Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
> 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.
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
@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 >> 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 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
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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 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 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 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 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 wrote: Hi Kurt and David, Thanks a lot for the insightful feedback! @Kurt: For the topic of checkpointing with Batch Scheduling, I totally agree
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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 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 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 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 > >> 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 > >> > 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
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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 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 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 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 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
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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 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 > 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 > 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 > 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
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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 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 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 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 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
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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 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 > 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 > 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
Hi Yun and Dawid, Dawid is correct in that: ``` 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 sources (ALL bounded == BATCH, STREAMING otherwise) ``` For allowing users to set the shuffling mode, we can consider it but I think we should be careful because if checkpointing is enabled (e.g. in STREAMING), then introducing a blocking shuffle will cause problems. The opposite, allowing pipelined execution for keyBy's in BATCH, may be ok. Kostas On Tue, Aug 18, 2020 at 11:40 AM Dawid Wysakowicz wrote: > > 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: > > Hi, > > Very thanks for bringing up this discussion! > > One more question is that does the BATCH and STREAMING mode also decides > the shuffle types and operators? I'm asking so because that even for blocking > mode, it should also benefit from keeping some edges to be pipeline if the > resources are known to be enough. Do we also consider to expose more > fine-grained control on the shuffle types? > > Best, > Yun > > > --Original Mail -- > Sender:Kostas Kloudas > Send Date:Tue Aug 18 02:24:21 2020 > Recipients:David Anderson > CC:dev , user > Subject:Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input >> >> 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 wrote: >> > >> > Kostas, >> > >> > I'm pleased to see some concrete details in this FLIP. >> > >> > I wonder
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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: > Hi, > > Very thanks for bringing up this discussion! > > One more question is that does the BATCH and STREAMING mode also > decides the shuffle types and operators? I'm asking so because that > even for blocking mode, it should also benefit from keeping some edges > to be pipeline if the resources are known to be enough. Do we also > consider to expose more fine-grained control on the shuffle types? > > Best, > Yun > > > --Original Mail -- > *Sender:*Kostas Kloudas > *Send Date:*Tue Aug 18 02:24:21 2020 > *Recipients:*David Anderson > *CC:*dev , user > *Subject:*Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded > Input > > 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 > wrote: > > > > Kostas, > > > > I'm pleased to see some concrete details in this FLIP. > > > > I wonder if the current proposal goes far enough in the dire
Re: Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
Hi, Very thanks for bringing up this discussion! One more question is that does the BATCH and STREAMING mode also decides the shuffle types and operators? I'm asking so because that even for blocking mode, it should also benefit from keeping some edges to be pipeline if the resources are known to be enough. Do we also consider to expose more fine-grained control on the shuffle types? Best, Yun --Original Mail -- Sender:Kostas Kloudas Send Date:Tue Aug 18 02:24:21 2020 Recipients:David Anderson CC:dev , user Subject:Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input 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 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 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] >>
Re: [DISCUSS] FLIP-134: DataStream Semantics for Bounded Input
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 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 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
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 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
Hi Kostas, Thanks for starting this discussion. The first part of this FLIP: "Batch vs Streaming Scheduling" looks reasonable to me. However, there is another dimension I think we should also take into consideration, which is whether checkpointing is enabled. This option is orthogonal (but not fully) to the boundedness and persistence of the input. For example, consider an arbitrary operator who uses state, we can enable checkpoint to achieve better failure recovery if the input is bounded and pipelined. And if the input is bounded and persistent, we can still use checkpointing, but we might need to checkpoint the offset of the intermediate result set of the operator. This would require much more work and we can defer this to the future. Beyond this dimension, there is another question to be asked. If the topology is mixed with some bounded and unbounded inputs, what would be the behavior? E.g. a join operator with one of its input bounded, and another input unbounded. Can we still use BATCH or STREAMING to define the schedule policy? What kind of failure recovery guarantee Flink can provide to the users. I don't have a clear answer for now, but just want to raise them up to seek some discussion. Best, Kurt On Wed, Aug 12, 2020 at 11:22 PM Kostas Kloudas 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 >
