Matt,
I read your email again and this one that you point out:
> What you also need to take into account is, how often topics are
> compacted, and how large the segment size is, because the active
segment
> is not subject to compaction.
Are you saying that compaction affects the rebuilding time ? Sorry, I am not
sure I understand what you meant by this in the current context.
-mohan
On 6/10/19, 10:22 AM, "Parthasarathy, Mohan" <mpart...@hpe.com> wrote:
Thanks. That helps me understand why recreating state might take time.
-mohan
On 6/9/19, 11:50 PM, "Matthias J. Sax" <matth...@confluent.io> wrote:
By default, Kafka Streams does not "close" windows.
To handle out-of-order data, windows are maintained until their
retention time passed, and are updated each time an out-of-order record
arrives (even if window-end time passed).
Cf
https://stackoverflow.com/questions/38935904/how-to-send-final-kafka-streams-aggregation-result-of-a-time-windowed-ktable
for details; note, that using `suppress()` and setting a grace-period
change the default behavior.
Hence, you usually want to keep windows around until you are sure that
no more out-of-order records for a window may arrive (what is usually
some time after the window end time). If 1 day is too large, you can of
course reduce the retention time accordingly.
If you use Interactive Queries, you would need to set retention time
large enough to allow your application to query the state. For this
case, retention time might be higher to keep state around for serving
queries, even if you know it won't be updated any longer.
For Kafka Streams, using 1 day as default provides a good out-of-the-box
experience. For production deployments, change the retention time base
on the need of the application make sense of course.
There is also one more config you might want to consider:
`windowstore.changelog.additional.retention.ms` -- it's 1 day by
default, too, and you might want to reduce it to reduce the amount of
data that is restored.
In general, there in nothing wrong with using stateful sets though, and
for large state, it's recommended to avoid long recovery times.
-Matthias
On 6/9/19 2:59 PM, Parthasarathy, Mohan wrote:
> Matt,
>
> Thanks for your response. I agree with you that there is no easy way
to answer this. I was trying to see what others experience is which could
simply be "Don't bother, in practice stateful set is better".
>
> Could you explain as to why there has to be more state than the
window size ? In a running application, as the data is being processed from a
topic, there is state being created depending on the stateful primitives. As
the window is closed, this state is not needed and I can see why grace period
has to be taken into account ? So, when would you need it for the whole store
retention time ? Could you clarify ?
>
> Thanks
> Mohan
>
> On 6/8/19, 11:18 PM, "Matthias J. Sax" <matth...@confluent.io> wrote:
>
> If depends how much state you need to restore and how much
restore-time
> you can accept in your application.
>
> The amount of data that needs to be restored, does not depend on
the
> window-size, but the store retention time (default 1 day,
configurable
> via `Materialized#withRetention()`). The window size (and grace
period,
> if case you use one) is a lower bound for the configurable
retention
> time though, ie, retention time >= grace-period >= window-size.
>
> What you also need to take into account is, how often topics are
> compacted, and how large the segment size is, because the active
segment
> is not subject to compaction.
>
> It's always hard to answer a question like this. I would
recommend to do
> some testing and benchmark fail over, by manually killing some
instances
> to simulate a crash. This should give the best insight -- tuning
the
> above parameters, you can see, what works for your application.
>
>
>
> -Matthias
>
> On 6/8/19 4:28 PM, Pavel Sapozhnikov wrote:
> > I suggest take a look at Strimzi project https://strimzi.io/
> >
> > Kafka operator deployed in Kubernetes environment.
> >
> > On Sat, Jun 8, 2019, 6:09 PM Parthasarathy, Mohan
<mpart...@hpe.com> wrote:
> >
> >> Hi,
> >>
> >> I have read several articles about this topic. We are soon
going to deploy
> >> our streaming apps inside k8s. My understanding from reading
these articles
> >> is that stateful set in k8s is not mandatory as the
application can rebuild
> >> its state if the state store is not present. Can people share
their
> >> experience or recommendation when it comes to deploying the
streaming apps
> >> on k8s ?
> >>
> >> Also, let us say the application is using a tumbling window of
5 mts. When
> >> an application restarts, is it correct to say that it has to
re-build the
> >> state only for that 5 minute window for the partitions that it
was handling
> >> before. I had an instance of such a restart where it was
running a long
> >> time in REBALANCE which makes me think that my understanding
is incorrect.
> >> In this case, the state store was available during the
restart. Can someone
> >> clarify ?
> >>
> >> Thanks
> >> Mohan
> >>
> >>
> >
>
>
>
