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https://issues.apache.org/jira/browse/KAFKA-8769?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16985408#comment-16985408
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Richard Yu commented on KAFKA-8769:
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[~vvcephei] [~mjsax] I'm not sure if you read the KIP, but on it, its
explicitly stated that the user is allowed control over which system of
tracking they want. If they don't want per key stream time tracking, they can
disable it (or enable it if they wish, we can have our default policy remain
with per partition tracking).
Seems like some users still have problems with per partition tracking anyways,
so I'd imagine that this would be a welcome fix for them. There are workarounds
which avoid making the low traffic problem worse, as I mentioned in earlier
discussion, but I don't know if that is acceptable for you.
> Consider computing stream time independently per key
> ----------------------------------------------------
>
> Key: KAFKA-8769
> URL: https://issues.apache.org/jira/browse/KAFKA-8769
> Project: Kafka
> Issue Type: Improvement
> Components: streams
> Reporter: John Roesler
> Priority: Major
> Labels: needs-discussion, needs-kip
>
> Currently, Streams uses a concept of "stream time", which is computed as the
> highest timestamp observed by stateful operators, per partition. This concept
> of time backs grace period, retention time, and suppression.
> For use cases in which data is produced to topics in roughly chronological
> order (as in db change capture), this reckoning is fine.
> Some use cases have a different pattern, though. For example, in IOT
> applications, it's common for sensors to save up quite a bit of data and then
> dump it all at once into the topic. See
> https://stackoverflow.com/questions/57082923/kafka-windowed-stream-make-grace-and-suppress-key-aware
> for a concrete example of the use case.
> I have heard of cases where each sensor dumps 24 hours' worth of data at a
> time into the topic. This results in a pattern in which, when reading a
> single partition, the operators observe a lot of consecutive records for one
> key that increase in timestamp for 24 hours, then a bunch of consecutive
> records for another key that are also increasing in timestamp over the same
> 24 hour period. With our current stream-time definition, this means that the
> partition's stream time increases while reading the first key's data, but
> then stays paused while reading the second key's data, since the second batch
> of records all have timestamps in the "past".
> E.g:
> {noformat}
> A@t0 (stream time: 0)
> A@t1 (stream time: 1)
> A@t2 (stream time: 2)
> A@t3 (stream time: 3)
> B@t0 (stream time: 3)
> B@t1 (stream time: 3)
> B@t2 (stream time: 3)
> B@t3 (stream time: 3)
> {noformat}
> This pattern results in an unfortunate compromise in which folks are required
> to set the grace period to the max expected time skew, for example 24 hours,
> or Streams will just drop the second key's data (since it is late). But, this
> means that if they want to use Suppression for "final results", they have to
> wait 24 hours for the result.
> This tradeoff is not strictly necessary, though, because each key represents
> a logically independent sequence of events. Tracking by partition is simply
> convenient, but typically not logically meaningful. That is, the partitions
> are just physically independent sequences of events, so it's convenient to
> track stream time at this granularity. It would be just as correct, and more
> useful for IOT-like use cases, to track time independently for each key.
> However, before considering this change, we need to solve the
> testing/low-traffic problem. This is the opposite issue, where a partition
> doesn't get enough traffic to advance stream time and results remain "stuck"
> in the suppression buffers. We can provide some mechanism to force the
> advancement of time across all partitions, for use in testing when you want
> to flush out all results, or in production when some topic is low volume. We
> shouldn't consider tracking time _more_ granularly until this problem is
> solved, since it would just make the low-traffic problem worse.
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