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• Reduce unnecessary downtime due to task restoration
• Make rebalance performance better for stream applications, A.K.A alleviating Stop-The-World Effect.
• Auto Better auto scaling support experience for KStream applications, including
  • Scale up capacity
  • Scale down capacity

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As we could see, there should be only exactly one learner task after each round of rebalance, and there should be exactly one corresponding active task at the same time. 

Algorithm Trade-offs

We open a special section to discuss the trade-offs of the new algorithm, because it's important to understand the change motivation and make the proposal more robust. 

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to help user define their customized strategy.

Implementation Plan

We want to callout this portion because the algorithm we are gonna design is fairly complicated so far. To make sure the delivery is smooth with fundamental changes of KStream internals, I build a separate Google Doc here that could be sharable to outline the step of changes. Feel free to give your feedback on this plan while reviewing the algorithm, because some of the changes are highly coupled with internal changes. Without these details, the algorithm is not making sense.

Compatibility, Deprecation, and Migration Plan

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