the StatefulSet to only one replica, the changelog
topics as well as the eventual output topic show the expected message
count.
So - can the issue somehow be related to rebalancing?
Best wishes
Karsten
Am Do., 28. März 2024 um 08:25 Uhr schrieb Karsten Stöckmann
:
>
> Hi Mangat,
>
&g
of exactly-once.
>
> Do let me know if that solves your problem. I am curious. if yes, then I
> would ask you to create an issue.
>
> Regards,
> Mangat
>
> On Wed, Mar 27, 2024 at 10:49 AM Karsten Stöckmann <
> karsten.stoeckm...@gmail.com> wrote:
>
> &g
the source topics. If there is no errors, then it should
> be persisted eventually.
>
> However, overall exactly-once provides a much tighter and better commit
> control. If you don't have scaling issue, I will strongly advise you to use
> EOS.
>
> Thanks,
> Mangat
>
>
>
zing the records from
> `folderTopicName`. You mentioned that it happens only when you start
> processing and the other table snapshot works fine. This gives me a feeling
> that the first records in the topic might not be deserialized properly.
>
> Regards,
> Mangat
>
> On T
ngs seem (!) to work as expected...
Best wishes,
Karsten
Bruno Cadonna schrieb am Mo., 25. März 2024, 17:01:
> Hi,
>
> That sounds worrisome!
>
> Could you please provide us with a minimal example that shows the issue
> you describe?
>
> That would help a lot!
>
> Best,
Hi,
are there circumstances that might lead to messages silently (i.e. without
any logged warnings or errors) disappearing from a topology?
Specifically, I've got a rather simple topology doing a series of FK left
joins and notice severe message loss in case the application is fired up
for the
ude
>
> On Fri, Feb 23, 2024 at 1:14 PM Karsten Stöckmann <
> karsten.stoeckm...@gmail.com> wrote:
>
> > Hi,
> >
> > I am observing somewhat unexpected (from my point of view) behaviour
> > while ke-key / re-partitioning operations in order to prepare
Hi,
I am observing somewhat unexpected (from my point of view) behaviour
while ke-key / re-partitioning operations in order to prepare a
KTable-KTable join.
Assume two (simplified) source data structures from two respective topics:
class User {
Long id; // PK
String name;
}
class Attribute
Hi,
is anyone here familiar with Quarkus Kafka Streams applications? If so
- is there a way to control output topic configuration when streaming
aggregate data into a sink like so:
KTable aggregate = ...;
aggregate.toStream().to("topic", );
-> Can I programmatically (or by application config)
e, there might also be ways to play with configs to cut down on
> latency to some extend, if e2e latency is your main concern. Again, I
> don't know the use case: for many case, sub-second latency is actually
> sufficient.
>
> HTH.
>
> -Matthias
>
> On 2/7/24 7:41 AM, Karsten Stö
nor a `repartition()` as explained in my
> previous reply. However, doing a `table.groupBy(...)` will set a new key
> and repartition the data to your needs.
>
>
> -Matthias
>
>
> On 2/1/24 1:12 AM, Karsten Stöckmann wrote:
> > Thanks so much for taking a look.
> Think of `folderTable` as fact-table and `personTable` as dimension table.
>
>
> KS will take care of everything else under the hood automatically.
>
>
> -Matthias
>
> On 1/30/24 11:25 AM, Karsten Stöckmann wrote:
> > Matthias, thanks for getting back on this. I'l
k1-fk2 -- is this on purpose? I guess it's
> unclear what you try to actually do to begin with? It sound like it's
> overall a self-join of the input topic on fk1 and fk2 ?
>
>
> -Matthias
>
> On 1/28/24 2:24 AM, Karsten Stöckmann wrote:
> > Hi all,
> >
> > just
Hi all,
just stumbled upon another Kafka Streams issue that keeps me busy these days.
Assume a (simplified) class A like this:
class A {
private Long id;
private String someContent;
private Long fk1;
private Long fk2;
// Getters and setters accordingly
}
Both fk1 and fk2
caling-issues-8620fd344a6d
> >
> article.
>
> Thanks,
> Dharin
>
> On Wed, Jan 24, 2024 at 12:55 PM Karsten Stöckmann <
> karsten.stoeckm...@gmail.com> wrote:
> >
> > Hi,
> >
> > we're currently in the process of evaluating Debezium and Kafka as
Hi,
we're currently in the process of evaluating Debezium and Kafka as a CDC
system for our Postgres database in order to build an indexing solution
(i.e. Solr or OpenSearch).
Debezium captures changes per table and propagates them into dedicated
Kafka topics each. The ingested tables originally
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