The Walrus storage protocol in the Sui ecosystem looks very high-end, but in actual use, it’s full of pitfalls. As a technical person who has been working on distributed storage for a long time, I have to say—official documentation only covers ideal scenarios; in real deployment, all kinds of issues crop up.

Let's start with RedStuff's 2D erasure coding scheme. This system is indeed ingenious in the paper: it treats data as an n×m symbol matrix, first applies RaptorQ encoding on columns to generate primary shards, then applies Reed-Solomon encoding on rows to produce secondary shards. Each node stores a pair of primary and secondary shards, and with one-third of the nodes, you can recover the complete data. The redundancy is controlled at 4 to 5 times, which is more efficient than some leading exchanges' 3 to 5 times replication, and also saves space compared to all-in-one platform's network-wide backups.

But efficiency doesn’t come for free; the cost amplifies several times under high load scenarios.

First, there's the computational overhead of encoding and decoding. RaptorQ, while recognized as an industry-standard fountain code, involves complex matrix operations. Especially when handling GB-sized files—testing with a 5GB AI model file shows that the encoding process consumes over 90% of the client’s CPU resources and takes more than 2 minutes. If your application requires frequent uploads, this overhead becomes a clear performance bottleneck. Long encoding times are one thing, but the resource consumption during decoding and reconstruction is equally staggering.