Distributed storage needs to truly take off, and data recovery efficiency is a crucial hurdle. Traditional solutions use RS erasure coding to ensure data security, which sounds good, but the reality is quite tough—restoring a piece of data requires downloading massive slices, running polynomial calculations, which takes a long time and consumes network bandwidth. The overall efficiency of the storage network is thus hampered, a common problem.

Walrus has a different approach. Its core competitiveness lies in the "Slice Collaborative Recovery" mechanism of RedStuff 2D encoding. The traditional method requires enough slices to restore data—fetch as many as needed. In contrast, Walrus establishes a tight logical mapping between primary slices and secondary slices, leveraging the reversible XOR operation. This allows recovery by fetching only a few primary and secondary slices. In other words, the number of slices needed is significantly reduced, and bandwidth consumption naturally drops.

Additionally, Walrus employs a "Proximity Recovery" tactic. The system prioritizes fetching slices from the node closest to the user, avoiding cross-region and cross-network data retrieval. Compared to traditional methods that transfer data from multiple remote nodes, this is a world of difference. Bandwidth pressure is eased, recovery speed is faster, and network efficiency is thus improved.