Thanks for your questions, I would say that your understanding sounds correct based on what you described but I'll try to add some clarity. The basic idea is that, as you said, any keys that are processed before time T will go to partition 1. All of those keys should then continue to be routed to partition 1 for the remainder of the app's lifetime, if you care about maintaining correct history/"state" for that key (I'll come back to this in the next paragraph). After the time T, new keys that weren't processed prior to T may be routed to either partition, provided they are similarly mapped to the same partition forever after. It's up to the user to enforce this, perhaps by trying to keep track of all keys but that is likely to be impractical. This feature is generally more targeted at cases where the partition mapping is "obvious" enough to compute without needing to maintain a history of all keys and their original partition: for example, imagine an application that processes user account information. You can scale out to a partition per user, and add a new partition each time someone opens a new account. When they open that account they get a userID number, starting with #0 and counting up from there. In that case, the partition for any records pertaining to a given account would just be its userID.
I hope that clears up the kind of intended use case we're targeting with this feature. That said, another important and equally viable use case that I neglected to mention in the KIP is fully stateless applications. Technically this feature can produce correct results for applications that are at least one of (a) statically partitioned, or (b) completely stateless. However, the stateless case is a bit stickier since even if the Streams application itself doesn't care about maintaining the same mapping of key to partition, it could for example be feeding into a downstream application which *does* need to maintain state, and which would wind up "losing" the history for any keys that changed partition. I kind of felt like opening this feature up to stateless applications would be asking for trouble and make it too easy for people to shoot themselves in the foot. That said, I'm open to discussion on this point if you feel like the benefits here outweigh the risks. I'm also happy to consider modifying the API so that it could naturally be expanded to include stateless applications in the future, even if we decide against allowing that use case in the first iteration of the feature. Thoughts? Sophie On Wed, Oct 19, 2022 at 7:46 AM Colt McNealy <c...@littlehorse.io> wrote: > Sophie, > > Thank you for the KIP! Choosing the number of partitions in a Streams app > is a tricky task because of how difficult it is to re-partition; I'm glad > you're working on an improvement. I've got two questions: > > First, `StaticStreamsPartitioner` is an interface that we (Streams users) > must implement, I'm trying to understand how it would work. For example, > let's say there's some point in time 'T' before which we have 1 partition. > Then we decide to increase the partition count to 2 at time T. From my > understanding, all keys that had passed through the Streams app before time > T must end up on partition 1 if they appear again in the input topics; but > any new keys are allowed to be sent to partition 2. Is that correct? And > (pardon the naive question) how is this achieved without keeping track of > all keys that have been seen at any point? > > Secondly, will this feature work with applications that use interactive > queries? > > Thank you very much, > Colt McNealy > *Founder, LittleHorse.io* > > > On Tue, Oct 18, 2022 at 9:34 PM Sophie Blee-Goldman > <sop...@confluent.io.invalid> wrote: > > > Hey all, > > > > I'd like to propose a new autoscaling feature for Kafka Streams > > applications which can follow the constraint of static partitioning. For > > further details please refer to the KIP document: > > > > > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-878%3A+Autoscaling+for+Statically+Partitioned+Streams > > > > This feature will be targeted for 3.4 but may not be fully implemented > > until the following release, 3.5. > > > > Please give this a read and let me know what you think! > > > > Cheers, > > Sophie > > >
