Thanks again, that makes things clear. I still have some questions here
then.
A. For each record we read, we do two updates
1. Changelog topic of the state store.
2. Output topic aka sink.
Does the Kafka stream app make sure that either both are committed or
neither?
B. Out Input topic actually has the as (a,b,c), but we partition with only
(a). We do this because we have different compaction requirements than the
partitions. It will still work as all (a,b,c) records will go to the same
partition. Now in aggregation, we group by (a,b,c). In such case what will
be the partition key for the changelog topic?
Note that we use low-level processor API and don't commit ourselves.
Regards,
Mangat
On Thu, Apr 8, 2021 at 5:37 PM Guozhang Wang <wangg...@gmail.com> wrote:
> Hi Mangat,
>
> Please see my replies inline below.
>
> On Thu, Apr 8, 2021 at 5:34 AM mangat rai <mangatm...@gmail.com> wrote:
>
> > @Guozhang Wang
> >
> > Thanks for the reply. Indeed I am finding it difficult to explain this
> > state. I checked the code many times. There can be a bug but I fail to
> see
> > it. There are several things about the Kafka streams that I don't
> > understand, which makes it harder for me to reason.
> >
> > 1. What is the partition key for the changelog topics? Is it the same as
> > the Input key or the state store key? Or maybe the thread specifies the
> > partition as it knows the input partition it is subscribed to? If the
> input
> > topic and state store are differently partitioned then we can explain the
> > issue here.
> >
>
> In Kafka Stream's changelog, the "partition key" of Kafka messages is the
> same as the "message key" itself. And the message key is the same as the
> state store key.
>
> Since the state store here should be storing the running aggregate, it
> means that the partition key is the same as the aggregated key.
>
> If you are doing a group-by aggregation here, where the group-by keys are
> different from the source input topic's keys, hence the state store keys
> would be different with the input topic keys.
>
>
> > 2. Is there a background thread to persist in the state store when
> caching
> > is disabled? When will the app commit the log for the input topic? Is it
> > when sink writes into the output topic or when the state store writes
> into
> > the changelog topic? Because, if the app commits the record before the
> data
> > was written to changelog topic then we can again explain this state
> >
> > The commit happens *after* the local state store, as well as the
> changelog records sent by the Streams' producers, have been flushed. I.e.
> if there's a failure in between, you would re-process some source records
> and hence cause duplicates, but no data loss (a.k.a. the at_least_once
> semantics).
>
>
>
> > >You may also consider upgrading to 2.6.x or higher version and see if
> this
> > issue goes away.
> > Do you mean the client or the Kafka broker? I will be upgrading the
> client
> > to 2.7.0 soon.
> >
> > I meant the client.
>
>
> > Sadly looking into the timestamp will not help much as we use some
> business
> > time field to set the record timestamp. If I am right, there is no way
> now
> > to know that when a Producer wrote a record in a Kafka topic.
> >
> > Regards,
> > Mangat
> >
> >
> >
> > On Wed, Apr 7, 2021 at 6:22 PM Guozhang Wang <wangg...@gmail.com> wrote:
> >
> > > Hello Mangat,
> > >
> > > With at least once, although some records maybe processed multiple
> times
> > > their process ordering should not be violated, so what you observed is
> > not
> > > expected. What caught my eyes are this section in your output
> changelogs
> > > (high-lighted):
> > >
> > > Key1, V1
> > > Key1, null
> > > Key1, V1
> > > Key1, null (processed again)
> > > Key1, V2
> > > Key1, null
> > >
> > > *Key1, V1Key1,V2*
> > > Key1, V2+V1 (I guess we didn't process V2 tombstone yet but reprocessed
> > V1
> > > again due to reassignment)
> > >
> > > They seem to be the result of first receiving a tombstone which removes
> > V1
> > > and then a new record that adds V2. However, since caching is disabled
> > you
> > > should get
> > >
> > > *Key1,V1*
> > > *Key1,null*
> > > *Key1,V2*
> > >
> > > instead; without the actual code snippet I cannot tell more what's
> > > happening here. If you can look into the logs you can record each time
> > when
> > > partition migrates, how many records from the changelog was replayed to
> > > restore the store, and from which offset on the input topic does
> Streams
> > > resume processing. You may also consider upgrading to 2.6.x or higher
> > > version and see if this issue goes away.
> > >
> > >
> > > Guozhang
> > >
> > > On Tue, Apr 6, 2021 at 8:38 AM mangat rai <mangatm...@gmail.com>
> wrote:
> > >
> > > > Hey,
> > > >
> > > > We have the following setup in our infrastructure.
> > > >
> > > > 1. Kafka - 2.5.1
> > > > 2. Apps use kafka streams `org.apache.kafka` version 2.5.1 library
> > > > 3. Low level processor API is used with *atleast-once* semantics
> > > > 4. State stores are *in-memory* with *caching disabled* and
> > *changelog
> > > > enabled*
> > > >
> > > >
> > > > Is it possible that during state replication and partition
> > reassignment,
> > > > the input data is not always applied to the state store?
> > > >
> > > > 1. Let's say the input topic is having records like following
> > > >
> > > > ```
> > > > Key1, V1
> > > > Key1, null (tombstone)
> > > > Key1, V2
> > > > Key1, null
> > > > Key1, V3
> > > > Key1, V4
> > > > ```
> > > > 2. The app has an aggregation function which takes these record and
> > > update
> > > > the state store so that changelog shall be
> > > >
> > > > ```
> > > > Key1, V1
> > > > Key1, null (tombstone)
> > > > Key1, V2
> > > > Key1, null
> > > > Key1, V3
> > > > Key1, V3 + V4
> > > > ```
> > > > Let's say the partition responsible for processing the above key was
> > > > several times reallocated to different threads due to some infra
> issues
> > > we
> > > > are having(in Kubernetes where we run the app, not the Kafka
> cluster).
> > > >
> > > > I see the following record in the changelogs
> > > >
> > > > ```
> > > > Key1, V1
> > > > Key1, null
> > > > Key1, V1
> > > > Key1, null (processed again)
> > > > Key1, V2
> > > > Key1, null
> > > > Key1, V1
> > > > Key1,V2
> > > > Key1, V2+V1 (I guess we didn't process V2 tombstone yet but
> reprocessed
> > > V1
> > > > again due to reassignment)
> > > > Key1,V1 (V2 is gone as there was a tombstone, but then V1 tombstone
> > > should
> > > > have been applied also!!)
> > > > Key1, V2+V1 (it is back!!!)
> > > > Key1,V1
> > > > Key1, V1 + V2 + V3 (This is the final state)!
> > > > ```
> > > >
> > > > If you see this means several things
> > > > 1. The state is always correctly applied locally (in developer
> laptop),
> > > > where there were no reassignments.
> > > > 2. The records are processed multiple times, which is understandable
> as
> > > we
> > > > have at least symantics here.
> > > > 3. As long as we re-apply the same events in the same orders we are
> > > golden
> > > > but looks like some records are skipped, but here it looks as if we
> > have
> > > > multiple consumers reading and update the same topics, leading to
> race
> > > > conditions.
> > > >
> > > > Is there any way, Kafka streams' state replication could lead to
> such a
> > > > race condition?
> > > >
> > > > Regards,
> > > > Mangat
> > > >
> > >
> > >
> > > --
> > > -- Guozhang
> > >
> >
>
>
> --
> -- Guozhang
>
