Hi Juho,
* does the output of the streaming job contain any data, which is not >> contained in the batch > > > No. > > * do you know if all lost records are contained in the last savepoint you >> took before the window fired? This would mean that no records are lost >> after the last restore. > > > I haven't built the tooling required to check all IDs like that, but yes, > that's my understanding currently. To check that I would need to: > - kill the stream only once on a given day (so that there's only one > savepoint creation & restore) > - next day or later: save all missing ids from batch output comparison > - next day or later: read the savepoint with bravo & check that it > contains all of those missing IDs > > However I haven't built the tooling for that yet. Do you think it's > necessary to verify that this assumption holds? > It would be another data point and might help us to track down the problem. Wether it is worth doing it, depends on the result, i.e. wether the current assumption would be falsified or not, but we only know that in retrospect ;) > * could you please check the numRecordsOut metric for the WindowOperator >> (FlinkUI -> TaskMetrics -> Select TaskChain containing WindowOperator -> >> find metric)? Is the count reported there correct (no missing data)? > > > Is that metric the result of window trigger? If yes, you must mean that I > check the value of that metric on the next day after restore, so that it > only contains the count for the output of previous day's window? The > counter is reset to 0 when job starts (even when state is restored), right? > Yes, this metric would be incremented when the window is triggered. Yes, please check this metric after the window, during which the restore happened, is fired. If you don't have a MetricsReporter configured so far, I recommend to quickly register a Sl4jReporter to log out all metrics every X seconds (maybe even minutes for your use case): https://ci.apache.org/projects/flink/flink-docs-release-1.7/monitoring/metrics.html#slf4j-orgapacheflinkmetricsslf4jslf4jreporter. Then you don't need to go trough the WebUI and can keep a history of the metrics. > Otherwise, do you have any suggestions for how to instrument the code to > narrow down further where the data gets lost? To me it would make sense to > proceed with this, because the problem seems hard to reproduce outside of > our environment. > Let's focus on checking this metric above, to make sure that the WindowOperator is actually emitting less records than the overall number of keys in the state as your experiments suggest, and on sharing the code. > On Thu, Feb 14, 2019 at 10:57 AM Konstantin Knauf < > konstan...@ververica.com> wrote: > >> Hi Juho, >> >> you are right the problem has actually been narrowed down quite a bit >> over time. Nevertheless, sharing the code (incl. flink-conf.yaml) might be >> a good idea. Maybe something strikes the eye, that we have not thought >> about so far. If you don't feel comfortable sharing the code on the ML, >> feel free to send me a PM. >> >> Besides that, three more questions: >> >> * does the output of the streaming job contain any data, which is not >> contained in the batch output? >> * do you know if all lost records are contained in the last savepoint you >> took before the window fired? This would mean that no records are lost >> after the last restore. >> * could you please check the numRecordsOut metric for the WindowOperator >> (FlinkUI -> TaskMetrics -> Select TaskChain containing WindowOperator -> >> find metric)? Is the count reported there correct (no missing data)? >> >> Cheers, >> >> Konstantin >> >> >> >> >> On Wed, Feb 13, 2019 at 3:19 PM Gyula Fóra <gyula.f...@gmail.com> wrote: >> >>> Sorry not posting on the mail list was my mistake :/ >>> >>> >>> On Wed, 13 Feb 2019 at 15:01, Juho Autio <juho.au...@rovio.com> wrote: >>> >>>> Thanks for stepping in, did you post outside of the mailing list on >>>> purpose btw? >>>> >>>> This I did long time ago: >>>> >>>> To rule out for good any questions about sink behaviour, the job was >>>>> killed and started with an additional Kafka sink. >>>>> The same number of ids were missed in both outputs: KafkaSink & >>>>> BucketingSink. >>>> >>>> >>>> (I wrote about that On Oct 1, 2018 in this email thread) >>>> >>>> After that I did the savepoint analysis with Bravo. >>>> >>>> Currently I'm indeed trying to get suggestions how to debug further, >>>> for example, where to add additional kafka output, to catch where the data >>>> gets lost. That would probably be somewhere in Flink's internals. >>>> >>>> I could try to share the full code also, but IMHO the problem has been >>>> quite well narrowed down, considering that data can be found in savepoint, >>>> savepoint is successfully restored, and after restoring the data doesn't go >>>> to "user code" (like the reducer) any more. >>>> >>>> On Wed, Feb 13, 2019 at 3:47 PM Gyula Fóra <gyula.f...@gmail.com> >>>> wrote: >>>> >>>>> Hi Juho! >>>>> I think the reason you are not getting much answers here is because it >>>>> is very hard to debug this problem remotely. >>>>> Seemingly you do very normal operations, the state contains all the >>>>> required data and nobody else has hit a similar problem for ages. >>>>> >>>>> My best guess would be some bug with the deduplication or output >>>>> writing logic but without a complete code example its very hard to say >>>>> anything useful. >>>>> Did you try writing it to Kafka to see if the output is there? (that >>>>> way we could rule out the dedup probllem) >>>>> >>>>> Cheers, >>>>> Gyula >>>>> >>>>> On Wed, Feb 13, 2019 at 2:37 PM Juho Autio <juho.au...@rovio.com> >>>>> wrote: >>>>> >>>>>> Stefan (or anyone!), please, could I have some feedback on the >>>>>> findings that I reported on Dec 21, 2018? This is still a major blocker.. >>>>>> >>>>>> On Thu, Jan 31, 2019 at 11:46 AM Juho Autio <juho.au...@rovio.com> >>>>>> wrote: >>>>>> >>>>>>> Hello, is there anyone that could help with this? >>>>>>> >>>>>>> On Fri, Jan 11, 2019 at 8:14 AM Juho Autio <juho.au...@rovio.com> >>>>>>> wrote: >>>>>>> >>>>>>>> Stefan, would you have time to comment? >>>>>>>> >>>>>>>> On Wednesday, January 2, 2019, Juho Autio <juho.au...@rovio.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Bump – does anyone know if Stefan will be available to comment the >>>>>>>>> latest findings? Thanks. >>>>>>>>> >>>>>>>>> On Fri, Dec 21, 2018 at 2:33 PM Juho Autio <juho.au...@rovio.com> >>>>>>>>> wrote: >>>>>>>>> >>>>>>>>>> Stefan, I managed to analyze savepoint with bravo. It seems that >>>>>>>>>> the data that's missing from output *is* found in savepoint. >>>>>>>>>> >>>>>>>>>> I simplified my test case to the following: >>>>>>>>>> >>>>>>>>>> - job 1 has bee running for ~10 days >>>>>>>>>> - savepoint X created & job 1 cancelled >>>>>>>>>> - job 2 started with restore from savepoint X >>>>>>>>>> >>>>>>>>>> Then I waited until the next day so that job 2 has triggered the >>>>>>>>>> 24 hour window. >>>>>>>>>> >>>>>>>>>> Then I analyzed the output & savepoint: >>>>>>>>>> >>>>>>>>>> - compare job 2 output with the output of a batch pyspark script >>>>>>>>>> => find 4223 missing rows >>>>>>>>>> - pick one of the missing rows (say, id Z) >>>>>>>>>> - read savepoint X with bravo, filter for id Z => Z was found in >>>>>>>>>> the savepoint! >>>>>>>>>> >>>>>>>>>> How can it be possible that the value is in state but doesn't end >>>>>>>>>> up in output after state has been restored & window is eventually >>>>>>>>>> triggered? >>>>>>>>>> >>>>>>>>>> I also did similar analysis on the previous case where I >>>>>>>>>> savepointed & restored the job multiple times (5) within the same >>>>>>>>>> 24-hour >>>>>>>>>> window. A missing id that I drilled down to, was found in all of >>>>>>>>>> those >>>>>>>>>> savepoints, yet missing from the output that gets written at the end >>>>>>>>>> of the >>>>>>>>>> day. This is even more surprising: that the missing ID was written >>>>>>>>>> to the >>>>>>>>>> new savepoints also after restoring. Is the reducer state somehow >>>>>>>>>> decoupled >>>>>>>>>> from the window contents? >>>>>>>>>> >>>>>>>>>> Big thanks to bravo-developer Gyula for guiding me through to be >>>>>>>>>> able read the reducer state! >>>>>>>>>> https://github.com/king/bravo/pull/11 >>>>>>>>>> >>>>>>>>>> Gyula also had an idea for how to troubleshoot the missing data >>>>>>>>>> in a scalable way: I could add some "side effect kafka output" on >>>>>>>>>> individual operators. This should allow tracking more closely at >>>>>>>>>> which >>>>>>>>>> point the data gets lost. However, maybe this would have to be in >>>>>>>>>> some >>>>>>>>>> Flink's internal components, and I'm not sure which those would be. >>>>>>>>>> >>>>>>>>>> Cheers, >>>>>>>>>> Juho >>>>>>>>>> >>>>>>>>>> On Mon, Nov 19, 2018 at 11:52 AM Juho Autio <juho.au...@rovio.com> >>>>>>>>>> wrote: >>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> Hi Stefan, >>>>>>>>>>> >>>>>>>>>>> Bravo doesn't currently support reading a reducer state. I gave >>>>>>>>>>> it a try but couldn't get to a working implementation yet. If >>>>>>>>>>> anyone can >>>>>>>>>>> provide some insight on how to make this work, please share at >>>>>>>>>>> github: >>>>>>>>>>> https://github.com/king/bravo/pull/11 >>>>>>>>>>> >>>>>>>>>>> Thanks. >>>>>>>>>>> >>>>>>>>>>> On Tue, Oct 23, 2018 at 3:32 PM Juho Autio <juho.au...@rovio.com> >>>>>>>>>>> wrote: >>>>>>>>>>> >>>>>>>>>>>> I was glad to find that bravo had now been updated to support >>>>>>>>>>>> installing bravo to a local maven repo. >>>>>>>>>>>> >>>>>>>>>>>> I was able to load a checkpoint created by my job, thanks to >>>>>>>>>>>> the example provided in bravo README, but I'm still missing the >>>>>>>>>>>> essential >>>>>>>>>>>> piece. >>>>>>>>>>>> >>>>>>>>>>>> My code was: >>>>>>>>>>>> >>>>>>>>>>>> OperatorStateReader reader = new >>>>>>>>>>>> OperatorStateReader(env2, savepoint, "DistinctFunction"); >>>>>>>>>>>> DontKnowWhatTypeThisIs reducingState = >>>>>>>>>>>> reader.readKeyedStates(what should I put here?); >>>>>>>>>>>> >>>>>>>>>>>> I don't know how to read the values collected from reduce() >>>>>>>>>>>> calls in the state. Is there a way to access the reducing state of >>>>>>>>>>>> the >>>>>>>>>>>> window with bravo? I'm a bit confused how this works, because when >>>>>>>>>>>> I check >>>>>>>>>>>> with debugger, flink internally uses a ReducingStateDescriptor >>>>>>>>>>>> with name=window-contents, but still reading operator state for >>>>>>>>>>>> "DistinctFunction" didn't at least throw an exception >>>>>>>>>>>> ("window-contents" >>>>>>>>>>>> threw – obviously there's no operator by that name). >>>>>>>>>>>> >>>>>>>>>>>> Cheers, >>>>>>>>>>>> Juho >>>>>>>>>>>> >>>>>>>>>>>> On Mon, Oct 15, 2018 at 2:25 PM Juho Autio < >>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> Hi Stefan, >>>>>>>>>>>>> >>>>>>>>>>>>> Sorry but it doesn't seem immediately clear to me what's a >>>>>>>>>>>>> good way to use https://github.com/king/bravo. >>>>>>>>>>>>> >>>>>>>>>>>>> How are people using it? Would you for example modify >>>>>>>>>>>>> build.gradle somehow to publish the bravo as a library >>>>>>>>>>>>> locally/internally? >>>>>>>>>>>>> Or add code directly in the bravo project (locally) and run it >>>>>>>>>>>>> from there >>>>>>>>>>>>> (using an IDE, for example)? Also it doesn't seem like the bravo >>>>>>>>>>>>> gradle >>>>>>>>>>>>> project supports building a flink job jar, but if it does, how do >>>>>>>>>>>>> I do it? >>>>>>>>>>>>> >>>>>>>>>>>>> Thanks. >>>>>>>>>>>>> >>>>>>>>>>>>> On Thu, Oct 4, 2018 at 9:30 PM Juho Autio < >>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>>> Good then, I'll try to analyze the savepoints with Bravo. >>>>>>>>>>>>>> Thanks! >>>>>>>>>>>>>> >>>>>>>>>>>>>> > How would you assume that backpressure would influence your >>>>>>>>>>>>>> updates? Updates to each local state still happen >>>>>>>>>>>>>> event-by-event, in a >>>>>>>>>>>>>> single reader/writing thread. >>>>>>>>>>>>>> >>>>>>>>>>>>>> Sure, just an ignorant guess by me. I'm not familiar with >>>>>>>>>>>>>> most of Flink's internals. Any way high backpressure is not a >>>>>>>>>>>>>> seen on this >>>>>>>>>>>>>> job after it has caught up the lag, so at I thought it would be >>>>>>>>>>>>>> worth >>>>>>>>>>>>>> mentioning. >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Thu, Oct 4, 2018 at 6:24 PM Stefan Richter < >>>>>>>>>>>>>> s.rich...@data-artisans.com> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>>> Hi, >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Am 04.10.2018 um 16:08 schrieb Juho Autio < >>>>>>>>>>>>>>> juho.au...@rovio.com>: >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> > you could take a look at Bravo [1] to query your >>>>>>>>>>>>>>> savepoints and to check if the state in the savepoint complete >>>>>>>>>>>>>>> w.r.t your >>>>>>>>>>>>>>> expectations >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Thanks. I'm not 100% if this is the case, but to me it >>>>>>>>>>>>>>> seemed like the missed ids were being logged by the reducer >>>>>>>>>>>>>>> soon after the >>>>>>>>>>>>>>> job had started (after restoring a savepoint). But on the other >>>>>>>>>>>>>>> hand, after >>>>>>>>>>>>>>> that I also made another savepoint & restored that, so what I >>>>>>>>>>>>>>> could check >>>>>>>>>>>>>>> is: does that next savepoint have the missed ids that were >>>>>>>>>>>>>>> logged (a couple >>>>>>>>>>>>>>> of minutes before the savepoint was created, so there should've >>>>>>>>>>>>>>> been more >>>>>>>>>>>>>>> than enough time to add them to the state before the savepoint >>>>>>>>>>>>>>> was >>>>>>>>>>>>>>> triggered) or not. Any way, if I would be able to verify with >>>>>>>>>>>>>>> Bravo that >>>>>>>>>>>>>>> the ids are missing from the savepoint (even though reduced >>>>>>>>>>>>>>> logged that it >>>>>>>>>>>>>>> saw them), would that help in figuring out where they are lost? >>>>>>>>>>>>>>> Is there >>>>>>>>>>>>>>> some major difference compared to just looking at the final >>>>>>>>>>>>>>> output after >>>>>>>>>>>>>>> window has been triggered? >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> I think that makes a difference. For example, you can >>>>>>>>>>>>>>> investigate if there is a state loss or a problem with the >>>>>>>>>>>>>>> windowing. In >>>>>>>>>>>>>>> the savepoint you could see which keys exists and to which >>>>>>>>>>>>>>> windows they are >>>>>>>>>>>>>>> assigned. Also just to make sure there is no misunderstanding: >>>>>>>>>>>>>>> only >>>>>>>>>>>>>>> elements that are in the state at the start of a savepoint are >>>>>>>>>>>>>>> expected to >>>>>>>>>>>>>>> be part of the savepoint; all elements between start and >>>>>>>>>>>>>>> completion of the >>>>>>>>>>>>>>> savepoint are not expected to be part of the savepoint. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> > I also doubt that the problem is about backpressure after >>>>>>>>>>>>>>> restore, because the job will only continue running after the >>>>>>>>>>>>>>> state restore >>>>>>>>>>>>>>> is already completed. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Yes, I'm not suspecting that the state restoring would be >>>>>>>>>>>>>>> the problem either. My concern was about backpressure possibly >>>>>>>>>>>>>>> messing with >>>>>>>>>>>>>>> the updates of reducing state? I would tend to suspect that >>>>>>>>>>>>>>> updating the >>>>>>>>>>>>>>> state consistently is what fails, where heavy load / >>>>>>>>>>>>>>> backpressure might be >>>>>>>>>>>>>>> a factor. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> How would you assume that backpressure would influence your >>>>>>>>>>>>>>> updates? Updates to each local state still happen >>>>>>>>>>>>>>> event-by-event, in a >>>>>>>>>>>>>>> single reader/writing thread. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> On Thu, Oct 4, 2018 at 4:18 PM Stefan Richter < >>>>>>>>>>>>>>> s.rich...@data-artisans.com> wrote: >>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Hi, >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> you could take a look at Bravo [1] to query your savepoints >>>>>>>>>>>>>>>> and to check if the state in the savepoint complete w.r.t your >>>>>>>>>>>>>>>> expectations. I somewhat doubt that there is a general problem >>>>>>>>>>>>>>>> with the >>>>>>>>>>>>>>>> state/savepoints because many users are successfully running >>>>>>>>>>>>>>>> it on a large >>>>>>>>>>>>>>>> state and I am not aware of any data loss problems, but >>>>>>>>>>>>>>>> nothing is >>>>>>>>>>>>>>>> impossible. What the savepoint does is also straight forward: >>>>>>>>>>>>>>>> iterate a db >>>>>>>>>>>>>>>> snapshot and write all key/value pairs to disk, so all data >>>>>>>>>>>>>>>> that was in the >>>>>>>>>>>>>>>> db at the time of the savepoint, should show up. I also doubt >>>>>>>>>>>>>>>> that the >>>>>>>>>>>>>>>> problem is about backpressure after restore, because the job >>>>>>>>>>>>>>>> will only >>>>>>>>>>>>>>>> continue running after the state restore is already completed. >>>>>>>>>>>>>>>> Did you >>>>>>>>>>>>>>>> check if you are using exactly-one-semantics or at-least-once >>>>>>>>>>>>>>>> semantics? >>>>>>>>>>>>>>>> Also did you check that the kafka consumer start position is >>>>>>>>>>>>>>>> configured >>>>>>>>>>>>>>>> properly [2]? Are watermarks generated as expected after >>>>>>>>>>>>>>>> restore? >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> One more unrelated high-level comment that I have: for a >>>>>>>>>>>>>>>> granularity of 24h windows, I wonder if it would not make >>>>>>>>>>>>>>>> sense to use a >>>>>>>>>>>>>>>> batch job instead? >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Best, >>>>>>>>>>>>>>>> Stefan >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> [1] https://github.com/king/bravo >>>>>>>>>>>>>>>> [2] >>>>>>>>>>>>>>>> https://ci.apache.org/projects/flink/flink-docs-stable/dev/connectors/kafka.html#kafka-consumers-start-position-configuration >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Am 04.10.2018 um 14:53 schrieb Juho Autio < >>>>>>>>>>>>>>>> juho.au...@rovio.com>: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Thanks for the suggestions! >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> > In general, it would be tremendously helpful to have a >>>>>>>>>>>>>>>> minimal working example which allows to reproduce the problem. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Definitely. The problem with reproducing has been that this >>>>>>>>>>>>>>>> only seems to happen in the bigger production data volumes. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> That's why I'm hoping to find a way to debug this with the >>>>>>>>>>>>>>>> production data. With that it seems to consistently cause some >>>>>>>>>>>>>>>> misses every >>>>>>>>>>>>>>>> time the job is killed/restored. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> > check if it happens for shorter windows, like 1h etc >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> What would be the benefit of that compared to 24h window? >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> > simplify the job to not use a reduce window but simply a >>>>>>>>>>>>>>>> time window which outputs the window events. Then counting the >>>>>>>>>>>>>>>> input and >>>>>>>>>>>>>>>> output events should allow you to verify the results. If you >>>>>>>>>>>>>>>> are not seeing >>>>>>>>>>>>>>>> missing events, then it could have something to do with the >>>>>>>>>>>>>>>> reducing state >>>>>>>>>>>>>>>> used in the reduce function. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Hm, maybe, but not sure how useful that would be, because >>>>>>>>>>>>>>>> it wouldn't yet prove that it's related to reducing, because >>>>>>>>>>>>>>>> not having a >>>>>>>>>>>>>>>> reduce function could also mean smaller load on the job, which >>>>>>>>>>>>>>>> might alone >>>>>>>>>>>>>>>> be enough to make the problem not manifest. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Is there a way to debug what goes into the reducing state >>>>>>>>>>>>>>>> (including what gets removed or overwritten and what >>>>>>>>>>>>>>>> restored), if that >>>>>>>>>>>>>>>> makes sense..? Maybe some suitable logging could be used to >>>>>>>>>>>>>>>> prove that the >>>>>>>>>>>>>>>> lost data is written to the reducing state (or at least asked >>>>>>>>>>>>>>>> to be >>>>>>>>>>>>>>>> written), but not found any more when the window closes and >>>>>>>>>>>>>>>> state is >>>>>>>>>>>>>>>> flushed? >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> On configuration once more, we're using RocksDB state >>>>>>>>>>>>>>>> backend with asynchronous incremental checkpointing. The state >>>>>>>>>>>>>>>> is restored >>>>>>>>>>>>>>>> from savepoints though, we haven't been using those >>>>>>>>>>>>>>>> checkpoints in these >>>>>>>>>>>>>>>> tests (although they could be used in case of crashes – but we >>>>>>>>>>>>>>>> haven't had >>>>>>>>>>>>>>>> those now). >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> On Thu, Oct 4, 2018 at 3:25 PM Till Rohrmann < >>>>>>>>>>>>>>>> trohrm...@apache.org> wrote: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Hi Juho, >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> another idea to further narrow down the problem could be >>>>>>>>>>>>>>>>> to simplify the job to not use a reduce window but simply a >>>>>>>>>>>>>>>>> time window >>>>>>>>>>>>>>>>> which outputs the window events. Then counting the input and >>>>>>>>>>>>>>>>> output events >>>>>>>>>>>>>>>>> should allow you to verify the results. If you are not seeing >>>>>>>>>>>>>>>>> missing >>>>>>>>>>>>>>>>> events, then it could have something to do with the reducing >>>>>>>>>>>>>>>>> state used in >>>>>>>>>>>>>>>>> the reduce function. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> In general, it would be tremendously helpful to have a >>>>>>>>>>>>>>>>> minimal working example which allows to reproduce the problem. >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Cheers, >>>>>>>>>>>>>>>>> Till >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> On Thu, Oct 4, 2018 at 2:02 PM Andrey Zagrebin < >>>>>>>>>>>>>>>>> and...@data-artisans.com> wrote: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> Hi Juho, >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> can you try to reduce the job to minimal reproducible >>>>>>>>>>>>>>>>>> example and share the job and input? >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> For example: >>>>>>>>>>>>>>>>>> - some simple records as input, e.g. tuples of primitive >>>>>>>>>>>>>>>>>> types saved as cvs >>>>>>>>>>>>>>>>>> - minimal deduplication job which processes them and >>>>>>>>>>>>>>>>>> misses records >>>>>>>>>>>>>>>>>> - check if it happens for shorter windows, like 1h etc >>>>>>>>>>>>>>>>>> - setup which you use for the job, ideally >>>>>>>>>>>>>>>>>> locally reproducible or cloud >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> Best, >>>>>>>>>>>>>>>>>> Andrey >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> On 4 Oct 2018, at 11:13, Juho Autio <juho.au...@rovio.com> >>>>>>>>>>>>>>>>>> wrote: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> Sorry to insist, but we seem to be blocked for any >>>>>>>>>>>>>>>>>> serious usage of state in Flink if we can't rely on it to >>>>>>>>>>>>>>>>>> not miss data in >>>>>>>>>>>>>>>>>> case of restore. >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> Would anyone have suggestions for how to troubleshoot >>>>>>>>>>>>>>>>>> this? So far I have verified with DEBUG logs that our reduce >>>>>>>>>>>>>>>>>> function gets >>>>>>>>>>>>>>>>>> to process also the data that is missing from window output. >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> On Mon, Oct 1, 2018 at 11:56 AM Juho Autio < >>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> Hi Andrey, >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> To rule out for good any questions about sink behaviour, >>>>>>>>>>>>>>>>>>> the job was killed and started with an additional Kafka >>>>>>>>>>>>>>>>>>> sink. >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> The same number of ids were missed in both outputs: >>>>>>>>>>>>>>>>>>> KafkaSink & BucketingSink. >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> I wonder what would be the next steps in debugging? >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> On Fri, Sep 21, 2018 at 3:49 PM Juho Autio < >>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Thanks, Andrey. >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> > so it means that the savepoint does not loose at >>>>>>>>>>>>>>>>>>>> least some dropped records. >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> I'm not sure what you mean by that? I mean, it was >>>>>>>>>>>>>>>>>>>> known from the beginning, that not everything is lost >>>>>>>>>>>>>>>>>>>> before/after >>>>>>>>>>>>>>>>>>>> restoring a savepoint, just some records around the time >>>>>>>>>>>>>>>>>>>> of restoration. >>>>>>>>>>>>>>>>>>>> It's not 100% clear whether records are lost before making >>>>>>>>>>>>>>>>>>>> a savepoint or >>>>>>>>>>>>>>>>>>>> after restoring it. Although, based on the new DEBUG logs >>>>>>>>>>>>>>>>>>>> it seems more >>>>>>>>>>>>>>>>>>>> like losing some records that are seen ~soon after >>>>>>>>>>>>>>>>>>>> restoring. It seems like >>>>>>>>>>>>>>>>>>>> Flink would be somehow confused either about the restored >>>>>>>>>>>>>>>>>>>> state vs. new >>>>>>>>>>>>>>>>>>>> inserts to state. This could also be somehow linked to the >>>>>>>>>>>>>>>>>>>> high back >>>>>>>>>>>>>>>>>>>> pressure on the kafka source while the stream is catching >>>>>>>>>>>>>>>>>>>> up. >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> > If it is feasible for your setup, I suggest to insert >>>>>>>>>>>>>>>>>>>> one more map function after reduce and before sink. >>>>>>>>>>>>>>>>>>>> > etc. >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Isn't that the same thing that we discussed before? >>>>>>>>>>>>>>>>>>>> Nothing is sent to BucketingSink before the window closes, >>>>>>>>>>>>>>>>>>>> so I don't see >>>>>>>>>>>>>>>>>>>> how it would make any difference if we replace the >>>>>>>>>>>>>>>>>>>> BucketingSink with a map >>>>>>>>>>>>>>>>>>>> function or another sink type. We don't create or restore >>>>>>>>>>>>>>>>>>>> savepoints during >>>>>>>>>>>>>>>>>>>> the time when BucketingSink gets input or has open buckets >>>>>>>>>>>>>>>>>>>> – that happens >>>>>>>>>>>>>>>>>>>> at a much later time of day. I would focus on figuring out >>>>>>>>>>>>>>>>>>>> why the records >>>>>>>>>>>>>>>>>>>> are lost while the window is open. But I don't know how to >>>>>>>>>>>>>>>>>>>> do that. Would >>>>>>>>>>>>>>>>>>>> you have any additional suggestions? >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> On Fri, Sep 21, 2018 at 3:30 PM Andrey Zagrebin < >>>>>>>>>>>>>>>>>>>> and...@data-artisans.com> wrote: >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Hi Juho, >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> so it means that the savepoint does not loose at least >>>>>>>>>>>>>>>>>>>>> some dropped records. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> If it is feasible for your setup, I suggest to insert >>>>>>>>>>>>>>>>>>>>> one more map function after reduce and before sink. >>>>>>>>>>>>>>>>>>>>> The map function should be called right after window >>>>>>>>>>>>>>>>>>>>> is triggered but before flushing to s3. >>>>>>>>>>>>>>>>>>>>> The result of reduce (deduped record) could be logged >>>>>>>>>>>>>>>>>>>>> there. >>>>>>>>>>>>>>>>>>>>> This should allow to check whether the processed >>>>>>>>>>>>>>>>>>>>> distinct records were buffered in the state after the >>>>>>>>>>>>>>>>>>>>> restoration from the >>>>>>>>>>>>>>>>>>>>> savepoint or not. If they were buffered we should see >>>>>>>>>>>>>>>>>>>>> that there was an >>>>>>>>>>>>>>>>>>>>> attempt to write them to the sink from the state. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Another suggestion is to try to write records to some >>>>>>>>>>>>>>>>>>>>> other sink or to both. >>>>>>>>>>>>>>>>>>>>> E.g. if you can access file system of workers, maybe >>>>>>>>>>>>>>>>>>>>> just into local files and check whether the records are >>>>>>>>>>>>>>>>>>>>> also dropped there. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Best, >>>>>>>>>>>>>>>>>>>>> Andrey >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> On 20 Sep 2018, at 15:37, Juho Autio < >>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Hi Andrey! >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> I was finally able to gather the DEBUG logs that you >>>>>>>>>>>>>>>>>>>>> suggested. In short, the reducer logged that it processed >>>>>>>>>>>>>>>>>>>>> at least some of >>>>>>>>>>>>>>>>>>>>> the ids that were missing from the output. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> "At least some", because I didn't have the job running >>>>>>>>>>>>>>>>>>>>> with DEBUG logs for the full 24-hour window period. So I >>>>>>>>>>>>>>>>>>>>> was only able to >>>>>>>>>>>>>>>>>>>>> look up if I can find *some* of the missing ids in >>>>>>>>>>>>>>>>>>>>> the DEBUG logs. Which I did indeed. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> I changed the DistinctFunction.java to do this: >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> @Override >>>>>>>>>>>>>>>>>>>>> public Map<String, String> reduce(Map<String, >>>>>>>>>>>>>>>>>>>>> String> value1, Map<String, String> value2) { >>>>>>>>>>>>>>>>>>>>> LOG.debug("DistinctFunction.reduce returns: >>>>>>>>>>>>>>>>>>>>> {}={}", value1.get("field"), value1.get("id")); >>>>>>>>>>>>>>>>>>>>> return value1; >>>>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Then: >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> vi flink-1.6.0/conf/log4j.properties >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> log4j.logger.org.apache.flink.streaming.runtime.tasks.StreamTask=DEBUG >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> log4j.logger.com.rovio.ds.flink.uniqueid.DistinctFunction=DEBUG >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Then I ran the following kind of test: >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> - Cancelled the on-going job with savepoint created at >>>>>>>>>>>>>>>>>>>>> ~Sep 18 08:35 UTC 2018 >>>>>>>>>>>>>>>>>>>>> - Started a new cluster & job with DEBUG enabled at >>>>>>>>>>>>>>>>>>>>> ~09:13, restored from that previous cluster's savepoint >>>>>>>>>>>>>>>>>>>>> - Ran until caught up offsets >>>>>>>>>>>>>>>>>>>>> - Cancelled the job with a new savepoint >>>>>>>>>>>>>>>>>>>>> - Started a new job _without_ DEBUG, which restored >>>>>>>>>>>>>>>>>>>>> the new savepoint, let it keep running so that it will >>>>>>>>>>>>>>>>>>>>> eventually write the >>>>>>>>>>>>>>>>>>>>> output >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Then on the next day, after results had been flushed >>>>>>>>>>>>>>>>>>>>> when the 24-hour window closed, I compared the results >>>>>>>>>>>>>>>>>>>>> again with a batch >>>>>>>>>>>>>>>>>>>>> version's output. And found some missing ids as usual. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> I drilled down to one specific missing id (I'm >>>>>>>>>>>>>>>>>>>>> replacing the actual value with AN12345 below), which was >>>>>>>>>>>>>>>>>>>>> not found in the >>>>>>>>>>>>>>>>>>>>> stream output, but was found in batch output & flink >>>>>>>>>>>>>>>>>>>>> DEBUG logs. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Related to that id, I gathered the following >>>>>>>>>>>>>>>>>>>>> information: >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> 2018-09-18~09:13:21,000 job started & savepoint is >>>>>>>>>>>>>>>>>>>>> restored >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> 2018-09-18 09:14:29,085 missing id is processed for >>>>>>>>>>>>>>>>>>>>> the first time, proved by this log line: >>>>>>>>>>>>>>>>>>>>> 2018-09-18 09:14:29,085 DEBUG >>>>>>>>>>>>>>>>>>>>> com.rovio.ds.flink.uniqueid.DistinctFunction >>>>>>>>>>>>>>>>>>>>> - >>>>>>>>>>>>>>>>>>>>> DistinctFunction.reduce returns: s.aid1=AN12345 >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> 2018-09-18 09:15:14,264 first synchronous part of >>>>>>>>>>>>>>>>>>>>> checkpoint >>>>>>>>>>>>>>>>>>>>> 2018-09-18 09:15:16,544 first asynchronous part of >>>>>>>>>>>>>>>>>>>>> checkpoint >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> ( >>>>>>>>>>>>>>>>>>>>> more occurrences of checkpoints (~1 min checkpointing >>>>>>>>>>>>>>>>>>>>> time + ~1 min delay before next) >>>>>>>>>>>>>>>>>>>>> / >>>>>>>>>>>>>>>>>>>>> more occurrences of DistinctFunction.reduce >>>>>>>>>>>>>>>>>>>>> ) >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> 2018-09-18 09:23:45,053 missing id is processed for >>>>>>>>>>>>>>>>>>>>> the last time >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> 2018-09-18~10:20:00,000 savepoint created & job >>>>>>>>>>>>>>>>>>>>> cancelled >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> To be noted, there was high backpressure after >>>>>>>>>>>>>>>>>>>>> restoring from savepoint until the stream caught up with >>>>>>>>>>>>>>>>>>>>> the kafka offsets. >>>>>>>>>>>>>>>>>>>>> Although, our job uses assign timestamps & watermarks on >>>>>>>>>>>>>>>>>>>>> the flink kafka >>>>>>>>>>>>>>>>>>>>> consumer itself, so event time of all partitions is >>>>>>>>>>>>>>>>>>>>> synchronized. As >>>>>>>>>>>>>>>>>>>>> expected, we don't get any late data in the late data >>>>>>>>>>>>>>>>>>>>> side output. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> From this we can see that the missing ids are >>>>>>>>>>>>>>>>>>>>> processed by the reducer, but they must get lost >>>>>>>>>>>>>>>>>>>>> somewhere before the >>>>>>>>>>>>>>>>>>>>> 24-hour window is triggered. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> I think it's worth mentioning once more that the >>>>>>>>>>>>>>>>>>>>> stream doesn't miss any ids if we let it's running >>>>>>>>>>>>>>>>>>>>> without interruptions / >>>>>>>>>>>>>>>>>>>>> state restoring. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> What's next? >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> On Wed, Aug 29, 2018 at 3:49 PM Andrey Zagrebin < >>>>>>>>>>>>>>>>>>>>> and...@data-artisans.com> wrote: >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Hi Juho, >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> > only when the 24-hour window >>>>>>>>>>>>>>>>>>>>>> triggers, BucketingSink gets a burst of input >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> This is of course totally true, my understanding is >>>>>>>>>>>>>>>>>>>>>> the same. We cannot exclude problem there for sure, just >>>>>>>>>>>>>>>>>>>>>> savepoints are >>>>>>>>>>>>>>>>>>>>>> used a lot w/o problem reports and BucketingSink is >>>>>>>>>>>>>>>>>>>>>> known to be problematic >>>>>>>>>>>>>>>>>>>>>> with s3. That is why, I asked you: >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> > You also wrote that the timestamps of lost event >>>>>>>>>>>>>>>>>>>>>> are 'probably' around the time of the savepoint, if it >>>>>>>>>>>>>>>>>>>>>> is not yet for sure >>>>>>>>>>>>>>>>>>>>>> I would also check it. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Although, bucketing sink might loose any data at the >>>>>>>>>>>>>>>>>>>>>> end of the day (also from the middle). The fact, that it >>>>>>>>>>>>>>>>>>>>>> is always around >>>>>>>>>>>>>>>>>>>>>> the time of taking a savepoint and not random, is surely >>>>>>>>>>>>>>>>>>>>>> suspicious and >>>>>>>>>>>>>>>>>>>>>> possible savepoint failures need to be investigated. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Regarding the s3 problem, s3 doc says: >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> > The caveat is that if you make a HEAD or GET >>>>>>>>>>>>>>>>>>>>>> request to the key name (to find if the object exists) >>>>>>>>>>>>>>>>>>>>>> before creating the >>>>>>>>>>>>>>>>>>>>>> object, Amazon S3 provides 'eventual consistency' for >>>>>>>>>>>>>>>>>>>>>> read-after-write. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> The algorithm you suggest is how it is roughly >>>>>>>>>>>>>>>>>>>>>> implemented now (BucketingSink.openNewPartFile). My >>>>>>>>>>>>>>>>>>>>>> understanding is that >>>>>>>>>>>>>>>>>>>>>> 'eventual consistency’ means that even if you just >>>>>>>>>>>>>>>>>>>>>> created file (its name >>>>>>>>>>>>>>>>>>>>>> is key) it can be that you do not get it in the list or >>>>>>>>>>>>>>>>>>>>>> exists (HEAD) >>>>>>>>>>>>>>>>>>>>>> returns false and you risk to rewrite the previous part. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> The BucketingSink was designed for a standard file >>>>>>>>>>>>>>>>>>>>>> system. s3 is used over a file system wrapper atm but >>>>>>>>>>>>>>>>>>>>>> does not always >>>>>>>>>>>>>>>>>>>>>> provide normal file system guarantees. See also last >>>>>>>>>>>>>>>>>>>>>> example in [1]. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Cheers, >>>>>>>>>>>>>>>>>>>>>> Andrey >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> [1] >>>>>>>>>>>>>>>>>>>>>> https://codeburst.io/quick-explanation-of-the-s3-consistency-model-6c9f325e3f82 >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> On 29 Aug 2018, at 12:11, Juho Autio < >>>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Andrey, thank you very much for the debugging >>>>>>>>>>>>>>>>>>>>>> suggestions, I'll try them. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> In the meanwhile two more questions, please: >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> > Just to keep in mind this problem with s3 and >>>>>>>>>>>>>>>>>>>>>> exclude it for sure. I would also check whether the size >>>>>>>>>>>>>>>>>>>>>> of missing events >>>>>>>>>>>>>>>>>>>>>> is around the batch size of BucketingSink or not. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Fair enough, but I also want to focus on debugging >>>>>>>>>>>>>>>>>>>>>> the most probable subject first. So what do you think >>>>>>>>>>>>>>>>>>>>>> about this – true or >>>>>>>>>>>>>>>>>>>>>> false: only when the 24-hour window triggers, >>>>>>>>>>>>>>>>>>>>>> BucketinSink gets a burst of >>>>>>>>>>>>>>>>>>>>>> input. Around the state restoring point (middle of the >>>>>>>>>>>>>>>>>>>>>> day) it doesn't get >>>>>>>>>>>>>>>>>>>>>> any input, so it can't lose anything either. Isn't this >>>>>>>>>>>>>>>>>>>>>> true, or have I >>>>>>>>>>>>>>>>>>>>>> totally missed how Flink works in triggering window >>>>>>>>>>>>>>>>>>>>>> results? I would not >>>>>>>>>>>>>>>>>>>>>> expect there to be any optimization that speculatively >>>>>>>>>>>>>>>>>>>>>> triggers early >>>>>>>>>>>>>>>>>>>>>> results of a regular time window to the downstream >>>>>>>>>>>>>>>>>>>>>> operators. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> > The old BucketingSink has in general problem with >>>>>>>>>>>>>>>>>>>>>> s3. Internally BucketingSink queries s3 as a file system >>>>>>>>>>>>>>>>>>>>>> to list already >>>>>>>>>>>>>>>>>>>>>> written file parts (batches) and determine index of the >>>>>>>>>>>>>>>>>>>>>> next part to start. >>>>>>>>>>>>>>>>>>>>>> Due to eventual consistency of checking file existence >>>>>>>>>>>>>>>>>>>>>> in s3 [1], the >>>>>>>>>>>>>>>>>>>>>> BucketingSink can rewrite the previously written part >>>>>>>>>>>>>>>>>>>>>> and basically loose >>>>>>>>>>>>>>>>>>>>>> it. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> I was wondering, what does S3's "read-after-write >>>>>>>>>>>>>>>>>>>>>> consistency" (mentioned on the page you linked) actually >>>>>>>>>>>>>>>>>>>>>> mean. It seems >>>>>>>>>>>>>>>>>>>>>> that this might be possible: >>>>>>>>>>>>>>>>>>>>>> - LIST keys, find current max index >>>>>>>>>>>>>>>>>>>>>> - choose next index = max + 1 >>>>>>>>>>>>>>>>>>>>>> - HEAD next index: if it exists, keep adding + >>>>>>>>>>>>>>>>>>>>>> 1 until key doesn't exist on S3 >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> But definitely sounds easier if a sink keeps track of >>>>>>>>>>>>>>>>>>>>>> files in a way that's guaranteed to be consistent. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Cheers, >>>>>>>>>>>>>>>>>>>>>> Juho >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> On Mon, Aug 27, 2018 at 2:04 PM Andrey Zagrebin < >>>>>>>>>>>>>>>>>>>>>> and...@data-artisans.com> wrote: >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> Hi, >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> true, StreamingFileSink does not support s3 in >>>>>>>>>>>>>>>>>>>>>>> 1.6.0, it is planned for the next 1.7 release, sorry >>>>>>>>>>>>>>>>>>>>>>> for confusion. >>>>>>>>>>>>>>>>>>>>>>> The old BucketingSink has in general problem with >>>>>>>>>>>>>>>>>>>>>>> s3. Internally BucketingSink queries s3 as a file system >>>>>>>>>>>>>>>>>>>>>>> to list already written file parts (batches) and >>>>>>>>>>>>>>>>>>>>>>> determine index of the next part to start. Due to >>>>>>>>>>>>>>>>>>>>>>> eventual consistency of >>>>>>>>>>>>>>>>>>>>>>> checking file existence in s3 [1], the BucketingSink >>>>>>>>>>>>>>>>>>>>>>> can rewrite the >>>>>>>>>>>>>>>>>>>>>>> previously written part and basically loose it. It >>>>>>>>>>>>>>>>>>>>>>> should be fixed for >>>>>>>>>>>>>>>>>>>>>>> StreamingFileSink in 1.7 where Flink keeps its own >>>>>>>>>>>>>>>>>>>>>>> track of written parts >>>>>>>>>>>>>>>>>>>>>>> and does not rely on s3 as a file system. >>>>>>>>>>>>>>>>>>>>>>> I also include Kostas, he might add more details. >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> Just to keep in mind this problem with s3 and >>>>>>>>>>>>>>>>>>>>>>> exclude it for sure I would also check whether the >>>>>>>>>>>>>>>>>>>>>>> size of missing events >>>>>>>>>>>>>>>>>>>>>>> is around the batch size of BucketingSink or not. You >>>>>>>>>>>>>>>>>>>>>>> also wrote that the >>>>>>>>>>>>>>>>>>>>>>> timestamps of lost event are 'probably' around the time >>>>>>>>>>>>>>>>>>>>>>> of the savepoint, >>>>>>>>>>>>>>>>>>>>>>> if it is not yet for sure I would also check it. >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> Have you already checked the log files of job >>>>>>>>>>>>>>>>>>>>>>> manager and task managers for the job running before >>>>>>>>>>>>>>>>>>>>>>> and after the restore >>>>>>>>>>>>>>>>>>>>>>> from the check point? Is everything successful there, >>>>>>>>>>>>>>>>>>>>>>> no errors, relevant >>>>>>>>>>>>>>>>>>>>>>> warnings or exceptions? >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> As the next step, I would suggest to log all >>>>>>>>>>>>>>>>>>>>>>> encountered events in DistinctFunction.reduce if >>>>>>>>>>>>>>>>>>>>>>> possible for production >>>>>>>>>>>>>>>>>>>>>>> data and check whether the missed events are eventually >>>>>>>>>>>>>>>>>>>>>>> processed before or >>>>>>>>>>>>>>>>>>>>>>> after the savepoint. The following log message >>>>>>>>>>>>>>>>>>>>>>> indicates a border between >>>>>>>>>>>>>>>>>>>>>>> the events that should be included into the savepoint >>>>>>>>>>>>>>>>>>>>>>> (logged before) or >>>>>>>>>>>>>>>>>>>>>>> not: >>>>>>>>>>>>>>>>>>>>>>> “{} ({}, synchronous part) in thread {} took {} ms” >>>>>>>>>>>>>>>>>>>>>>> (template) >>>>>>>>>>>>>>>>>>>>>>> Also check if the savepoint has been overall >>>>>>>>>>>>>>>>>>>>>>> completed: >>>>>>>>>>>>>>>>>>>>>>> "{} ({}, asynchronous part) in thread {} took {} ms." >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> Best, >>>>>>>>>>>>>>>>>>>>>>> Andrey >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> [1] >>>>>>>>>>>>>>>>>>>>>>> https://docs.aws.amazon.com/AmazonS3/latest/dev/Introduction.html >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> On 24 Aug 2018, at 20:41, Juho Autio < >>>>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> Hi, >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> Using StreamingFileSink is not a convenient option >>>>>>>>>>>>>>>>>>>>>>> for production use for us as it doesn't support s3*. I >>>>>>>>>>>>>>>>>>>>>>> could use >>>>>>>>>>>>>>>>>>>>>>> StreamingFileSink just to verify, but I don't see much >>>>>>>>>>>>>>>>>>>>>>> point in doing so. >>>>>>>>>>>>>>>>>>>>>>> Please consider my previous comment: >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> > I realized that BucketingSink must not play any >>>>>>>>>>>>>>>>>>>>>>> role in this problem. This is because only when the >>>>>>>>>>>>>>>>>>>>>>> 24-hour window >>>>>>>>>>>>>>>>>>>>>>> triggers, BucketingSink gets a burst of input. Around >>>>>>>>>>>>>>>>>>>>>>> the state restoring >>>>>>>>>>>>>>>>>>>>>>> point (middle of the day) it doesn't get any input, so >>>>>>>>>>>>>>>>>>>>>>> it can't lose >>>>>>>>>>>>>>>>>>>>>>> anything either (right?). >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> I could also use a kafka sink instead, but I can't >>>>>>>>>>>>>>>>>>>>>>> imagine how there could be any difference. It's very >>>>>>>>>>>>>>>>>>>>>>> real that the sink >>>>>>>>>>>>>>>>>>>>>>> doesn't get any input for a long time until the 24-hour >>>>>>>>>>>>>>>>>>>>>>> window closes, and >>>>>>>>>>>>>>>>>>>>>>> then it quickly writes out everything because it's not >>>>>>>>>>>>>>>>>>>>>>> that much data >>>>>>>>>>>>>>>>>>>>>>> eventually for the distinct values. >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> Any ideas for debugging what's happening around the >>>>>>>>>>>>>>>>>>>>>>> savepoint & restoration time? >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> *) I actually implemented StreamingFileSink as an >>>>>>>>>>>>>>>>>>>>>>> alternative sink. This was before I came to realize >>>>>>>>>>>>>>>>>>>>>>> that most likely the >>>>>>>>>>>>>>>>>>>>>>> sink component has nothing to do with the data loss >>>>>>>>>>>>>>>>>>>>>>> problem. I tried it >>>>>>>>>>>>>>>>>>>>>>> with s3n:// path just to see an exception being thrown. >>>>>>>>>>>>>>>>>>>>>>> In the source code >>>>>>>>>>>>>>>>>>>>>>> I indeed then found an explicit check for the target >>>>>>>>>>>>>>>>>>>>>>> path scheme to be >>>>>>>>>>>>>>>>>>>>>>> "hdfs://". >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> On Fri, Aug 24, 2018 at 7:49 PM Andrey Zagrebin < >>>>>>>>>>>>>>>>>>>>>>> and...@data-artisans.com> wrote: >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> Ok, I think before further debugging the window >>>>>>>>>>>>>>>>>>>>>>>> reduced state, >>>>>>>>>>>>>>>>>>>>>>>> could you try the new ‘StreamingFileSink’ [1] >>>>>>>>>>>>>>>>>>>>>>>> introduced in Flink 1.6.0 instead of the previous >>>>>>>>>>>>>>>>>>>>>>>> 'BucketingSink’? >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> Cheers, >>>>>>>>>>>>>>>>>>>>>>>> Andrey >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> [1] >>>>>>>>>>>>>>>>>>>>>>>> https://ci.apache.org/projects/flink/flink-docs-stable/dev/connectors/streamfile_sink.html >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> On 24 Aug 2018, at 18:03, Juho Autio < >>>>>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> Yes, sorry for my confusing comment. I just meant >>>>>>>>>>>>>>>>>>>>>>>> that it seems like there's a bug somewhere now that >>>>>>>>>>>>>>>>>>>>>>>> the output is missing >>>>>>>>>>>>>>>>>>>>>>>> some data. >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> > I would wait and check the actual output in s3 >>>>>>>>>>>>>>>>>>>>>>>> because it is the main result of the job >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> Yes, and that's what I have already done. There >>>>>>>>>>>>>>>>>>>>>>>> seems to be always some data loss with the production >>>>>>>>>>>>>>>>>>>>>>>> data volumes, if the >>>>>>>>>>>>>>>>>>>>>>>> job has been restarted on that day. >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> Would you have any suggestions for how to debug >>>>>>>>>>>>>>>>>>>>>>>> this further? >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> Many thanks for stepping in. >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> On Fri, Aug 24, 2018 at 6:37 PM Andrey Zagrebin < >>>>>>>>>>>>>>>>>>>>>>>> and...@data-artisans.com> wrote: >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Hi Juho, >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> So it is a per key deduplication job. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Yes, I would wait and check the actual output in >>>>>>>>>>>>>>>>>>>>>>>>> s3 because it is the main result of the job and >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> > The late data around the time of taking >>>>>>>>>>>>>>>>>>>>>>>>> savepoint might be not included into the savepoint >>>>>>>>>>>>>>>>>>>>>>>>> but it should be behind >>>>>>>>>>>>>>>>>>>>>>>>> the snapshotted offset in Kafka. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> is not a bug, it is a possible behaviour. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> The savepoint is a snapshot of the data in >>>>>>>>>>>>>>>>>>>>>>>>> transient which is already consumed from Kafka. >>>>>>>>>>>>>>>>>>>>>>>>> Basically the full contents of the window result >>>>>>>>>>>>>>>>>>>>>>>>> is split between the savepoint and what can come >>>>>>>>>>>>>>>>>>>>>>>>> after the savepoint'ed >>>>>>>>>>>>>>>>>>>>>>>>> offset in Kafka but before the window result is >>>>>>>>>>>>>>>>>>>>>>>>> written into s3. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Allowed lateness should not affect it, I am just >>>>>>>>>>>>>>>>>>>>>>>>> saying that the final result in s3 should include all >>>>>>>>>>>>>>>>>>>>>>>>> records after it. >>>>>>>>>>>>>>>>>>>>>>>>> This is what should be guaranteed but not the >>>>>>>>>>>>>>>>>>>>>>>>> contents of the intermediate savepoint. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Cheers, >>>>>>>>>>>>>>>>>>>>>>>>> Andrey >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> On 24 Aug 2018, at 16:52, Juho Autio < >>>>>>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Thanks for your answer! >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> I check for the missed data from the final output >>>>>>>>>>>>>>>>>>>>>>>>> on s3. So I wait until the next day, then run the >>>>>>>>>>>>>>>>>>>>>>>>> same thing re-implemented >>>>>>>>>>>>>>>>>>>>>>>>> in batch, and compare the output. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> > The late data around the time of taking >>>>>>>>>>>>>>>>>>>>>>>>> savepoint might be not included into the savepoint >>>>>>>>>>>>>>>>>>>>>>>>> but it should be behind >>>>>>>>>>>>>>>>>>>>>>>>> the snapshotted offset in Kafka. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Yes, I would definitely expect that. It seems like >>>>>>>>>>>>>>>>>>>>>>>>> there's a bug somewhere. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> > Then it should just come later after the restore >>>>>>>>>>>>>>>>>>>>>>>>> and should be reduced within the allowed lateness >>>>>>>>>>>>>>>>>>>>>>>>> into the final result >>>>>>>>>>>>>>>>>>>>>>>>> which is saved into s3. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Well, as far as I know, allowed lateness doesn't >>>>>>>>>>>>>>>>>>>>>>>>> play any role here, because I started running the job >>>>>>>>>>>>>>>>>>>>>>>>> with >>>>>>>>>>>>>>>>>>>>>>>>> allowedLateness=0, and still get the data loss, while >>>>>>>>>>>>>>>>>>>>>>>>> my late data output >>>>>>>>>>>>>>>>>>>>>>>>> doesn't receive anything. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> > Also, is this `DistinctFunction.reduce` just an >>>>>>>>>>>>>>>>>>>>>>>>> example or the actual implementation, basically >>>>>>>>>>>>>>>>>>>>>>>>> saving just one of records >>>>>>>>>>>>>>>>>>>>>>>>> inside the 24h window in s3? then what is missing >>>>>>>>>>>>>>>>>>>>>>>>> there? >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Yes, it's the actual implementation. Note that >>>>>>>>>>>>>>>>>>>>>>>>> there's a keyBy before the DistinctFunction. So >>>>>>>>>>>>>>>>>>>>>>>>> there's one record for each >>>>>>>>>>>>>>>>>>>>>>>>> key (which is the combination of a couple of fields). >>>>>>>>>>>>>>>>>>>>>>>>> In practice I've seen >>>>>>>>>>>>>>>>>>>>>>>>> that we're missing ~2000-4000 elements on each >>>>>>>>>>>>>>>>>>>>>>>>> restore, and the total >>>>>>>>>>>>>>>>>>>>>>>>> output is obviously much more than that. >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> Here's the full code for the key selector: >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> public class MapKeySelector implements >>>>>>>>>>>>>>>>>>>>>>>>> KeySelector<Map<String,String>, Object> { >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> private final String[] fields; >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> public MapKeySelector(String... fields) { >>>>>>>>>>>>>>>>>>>>>>>>> this.fields = fields; >>>>>>>>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> @Override >>>>>>>>>>>>>>>>>>>>>>>>> public Object getKey(Map<String, String> >>>>>>>>>>>>>>>>>>>>>>>>> event) throws Exception { >>>>>>>>>>>>>>>>>>>>>>>>> Tuple key = >>>>>>>>>>>>>>>>>>>>>>>>> Tuple.getTupleClass(fields.length).newInstance(); >>>>>>>>>>>>>>>>>>>>>>>>> for (int i = 0; i < fields.length; i++) { >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> key.setField(event.getOrDefault(fields[i], ""), i); >>>>>>>>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>>>>>>>> return key; >>>>>>>>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> And a more exact example on how it's used: >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> .keyBy(new MapKeySelector("ID", >>>>>>>>>>>>>>>>>>>>>>>>> "PLAYER_ID", "FIELD", "KEY_NAME", "KEY_VALUE")) >>>>>>>>>>>>>>>>>>>>>>>>> .timeWindow(Time.days(1)) >>>>>>>>>>>>>>>>>>>>>>>>> .reduce(new DistinctFunction()) >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> On Fri, Aug 24, 2018 at 5:26 PM Andrey Zagrebin < >>>>>>>>>>>>>>>>>>>>>>>>> and...@data-artisans.com> wrote: >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> Hi Juho, >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> Where exactly does the data miss? When do you >>>>>>>>>>>>>>>>>>>>>>>>>> notice that? >>>>>>>>>>>>>>>>>>>>>>>>>> Do you check it: >>>>>>>>>>>>>>>>>>>>>>>>>> - debugging `DistinctFunction.reduce` right after >>>>>>>>>>>>>>>>>>>>>>>>>> resume in the middle of the day >>>>>>>>>>>>>>>>>>>>>>>>>> or >>>>>>>>>>>>>>>>>>>>>>>>>> - some distinct records miss in the final output >>>>>>>>>>>>>>>>>>>>>>>>>> of BucketingSink in s3 after window result is >>>>>>>>>>>>>>>>>>>>>>>>>> actually triggered and saved >>>>>>>>>>>>>>>>>>>>>>>>>> into s3 at the end of the day? is this the main >>>>>>>>>>>>>>>>>>>>>>>>>> output? >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> The late data around the time of taking savepoint >>>>>>>>>>>>>>>>>>>>>>>>>> might be not included into the savepoint but it >>>>>>>>>>>>>>>>>>>>>>>>>> should be behind the >>>>>>>>>>>>>>>>>>>>>>>>>> snapshotted offset in Kafka. Then it should just >>>>>>>>>>>>>>>>>>>>>>>>>> come later after the >>>>>>>>>>>>>>>>>>>>>>>>>> restore and should be reduced within the allowed >>>>>>>>>>>>>>>>>>>>>>>>>> lateness into the final >>>>>>>>>>>>>>>>>>>>>>>>>> result which is saved into s3. >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> Also, is this `DistinctFunction.reduce` just an >>>>>>>>>>>>>>>>>>>>>>>>>> example or the actual implementation, basically >>>>>>>>>>>>>>>>>>>>>>>>>> saving just one of records >>>>>>>>>>>>>>>>>>>>>>>>>> inside the 24h window in s3? then what is missing >>>>>>>>>>>>>>>>>>>>>>>>>> there? >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> Cheers, >>>>>>>>>>>>>>>>>>>>>>>>>> Andrey >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> On 23 Aug 2018, at 15:42, Juho Autio < >>>>>>>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> I changed to allowedLateness=0, no change, still >>>>>>>>>>>>>>>>>>>>>>>>>> missing data when restoring from savepoint. >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> On Tue, Aug 21, 2018 at 10:43 AM Juho Autio < >>>>>>>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>> I realized that BucketingSink must not play any >>>>>>>>>>>>>>>>>>>>>>>>>>> role in this problem. This is because only when the >>>>>>>>>>>>>>>>>>>>>>>>>>> 24-hour window >>>>>>>>>>>>>>>>>>>>>>>>>>> triggers, BucketinSink gets a burst of input. >>>>>>>>>>>>>>>>>>>>>>>>>>> Around the state restoring >>>>>>>>>>>>>>>>>>>>>>>>>>> point (middle of the day) it doesn't get any input, >>>>>>>>>>>>>>>>>>>>>>>>>>> so it can't lose >>>>>>>>>>>>>>>>>>>>>>>>>>> anything either (right?). >>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>> I will next try removing the allowedLateness >>>>>>>>>>>>>>>>>>>>>>>>>>> entirely from the equation. >>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>> In the meanwhile, please let me know if you have >>>>>>>>>>>>>>>>>>>>>>>>>>> any suggestions for debugging the lost data, for >>>>>>>>>>>>>>>>>>>>>>>>>>> example what logs to >>>>>>>>>>>>>>>>>>>>>>>>>>> enable. >>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>> We use FlinkKafkaConsumer010 btw. Are there any >>>>>>>>>>>>>>>>>>>>>>>>>>> known issues with that, that could contribute to >>>>>>>>>>>>>>>>>>>>>>>>>>> lost data when restoring a >>>>>>>>>>>>>>>>>>>>>>>>>>> savepoint? >>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>> On Fri, Aug 17, 2018 at 4:23 PM Juho Autio < >>>>>>>>>>>>>>>>>>>>>>>>>>> juho.au...@rovio.com> wrote: >>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> Some data is silently lost on my Flink stream >>>>>>>>>>>>>>>>>>>>>>>>>>>> job when state is restored from a savepoint. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> Do you have any debugging hints to find out >>>>>>>>>>>>>>>>>>>>>>>>>>>> where exactly the data gets dropped? >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> My job gathers distinct values using a 24-hour >>>>>>>>>>>>>>>>>>>>>>>>>>>> window. It doesn't have any custom state >>>>>>>>>>>>>>>>>>>>>>>>>>>> management. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> When I cancel the job with savepoint and >>>>>>>>>>>>>>>>>>>>>>>>>>>> restore from that savepoint, some data is missed. >>>>>>>>>>>>>>>>>>>>>>>>>>>> It seems to be losing >>>>>>>>>>>>>>>>>>>>>>>>>>>> just a small amount of data. The event time of >>>>>>>>>>>>>>>>>>>>>>>>>>>> lost data is probably around >>>>>>>>>>>>>>>>>>>>>>>>>>>> the time of savepoint. In other words the rest of >>>>>>>>>>>>>>>>>>>>>>>>>>>> the time window is not >>>>>>>>>>>>>>>>>>>>>>>>>>>> entirely missed – collection works correctly also >>>>>>>>>>>>>>>>>>>>>>>>>>>> for (most of the) events >>>>>>>>>>>>>>>>>>>>>>>>>>>> that come in after restoring. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> When the job processes a full 24-hour window >>>>>>>>>>>>>>>>>>>>>>>>>>>> without interruptions it doesn't miss anything. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> Usually the problem doesn't happen in test >>>>>>>>>>>>>>>>>>>>>>>>>>>> environments that have smaller parallelism and >>>>>>>>>>>>>>>>>>>>>>>>>>>> smaller data volumes. But in >>>>>>>>>>>>>>>>>>>>>>>>>>>> production volumes the job seems to be >>>>>>>>>>>>>>>>>>>>>>>>>>>> consistently missing at least >>>>>>>>>>>>>>>>>>>>>>>>>>>> something on every restore. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> This issue has consistently happened since the >>>>>>>>>>>>>>>>>>>>>>>>>>>> job was initially created. It was at first run on >>>>>>>>>>>>>>>>>>>>>>>>>>>> an older version of Flink >>>>>>>>>>>>>>>>>>>>>>>>>>>> 1.5-SNAPSHOT and it still happens on both Flink >>>>>>>>>>>>>>>>>>>>>>>>>>>> 1.5.2 & 1.6.0. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> I'm wondering if this could be for example some >>>>>>>>>>>>>>>>>>>>>>>>>>>> synchronization issue between the kafka consumer >>>>>>>>>>>>>>>>>>>>>>>>>>>> offsets vs. what's been >>>>>>>>>>>>>>>>>>>>>>>>>>>> written by BucketingSink? >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> 1. Job content, simplified >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> kafkaStream >>>>>>>>>>>>>>>>>>>>>>>>>>>> .flatMap(new >>>>>>>>>>>>>>>>>>>>>>>>>>>> ExtractFieldsFunction()) >>>>>>>>>>>>>>>>>>>>>>>>>>>> .keyBy(new MapKeySelector(1, 2, >>>>>>>>>>>>>>>>>>>>>>>>>>>> 3, 4)) >>>>>>>>>>>>>>>>>>>>>>>>>>>> .timeWindow(Time.days(1)) >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> .allowedLateness(allowedLateness) >>>>>>>>>>>>>>>>>>>>>>>>>>>> .sideOutputLateData(lateDataTag) >>>>>>>>>>>>>>>>>>>>>>>>>>>> .reduce(new DistinctFunction()) >>>>>>>>>>>>>>>>>>>>>>>>>>>> .addSink(sink) >>>>>>>>>>>>>>>>>>>>>>>>>>>> // use a fixed number of output >>>>>>>>>>>>>>>>>>>>>>>>>>>> partitions >>>>>>>>>>>>>>>>>>>>>>>>>>>> .setParallelism(8)) >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> /** >>>>>>>>>>>>>>>>>>>>>>>>>>>> * Usage: .keyBy("the", "distinct", >>>>>>>>>>>>>>>>>>>>>>>>>>>> "fields").reduce(new DistinctFunction()) >>>>>>>>>>>>>>>>>>>>>>>>>>>> */ >>>>>>>>>>>>>>>>>>>>>>>>>>>> public class DistinctFunction implements >>>>>>>>>>>>>>>>>>>>>>>>>>>> ReduceFunction<java.util.Map<String, String>> { >>>>>>>>>>>>>>>>>>>>>>>>>>>> @Override >>>>>>>>>>>>>>>>>>>>>>>>>>>> public Map<String, String> >>>>>>>>>>>>>>>>>>>>>>>>>>>> reduce(Map<String, String> value1, Map<String, >>>>>>>>>>>>>>>>>>>>>>>>>>>> String> value2) { >>>>>>>>>>>>>>>>>>>>>>>>>>>> return value1; >>>>>>>>>>>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> 2. State configuration >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> boolean enableIncrementalCheckpointing = true; >>>>>>>>>>>>>>>>>>>>>>>>>>>> String statePath = "s3n://bucket/savepoints"; >>>>>>>>>>>>>>>>>>>>>>>>>>>> new RocksDBStateBackend(statePath, >>>>>>>>>>>>>>>>>>>>>>>>>>>> enableIncrementalCheckpointing); >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> Checkpointing Mode Exactly Once >>>>>>>>>>>>>>>>>>>>>>>>>>>> Interval 1m 0s >>>>>>>>>>>>>>>>>>>>>>>>>>>> Timeout 10m 0s >>>>>>>>>>>>>>>>>>>>>>>>>>>> Minimum Pause Between Checkpoints 1m 0s >>>>>>>>>>>>>>>>>>>>>>>>>>>> Maximum Concurrent Checkpoints 1 >>>>>>>>>>>>>>>>>>>>>>>>>>>> Persist Checkpoints Externally Enabled (retain >>>>>>>>>>>>>>>>>>>>>>>>>>>> on cancellation) >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> 3. BucketingSink configuration >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> We use BucketingSink, I don't think there's >>>>>>>>>>>>>>>>>>>>>>>>>>>> anything special here, if not the fact that we're >>>>>>>>>>>>>>>>>>>>>>>>>>>> writing to S3. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> String outputPath = "s3://bucket/output >>>>>>>>>>>>>>>>>>>>>>>>>>>> "; >>>>>>>>>>>>>>>>>>>>>>>>>>>> BucketingSink<Map<String, String>> sink >>>>>>>>>>>>>>>>>>>>>>>>>>>> = new BucketingSink<Map<String, >>>>>>>>>>>>>>>>>>>>>>>>>>>> String>>(outputPath) >>>>>>>>>>>>>>>>>>>>>>>>>>>> .setBucketer(new >>>>>>>>>>>>>>>>>>>>>>>>>>>> ProcessdateBucketer()) >>>>>>>>>>>>>>>>>>>>>>>>>>>> .setBatchSize(batchSize) >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> .setInactiveBucketThreshold(inactiveBucketThreshold) >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> .setInactiveBucketCheckInterval(inactiveBucketCheckInterval); >>>>>>>>>>>>>>>>>>>>>>>>>>>> sink.setWriter(new IdJsonWriter()); >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> 4. Kafka & event time >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> My flink job reads the data from Kafka, using a >>>>>>>>>>>>>>>>>>>>>>>>>>>> BoundedOutOfOrdernessTimestampExtractor on the >>>>>>>>>>>>>>>>>>>>>>>>>>>> kafka consumer to >>>>>>>>>>>>>>>>>>>>>>>>>>>> synchronize watermarks accross all kafka >>>>>>>>>>>>>>>>>>>>>>>>>>>> partitions. We also write late >>>>>>>>>>>>>>>>>>>>>>>>>>>> data to side output, but nothing is written there >>>>>>>>>>>>>>>>>>>>>>>>>>>> – if it would, it could >>>>>>>>>>>>>>>>>>>>>>>>>>>> explain missed data in the main output (I'm also >>>>>>>>>>>>>>>>>>>>>>>>>>>> sure that our late data >>>>>>>>>>>>>>>>>>>>>>>>>>>> writing works, because we previously had some >>>>>>>>>>>>>>>>>>>>>>>>>>>> actual late data which ended >>>>>>>>>>>>>>>>>>>>>>>>>>>> up there). >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> 5. allowedLateness >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> It may be or may not be relevant that I have >>>>>>>>>>>>>>>>>>>>>>>>>>>> also enabled allowedLateness with 1 minute >>>>>>>>>>>>>>>>>>>>>>>>>>>> lateness on the 24-hour window: >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> If that makes sense, I could try removing >>>>>>>>>>>>>>>>>>>>>>>>>>>> allowedLateness entirely? That would be just to >>>>>>>>>>>>>>>>>>>>>>>>>>>> rule out that Flink doesn't >>>>>>>>>>>>>>>>>>>>>>>>>>>> have a bug that's related to restoring state in >>>>>>>>>>>>>>>>>>>>>>>>>>>> combination with the >>>>>>>>>>>>>>>>>>>>>>>>>>>> allowedLateness feature. After all, all of our >>>>>>>>>>>>>>>>>>>>>>>>>>>> data should be in a good >>>>>>>>>>>>>>>>>>>>>>>>>>>> enough order to not be late, given the max out of >>>>>>>>>>>>>>>>>>>>>>>>>>>> orderness used on kafka >>>>>>>>>>>>>>>>>>>>>>>>>>>> consumer timestamp extractor. >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>>> Thank you in advance! >>>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>> >>>> >>>> -- >>>> *Juho Autio* >>>> Senior Data Engineer >>>> >>>> Data Engineering, Games >>>> Rovio Entertainment Corporation >>>> Mobile: + 358 (0)45 313 0122 >>>> juho.au...@rovio.com >>>> www.rovio.com >>>> >>>> *This message and its attachments may contain confidential information >>>> and is intended solely for the attention and use of the named addressee(s). >>>> If you are not the intended recipient and / or you have received this >>>> message in error, please contact the sender immediately and delete all >>>> material you have received in this message. You are hereby notified that >>>> any use of the information, which you have received in error in whatsoever >>>> form, is strictly prohibited. Thank you for your co-operation.* >>>> >>> >> >> -- >> >> Konstantin Knauf | Solutions Architect >> >> +49 160 91394525 >> >> <https://www.ververica.com/> >> >> Follow us @VervericaData >> >> -- >> >> Join Flink Forward <https://flink-forward.org/> - The Apache Flink >> Conference >> >> Stream Processing | Event Driven | Real Time >> >> -- >> >> Data Artisans GmbH | Invalidenstrasse 115, 10115 Berlin, Germany >> >> -- >> Data Artisans GmbH >> Registered at Amtsgericht Charlottenburg: HRB 158244 B >> Managing Directors: Dr. Kostas Tzoumas, Dr. Stephan Ewen >> > > -- Konstantin Knauf | Solutions Architect +49 160 91394525 <https://www.ververica.com/> Follow us @VervericaData -- Join Flink Forward <https://flink-forward.org/> - The Apache Flink Conference Stream Processing | Event Driven | Real Time -- Data Artisans GmbH | Invalidenstrasse 115, 10115 Berlin, Germany -- Data Artisans GmbH Registered at Amtsgericht Charlottenburg: HRB 158244 B Managing Directors: Dr. Kostas Tzoumas, Dr. Stephan Ewen
