Hello,

Following our experimenting with MM2, we seem to have a hard time
recovering from lags created by increased traffic.

Since we're running MM2 on kubernetes, we can easily add more
resources/instances to our MM2 cluster, which should in theory increase the
clusters performance and allow us to eliminate accumulated lag faster.

In practice, we see that increasing the number of MM2 instances we have
does not drastically change the throughput per machine - newly created
instances have much lower throughput than instances which existed before
the increase.

This made me think about the partition assignment strategy used by MM2. If
I understand correctly, the default seems to be
partition.assignment.strategy = [class
org.apache.kafka.clients.consumer.RangeAssignor]

I believe changing this to round robin might help this? I tried setting it
up in my /config/connect-mirror-maker.properties file as one of each of the
following (assuming source / dest A / B respectively)

A.consumer.partition.assignment.strategy=org.apache.kafka.clients.consumer.RoundRobinAssignor
B.consumer.partition.assignment.strategy=org.apache.kafka.clients.consumer.RoundRobinAssignor
A.partition.assignment.strategy=org.apache.kafka.clients.consumer.RoundRobinAssignor
B.partition.assignment.strategy=org.apache.kafka.clients.consumer.RoundRobinAssignor
partition.assignment.strategy=org.apache.kafka.clients.consumer.RoundRobinAssignor
consumer.partition.assignment.strategy=org.apache.kafka.clients.consumer.RoundRobinAssignor
None of these seem to work. Am I doing it wrong? Is this setting even
applicable to MM2?

Thanks a lot,
Iftach

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