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Note that due to the complex nature of the problem and the heuristics (e.g. steps 2 and 4) applied in the algorithm above we expect to find an optimum (and not necessarily the best) sticky partition assignment.

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Alternatives Yet to be Considered

• The sticky partition assignment algorithm described above, as mentioned earlier in this KIP, favors fairness over stickiness (we may call it the fair yet sticky or stickiest fair strategy). Therefore, some partitions may change their consumer towards a fair assignment. This strategy is supposedly more complex to implement due to complex nature of calculating the most balanced assignment.
  An alternative strategy is to implement it so that stickiness take precedence and all previously assigned partitions are preserved; while the new consumers or partitions that need to be assigned do so towards the fairest possible assignment. We may call this alternative the sticky yet fair or fairest sticky strategy. This strategy would be more efficient as it exhibits less complexity and variance. After all, the previous valid assignments remain as they are and only a few new assignments have to be calculated.
  The fair strategy discussed in KIP-49 considers fairness only and does not take into consideration the partition assignments before the rebalance. Fairness only can also be supported as part of this KIP by switching off stickiness and always starting from an empty currentAssignment.

Notes

• KAFKA-2019: This JIRA describes an issue with the round robin assignor of the old consumer, which stems from multiple threads that each (old) consumer may have and how the threads of each consumer are assigned first before assigning partitions to other consumer threads. Since the new consumer is single threaded there is no such problem in its round robin strategy. It simply considers consumers one by one for each partition assignment, and when one consumer is assigned a partition, the next assignment starts by considering the next consumer in the list (and not from the start of the list again not from the same consumer that was just assigned). This removes the possibility of the issue reported in KAFKA-2019 surfacing in the new consumer's round robin assignor. In the sticky strategy this issue does not exist either, since every time an assignment is about to happen it starts with the consumer with least number of assignments. So there is no scenario in which a consumer is repeatedly assigned partitions as in KAFKA-2019 (unless that consumer is lagging behind other consumers on the number of partitions assigned).
  
  

Compatibility, Deprecation, and Migration Plan

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