Your observation is correct. Kafka Streams creates a task per partition.
As you have a shared state store over two operator, the tasks of both
input streams need to be merged to ensure co-partitioning.
Thus, task0 reads topic1 partition0 and topic2 partion0, and all other
task[123] only topic1 par
Hi,
this is not supported by the DSL layer. What you would need to do, is to
add a custom stateful transform() operator after there window
(`stream.groupByKey().aggregate().toStream().transform().to()`), that
buffers the output and remembers the latest result. Second, you would
schedule a punctuat
Hi,
I'd like to have your comments on the problem I met while testing my app
with kafka streams (0.10.2.1)
Roughly, my stream app has 2 input topics :
. the first one has 4 partitions (main data)
. the second one has only one partition and receives messages from time to
time
At first, I suppo
