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 >> >> >
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