THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
...
We will be adding following new configs:
| stream.rebalancing.mode Default: incremental Version 1.0 | The setting to help ensure no downtime upgrade of online application. Options : upgrading, incremental |
scale.down.timeout.ms Default: infinity Version 2.0 | Timeout in milliseconds to force terminate the stream worker when informed to be scaled down. |
learnerlearn.partial.rebalance Default : true Version 3.0 | If this config is set to true, new member will proactively trigger rebalance when it finishes restoring one task state each time, until it eventually finishes all the task replaying. Otherwise, new worker will batch the ready stage to ask for single round of rebalance. scale.down. timeout.msDefault: infinity Timeout in milliseconds to force terminate the |
| stream |
| . |
Default: incremental
The setting to help ensure no downtime upgrade of online application.
Options : upgrading, incremental
| worker.balancing.factor Default: 2 Version 4.0 | The tolerance of task imbalance factor between hosts to trigger rebalance. |
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.
...