actually ignore the event-time triggers. We would have to adjust the implementation of WindowOperator to do that. At the same time I see no problem with simply keeping them working. I am wondering if we should/could just leave them as they are. # Broadcast State As far as I am concerned there are no core semantical problems with the Broadcast State. As of now, it does not give any guarantees about the order in which the broadcast and non-broadcast sides are executed even in streaming. It also does not expose any mechanisms to implement an event/processing-time alignments (you cannot register timers in the broadcast side). I can't see any of the guarantees breaking in the BATCH mode. I do agree it would give somewhat nicer properties in BATCH if we consumed the broadcast side first. It would make the operation deterministic and let users implement a broadcast join properly on top of this method. Nevertheless I see it as an extension of the DataStream API for BATCH execution rather than making the DataStream API work for BATCH. Therefore I'd be fine with the leaving the Broadcast State out of the FLIP What do you think? On 01/09/2020 13:46, Aljoscha Krettek wrote: > Hmm, it seems I left out the Dev ML in my mail. Looping that back in.. > > > On 28.08.20 13:54, Dawid Wysakowicz wrote: >> @Aljoscha Let me bring back to the ML some of the points we discussed >> offline. >> >> Ad. 1 Yes I agree it's not just about scheduling. It includes more >> changes to the runtime. We might need to make it more prominent in the >> write up. >> >> Ad. 2 You have a good point here that switching the default value for >> TimeCharacteristic to INGESTION time might not be the best option as it >> might hide problems if we assign ingestion time, which is rarely a right >> choice for user programs. Maybe we could just go with the EVENT_TIME as >> the default? >> >> Ad. 4 That's a very good point! I do agree with you it would be better >> to change the behaviour of said methods for batch-style execution. Even >> though it changes the behaviour, the overall logic is still correct. >> Moreover I'd also recommend deprecating some of the relational-like >> methods, which we should rather redirect to the Table API. I added a >> section about it to the FLIP (mostly copying over your message). Let me >> know what you think about it. >> >> Best, >> >> Dawid >> >> On 25/08/2020 11:39, Aljoscha Krettek wrote: >>> Thanks for creating this FLIP! I think the general direction is very >>> good but I think there are some specifics that we should also put in >>> there and that we may need to discuss here as well. >>> >>> ## About batch vs streaming scheduling >>> >>> I think we shouldn't call it "scheduling", because the decision >>> between bounded and unbounded affects more than just scheduling. It >>> affects how we do network transfers and the semantics of time, among >>> other things. So maybe we should differentiate between batch-style and >>> streaming-style execution, though I'm not sure I like those terms >>> either. >>> >>> ## About processing-time support in batch >>> >>> It's not just about "batch" changing the default to ingestion time is >>> a change for stream processing as well. Actually, I don't know if >>> ingestion time even makes sense for batch processing. IIRC, with the >>> new sources we actually always have a timestamp, so this discussion >>> might be moot. Maybe Becket and/or Stephan (cc'ed) could chime in on >>> this. >>> >>> Also, I think it's right that we currently ignore processing-time >>> timers at the end of input in streaming jobs, but this has been a >>> source of trouble for users. See [1] and several discussions on the >>> ML. I'm also cc'ing Flavio here who also ran into this problem. I >>> think we should solve this quickly after laying the foundations of >>> bounded processing on the DataStream API. >>> >>> ## About broadcast state support >>> >>> I think as a low-hanging fruit we could just read the broadcast side >>> first and then switch to the regular input. We do need to be careful >>> with creating distributed deadlocks, though, so this might be trickier >>> than it seems at first. >>> >>> ## Loose ends and weird semantics >>> >>> There are some operations in the DataStream API that have semantics >>> that might make sense for stream processing but should behave >>> differently for batch. For example, KeyedStream.reduce() is >>> essentially a reduce on a GlobalWindow with a Trigger that fires on >>> every element. In DB terms it produces an UPSERT stream as an output, >>> if you get ten input elements for a key you also get ten output >>> records. For batch processing it might make more sense to instead only >>> produce one output record per key with the result of the aggregation. >>> This would be correct for downstream consumers that expect an UPSERT >>> stream but it would change the actual physical output stream that they >>> see. >>> >>> There might be other such operations in the DataStream API that have >>> slightly weird behaviour that doesn't make much sense when you do >>> bounded processing. >>> >>> Best, >>> Aljoscha >>> >>> [1] https://issues.apache.org/jira/browse/FLINK-18647 >>> >>> On 24.08.20 11:29, Kostas Kloudas wrote: >>>> Thanks a lot for the discussion! >>>> >>>> I will open a voting thread shortly! >>>> >>>> Kostas >>>> >>>> On Mon, Aug 24, 2020 at 9:46 AM Kostas Kloudas <[hidden email]> >>>> wrote: >>>>> >>>>> Hi Guowei, >>>>> >>>>> Thanks for the insightful comment! >>>>> >>>>> I agree that this can be a limitation of the current runtime, but I >>>>> think that this FLIP can go on as it discusses mainly the semantics >>>>> that the DataStream API will expose when applied on bounded data. >>>>> There will definitely be other FLIPs that will actually handle the >>>>> runtime-related topics. >>>>> >>>>> But it is good to document them nevertheless so that we start soon >>>>> ironing out the remaining rough edges. >>>>> >>>>> Cheers, >>>>> Kostas >>>>> >>>>> On Mon, Aug 24, 2020 at 9:16 AM Guowei Ma <[hidden email]> >>>>> wrote: >>>>>> >>>>>> Hi, Klou >>>>>> >>>>>> Thanks for your proposal. It's a very good idea. >>>>>> Just a little comment about the "Batch vs Streaming Scheduling". >>>>>> In the AUTOMATIC execution mode maybe we could not pick BATCH >>>>>> execution mode even if all sources are bounded. For example some >>>>>> applications would use the `CheckpointListener`, which is not >>>>>> available in the BATCH mode in current implementation. >>>>>> So maybe we need more checks in the AUTOMATIC execution mode. >>>>>> >>>>>> Best, >>>>>> Guowei >>>>>> >>>>>> >>>>>> On Thu, Aug 20, 2020 at 10:27 PM Kostas Kloudas >>>>>> <[hidden email]> wrote: >>>>>>> >>>>>>> Hi all, >>>>>>> >>>>>>> Thanks for the comments! >>>>>>> >>>>>>> @Dawid: "execution.mode" can be a nice alternative and from a quick >>>>>>> look it is not used currently by any configuration option. I will >>>>>>> update the FLIP accordingly. >>>>>>> >>>>>>> @David: Given that having the option to allow timers to fire at the >>>>>>> end of the job is already in the FLIP, I will leave it as is and I >>>>>>> will update the default policy to be "ignore processing time timers >>>>>>> set by the user". This will allow existing dataStream programs >>>>>>> to run >>>>>>> on bounded inputs. This update will affect point 2 in the >>>>>>> "Processing >>>>>>> Time Support in Batch" section. >>>>>>> >>>>>>> If these changes cover your proposals, then I would like to start a >>>>>>> voting thread tomorrow evening if this is ok with you. >>>>>>> >>>>>>> Please let me know until then. >>>>>>> >>>>>>> Kostas >>>>>>> >>>>>>> On Tue, Aug 18, 2020 at 3:54 PM David Anderson >>>>>>> <[hidden email]> wrote: >>>>>>>> >>>>>>>> Being able to optionally fire registered processing time timers >>>>>>>> at the end of a job would be interesting, and would help in (at >>>>>>>> least some of) the cases I have in mind. I don't have a better >>>>>>>> idea. >>>>>>>> >>>>>>>> David >>>>>>>> >>>>>>>> On Mon, Aug 17, 2020 at 8:24 PM Kostas Kloudas >>>>>>>> <[hidden email]> wrote: >>>>>>>>> >>>>>>>>> Hi Kurt and David, >>>>>>>>> >>>>>>>>> Thanks a lot for the insightful feedback! >>>>>>>>> >>>>>>>>> @Kurt: For the topic of checkpointing with Batch Scheduling, I >>>>>>>>> totally >>>>>>>>> agree with you that it requires a lot more work and careful >>>>>>>>> thinking >>>>>>>>> on the semantics. This FLIP was written under the assumption >>>>>>>>> that if >>>>>>>>> the user wants to have checkpoints on bounded input, he/she will >>>>>>>>> have >>>>>>>>> to go with STREAMING as the scheduling mode. Checkpointing for >>>>>>>>> BATCH >>>>>>>>> can be handled as a separate topic in the future. >>>>>>>>> >>>>>>>>> In the case of MIXED workloads and for this FLIP, the scheduling >>>>>>>>> mode >>>>>>>>> should be set to STREAMING. That is why the AUTOMATIC option sets >>>>>>>>> scheduling to BATCH only if all the sources are bounded. I am >>>>>>>>> not sure >>>>>>>>> what are the plans there at the scheduling level, as one could >>>>>>>>> imagine >>>>>>>>> in the future that in mixed workloads, we schedule first all the >>>>>>>>> bounded subgraphs in BATCH mode and we allow only one UNBOUNDED >>>>>>>>> subgraph per application, which is going to be scheduled after >>>>>>>>> all >>>>>>>>> Bounded ones have finished. Essentially the bounded subgraphs >>>>>>>>> will be >>>>>>>>> used to bootstrap the unbounded one. But, I am not aware of any >>>>>>>>> plans >>>>>>>>> towards that direction. >>>>>>>>> >>>>>>>>> >>>>>>>>> @David: The processing time timer handling is a topic that has >>>>>>>>> also >>>>>>>>> been discussed in the community in the past, and I do not >>>>>>>>> remember any >>>>>>>>> final conclusion unfortunately. >>>>>>>>> >>>>>>>>> In the current context and for bounded input, we chose to favor >>>>>>>>> reproducibility of the result, as this is expected in batch >>>>>>>>> processing >>>>>>>>> where the whole input is available in advance. This is why this >>>>>>>>> proposal suggests to not allow processing time timers. But I >>>>>>>>> understand your argument that the user may want to be able to >>>>>>>>> run the >>>>>>>>> same pipeline on batch and streaming this is why we added the two >>>>>>>>> options under future work, namely (from the FLIP): >>>>>>>>> >>>>>>>>> ``` >>>>>>>>> Future Work: In the future we may consider adding as options the >>>>>>>>> capability of: >>>>>>>>> * firing all the registered processing time timers at the end of >>>>>>>>> a job >>>>>>>>> (at close()) or, >>>>>>>>> * ignoring all the registered processing time timers at the end >>>>>>>>> of a job. >>>>>>>>> ``` >>>>>>>>> >>>>>>>>> Conceptually, we are essentially saying that we assume that batch >>>>>>>>> execution is assumed to be instantaneous and refers to a single >>>>>>>>> "point" in time and any processing-time timers for the future >>>>>>>>> may fire >>>>>>>>> at the end of execution or be ignored (but not throw an >>>>>>>>> exception). I >>>>>>>>> could also see ignoring the timers in batch as the default, if >>>>>>>>> this >>>>>>>>> makes more sense. >>>>>>>>> >>>>>>>>> By the way, do you have any usecases in mind that will help us >>>>>>>>> better >>>>>>>>> shape our processing time timer handling? >>>>>>>>> >>>>>>>>> Kostas >>>>>>>>> >>>>>>>>> On Mon, Aug 17, 2020 at 2:52 PM David Anderson >>>>>>>>> <[hidden email]> wrote: >>>>>>>>>> >>>>>>>>>> Kostas, >>>>>>>>>> >>>>>>>>>> I'm pleased to see some concrete details in this FLIP. >>>>>>>>>> >>>>>>>>>> I wonder if the current proposal goes far enough in the >>>>>>>>>> direction of recognizing the need some users may have for >>>>>>>>>> "batch" and "bounded streaming" to be treated differently. If >>>>>>>>>> I've understood it correctly, the section on scheduling allows >>>>>>>>>> me to choose STREAMING scheduling even if I have bounded >>>>>>>>>> sources. I like that approach, because it recognizes that even >>>>>>>>>> though I have bounded inputs, I don't necessarily want batch >>>>>>>>>> processing semantics. I think it makes sense to extend this >>>>>>>>>> idea to processing time support as well. >>>>>>>>>> >>>>>>>>>> My thinking is that sometimes in development and testing it's >>>>>>>>>> reasonable to run exactly the same job as in production, except >>>>>>>>>> with different sources and sinks. While it might be a >>>>>>>>>> reasonable default, I'm not convinced that switching a >>>>>>>>>> processing time streaming job to read from a bounded source >>>>>>>>>> should always cause it to fail. >>>>>>>>>> >>>>>>>>>> David >>>>>>>>>> >>>>>>>>>> On Wed, Aug 12, 2020 at 5:22 PM Kostas Kloudas >>>>>>>>>> <[hidden email]> wrote: >>>>>>>>>>> >>>>>>>>>>> Hi all, >>>>>>>>>>> >>>>>>>>>>> As described in FLIP-131 [1], we are aiming at deprecating the >>>>>>>>>>> DataSet >>>>>>>>>>> API in favour of the DataStream API and the Table API. After >>>>>>>>>>> this work >>>>>>>>>>> is done, the user will be able to write a program using the >>>>>>>>>>> DataStream >>>>>>>>>>> API and this will execute efficiently on both bounded and >>>>>>>>>>> unbounded >>>>>>>>>>> data. But before we reach this point, it is worth discussing >>>>>>>>>>> and >>>>>>>>>>> agreeing on the semantics of some operations as we transition >>>>>>>>>>> from the >>>>>>>>>>> streaming world to the batch one. >>>>>>>>>>> >>>>>>>>>>> This thread and the associated FLIP [2] aim at discussing >>>>>>>>>>> these issues >>>>>>>>>>> as these topics are pretty important to users and can lead to >>>>>>>>>>> unpleasant surprises if we do not pay attention. >>>>>>>>>>> >>>>>>>>>>> Let's have a healthy discussion here and I will be updating >>>>>>>>>>> the FLIP >>>>>>>>>>> accordingly. >>>>>>>>>>> >>>>>>>>>>> Cheers, >>>>>>>>>>> Kostas >>>>>>>>>>> >>>>>>>>>>> [1] >>>>>>>>>>> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158866741 >>>>>>>>>>> >>>>>>>>>>> [2] >>>>>>>>>>> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158871522 >>>>>>>>>>> >>> >> > |
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Thanks for the update Aljoscha!
I am not sure about the option for ignoring the Triggers. Do you mean to ignore all the Triggers including e.g. Flink's such as CountTrigger, EventTimeTrigger etc.? Won't it effectively disable the WindowOperator whatsoever. Or even worse make it unusable with ever growing state? I might be wrong here but aren't Triggers required for emitting results from WindowOperator? If I am correct we emit results only if a Trigger returns FIRE from on of onElement, onEventTime, onProcessingTime. Why do you think it does not work well with FAILing hard without this option? We could fail hard e.g. if the WindowAssigner#isEventTime returns false. As for the question with getProcessingTime(). From my point of view, it would be safe to simply return the current system time. I cannot think of any dangers if we do so. Moreover, frankly speaking I am not entirely sure what is the purpose of the method, other than injecting a clock in tests of built-in operators. Maybe it was a mistake to expose it in the user's API? Best, Dawid On 09/09/2020 14:12, Aljoscha Krettek wrote: > I updated the FLIP, you can check out the changes here: > https://cwiki.apache.org/confluence/pages/diffpagesbyversion.action?pageId=158871522&selectedPageVersions=16&selectedPageVersions=15 > > There is still the open question of what IGNORE means for > getProcessingTime(). > > Plus, I introduced a setting for ignoring Triggers because I think it > otherwise doesn't work well with FAILing hard on processing-time API > calls. I described it in the FLIP, so please have a look at the diff I > linked above. > > Aljoscha > > > On 08.09.20 11:35, Dawid Wysakowicz wrote: >>> The only one where I could see that users want different behaviour >>> BATCH jobs on the DataStream API. I agree that processing-time does >>> not make much sense in batch jobs. However, if users have written some >>> business logic using processing-time timers their jobs will silently >>> not work if we set the default to IGNORE. Setting it to FAIL would at >>> least make users aware that something is not right. >> I see your point. I was also undecided myself which option to use here. >> I went with IGNORE for the reason that I thought the common/the most >> prominent functions should work just out of the box without much >> additional tweaking. I found the case of "running the same program in >> BATCH and STREAM" one of such cases and therefore optimized the options >> for that case. That's why went with IGNORE instead of FAIL. Again I am >> good with either of the two. >> >>> I can also see a small group of users wanting processing-time timers >>> for BATCH. We could, for example, fire all processing-time timers at >>> the "end of input", then we also set the watermark to +Inf. >> I agree. I think this case would be covered with ENABLE + TRIGGER. I do >> agree though it makes sense to mention this case explicitly as the >> ENABLE option would behave slightly different in BATCH than in STREAM. >> Maybe not strictly speaking different, but would be worth explaining >> anyway. The way I heard from some people you can think of BATCH >> processing happening instantaneously in processing time. Therefore there >> can be no timers triggered in between records. In BATCH processing the >> only time when timers can be triggered is at the end of input. Or at >> least that is how I see it. >> >>> Another thing is: what should we do with new triggers that are set >>> after the end-of-input. If we have TRIGGER and users keep setting new >>> processing-time timers in the callback, would we continue firing them. >>> Or should the behaviour bee QUIESCE_AND_TRIGGER, where we work off >>> remaining timers but don't add new ones? Do we silently ignore adding >>> new ones? >> My take on this issue is that it should be good enough to have the >> QUIESCE_AND_TRIGGER behaviour with ignoring timers registered after the >> end of input. We can not fail hard in such scenario, unless we expose a >> flag saying the timer is after the end of input. Otherwise I can not see >> a way to correctly safe guard for this scenario. I can see some use >> cases that would benefit from allowing the timers registration, e.g. >> periodically checking if some external process finished. In my opinion >> this is a bit of a different topic, as it is actually an issue of >> inverting the control when an operator can finish. Right now it is the >> task that decides that the job/operator finishes at the end of input. >> >>> By the way, I assume WAIT means we wait for processing-time to >>> actually reach the time of pending timers? Or did you have something >>> else in mind with this? >> Yes, that's what I meant. I actually took the options from this >> issue[1], where there is some discussion on that topic as well. >> >> Best, >> >> Dawid >> >> [1] https://issues.apache.org/jira/browse/FLINK-18647 >> >> On 08/09/2020 10:57, Aljoscha Krettek wrote: >>> I agree with almost all of your points! >>> >>> The only one where I could see that users want different behaviour >>> BATCH jobs on the DataStream API. I agree that processing-time does >>> not make much sense in batch jobs. However, if users have written some >>> business logic using processing-time timers their jobs will silently >>> not work if we set the default to IGNORE. Setting it to FAIL would at >>> least make users aware that something is not right. >>> >>> I can also see a small group of users wanting processing-time timers >>> for BATCH. We could, for example, fire all processing-time timers at >>> the "end of input", then we also set the watermark to +Inf. >>> >>> Another thing is: what should we do with new triggers that are set >>> after the end-of-input. If we have TRIGGER and users keep setting new >>> processing-time timers in the callback, would we continue firing them. >>> Or should the behaviour bee QUIESCE_AND_TRIGGER, where we work off >>> remaining timers but don't add new ones? Do we silently ignore adding >>> new ones? >>> >>> By the way, I assume WAIT means we wait for processing-time to >>> actually reach the time of pending timers? Or did you have something >>> else in mind with this? >>> >>> Aljoscha >>> >>> On 08.09.20 09:19, Dawid Wysakowicz wrote: >>>> Hey Aljoscha >>>> >>>> A couple of thoughts for the two remaining TODOs in the doc: >>>> >>>> # Processing Time Support in BATCH/BOUNDED execution mode >>>> >>>> I think there are two somewhat orthogonal problems around this topic: >>>> 1. Firing processing timers at the end of the job >>>> 2. Having processing timers in the BATCH mode >>>> The way I see it there are three main use cases for different >>>> combinations of the aforementioned dimensions: >>>> 1. Regular streaming jobs: STREAM mode with UNBOUNDED sources >>>> - we do want to have processing timers >>>> - there is no end of the job >>>> 2. Debugging/Testing streaming jobs: STREAM mode with BOUNDED >>>> sources >>>> - we do want to have processing timers >>>> - we want to fire/wait for the timers at the end >>>> 3. batch jobs with DataStream API: >>>> - we do **NOT** want to have processing timers either during >>>> processing or at the end. We want to either fail-hard or ignore the >>>> timers. Generally speaking, in BATCH mode the processing timers do not >>>> make sense, therefore it would be better to fail-hard. It would be the >>>> safest option, as some of the user logic might depend on the >>>> processing >>>> timers. Failing hard would give the user opportunity to react to the >>>> changed behaviour. On the other hand if we want to make it possible to >>>> run exact same program both in STREAM and BATCH mode we must have an >>>> option to simply ignore processing timers. >>>> - we never want to actually trigger the timers. Neither >>>> during >>>> runtime nor at the end >>>> >>>> Having the above in mind, I am thinking if we should introduce two >>>> separate options: >>>> * processing-time.timers = ENABLE/FAIL/IGNORE >>>> * processing-time.on-end-of-input = CANCEL/WAIT/TRIGGER >>>> With the two options we can satisfy all the above cases. The default >>>> settings would be: >>>> STREAM: >>>> processing-time.timers = ENABLE >>>> processing-time.on-end-of-input = TRIGGER >>>> BATCH: >>>> processing-time.timers = IGNORE >>>> processing-time.on-end-of-input = CANCEL >>>> >>>> # Event time triggers >>>> I do say that from the implementation perspective, but I find it >>>> hard to >>>> actually ignore the event-time triggers. We would have to adjust the >>>> implementation of WindowOperator to do that. At the same time I see no >>>> problem with simply keeping them working. I am wondering if we >>>> should/could just leave them as they are. >>>> >>>> # Broadcast State >>>> As far as I am concerned there are no core semantical problems with >>>> the >>>> Broadcast State. As of now, it does not give any guarantees about the >>>> order in which the broadcast and non-broadcast sides are executed even >>>> in streaming. It also does not expose any mechanisms to implement an >>>> event/processing-time alignments (you cannot register timers in the >>>> broadcast side). I can't see any of the guarantees breaking in the >>>> BATCH >>>> mode. >>>> I do agree it would give somewhat nicer properties in BATCH if we >>>> consumed the broadcast side first. It would make the operation >>>> deterministic and let users implement a broadcast join properly on top >>>> of this method. Nevertheless I see it as an extension of the >>>> DataStream >>>> API for BATCH execution rather than making the DataStream API work for >>>> BATCH. Therefore I'd be fine with the leaving the Broadcast State out >>>> of the FLIP >>>> >>>> What do you think? >>>> >>>> On 01/09/2020 13:46, Aljoscha Krettek wrote: >>>>> Hmm, it seems I left out the Dev ML in my mail. Looping that back >>>>> in.. >>>>> >>>>> >>>>> On 28.08.20 13:54, Dawid Wysakowicz wrote: >>>>>> @Aljoscha Let me bring back to the ML some of the points we >>>>>> discussed >>>>>> offline. >>>>>> >>>>>> Ad. 1 Yes I agree it's not just about scheduling. It includes more >>>>>> changes to the runtime. We might need to make it more prominent >>>>>> in the >>>>>> write up. >>>>>> >>>>>> Ad. 2 You have a good point here that switching the default value >>>>>> for >>>>>> TimeCharacteristic to INGESTION time might not be the best option >>>>>> as it >>>>>> might hide problems if we assign ingestion time, which is rarely a >>>>>> right >>>>>> choice for user programs. Maybe we could just go with the >>>>>> EVENT_TIME as >>>>>> the default? >>>>>> >>>>>> Ad. 4 That's a very good point! I do agree with you it would be >>>>>> better >>>>>> to change the behaviour of said methods for batch-style execution. >>>>>> Even >>>>>> though it changes the behaviour, the overall logic is still correct. >>>>>> Moreover I'd also recommend deprecating some of the relational-like >>>>>> methods, which we should rather redirect to the Table API. I added a >>>>>> section about it to the FLIP (mostly copying over your message). >>>>>> Let me >>>>>> know what you think about it. >>>>>> >>>>>> Best, >>>>>> >>>>>> Dawid >>>>>> >>>>>> On 25/08/2020 11:39, Aljoscha Krettek wrote: >>>>>>> Thanks for creating this FLIP! I think the general direction is >>>>>>> very >>>>>>> good but I think there are some specifics that we should also >>>>>>> put in >>>>>>> there and that we may need to discuss here as well. >>>>>>> >>>>>>> ## About batch vs streaming scheduling >>>>>>> >>>>>>> I think we shouldn't call it "scheduling", because the decision >>>>>>> between bounded and unbounded affects more than just scheduling. It >>>>>>> affects how we do network transfers and the semantics of time, >>>>>>> among >>>>>>> other things. So maybe we should differentiate between batch-style >>>>>>> and >>>>>>> streaming-style execution, though I'm not sure I like those terms >>>>>>> either. >>>>>>> >>>>>>> ## About processing-time support in batch >>>>>>> >>>>>>> It's not just about "batch" changing the default to ingestion >>>>>>> time is >>>>>>> a change for stream processing as well. Actually, I don't know if >>>>>>> ingestion time even makes sense for batch processing. IIRC, with >>>>>>> the >>>>>>> new sources we actually always have a timestamp, so this discussion >>>>>>> might be moot. Maybe Becket and/or Stephan (cc'ed) could chime >>>>>>> in on >>>>>>> this. >>>>>>> >>>>>>> Also, I think it's right that we currently ignore processing-time >>>>>>> timers at the end of input in streaming jobs, but this has been a >>>>>>> source of trouble for users. See [1] and several discussions on the >>>>>>> ML. I'm also cc'ing Flavio here who also ran into this problem. I >>>>>>> think we should solve this quickly after laying the foundations of >>>>>>> bounded processing on the DataStream API. >>>>>>> >>>>>>> ## About broadcast state support >>>>>>> >>>>>>> I think as a low-hanging fruit we could just read the broadcast >>>>>>> side >>>>>>> first and then switch to the regular input. We do need to be >>>>>>> careful >>>>>>> with creating distributed deadlocks, though, so this might be >>>>>>> trickier >>>>>>> than it seems at first. >>>>>>> >>>>>>> ## Loose ends and weird semantics >>>>>>> >>>>>>> There are some operations in the DataStream API that have semantics >>>>>>> that might make sense for stream processing but should behave >>>>>>> differently for batch. For example, KeyedStream.reduce() is >>>>>>> essentially a reduce on a GlobalWindow with a Trigger that fires on >>>>>>> every element. In DB terms it produces an UPSERT stream as an >>>>>>> output, >>>>>>> if you get ten input elements for a key you also get ten output >>>>>>> records. For batch processing it might make more sense to instead >>>>>>> only >>>>>>> produce one output record per key with the result of the >>>>>>> aggregation. >>>>>>> This would be correct for downstream consumers that expect an >>>>>>> UPSERT >>>>>>> stream but it would change the actual physical output stream that >>>>>>> they >>>>>>> see. >>>>>>> >>>>>>> There might be other such operations in the DataStream API that >>>>>>> have >>>>>>> slightly weird behaviour that doesn't make much sense when you do >>>>>>> bounded processing. >>>>>>> >>>>>>> Best, >>>>>>> Aljoscha >>>>>>> >>>>>>> [1] https://issues.apache.org/jira/browse/FLINK-18647 >>>>>>> >>>>>>> On 24.08.20 11:29, Kostas Kloudas wrote: >>>>>>>> Thanks a lot for the discussion! >>>>>>>> >>>>>>>> I will open a voting thread shortly! >>>>>>>> >>>>>>>> Kostas >>>>>>>> >>>>>>>> On Mon, Aug 24, 2020 at 9:46 AM Kostas Kloudas >>>>>>>> <[hidden email]> >>>>>>>> wrote: >>>>>>>>> >>>>>>>>> Hi Guowei, >>>>>>>>> >>>>>>>>> Thanks for the insightful comment! >>>>>>>>> >>>>>>>>> I agree that this can be a limitation of the current runtime, >>>>>>>>> but I >>>>>>>>> think that this FLIP can go on as it discusses mainly the >>>>>>>>> semantics >>>>>>>>> that the DataStream API will expose when applied on bounded data. >>>>>>>>> There will definitely be other FLIPs that will actually handle >>>>>>>>> the >>>>>>>>> runtime-related topics. >>>>>>>>> >>>>>>>>> But it is good to document them nevertheless so that we start >>>>>>>>> soon >>>>>>>>> ironing out the remaining rough edges. >>>>>>>>> >>>>>>>>> Cheers, >>>>>>>>> Kostas >>>>>>>>> >>>>>>>>> On Mon, Aug 24, 2020 at 9:16 AM Guowei Ma <[hidden email]> >>>>>>>>> wrote: >>>>>>>>>> >>>>>>>>>> Hi, Klou >>>>>>>>>> >>>>>>>>>> Thanks for your proposal. It's a very good idea. >>>>>>>>>> Just a little comment about the "Batch vs Streaming Scheduling". >>>>>>>>>> In the AUTOMATIC execution mode maybe we could not pick BATCH >>>>>>>>>> execution mode even if all sources are bounded. For example some >>>>>>>>>> applications would use the `CheckpointListener`, which is not >>>>>>>>>> available in the BATCH mode in current implementation. >>>>>>>>>> So maybe we need more checks in the AUTOMATIC execution mode. >>>>>>>>>> >>>>>>>>>> Best, >>>>>>>>>> Guowei >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On Thu, Aug 20, 2020 at 10:27 PM Kostas Kloudas >>>>>>>>>> <[hidden email]> wrote: >>>>>>>>>>> >>>>>>>>>>> Hi all, >>>>>>>>>>> >>>>>>>>>>> Thanks for the comments! >>>>>>>>>>> >>>>>>>>>>> @Dawid: "execution.mode" can be a nice alternative and from a >>>>>>>>>>> quick >>>>>>>>>>> look it is not used currently by any configuration option. I >>>>>>>>>>> will >>>>>>>>>>> update the FLIP accordingly. >>>>>>>>>>> >>>>>>>>>>> @David: Given that having the option to allow timers to fire >>>>>>>>>>> at the >>>>>>>>>>> end of the job is already in the FLIP, I will leave it as is >>>>>>>>>>> and I >>>>>>>>>>> will update the default policy to be "ignore processing time >>>>>>>>>>> timers >>>>>>>>>>> set by the user". This will allow existing dataStream programs >>>>>>>>>>> to run >>>>>>>>>>> on bounded inputs. This update will affect point 2 in the >>>>>>>>>>> "Processing >>>>>>>>>>> Time Support in Batch" section. >>>>>>>>>>> >>>>>>>>>>> If these changes cover your proposals, then I would like to >>>>>>>>>>> start a >>>>>>>>>>> voting thread tomorrow evening if this is ok with you. >>>>>>>>>>> >>>>>>>>>>> Please let me know until then. >>>>>>>>>>> >>>>>>>>>>> Kostas >>>>>>>>>>> >>>>>>>>>>> On Tue, Aug 18, 2020 at 3:54 PM David Anderson >>>>>>>>>>> <[hidden email]> wrote: >>>>>>>>>>>> >>>>>>>>>>>> Being able to optionally fire registered processing time >>>>>>>>>>>> timers >>>>>>>>>>>> at the end of a job would be interesting, and would help in >>>>>>>>>>>> (at >>>>>>>>>>>> least some of) the cases I have in mind. I don't have a better >>>>>>>>>>>> idea. >>>>>>>>>>>> >>>>>>>>>>>> David >>>>>>>>>>>> >>>>>>>>>>>> On Mon, Aug 17, 2020 at 8:24 PM Kostas Kloudas >>>>>>>>>>>> <[hidden email]> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>> Hi Kurt and David, >>>>>>>>>>>>> >>>>>>>>>>>>> Thanks a lot for the insightful feedback! >>>>>>>>>>>>> >>>>>>>>>>>>> @Kurt: For the topic of checkpointing with Batch >>>>>>>>>>>>> Scheduling, I >>>>>>>>>>>>> totally >>>>>>>>>>>>> agree with you that it requires a lot more work and careful >>>>>>>>>>>>> thinking >>>>>>>>>>>>> on the semantics. This FLIP was written under the assumption >>>>>>>>>>>>> that if >>>>>>>>>>>>> the user wants to have checkpoints on bounded input, he/she >>>>>>>>>>>>> will >>>>>>>>>>>>> have >>>>>>>>>>>>> to go with STREAMING as the scheduling mode. Checkpointing >>>>>>>>>>>>> for >>>>>>>>>>>>> BATCH >>>>>>>>>>>>> can be handled as a separate topic in the future. >>>>>>>>>>>>> >>>>>>>>>>>>> In the case of MIXED workloads and for this FLIP, the >>>>>>>>>>>>> scheduling >>>>>>>>>>>>> mode >>>>>>>>>>>>> should be set to STREAMING. That is why the AUTOMATIC option >>>>>>>>>>>>> sets >>>>>>>>>>>>> scheduling to BATCH only if all the sources are bounded. I am >>>>>>>>>>>>> not sure >>>>>>>>>>>>> what are the plans there at the scheduling level, as one >>>>>>>>>>>>> could >>>>>>>>>>>>> imagine >>>>>>>>>>>>> in the future that in mixed workloads, we schedule first all >>>>>>>>>>>>> the >>>>>>>>>>>>> bounded subgraphs in BATCH mode and we allow only one >>>>>>>>>>>>> UNBOUNDED >>>>>>>>>>>>> subgraph per application, which is going to be scheduled >>>>>>>>>>>>> after >>>>>>>>>>>>> all >>>>>>>>>>>>> Bounded ones have finished. Essentially the bounded subgraphs >>>>>>>>>>>>> will be >>>>>>>>>>>>> used to bootstrap the unbounded one. But, I am not aware >>>>>>>>>>>>> of any >>>>>>>>>>>>> plans >>>>>>>>>>>>> towards that direction. >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> @David: The processing time timer handling is a topic that >>>>>>>>>>>>> has >>>>>>>>>>>>> also >>>>>>>>>>>>> been discussed in the community in the past, and I do not >>>>>>>>>>>>> remember any >>>>>>>>>>>>> final conclusion unfortunately. >>>>>>>>>>>>> >>>>>>>>>>>>> In the current context and for bounded input, we chose to >>>>>>>>>>>>> favor >>>>>>>>>>>>> reproducibility of the result, as this is expected in batch >>>>>>>>>>>>> processing >>>>>>>>>>>>> where the whole input is available in advance. This is why >>>>>>>>>>>>> this >>>>>>>>>>>>> proposal suggests to not allow processing time timers. But I >>>>>>>>>>>>> understand your argument that the user may want to be able to >>>>>>>>>>>>> run the >>>>>>>>>>>>> same pipeline on batch and streaming this is why we added >>>>>>>>>>>>> the two >>>>>>>>>>>>> options under future work, namely (from the FLIP): >>>>>>>>>>>>> >>>>>>>>>>>>> ``` >>>>>>>>>>>>> Future Work: In the future we may consider adding as options >>>>>>>>>>>>> the >>>>>>>>>>>>> capability of: >>>>>>>>>>>>> * firing all the registered processing time timers at the >>>>>>>>>>>>> end of >>>>>>>>>>>>> a job >>>>>>>>>>>>> (at close()) or, >>>>>>>>>>>>> * ignoring all the registered processing time timers at >>>>>>>>>>>>> the end >>>>>>>>>>>>> of a job. >>>>>>>>>>>>> ``` >>>>>>>>>>>>> >>>>>>>>>>>>> Conceptually, we are essentially saying that we assume that >>>>>>>>>>>>> batch >>>>>>>>>>>>> execution is assumed to be instantaneous and refers to a >>>>>>>>>>>>> single >>>>>>>>>>>>> "point" in time and any processing-time timers for the future >>>>>>>>>>>>> may fire >>>>>>>>>>>>> at the end of execution or be ignored (but not throw an >>>>>>>>>>>>> exception). I >>>>>>>>>>>>> could also see ignoring the timers in batch as the >>>>>>>>>>>>> default, if >>>>>>>>>>>>> this >>>>>>>>>>>>> makes more sense. >>>>>>>>>>>>> >>>>>>>>>>>>> By the way, do you have any usecases in mind that will >>>>>>>>>>>>> help us >>>>>>>>>>>>> better >>>>>>>>>>>>> shape our processing time timer handling? >>>>>>>>>>>>> >>>>>>>>>>>>> Kostas >>>>>>>>>>>>> >>>>>>>>>>>>> On Mon, Aug 17, 2020 at 2:52 PM David Anderson >>>>>>>>>>>>> <[hidden email]> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>> Kostas, >>>>>>>>>>>>>> >>>>>>>>>>>>>> I'm pleased to see some concrete details in this FLIP. >>>>>>>>>>>>>> >>>>>>>>>>>>>> I wonder if the current proposal goes far enough in the >>>>>>>>>>>>>> direction of recognizing the need some users may have for >>>>>>>>>>>>>> "batch" and "bounded streaming" to be treated >>>>>>>>>>>>>> differently. If >>>>>>>>>>>>>> I've understood it correctly, the section on scheduling >>>>>>>>>>>>>> allows >>>>>>>>>>>>>> me to choose STREAMING scheduling even if I have bounded >>>>>>>>>>>>>> sources. I like that approach, because it recognizes that >>>>>>>>>>>>>> even >>>>>>>>>>>>>> though I have bounded inputs, I don't necessarily want batch >>>>>>>>>>>>>> processing semantics. I think it makes sense to extend this >>>>>>>>>>>>>> idea to processing time support as well. >>>>>>>>>>>>>> >>>>>>>>>>>>>> My thinking is that sometimes in development and testing >>>>>>>>>>>>>> it's >>>>>>>>>>>>>> reasonable to run exactly the same job as in production, >>>>>>>>>>>>>> except >>>>>>>>>>>>>> with different sources and sinks. While it might be a >>>>>>>>>>>>>> reasonable default, I'm not convinced that switching a >>>>>>>>>>>>>> processing time streaming job to read from a bounded source >>>>>>>>>>>>>> should always cause it to fail. >>>>>>>>>>>>>> >>>>>>>>>>>>>> David >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Wed, Aug 12, 2020 at 5:22 PM Kostas Kloudas >>>>>>>>>>>>>> <[hidden email]> wrote: >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Hi all, >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> As described in FLIP-131 [1], we are aiming at deprecating >>>>>>>>>>>>>>> the >>>>>>>>>>>>>>> DataSet >>>>>>>>>>>>>>> API in favour of the DataStream API and the Table API. >>>>>>>>>>>>>>> After >>>>>>>>>>>>>>> this work >>>>>>>>>>>>>>> is done, the user will be able to write a program using the >>>>>>>>>>>>>>> DataStream >>>>>>>>>>>>>>> API and this will execute efficiently on both bounded and >>>>>>>>>>>>>>> unbounded >>>>>>>>>>>>>>> data. But before we reach this point, it is worth >>>>>>>>>>>>>>> discussing >>>>>>>>>>>>>>> and >>>>>>>>>>>>>>> agreeing on the semantics of some operations as we >>>>>>>>>>>>>>> transition >>>>>>>>>>>>>>> from the >>>>>>>>>>>>>>> streaming world to the batch one. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> This thread and the associated FLIP [2] aim at discussing >>>>>>>>>>>>>>> these issues >>>>>>>>>>>>>>> as these topics are pretty important to users and can >>>>>>>>>>>>>>> lead to >>>>>>>>>>>>>>> unpleasant surprises if we do not pay attention. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Let's have a healthy discussion here and I will be updating >>>>>>>>>>>>>>> the FLIP >>>>>>>>>>>>>>> accordingly. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Cheers, >>>>>>>>>>>>>>> Kostas >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> [1] >>>>>>>>>>>>>>> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158866741 >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> [2] >>>>>>>>>>>>>>> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=158871522 >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> > |
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Thanks for the explanation! Makes sense now! What do you think about
adding this behaviour of WindowAssigner in streaming mode as well? I mean the behaviour of emitting at the end of a Window. I think it would make sense in the STREAM mode as well and keep the two modes more aligned. Best, Dawid On 10/09/2020 11:42, Aljoscha Krettek wrote: > On 10.09.20 11:30, Dawid Wysakowicz wrote: >> I am not sure about the option for ignoring the Triggers. Do you mean to >> ignore all the Triggers including e.g. Flink's such as CountTrigger, >> EventTimeTrigger etc.? Won't it effectively disable the WindowOperator >> whatsoever. Or even worse make it unusable with ever growing state? I >> might be wrong here but aren't Triggers required for emitting results >> from WindowOperator? If I am correct we emit results only if a Trigger >> returns FIRE from on of onElement, onEventTime, onProcessingTime. Why do >> you think it does not work well with FAILing hard without this option? >> We could fail hard e.g. if the WindowAssigner#isEventTime returns false. > > The problem I'm trying to solve are mixed Triggers. Say you have a > Trigger that does "fire when watermark passes maxTimestamp() but also > fire every 5 minutes in processing time and when the watermark passes > maxTimestamp() fire for every 5 new records". This is something that > the Beam API for example allows users to specify and is something that > I think is potentially valuable in the real world. > > Ignoring Triggers would mean that we always fire on the maxTimestamp() > by hardcoding this in a WindowOperator that we use for BATCH > execution. With this, the WindowAssigner becomes the only thing that > changes. This is similar to how Beam treats windows, where the > WindowAssigner carries semantic content but the Trigger is only for > optimizing streaming emission, which you don't need for BATCH where > you always have a "perfect watermark". > > Coming back to the initial example, such a Trigger would not work if > we FAIL hard for processing-time on BATCH, which I'm suggesting > because we otherwise have potentially surprising results if business > logic depends on processing-time timers. For Windows, on the other > hand, we could get around it by agreeing that Triggers are ignored for > BATCH. > >> As for the question with getProcessingTime(). From my point of view, it >> would be safe to simply return the current system time. I cannot think >> of any dangers if we do so. Moreover, frankly speaking I am not entirely >> sure what is the purpose of the method, other than injecting a clock in >> tests of built-in operators. Maybe it was a mistake to expose it in the >> user's API? > > I agree, it was a mistake to expose getProcessingTime(). And I also > think the same about getCurrentWatermark(), but that's neither here > nor there. 😅 I then also agree to just return the current time, as > you said. I will change the FLIP for this. > > Aljoscha |
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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