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From Dmitry Minkovsky <dminkov...@gmail.com>
Subject Re: Kafka Streams topology does not replay correctly
Date Wed, 17 Jan 2018 17:10:09 GMT
I have read through the source, following StreamThread ->
AssignedStreamTasks -> StreamTask -> PartitionGroup -> RecordQueue. I see,
as you said, that everything depends on what records the consumer initially
returns from poll.

I wonder how this problem might be solved in the bigger picture. Certainly
I'm not the only person who is interested in reprocessing their history.
For me that was in the top 3 coolest things about the stream-based approach
to modeling an application. If you can't reprocess your application, it's
almost not worth modeling it this way.

Now, certainly I will find ways to make adjustments to make this
re-processable. But it would be cool if this was a first-class things in
the framework. It seems like it would require the user to specify the
runtime dependencies between streams.

On Wed, Jan 17, 2018 at 8:25 AM, Dmitry Minkovsky <dminkovsky@gmail.com>
wrote:

> > That depends what data the consumer fetches and this part is hard to
> predict. For this reason, you need to buffer multiple records in a
> store, in case data does not arrive in the order and you need it
> (between different topics) and later do the processing in the correct
> order when you got all data you need. Does this make sense?
>
> I understand. Thanks for the explanation. That’s what I concluded when I
> was wondering you were talking about buffers. All this time I though the
> StreamThread did this to some extent. Fundamental misconception on my part.
> So the “best effort” synchronization doesn’t apply at all across
> independent streams? Only applies in the case of joins? Does it apply in
> the case of merge?
>
> Thank you,
> Dmitry
>
>
> ср, 17 янв. 2018 г. в 2:39, Matthias J. Sax <matthias@confluent.io>:
>
>> >>> The KStream has incoming events, and #transform() will
>> >>> let me mount the store and use it how I please. Within an application
>> >>> instance, any other KStream#transform()s using the same store will
>> see the
>> >>> same data in real time.
>>
>> That sounds basically correct. But you don't know the order (between
>> different topics) in which you will receive the data.
>>
>> >>> Will the topology call the join transform before the settings-confirm
>> >>> transform before the settings-update transform?
>>
>> That depends what data the consumer fetches and this part is hard to
>> predict. For this reason, you need to buffer multiple records in a
>> store, in case data does not arrive in the order and you need it
>> (between different topics) and later do the processing in the correct
>> order when you got all data you need. Does this make sense?
>>
>> This is the underlying problem for KStream-KTable join, too. If might
>> happen hat we get 100 KTable records that we all process before we
>> receive 100 KStream records. For the correct result it might be required
>> to get 50 KTable and 50 KStream in the first poll call and the rest in
>> the second. But we don't know and just process whatever we get.
>>
>>
>> -Matthias
>>
>>
>> On 1/16/18 7:14 PM, Dmitry Minkovsky wrote:
>> > I meant “Thanks, yes I will try replacing...”
>> >
>> > вт, 16 янв. 2018 г. в 22:12, Dmitry Minkovsky <dminkovsky@gmail.com>:
>> >
>> >> Thanks, yes try replacing the KStream-KTable joins with
>> >> KStream#transform()s and a store. Not sure why you mean I’d need to
>> buffer
>> >> multiple records. The KStream has incoming events, and #transform()
>> will
>> >> let me mount the store and use it how I please. Within an application
>> >> instance, any other KStream#transform()s using the same store will see
>> the
>> >> same data in real time.
>> >>
>> >> Now suppose I have three topics, each with events like this, each on
>> their
>> >> own KStream:
>> >>
>> >> T1 join
>> >> T2 settings-confirm
>> >> T3 settings-update
>> >>
>> >> Will the topology call the join transform before the settings-confirm
>> >> transform before the settings-update transform?
>> >>
>> >>
>> >>
>> >> вт, 16 янв. 2018 г. в 21:39, Matthias J. Sax <matthias@confluent.io>:
>> >>
>> >>> You have more flexibility of course and thus can get better results.
>> But
>> >>> your code must be able to buffer multiple records from the KTable and
>> >>> KStream input and also store the corresponding timestamps to perform
>> the
>> >>> join correctly. It's not trivial but also also not rocket-science.
>> >>>
>> >>> If we need stronger guarantees, it's the best way to follow though
>> atm,
>> >>> until we have addressed those issues. Planned for 1.2.0 release.
>> >>>
>> >>> -Matthias
>> >>>
>> >>>
>> >>> On 1/16/18 5:34 PM, Dmitry Minkovsky wrote:
>> >>>> Right now I am thinking of re-writing anything that has these
>> >>> problematic
>> >>>> KStream/KTable joins as KStream#transform() wherein the state store
>> is
>> >>>> manually used. Does that makes sense as an option for me?
>> >>>>
>> >>>> -Dmitry
>> >>>>
>> >>>> On Tue, Jan 16, 2018 at 6:08 PM, Dmitry Minkovsky <
>> dminkovsky@gmail.com
>> >>>>
>> >>>> wrote:
>> >>>>
>> >>>>> Earlier today I posted this question to SO
>> >>>>> <
>> >>> https://stackoverflow.com/questions/48287840/kafka-
>> streams-topology-does-not-replay-correctly
>> >>>>
>> >>>>> :
>> >>>>>
>> >>>>>> I have a topology that looks like this:
>> >>>>>
>> >>>>>     KTable<ByteString, User> users = topology.table(USERS,
>> >>>>> Consumed.with(byteStringSerde, userSerde), Materialized.as(USERS));
>> >>>>>
>> >>>>>     KStream<ByteString, JoinRequest> joinRequests =
>> >>>>> topology.stream(JOIN_REQUESTS, Consumed.with(byteStringSerde,
>> >>>>> joinRequestSerde))
>> >>>>>         .mapValues(entityTopologyProcessor::userNew)
>> >>>>>         .to(USERS, Produced.with(byteStringSerde, userSerde));
>> >>>>>
>> >>>>>     topology.stream(SETTINGS_CONFIRM_REQUESTS,
>> >>>>> Consumed.with(byteStringSerde, settingsConfirmRequestSerde))
>> >>>>>         .join(users, entityTopologyProcessor::userSettingsConfirm,
>> >>>>> Joined.with(byteStringSerde, settingsConfirmRequestSerde,
>> userSerde))
>> >>>>>         .to(USERS, Produced.with(byteStringSerde, userSerde));
>> >>>>>
>> >>>>>     topology.stream(SETTINGS_UPDATE_REQUESTS,
>> >>>>> Consumed.with(byteStringSerde, settingsUpdateRequestSerde))
>> >>>>>         .join(users, entityTopologyProcessor::userSettingsUpdate,
>> >>>>> Joined.with(byteStringSerde, settingsUpdateRequestSerde, userSerde))
>> >>>>>         .to(USERS, Produced.with(byteStringSerde, userSerde));
>> >>>>>
>> >>>>>> At runtime this topology works fine. Users are created with
join
>> >>>>> requests. They confirm their settings with settings confirm
>> requests.
>> >>> They
>> >>>>> update their settings with settings update requests.
>> >>>>>>
>> >>>>>> However, reprocessing this topology does not produce the
original
>> >>>>> results. Specifically, the settings update joiner does not see
the
>> user
>> >>>>> that resulted from the settings confirm joiner, even though
in
>> terms of
>> >>>>> timestamps, many seconds elapse from the time the user is created,
>> to
>> >>> the
>> >>>>> time the user is confirmed to the time the user updates their
>> settings.
>> >>>>>>
>> >>>>>> I'm at a loss. I've tried turning off caching/logging on
the user
>> >>> table.
>> >>>>> No idea what to do to make this reprocess properly.
>> >>>>>
>> >>>>> ----
>> >>>>>
>> >>>>> The response by Matthias, also on SO:
>> >>>>>
>> >>>>>> A KStream-KTable join is not 100% deterministic (and might
never
>> >>> become
>> >>>>> 100% deterministic). We are aware of the problem and discuss
>> >>> solutions, to
>> >>>>> at least mitigate the issue.
>> >>>>>>
>> >>>>>> One problem is, that if a Consumer fetches from the brokers,
we
>> cannot
>> >>>>> control easily for which topics and/or partitions the broker
returns
>> >>> data.
>> >>>>> And depending on the order in which we receive data from the
broker,
>> >>> the
>> >>>>> result might slightly differ.
>> >>>>>>
>> >>>>>> One related issue: https://issues.apache.org/
>> jira/browse/KAFKA-3514
>> >>>>>>
>> >>>>>> This blog post might help, too: https://www.confluent.io/blog/
>> >>>>> crossing-streams-joins-apache-kafka/
>> >>>>>
>> >>>>> ----
>> >>>>>
>> >>>>> I don't really know what to do with this response. I have been
>> aware of
>> >>>>> some "slight" discrepancy that might occur in edge cases with
>> >>>>> KStream-KTable joins for some time now, but what I'm seeing
is not a
>> >>> slight
>> >>>>> discrepancy but very different results.
>> >>>>>
>> >>>>> I looked at the JIRA Matthias linked
>> >>>>> <https://issues.apache.org/jira/browse/KAFKA-3514>. However,
my
>> data
>> >>> has
>> >>>>> no late arriving records. I don't know about the empty buffers.
I
>> have
>> >>> read
>> >>>>> the blog post he linked several times already.
>> >>>>>
>> >>>>> Can someone please suggest how I may obviate this problem? For
>> example
>> >>>>>
>> >>>>>    - Would it make sense for me to try launching the topology
with
>> >>> fewer
>> >>>>>    threads during the reprocess?
>> >>>>>    - Would it make sense for launch the topology with fewer
input
>> >>> tasks?
>> >>>>>    - Would it make sense to increase size of the stream buffer?
>> >>>>>
>> >>>>> I am at a total loss at this point. I cannot believe that there
is
>> >>> nothing
>> >>>>> I can do to replay this data and perform the migration I am
trying
>> to
>> >>>>> perform, in order to release a next version of my application.
Am I
>> >>> totally
>> >>>>> screwed?
>> >>>>>
>> >>>>>
>> >>>>> Thank you,
>> >>>>> Dmitry
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>
>> >>>
>> >>>
>> >
>>
>>

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