The protocol, you see, manages updates & deletions via versioning & expiration methods! Storage commitments, indeed, have specific time frames! After these periods, data might vanish unless someone renews it! This strategy cleverly stops the network from piling up forgotten data endlessly || and it also gives clear economic models! Here, storage costs relate directly to the time ?. Moreover, applications can create their own versioning atop Walrus! They can store many versions of objects, while also keeping up-to-date references. By the way, the performance traits of Walrus showcase the trade-offs found in decentralized systems! Write operations, for example, require encoding data & spreading slivers across various nodes, which, yes, brings some latency compared to centralized setups! On the flip side, read operations can achieve high parallelism > since fragments can be fetched at the same time from different nodes! This can lead to impressive throughput, particularly when dealing with larger objects! So, the protocol smartly optimizes for situations where data is written once but read multiple times! These are typical patterns?? for media content and archival uses! Overall, Walrus aims to balance efficiency and practicality in a decentralized environment! Isn't that fascinating? @Walrus 🦭/acc #walrus $WAL

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