btw. in case you didn't find out yet (I just discovered this...), you
can get the entire topology by starting the stream, waiting a bit and
then printing "KafkaStreams.toString()" to console.
I found it useful and cool :)
On Tue, Jan 17, 2017 at 3:19 PM, Matthias J. Sax wrote:
> Sorry for answe
Sorry for answering late.
The mapping from partitions to threads also depend on the structure of
your topology. As you mention that you have a quite complex one, I
assume that this is the reason for the uneven distribution. I you want
to dig deeper, it would be helpful to know the structure of you
I meant I have 7 topics and each has 12 partitions. Considering that I have 4
streaming threads per node, I was expecting to see each thread process 1
partition from each topics and 7 partitions total per streaming thread. But
that’s not the case. Or perhaps you are saying the number of streamin
What does the processing topology of your Kafka Streams application look
like, and what's the exact topic and partition configuration? You say you
have 12 partitions in your cluster, presumably across 7 topics -- that
means that most topics have just a single partition. Depending on your
topology
Hi,
I have 3 kafka brokers, each with 4 disks. I have 12 partitions. I have 3 kafka
streams nodes. Each is configured to have 4 streaming threads. My topology is
quite complex and I have 7 topics and lots of joins and states.
What I have noticed is that each of the 3 kafka streams nodes gets co
