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 (e.g. if you have defined that single-partition topics A, B, C
must be joined), Kafka Streams is forced to let one of your three Streams
nodes process "more" topics/partitions than the other two nodes.

-Michael



On Mon, Jan 9, 2017 at 6:52 PM, Ara Ebrahimi <ara.ebrah...@argyledata.com>
wrote:

> 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
> configured to process variables number of partitions of a topic. One node
> is assigned to process 2 partitions of topic a and another one gets
> assigned 5. Hence I end up with nonuniform throughput across these nodes.
> One node ends up processing more data than the other.
>
> What’s going on? How can I make sure partitions assignment to kafka
> streams nodes is uniform?
>
> On a similar topic, is there a way to make sure partition assignment to
> disks across kafka brokers is also uniform? Even if I use a round-robin one
> to pin partitions to broker, but there doesn’t seem to be a way to
> uniformly pin partitions to disks. Or maybe I’m missing something here? I
> end up with 2 partitions of topic a on disk 1 and 3 partitions of topic a
> on disk 2. It’s a bit variable. Not totally random, but it’s not uniformly
> distributed either.
>
> Ara.
>
>
>
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