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> 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>:
>
>> 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!
>>
>
>
