For what it's worth, we run 32 partitions per topic and have also observed
imbalanced balancing, where a large number of A partitions are assigned to
worker 1 and a large number of B partitions are assigned to worker 2, leading
to imbalanced load. Nothing super bad for us yet but the effect is not
The assignment strategy cannot be configures in Kafka Streams atm.
How many partitions do you have in your topics? If I read in-between the
lines, it seems that all topics have just one partition? For this case,
it will be hard to scale out, as Kafka Streams scales via the number of
partitions...
The assignment strategy cannot be configures in Kafka Streams atm.
How many partitions do you have in your topics? If I read in-between the
lines, it seems that all topics have just one partition? For this case,
it will be hard to scale out, as Kafka Streams scales via the number of
partitions...
Hi,
I am using Kafka Streams for one of our application. The application has
several type of topics like the initial ingestion topics and the live
stream topic, all are sharing the same state in continuous fashion.
My problem is that the the assignment of these topics/partitions where I am
observ
