If confusion is the problem, then totally agree no point adding more
knobs. Perhaps you're right that users don't /really/ want
processing-time semantics. Just /think/ they want them until they start
considering replay/catch-up scenarios. I guess people rarely think about
those from the start (I sure didn't).
Cheers,
Michał
On 16/06/17 17:54, Jay Kreps wrote:
I think the question is when do you actually /want/ processing time
semantics? There are definitely times when its safe to assume the two
are close enough that a little lossiness doesn't matter much but it is
pretty hard to make assumptions about when the processing time is and
has been hard for us to think of a use case where its actually desirable.
I think mostly what we've seen is confusion about the core concepts:
* stream -- immutable events that occur
* tables (including windows) -- current state of the world
If the root problem is confusion adding knobs never makes it better.
If the root problem is we're missing important use cases that justify
the additional knobs then i think it's good to try to really
understand them. I think there could be use cases around systems that
don't take updates, example would be email, twitter, and some metrics
stores.
One solution that would be less complexity inducing than allowing new
semantics, but might help with the use cases we need to collect, would
be to add a new operator in the DSL. Something like .freezeAfter(30,
TimeUnit.SECONDS) that collects all updates for a given window and
both emits and enforces a single output after 30 seconds after the
advancement of stream time and remembers that it is omitted,
suppressing all further output (so the output is actually a KStream).
This might or might not depend on wall clock time. Perhaps this is in
fact what you are proposing?
-Jay
On Fri, Jun 16, 2017 at 2:38 AM, Michal Borowiecki
<michal.borowie...@openbet.com <mailto:michal.borowie...@openbet.com>>
wrote:
I wonder if it's a frequent enough use case that Kafka Streams
should consider providing this out of the box - this was asked for
multiple times, right?
Personally, I agree totally with the philosophy of "no final
aggregation", as expressed by Eno's post, but IMO that is
predicated totally on event-time semantics.
If users want processing-time semantics then, as the docs already
point out, there is no such thing as a late-arriving record -
every record just falls in the currently open window(s), hence the
notion of final aggregation makes perfect sense, from the
usability point of view.
The single abstraction of "stream time" proves leaky in some cases
(e.g. for punctuate method - being addressed in KIP-138). Perhaps
this is another case where processing-time semantics warrant
explicit handling in the api - but of course, only if there's
sufficient user demand for this.
What I could imagine is a new type of time window
(ProcessingTimeWindow?), that if used in an aggregation, the
underlying processor would force the WallclockTimestampExtractor
(KAFKA-4144 enables that) and would use the system-time
punctuation (KIP-138) to send the final aggregation value once the
window has expired and could be configured to not send
intermediate updates while the window was open.
Of course this is just a helper for the users, since they can
implement it all themselves using the low-level API, as Matthias
pointed out already. Just seems there's recurring interest in this.
Again, this only makes sense for processing time semantics. For
event-time semantics I find the arguments for "no final
aggregation" totally convincing.
Cheers,
Michał
On 16/06/17 00:08, Matthias J. Sax wrote:
Hi Paolo,
This SO question might help, too:
https://stackoverflow.com/questions/38935904/how-to-send-final-kafka-streams-aggregation-result-of-a-time-windowed-ktable
<https://stackoverflow.com/questions/38935904/how-to-send-final-kafka-streams-aggregation-result-of-a-time-windowed-ktable>
For Streams, the basic model is based on "change" and we report updates
to the "current" result immediately reducing latency to a minimum.
Last, if you say it's going to fall into the next window, you won't get
event time semantics but you fall back processing time semantics, that
cannot provide exact results....
If you really want to trade-off correctness version getting (late)
updates and want to use processing time semantics, you should configure
WallclockTimestampExtractor and implement a "update deduplication"
operator using table.toStream().transform(). You can attached a state to
your transformer and store all update there (ie, newer update overwrite
older updates). Punctuations allow you to emit "final" results for
windows for which "window end time" passed.
-Matthias
On 6/15/17 9:21 AM, Paolo Patierno wrote:
Hi Eno,
regarding closing window I think that it's up to the streaming application.
I mean ...
If I want something like I described, I know that a value outside my 5 seconds window
will be taken into account for the next processing (in the next 5 seconds). I don't think
I'm losing a record, I am ware that this record will fall in the next
"processing" window. Btw I'll take a look at your article ! Thanks !
Paolo
Paolo Patierno
Senior Software Engineer (IoT) @ Red Hat
Microsoft MVP on Windows Embedded & IoT
Microsoft Azure Advisor
Twitter : @ppatierno<http://twitter.com/ppatierno>
<http://twitter.com/ppatierno>
Linkedin : paolopatierno<http://it.linkedin.com/in/paolopatierno>
<http://it.linkedin.com/in/paolopatierno>
Blog : DevExperience<http://paolopatierno.wordpress.com/>
<http://paolopatierno.wordpress.com/>
________________________________
From: Eno Thereska<eno.there...@gmail.com> <mailto:eno.there...@gmail.com>
Sent: Thursday, June 15, 2017 3:57 PM
To:us...@kafka.apache.org <mailto:us...@kafka.apache.org>
Subject: Re: Kafka Streams vs Spark Streaming : reduce by window
Hi Paolo,
Yeah, so if you want fewer records, you should actually "not" disable
cache. If you disable cache you'll get all the records as you described.
About closing windows: if you close a window and a late record arrives that
should have been in that window, you basically lose the ability to process that
record. In Kafka Streams we are robust to that, in that we handle late arriving
records. There is a comparison here for example when we compare it to other
methods that depend on watermarks or
triggers:https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model/
<https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model/>
<https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model/>
<https://www.confluent.io/blog/watermarks-tables-event-time-dataflow-model/>
Eno
On 15 Jun 2017, at 14:57, Paolo Patierno<ppatie...@live.com>
<mailto:ppatie...@live.com> wrote:
Hi Emo,
thanks for the reply !
Regarding the cache I'm already using CACHE_MAX_BYTES_BUFFERING_CONFIG = 0
(so disabling cache).
Regarding the interactive query API (I'll take a look) it means that it's
up to the application doing something like we have oob with Spark.
May I ask what do you mean with "We don’t believe in closing windows" ?
Isn't it much more code that user has to write for having the same result ?
I'm exploring Kafka Streams and it's very powerful imho even because the
usage is pretty simple but this scenario could have a lack against Spark.
Thanks,
Paolo.
Paolo Patierno
Senior Software Engineer (IoT) @ Red Hat
Microsoft MVP on Windows Embedded & IoT
Microsoft Azure Advisor
Twitter : @ppatierno<http://twitter.com/ppatierno>
<http://twitter.com/ppatierno>
Linkedin : paolopatierno<http://it.linkedin.com/in/paolopatierno>
<http://it.linkedin.com/in/paolopatierno>
Blog : DevExperience<http://paolopatierno.wordpress.com/>
<http://paolopatierno.wordpress.com/>
________________________________
From: Eno Thereska<eno.there...@gmail.com> <mailto:eno.there...@gmail.com>
Sent: Thursday, June 15, 2017 1:45 PM
To:us...@kafka.apache.org <mailto:us...@kafka.apache.org>
Subject: Re: Kafka Streams vs Spark Streaming : reduce by window
Hi Paolo,
That is indeed correct. We don’t believe in closing windows in Kafka
Streams.
You could reduce the number of downstream records by using record
caches:http://docs.confluent.io/current/streams/developer-guide.html#record-caches-in-the-dsl
<http://docs.confluent.io/current/streams/developer-guide.html#record-caches-in-the-dsl>
<http://docs.confluent.io/current/streams/developer-guide.html#record-caches-in-the-dsl>
<http://docs.confluent.io/current/streams/developer-guide.html#record-caches-in-the-dsl>.
Alternatively you can just query the KTable whenever you want using the
Interactive Query APIs (so when you query dictates what data you receive), see
thishttps://www.confluent.io/blog/unifying-stream-processing-and-interactive-queries-in-apache-kafka/
<https://www.confluent.io/blog/unifying-stream-processing-and-interactive-queries-in-apache-kafka/>
<https://www.confluent.io/blog/unifying-stream-processing-and-interactive-queries-in-apache-kafka/>
<https://www.confluent.io/blog/unifying-stream-processing-and-interactive-queries-in-apache-kafka/>
Thanks
Eno
On Jun 15, 2017, at 2:38 PM, Paolo Patierno<ppatie...@live.com>
<mailto:ppatie...@live.com> wrote:
Hi,
using the streams library I noticed a difference (or there is a lack of
knowledge on my side)with Apache Spark.
Imagine following scenario ...
I have a source topic where numeric values come in and I want to check the
maximum value in the latest 5 seconds but ... putting the max value into a
destination topic every 5 seconds.
This is what happens with reduceByWindow method in Spark.
I'm using reduce on a KStream here that process the max value taking into
account previous values in the latest 5 seconds but the final value is put into
the destination topic for each incoming value.
For example ...
An application sends numeric values every 1 second.
With Spark ... the source gets values every 1 second, process max in a
window of 5 seconds, puts the max into the destination every 5 seconds (so when
the window ends). If the sequence is 21, 25, 22, 20, 26 the output will be just
26.
With Kafka Streams ... the source gets values every 1 second, process max
in a window of 5 seconds, puts the max into the destination every 1 seconds (so
every time an incoming value arrives). Of course, if for example the sequence
is 21, 25, 22, 20, 26 ... the output will be 21, 25, 25, 25, 26.
Is it possible with Kafka Streams ? Or it's something to do at application
level ?
Thanks,
Paolo
Paolo Patierno
Senior Software Engineer (IoT) @ Red Hat
Microsoft MVP on Windows Embedded & IoT
Microsoft Azure Advisor
Twitter : @ppatierno<http://twitter.com/ppatierno>
<http://twitter.com/ppatierno>
Linkedin : paolopatierno<http://it.linkedin.com/in/paolopatierno>
<http://it.linkedin.com/in/paolopatierno>
Blog : DevExperience<http://paolopatierno.wordpress.com/>
<http://paolopatierno.wordpress.com/>
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