#Walrus uses erasure coding instead of storing complete copies across multiple nodes, which fundamentally changes the economics and scalability of decentralized storage. The approach is mathematically elegant rather than computationally wasteful.
Erasure coding takes a file and encodes it into fragments where you only need a subset to reconstruct the original. If you encode data into 100 pieces and any 67 can rebuild the file, you get fault tolerance without storing 100 complete copies. This means a node can go offline, get corrupted, or disappear entirely without data loss, but you're storing maybe 1.5x to 2x the original data across the network rather than 5x or 10x with full replication.
The reliability comes from the mathematical guarantee. With traditional replication, losing access to specific nodes means losing specific copies. With erasure coding, losing random nodes just reduces your redundancy margin until you fall below the reconstruction threshold. The system treats node failures as expected statistical events rather than catastrophic losses requiring perfect copies everywhere.
This creates different operational characteristics. Storage nodes don't need to be perfectly reliable because the encoding assumes some will fail. You can use cheaper hardware, tolerate more network variability, and still guarantee data availability. The system becomes resilient through mathematics rather than brute force redundancy, which lowers operational costs and makes decentralized storage economically competitive with centralized alternatives.
The computational tradeoff is that reconstructing data requires processing those fragments through the erasure code algorithm, which takes more CPU than just fetching a complete copy. But storage is expensive and persistent while computation is cheap and momentary, so you optimize for the constraint that matters long-term. For blockchain applications where data needs to persist indefinitely but might be accessed infrequently, this tradeoff makes sense. @Walrus 🦭/acc $WAL
