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Machine Learning (cs.LG)。搜狗输入法下载对此有专业解读
There are a couple ways mitigate this drawback, both of which are outside the scope of this article. One is “garbage collection”: pruning tombstones from CRDTs, which prevents you from merging states with any changes made before the tombstones were removed. Another is creating an efficient format to encode the data. You can also combine these methods. Research suggests that this can result in as little as 50% overhead compared to the “plain” data CRDTs: The Hard Parts A talk on the latest research on CRDTs, originally given at the Hydra distributed computing conference on 6 July 2020.References: https://martin.kleppmann.co... youtu.be/x7drE24geUw?t=3587 . If you’d like to skip ahead and see some of this optimization in action, check out the final part in this series: Making CRDTs 98% More Efficient Making CRDTs 98% More Efficient | jakelazaroff.com State-based CRDTs grow monotonically, but that doesn't mean they can't be efficient. We'll learn how to compress the pixel editor state by 98%. jakelazaroff.com/words/making-crdts-98-percent-more-efficient/ . ↩，更多细节参见91视频
Drumroll, please!。业内人士推荐体育直播作为进阶阅读