Mathias, This apparently happens because we have more than 1 source topic. We have 3 source topics in the same application. So it seems like the task assignment algorithm creates topologies not for one specific topic at a time but the total partitions across all source topics consumed in an application instance. Because we have some code dependencies between these 3 source topics we can’t separate them into 3 applications at this time. Hence the reason I want to get the task assignment algorithm basically do a uniform and simple task assignment PER source topic.
Ara. On Mar 25, 2017, at 5:21 PM, Matthias J. Sax <matth...@confluent.io<mailto:matth...@confluent.io>> wrote: ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ From: "Matthias J. Sax" <matth...@confluent.io<mailto:matth...@confluent.io>> Subject: Re: more uniform task assignment across kafka stream nodes Date: March 25, 2017 at 5:21:47 PM PDT To: users@kafka.apache.org<mailto:users@kafka.apache.org> Reply-To: <users@kafka.apache.org<mailto:users@kafka.apache.org>> Hi, I am wondering why this happens in the first place. Streams, load-balanced over all running instances, and each instance should be the same number of tasks (and thus partitions) assigned. What is the overall assignment? Do you have StandyBy tasks configured? What version do you use? -Matthias On 3/24/17 8:09 PM, Ara Ebrahimi wrote: Hi, Is there a way to tell kafka streams to uniformly assign partitions across instances? If I have n kafka streams instances running, I want each to handle EXACTLY 1/nth number of partitions. No dynamic task assignment logic. Just dumb 1/n assignment. Here’s our scenario. Lets say we have an “source" topic with 8 partitions. We also have 2 kafka streams instances. Each instances get assigned to handle 4 “source" topic partitions. BUT then we do a few maps and an aggregate. So data gets shuffled around. The map function uniformly distributes these across all partitions (I can verify that by looking at the partition offsets). After the map what I notice by looking at the topology is that one kafka streams instance get assigned to handle say 2 aggregate repartition topics and the other one gets assigned 6. Even worse, on bigger clusters (say 4 instances) we see say 2 nodes gets assigned downstream aggregate repartition topics and 2 other nodes assigned NOTHING to handle. Ara. ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________ ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. Thank you in advance for your cooperation. ________________________________