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allocation of the required size, copy our tasks into it, and return
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Airplane designers have done their best to minimize the pain. Since the nineteen-thirties, cabins have evolved from wicker chairs in bare metal tubes to mood-lit cocoons full of curved surfaces. The air has been pressurized so that pilots can fly up to smoother skies. The service carts have been lightened with composites and braced with built-in brakes and stay-closed drawers. Should the plane suddenly drop, the passenger seats can bear a force of sixteen g’s; the jump seats have harnesses to hold down the crew. Should a fire break out, the walls, seat backs, and tray tables are made with self-extinguishing polymers that won’t give off toxic fumes. Floor lights will guide the passengers down darkened aisles, to evacuation slides that inflate and unfurl to the land or water below.
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/ . ↩