Hey, Actually I think Fabian initial message was incorrect. As far as I can see in the code of WindowOperator (last lines of org.apache.flink.streaming.runtime.operators.windowing.WindowOperator#processElement ), the element is sent to late side output if it is late AND it wasn’t assigned to any of the existing windows (because they were late as well). In other words, it should work as you Juho are wishing: element should be marked as late once they are overdue/late for the window after one full day.
I have tested it and it works as expected. Following program: https://gist.github.com/pnowojski/8cd650170925cf35be521cf236f1d97a <https://gist.github.com/pnowojski/8cd650170925cf35be521cf236f1d97a> Prints only ONE number to the standard err: > 1394 And there is nothing on the side output. Piotrek > On 11 May 2018, at 12:32, Juho Autio <juho.au...@rovio.com> wrote: > > Thanks. What I still don't get is why my message got filtered in the first > place. Even if the allowed lateness filtering would be done "on the window", > data should not be dropped as late if it's not in fact late by more than the > allowedLateness setting. > > Assuming that these conditions hold: > - messages (and thus the extracted timestamps) were not out of order by more > than 5 secods (as far as I didn't make any mistake in my partition-level > analysis) > - allowedLateness=1 minute > - watermarks are assigned on kafka consumer meaning that they are > synchronized across all partitions > > I don't see how the watermark could have ever been more than 5 seconds > further when the message arrives on the isElementLate filter. Do you have any > idea on this? Is there some existing test that simulates out of order input > to flink's kafka consumer? I could try to build a test case based on that to > possibly reproduce my problem. I'm not sure how to gather enough debug > information on the production stream so that it would clearly show the > watermarks, how they progressed on each kafka partition & later in the chain > in case isElementLate filters something. > > On Fri, May 11, 2018 at 12:12 PM, Fabian Hueske <fhue...@gmail.com > <mailto:fhue...@gmail.com>> wrote: > Hi Juho, > > Thanks for bringing up this topic! I share your intuition. > IMO, records should only be filtered out and send to a side output if any of > the windows they would be assigned to is closed already. > > I had a look into the code and found that records are filtered out as late > based on the following condition: > > protected boolean isElementLate(StreamRecord<IN> element){ > return (windowAssigner.isEventTime()) && > (element.getTimestamp() + allowedLateness <= > internalTimerService.currentWatermark()); > } > > > This code shows that your analysis is correct. > Records are filtered out based on their timestamp and the current watermark, > even though they arrive before the window is closed. > > OTOH, filtering out records based on the window they would end up in can also > be tricky if records are assigned to multiple windows (e.g., sliding windows). > In this case, a side-outputted records could still be in some windows and not > in others. > > @Aljoscha (CC) Might have an explanation for the current behavior. > > Thanks, > Fabian > > > 2018-05-11 10:55 GMT+02:00 Juho Autio <juho.au...@rovio.com > <mailto:juho.au...@rovio.com>>: > I don't understand why I'm getting some data discarded as late on my Flink > stream job a long time before the window even closes. > > I can not be 100% sure, but to me it seems like the kafka consumer is > basically causing the data to be dropped as "late", not the window. I didn't > expect this to ever happen? > > I have a Flink stream job that gathers distinct values using a 24-hour > window. It reads the data from Kafka, using a > BoundedOutOfOrdernessTimestampExtractor on the kafka consumer to synchronize > watermarks accross all kafka partitions. The maxOutOfOrderness of the > extractor is set to 10 seconds. > > I have also enabled allowedLateness with 1 minute lateness on the 24-hour > window: > > .timeWindow(Time.days(1)) > .allowedLateness(Time.minutes(1)) > .sideOutputLateData(lateDataTag) > .reduce(new DistinctFunction()) > > I have used accumulators to see that there is some late data. I have had > multiple occurrences of those. > > Now focusing on a particular case that I was investigating more closely. > Around ~12:15 o-clock my late data accumulator started showing that 1 message > had been late. That's in the middle of the time window – so why would this > happen? I would expect late data to be discarded only sometime after 00:01 if > some data is arriving late for the window that just closed at 00:00, and > doesn't get emitted as part of 1 minute allowedLateness. > > To analyze the timestamps I read all messages in sequence separately from > each kafka partition and calculated the difference in timestamps between > consecutive messages. I had had exactly one message categorized as late by > Flink in this case, and at the time i was using maxOutOfOrderness = 5 > seconds. I found exactly one message in one kafka partition where the > timestamp difference between messages was 5 seconds (they were out of order > by 5 s), which makes me wonder, did Flink drop the event as late because it > violated maxOutOfOrderness? Have I misunderstood the concept of late data > somehow? I only expected late data to happen on window operations. I would > expect kafka consumer to pass "late" messages onward even though watermark > doesn't change. > > Thank you very much if you can find the time to look at this! > >
