Ideal value for Kafka Connect Distributed tasks.max configuration setting?

We had a problem with the distribution of the workload between the Kafka-Connect(5.1.2) instances, caused by the high number of tasks.max than the number of partitions.

In our case, there were 10 Kafka Connect tasks and 3 partitions of the topic which is to be consumed. 3 of those 10 workers are assigned to the 3 partitions of the topic and the other 7 are not assigned to any partitions(which is expected) but the Kafka Connect were distributing the tasks evenly, without considering their workload. So we were ending up with a task distribution to our instances where some instances are staying idle( because they are not assigned to any unempty worker ) or some instances are working more than the others.

To come up with the issue, we set tasks.max equal to number of partitions of our topics.

It is really unexpected for us to see that Kafka Connect does not consider tasks' assignments while rebalancing. Also, I couldn't find any documentation for the tasks.max setting.

In a Kafka Connect sink, the tasks are essentially consumer threads and receive partitions to read from. If you have 10 partitions and have tasks.max set to 5, each task with receive 2 partitions to read from and track the offsets. If you have configured tasks.max to a number above the partition count Connect will launch a number of tasks equal to the partitions of the topics it's reading.

If you change the partition count of the topic you'll have to relaunch your connect task, if tasks.max is still greater than the partition count, Connect will start that many tasks.

edit, just discovered ConnectorContext: https://kafka.apache.org/0100/javadoc/org/apache/kafka/connect/connector/ConnectorContext.html

The connector will have to be written to include this but it looks like Connect has the ability to reconfigure a connector if there's a topic change (partitions added/removed).